Opuntia ficus-indica (L.) Mill., commonly known as prickly pear, is a versatile plant widely used for food, medicinal, and cosmetic purposes in various regions of the Mediterranean and North Africa. This study provides a comparative ethnobotanical review of prickly pear uses in Algeria, Morocco, Tunisia, and Italy. In total, 74 articles were selected from ethnobotanical uses in Italy, Algeria, Morocco and Tunisia. In the Maghreb, the plant is used for fruit production and processed products, such as jams, oils, and flours, as well as for traditional medicinal purposes. In Algeria and Morocco, the fruits and cladodes are used to treat digestive disorders, diabetes, and skin diseases. In Tunisia, products derived from O. ficus-indica are also applied in the cosmetic industry and for erosion control. In Italy, particularly in Sicily and Calabria, the prickly pear is a vital resource, used for both food consumption and for medicinal purposes. The cladodes, rich in mucilage, are applied as topical remedies for skin problems, while the fruits are a key ingredient in the preparation of traditional desserts. Furthermore, O. ficus-indica has historically been used as forage and to produce natural dyes. Results indicate that the versatility of this species, combined with its ability to adapt to extreme climates, makes it a valuable resource for the development of new nutraceutical and cosmetic products. However, further scientific research is necessary to explore the bio-functional potential of this plant and to promote its broader and more sustainable use, especially in arid and semi-arid regions.

Practice guidelines recommend fixed combinations of calcipotriol, a topical vitamin D analogue, and betamethasone dipropionate, a high potency corticosteroid, as first line topical treatment for mild to moderate plaque psoriasis of the body and scalp. A new foaming lotion for treatment of Psoriasis was developed and patented by the Spanish Ministry of Industry, Trade and Tourism (Invention patent reference number 202030824). The foaming lotion is composed of clobetasol, papaverine hydrochloride, spironolactone, milk-peptide-complex and propylene glycol. Three cases with moderate Psoriasis aged 34, 36 and 66 years old were treated with our new foaming lotion for 7-8 days. The three patients reported important improvement in the itching sensations and remission of the scaled lesions. Before and after application of the new foaming lotion, Psoriasis Area Severity Index (PASI) scores improved in the first patient from 24.3 to 1.8, in the second patient from 26.1 to 1.8, and in the third patient from 27 to 1.8. Our results show the short-term effectiveness of the new foaming lotion in treating moderate and extensive Psoriasis. Long follow-up is needed to evaluate the remission period of Psoriasis and possible side effects of the new topical treatment.

A persistent problem in the pharmaceutical industry that has existed for centuries is the prevalence of counterfeit drugs, and the World Health Organization (WHO) estimates that millions of people are affected by this issue each year. In particular, 4 out of 10 drugs in poor or underdeveloped countries may be adulterated, which can lead to severe adverse events. To address this problem, many countries have implemented regulatory compliance measures such as pharmaceutical drug serialization, which requires the unique identification of every drug package manufactured. This enables tracking and tracing of individual packages throughout the supply chain and helps to prevent counterfeit drugs from entering the market. In this paper, we conduct a systematic review of the serialization process evaluation and its impact on the pharmaceutical industry. We discuss the benefits of pharmaceutical drug serialization, including its ability to improve drug security and reduce adverse events and investigations. We also examine the challenges associated with implementing serialization processes and the regulatory requirements necessary for compliance. Finally, we explore the various tracking and tracing technologies used in serialization processes and their effectiveness in preventing the distribution of counterfeit drugs. Overall, this paper highlights the importance of pharmaceutical drug serialization in ensuring the safety and efficacy of drugs in the healthcare industry, particularly in poor or underdeveloped countries where the problem of adulterated drugs is especially prevalent.

Occupational lead (Pb) exposure continues to be an important public health problem globally, yet data is lacking on the associated risks and resources available for the prevention of Pb related diseases in low middle income countries (LMICs) like The Gambia. In this study, we used a case-control design to compare blood lead levels (BLLs) between the exposed (auto repair workers) and unexposed (healthcare workers) populations in The Greater Banjul Area. The data was analyzed using Chi square test of independence to determine the characteristic factors associated with BLL. Multivariate logistic regression was used to test the relationship between BLLs for auto repair and healthcare workers and their experiences. The results of this study indicated 82.1% of cases had higher BLLs compared to 52.9% of controls. Also, the proportion of workers with elevated BLLs was higher for certain factors including more than 80% of those with greater than 10 of years job experience, more than 70% of those who worked greater than 5 days a week, worked more than 8 hours per day, did not use personal protective equipment (PPE), were illiterate or had no formal education, and smoked or ate at work. The study results have implications for policies and practice in the auto repair industry and related workplaces in The Gambia and other LMICs with similar settings. Based on the findings of this study, it is essential to initiate discussions to establish national occupational health policies in The Gambia aimed at protecting workers and the general population.

There have been various concerns about the environmental impact of construction works. This generates a need to take a more proactive approach in evaluating the environmental impacts of construction operations and further explore ways to reduce the environmental impacts. Enormous opportunities exist within the building industry to achieve a reduction in greenhouse gas emissions. The aim of the study is to develop a framework for the evaluation of improvement opportunities for environmental impact for onsite and offsite building construction works using life cycle assessment (LCA) and value-stream-mapping concepts. Various tools for LCA exist; however, there is a need for the development of an LCA framework and improvement opportunities that can be localized to various communities to evaluate improvement opportunities for building construction. This study conducts a review of methods to evaluate the LCA of buildings on local construction sites. A procedure for establishing improvement opportunities is also developed. Based on the author’s knowledge and experience, including site visits, using value stream mapping (VSM) techniques, a conceptual framework of the present state map and future state map of residential construction works was developed. The study presents a procedure for the evaluation of improvement opportunities for the environmental impacts of construction operations.

The building construction industry holds a crucial role in the reduction of greenhouse gas emissions globally. The targets for greenhouse gas emissions may not be achieved without a defined strategic plan to meet up with the set targets from various sectors of the economy. Recognizing the enormous potential that the building industry holds in contributing to global greenhouse gas GHG emission reduction, this study describes a framework on how optimization techniques can be used as a guide for emission reduction targets for the housing sector using illustrations of the onsite and offsite building construction industry. Given that some of the GHG gases are also sources of air pollution, this study includes a discussion on how the effort to address air pollution can be used to find a consensus towards addressing the concern about GHG emissions. This study presents procedures for simplified methods of estimation of GHG emissions that various municipalities around the globe can use to estimate and report the emissions from the building construction industry. The study presents a unifying strategy for emission management. The study also demonstrates how programming methods can be applied to GHG emissions management. The approach used in this study is transferable to other industries. The study recommends a unifying strategy for the management and control of emissions in the building construction industry. The study also recommends a coordinated effort in sharing best practices for emission control and management from all jurisdictions globally. In the effort to reduce global emission targets, further studies like this and its expansion is recommended for all sectors of the global economy. It is recommended that these studies should be followed by a concrete effort to achieve good implementation of sustainable emission reduction targets globally.

The construction industry remains one of the most hazardous sectors globally, with Nigeria experiencing a high incidence of workplace accidents despite the adoption of Occupational Health and Safety Management (OHSM) frameworks. This study evaluated the effectiveness of OHSM practices in improving workplace safety across construction companies in Nigeria’s coastal cities. A cross-sectional design was employed, combining quantitative surveys of construction workers (n = 1,400) with qualitative interviews of 35 managers and supervisors. Quantitative data were analyzed using SPSS version 28, while thematic analysis was applied to qualitative responses. Findings revealed a generally positive perception of OHSM, with 54.4% of workers rating OHS policy effectiveness as “Good” and 52.0% rating health outcomes as “Good.” However, accident frequency remained a concern, with 46.4% reporting accidents occurred “Occasionally” and 31.9% acknowledging them as “Frequent” or “Very Frequent.” Comparative analysis showed indigenous firms were rated higher in policy effectiveness and health outcomes but also reported slightly higher accident frequencies than international firms. Thematic analysis identified five key monitoring and evaluation strategies including routine inspections, regular training, audits, behavioural reinforcement, and access control, Also, five measures of OHSM effectiveness, including compliance observation, incident tracking, KPIs, employee feedback, and benchmarking. OHSM was found to positively influence project outcomes by reducing compensation costs, enhancing reputation, and improving supervision and quality of work. OHSM practices in Nigeria’s construction sector are perceived as effective in policy and health outcomes, yet accident rates remain a critical challenge. The study underscores the importance of continuous training, stricter enforcement, behavioural reinforcement, and systematic performance evaluation.

Background: Building Information Modelling (BIM) improves safety planning in construction by enabling visualization and simulation to identify and reduce risks. However, its adoption in Nigeria is limited. This study examines the application of BIM in enhancing safety and environmental performance on construction sites in Nigeria. Methodology: A quantitative cross-sectional survey was conducted using a structured online questionnaire distributed to professionals in Nigeria’s construction industry. A purposive sampling method was employed to target respondents with relevant BIM experience. Data were analysed using SPSS version 28, applying descriptive statistics, chi-square tests, and logistic regression at a 5% significance level. Result: Findings show that BIM was fully adopted by 7.0% of organizations, with only 19.8% of respondents using it to identify safety hazards during planning. While 76.8% reported no notable safety benefit, 19.5% identified improved risk management as the key benefit. Most respondents (80.2%) reported no noticeable environmental benefits. Among those who did, improved energy efficiency was the most cited benefit (16.4%). Respondents with 10 or more years of experience were significantly more likely to report enhanced safety and environmental outcomes (AOR = 4.555; p = 0.003) and adequate BIM utilization (AOR = 3.255; p = 0.023). Those with intermediate BIM experience were also more likely to report high enhancement (AOR = 2.857; p = 0.039) and effective tool use (AOR = 2.881; p = 0.050). Conclusion: This study revealed that BIM has the potential to improve construction outcomes in Nigeria if supported by training, experience, and structured implementation.

Background: Tobacco control policies nationwide have contributed to a substantial decline in cigarette and tobacco use, with particularly sharp reductions observed in states such as California that have implemented restrictive bans, strong prevention measures, and high excise taxes. While these policies have led to overall decreases in tobacco use, progress has not necessarily been distributed equally across racial groups. Understanding long-term trends by race is critical for addressing equity gaps in tobacco prevention and control. Evidence suggests that some racialized groups may experience slower or delayed declines, raising concerns about equity in public health gains. Methods: We analyzed data from the California Health Interview Survey (CHIS) spanning 2003–2023. Trends in current smoking were examined separately for non-Latino Black and non-Latino White adults. We also assessed current use of flavored tobacco products, given California’s statewide ban enacted in 2021. Changes were evaluated in both absolute terms (percentage point declines) and relative terms (percent reduction from baseline). Results: Smoking prevalence declined from 17.2% in 2003 to 5.2% in 2023 among White adults and from 19.9% to 9.0% among Black adults. This represents a 12.0 percentage point (69.8%) decline for Whites compared with a 10.9 percentage point (54.8%) decline for Blacks. For flavored tobacco use, prevalence decreased from 8.0% to 4.7% among White adults but only from 11.9% to 10.8% among Black adults. This corresponds to a 3.3 percentage point (41.3%) decline for Whites compared with a 1.1 percentage point (9.2%) decline for Blacks. Conclusions: Although both Black and White adults in California experienced reductions in smoking over the past two decades, White adults showed larger declines in both absolute and relative terms. Disparities were even more pronounced for flavored tobacco use, where declines were minimal among Black adults despite the statewide ban. These findings suggest that Black populations in California may have been left behind by tobacco control progress, especially regarding flavored products. Given the history of targeted marketing by the tobacco industry, the role of flavors in increasing dependence, and reduced access to cessation resources in Black communities, targeted policies and culturally tailored interventions are needed to ensure equitable reductions in tobacco use. Greater attention to flavored tobacco in Black communities may help narrow these disparities and advance California’s tobacco endgame goals.

Background: Tobacco endgame, defined as elimination of commercial tobacco sales The U.S. tobacco control landscape is a complex, adaptive system shaped by diverse stakeholders, evolving products and regulations, shifting social norms, and the strategic countermeasures of a powerful industry. Managing such complexity requires more than isolated interventions—it demands a coordinated, enterprise-wide approach that accounts for dynamic interactions, feedback loops, and emergent risks. Objective: Drawing on complex systems thinking, Zachman enterprise architecture model, and public health best practices, we conceptualize tobacco control as an evolving enterprise progressing through six interconnected phases: (1) Conception & Initiation, (2) Policy & System Design, (3) Implementation & Operation, (4) Evaluation & Adaptation, (5) Consolidation & Endgame Transition, and (6) Sustainment or Sunset. Each phase incorporates governance structures, performance benchmarks, and transition criteria designed to manage interdependence and reduce systemic vulnerabilities. Results: The lifecycle framing emphasizes how tobacco control in the U.S. can evolve as a complex, adaptive enterprise—integrating public health objectives with legal, operational, and cultural change processes. This model supports strategic sequencing, cross-sector alignment, and risk mitigation against emergent industry tactics, enabling a resilient and measurable pathway to the endgame. Conclusions: Seeing tobacco control as a complex enterprise that operates under a lifecycle model may offer a roadmap for achieving and sustaining the tobacco endgame. Using this approach may enhance policy coherence, resource efficiency, and adaptability, ensuring tobacco endgame is achieved.

The pharmaceutical industry is undergoing a profound transformation driven by advancements in Information Technology (IT), with Enterprise Resource Planning (ERP) systems playing a pivotal role in reshaping operations. These systems offer integrated solutions that streamline key business processes, such as production, inventory management, supply chain optimization, regulatory compliance, and data integration, contributing significantly to operational efficiency and organizational agility. This paper explores the evolution and impact of ERP systems within the pharmaceutical sector, highlighting their contributions to overcoming the industry’s inherent challenges, including complex regulatory requirements, the need for accurate and real-time data, and the demand for supply chain resilience. The integration of cloud-based ERP solutions, the incorporation of emerging technologies like Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), and enhanced data analytics capabilities have revolutionized pharmaceutical IT. These advancements not only reduce operational costs, improve forecasting accuracy, and enhance collaboration but also ensure compliance with stringent global regulations, such as Good Manufacturing Practices (GMP) and FDA guidelines. Moreover, ERP systems have been instrumental in managing the pharmaceutical supply chain, ensuring product traceability, and improving inventory control and order fulfillment processes. This manuscript examines how ERP systems enable pharmaceutical companies to maintain high standards of product quality, improve decision-making, and ensure the safety and efficacy of drugs through robust tracking and auditing mechanisms. A case study of a pharmaceutical company that implemented an ERP system demonstrates the tangible benefits, including increased operational efficiency, improved compliance rates, and enhanced customer satisfaction. However, despite the clear advantages, challenges such as customization complexities, data integration issues, and resistance to change remain. As the pharmaceutical industry continues to evolve, ERP systems will remain a cornerstone of digital transformation, facilitating smarter decision-making, better resource management, and enhanced collaboration across global operations. This paper also identifies future trends, including the potential of AI and blockchain technologies in further strengthening ERP systems and transforming the pharmaceutical landscape.

Background: Electronic cigarette (e-cigarette) use and vaping tobacco have increased rapidly worldwide, raising concerns about their health effects, social acceptability, and regulatory challenges. In many countries, e-cigarettes are more commonly used by individuals from higher socioeconomic status (SES) backgrounds, who, in theory, should have greater knowledge about e-cigarettes and their associated risks. However, it remains unclear why a group with more knowledge about e-cigarette risks would also hold more positive attitudes toward vaping and exhibit higher usage rates — a phenomenon that may represent a knowledge-behavior paradox. Understanding this paradox, along with the complex relationships between e-cigarette knowledge, attitudes, and behaviors, is critical for informing effective public health interventions, campaigns, social media messaging, and regulatory policies. Objectives: This study aimed to evaluate the complex relationship between SES, e-cigarette knowledge, pro-vaping attitudes, and e-cigarette use. Methods: The SMOKES Study (Study of Measurement of Knowledge and Examination of Support for Tobacco Control Policies) used a multi-center, cross-sectional design, collecting data from 2,403 college and university students across 15 provinces in Iran (covering nearly half of the country's provinces). The survey measured family income, age, sex, ethnicity, e-cigarette use, knowledge, and attitudes. Structural Equation Modeling (SEM) was employed to examine the interrelations between SES, knowledge, attitudes, and behavior, while adjusting for age, sex, and ethnic minority status. Results: SEM analysis confirmed the hypothesized paradox. Although greater knowledge about e-cigarettes was linked to less favorable attitudes toward vaping and lower use, pro-vaping attitudes emerged as the strongest predictor of vaping behavior, while knowledge played a weaker protective role. Notably, individuals with higher SES simultaneously showed higher knowledge and, paradoxically, more pro-e-cigarette attitudes and greater usage. Female students and ethnic minority students reported higher correct knowledge and lower pro-vaping attitudes and use. Although age and higher family income were associated with more favorable attitudes, they did not directly predict vaping behavior. These results suggest that for higher SES individuals, poor knowledge is not the main driver of e-cigarette use; rather, their pro-e-cigarette attitudes, which seem to outweigh the influence of knowledge, play a key role. Conclusions: Although individuals from higher SES backgrounds report greater correct knowledge about e-cigarettes, this knowledge does not necessarily translate into reduced positive attitudes or lower usage. This study highlights the complexity of these paradoxical effects and suggests that public health strategies need to go beyond simple education and knowledge-based interventions. Targeted approaches should address industry messaging, challenge misconceptions, and strengthen regulatory efforts to reduce e-cigarette use among young adults, including those from higher SES backgrounds.

The pharmaceutical supply chain has become increasingly complex and vulnerable to various risks, including counterfeit drugs, diversion, and fraud. As these challenges threaten patient safety and the integrity of global healthcare systems, serialization has emerged as a pivotal innovation in pharmaceutical logistics and regulatory compliance. Serialization involves assigning unique identifiers to individual drug packages, enabling precise tracking and authentication at every stage of the supply chain. This process provides unprecedented transparency, enhances product security, and facilitates real-time monitoring of pharmaceutical products as they move from manufacturers to end consumers. Despite its potential to revolutionize pharmaceutical traceability, the integration of serialization technologies faces numerous obstacles. These include high implementation costs, regulatory inconsistencies across regions, and the technological challenges of managing vast amounts of data. Moreover, the complex, multi-tiered nature of the global supply chain introduces additional risks related to data integrity, cybersecurity, and interoperability between systems. As pharmaceutical companies seek to navigate these challenges, innovations in serialization technology—such as blockchain, artificial intelligence (AI), the Internet of Things (IoT), and radio frequency identification (RFID)—are providing promising solutions to enhance efficiency, reduce fraud, and increase visibility. This manuscript explores both the innovative advancements and the key challenges associated with the integration of serialization in the pharmaceutical supply chain. It delves into the evolving regulatory landscape, highlighting the need for global harmonization of serialization standards, and examines the impact of serialization on securing pharmaceutical distribution networks. Additionally, the paper emphasizes the importance of collaboration among manufacturers, technology providers, and regulatory bodies in overcoming implementation barriers and realizing the full potential of serialization. As the pharmaceutical industry moves towards a more interconnected and data-driven future, serialization promises to play a central role in shaping the next generation of drug safety and supply chain management. By addressing the hurdles to adoption and leveraging emerging technologies, the pharmaceutical sector can create a more secure, transparent, and efficient supply chain that better serves public health and fosters greater trust among consumers and healthcare professionals alike.

The synthesis and optimization of Active Pharmaceutical Ingredients (APIs) is fundamental to pharmaceutical drug development, directly influencing drug efficacy, safety, and cost-effectiveness. Over recent years, significant advancements in synthetic methodologies and manufacturing technologies have transformed API production. This manuscript provides an overview of the latest innovations in API synthesis, focusing on key techniques such as green chemistry, continuous flow chemistry, biocatalysis, and automation. Green chemistry principles, including solvent substitution and catalytic reactions, have enhanced sustainability by reducing waste and energy consumption. Continuous flow chemistry offers improved reaction control, scalability, and safety, while biocatalysis provides an eco-friendly alternative for synthesizing complex and chiral APIs. Additionally, the integration of automation and advanced process control using machine learning and real-time monitoring has optimized production efficiency and consistency. The manuscript also discusses the challenges associated with regulatory compliance and quality assurance, highlighting the role of advanced analytical techniques such as HPLC, NMR, and mass spectrometry in ensuring API purity. Looking ahead, personalized medicine and smart manufacturing technologies, including blockchain for traceability, are expected to drive further innovation in API production. This review concludes by emphasizing the need for continued advancements in sustainability, efficiency, and scalability to meet the evolving demands of the pharmaceutical industry, ultimately enabling the development of safer, more effective, and environmentally responsible medicines.

Bead jewellery is common in all Ghanaian communities. Bead jewellers are well-known for their important bead jewellery production and have advanced to the Master craft level. The industry can be utilized if the industry's difficulties are discovered and remedied. The study area for the research includes Ghana's Eastern, Ashanti, and Greater Accra Regions. The study's population included bead jewellers, bead jewellery firms, bead dealers, bead aficionados, clients, and bead makers. With a sample size of 30, data was gathered mostly through direct observations, interviews, and document reviews. The study went over the procedure for securing land for a bead jewellery workshop. According to a survey conducted by the Ghanaian Chamber of Commerce and Industry (GCCII), Ghanaian bead jewellers should use qualified designers. Cost pooling in legal services, accounting, and consulting is likely to save money.

Pharmaceutical supply chains are critical to ensuring the availability of safe and effective medications, yet they face numerous challenges that can jeopardize public health. This article provides a comprehensive analysis of the key issues impacting pharmaceutical supply chains, including regulatory compliance, demand forecasting, supply chain visibility, quality assurance, and geopolitical risks. Regulatory compliance remains a significant concern due to the stringent guidelines imposed by authorities such as the FDA and EMA, which can lead to increased operational costs and time delays. Additionally, traditional demand forecasting methods often fail to accurately predict fluctuations in drug demand, resulting in stockouts or excess inventory. Limited supply chain visibility further complicates these challenges, hindering timely decision-making and operational efficiency. Quality assurance is paramount, as maintaining the integrity of pharmaceutical products throughout the supply chain is crucial to preventing costly recalls and ensuring patient safety. Moreover, the globalization of supply chains introduces vulnerabilities to geopolitical risks, trade disputes, and natural disasters. In response to these issues, this article outlines strategic recommendations for optimizing pharmaceutical supply chains. These include leveraging advanced analytics and IoT technologies to enhance demand forecasting and visibility, strengthening compliance through automated systems and training, fostering collaboration among stakeholders, implementing robust risk management frameworks, and investing in quality management systems. By adopting these strategies, pharmaceutical companies can enhance the efficiency and resilience of their supply chains, ultimately ensuring the continuous availability of essential medications for patients worldwide. This analysis serves as a critical resource for industry professionals seeking to navigate the complexities of pharmaceutical supply chains in an increasingly dynamic global environment.

Drug shortages represent a significant and growing challenge within the pharmaceutical supply chain, with profound implications for patient care, public health, and healthcare costs. This manuscript provides a comprehensive examination of the causes and impacts of drug shortages, highlighting the multifaceted nature of this issue. Key factors contributing to shortages include manufacturing complications, limited availability of active pharmaceutical ingredients (APIs), market dynamics that discourage the production of less profitable medications, and regulatory challenges that slow down the approval process for new manufacturing capacities. The consequences of these shortages are far-reaching. Patients often face treatment delays, which can lead to adverse health outcomes, increased hospitalization rates, and even mortality. Healthcare providers experience heightened operational costs as they seek alternative therapies and manage complications resulting from inadequate treatment. Furthermore, the frequent occurrence of drug shortages erodes public trust in both the healthcare system and the pharmaceutical industry, leading to decreased patient adherence to prescribed therapies. To mitigate the impacts of drug shortages, this manuscript proposes several strategic solutions, including enhanced communication among stakeholders, diversification of supply sources, increased regulatory flexibility, and collaborative approaches between public and private sectors. Additionally, raising awareness among healthcare providers and patients regarding the causes and potential alternatives can empower stakeholders to navigate shortages effectively. Ultimately, addressing drug shortages necessitates a proactive and coordinated effort from all participants in the pharmaceutical supply chain. By implementing these strategies, stakeholders can enhance the resilience of the supply chain, ensuring that essential medications remain accessible and that patient care is not compromised. The findings of this manuscript underscore the urgent need for ongoing vigilance and collaborative action to tackle the challenges posed by drug shortages, safeguarding public health and improving healthcare outcomes globally.

Pharmaceutical Product serialization aims to assign distinct serial numbers to items within a pharmaceutical supply chain. However, this process faces several security challenges like Theft of valid serial numbers may occur, enabling the labelling of counterfeit products. Therefore, it's essential to ensure the uniqueness of serial numbers can be verified at any point in the product's lifecycle within the supply chain. Intimidatory nodes along the distribution network could corrupt planned changes of custody for products. Ensuring verifiability of compliance with these changes is crucial. Manufacturers and consumers need assurance that perishable goods with expired shelf lives are appropriately discarded. In this paper, we review a product serialization method leveraging blockchain technology to address these security concerns within a multi-party perishable goods supply chain. Blockchains offer potential solutions by providing a secure platform for data sharing in multi-party environments, enhancing security and transparency. Within Blockchain technology, each distribution partner is registered to uphold transparency regarding drug information. The system facilitates real-time transfer of ownership changes, recording them as blocks with date and time stamps. This ensures visibility to all partners in real time, maintaining the authenticity of drugs. This article aims to outline how Blockchain technology benefits the pharmaceutical industry by enhancing traceability and trackability of drugs throughout the entire pharmaceutical supply chain.

This study examines the influence of socio-demographic factors on work-family conflict among management staff in star-rated hotels in the Accra Metropolis. The research aims to explore how variables such as gender, age, marital status, and educational background impact the experiences of work-family conflict among hotel managers. A quantitative research design was employed, and data were collected through structured questionnaires distributed to a purposive sample of hotel managers. The findings reveal significant associations between socio-demographic characteristics and work-family conflict, highlighting the complexities of balancing work responsibilities with family obligations in the hospitality industry. The implications of the study underscore the importance of tailored interventions to support the well-being of hotel managers, while the recommendations emphasise the implementation of work-life balance programs, support for family obligations, promotion of diversity and inclusion, and continuous training and development. It is recommended that hotel organisations create a supportive work environment that enhances the job satisfaction and productivity of their management staff. This study contributes valuable insights to the existing literature on work-family conflict in the hospitality sector and provides a foundation for future research endeavours in this area.

Consumer behavior is evolving, demanding a wide range of products with fast shipping and reliable service. The automotive aftermarket industry, worth billions, requires efficient distribution systems to stay competitive. Manufacturers strive to balance growth with product and service excellence. Distributors and retailers face the challenge of maintaining competitive pricing while keeping inventory levels low. An adequate supply chain and accurate product data are crucial for product availability and reducing stock issues. This ultimately increases profits and customer satisfaction.

Failure prediction can be achieved through prognostics, which provides timely warnings before failure. Failure prediction is crucial in an effective prognostic system, allowing preventive maintenance actions to avoid downtime. The prognostics problem involves estimating the remaining useful life (RUL) of a system or component at any given time. The RUL is defined as the time from the current time to the time of failure. The goal is to make accurate predictions close to the failure time to provide early warnings. J S Grewal and J. Grewal provide a comprehensive definition of RUL in their paper "The Kalman Filter approach to RUL estimation." A process is a quadruple (XU f P), where X is the state space, U is the control space, P is the set of possible paths, and f represents the transition between states. The process involves applying control values to change the system's state over time.

The technologies for cloud ERP (Enterprise Resource Planning) have revolutionized the field of information technologies. Any kind of business can benefit from their flexibility, affordability, scalability, adaptation, availability, and customizable data. An advancement of classic ERP, cloud enterprise resource planning (C-ERP) provides the benefits of cloud computing (CC), including resource elasticity and ease of use. The rise of cloud computing affects on-premise ERP systems in terms of architecture and cost. Cloud-based ERP systems make the claim to be appropriate for digital corporate settings. System quality, security, vendor lock-in, and data accessibility are recognized as the technological issues. Industry 4.0 refers to the re-engineering and revitalization of modern factories through the integration of cloud-based operations, industrial internet connectivity, additive manufacturing, and cybersecurity platforms. One of the four main pillars of Industry 4.0, cloud-based Enterprise Resource Planning (Cloud ERP), is a component of cloud operations that aids in achieving greater standards of sustainable performance.

Currently, Drug Counterfeiting is the biggest challenge facing the pharmaceutical industry. They are encountering this threat due to high market demand for the drugs and their profit margin. The lack of data transparency and traceability also lured criminals into the counterfeiting of drugs which, is impacting people’s health and put their life in danger. Through the drug supply chain, a substantial portion of counterfeit drugs are injected and distributed through the healthcare supply chain network, so the supply chain plays a vital role in drug distribution and impacts patient lives. Through digitalization in the healthcare sector, Distributed Ledger Technology (DLT) provides a platform with ground-breaking results by providing a system for drug traceability with consideration of the critical requirements of transparency, privacy, and authenticity without involving any third party. In DLT, each distribution partner is registered to maintain transparency with the drug information. Real-time transfer of information about the change of ownership with date and time in the form of blocks gives visibility to all the partners in real time about the authenticity of drugs. This article will give information about the benefits of Distributed Ledger Technology to the pharmaceutical industry and the traceability of drugs from end-to-end of the pharmaceutical supply chain.

The role of enterprise applications in pharmaceutical industries is driving the digital transformation of various critical processes, and one process benefiting from this innovation is pharmaceutical drug traceability. This industry grapples with challenges like a lack of transparency, difficulties in tracking products, a deficit of trust, and issues related to shipping expired products. To address these concerns, blockchain technology as an enterprise application has been harnessed as a solution. Notably, counterfeit drug prevention emerged as the most prevalent category, aligning with the pharmaceutical industry's primary objective. Blockchain technology is an emerging innovation that is finding enterprise applications in various industries, including healthcare. In the healthcare sector, Blockchain networks are being utilized to securely store and exchange patient data across hospitals, diagnostic laboratories, pharmacies, and medical practitioners. These enterprise applications can effectively identify and mitigate critical errors, including potentially hazardous ones within the realm of healthcare. Consequently, this enterprise technology holds the promise of enhancing the efficiency, security, and transparency of medical data sharing within the healthcare system. Moreover, it offers valuable tools for medical institutions to gain insights and improve the analysis of medical records. It visually represents the diverse capabilities, enablers, and the unified workflow process of Blockchain technology in supporting healthcare on a global scale. Additionally, the paper presents a thorough discussion of fourteen significant applications of Blockchain in healthcare, underscoring its pivotal role in addressing issues like deception in clinical trials.

Pharmaceutical drug traceability is a regulatory compliance adopted by most nations in the world. A comprehensive analysis was carried out to explain the benefits of adopting enterprise system for pharmaceutical drug traceability. Counterfeit drugs are medicines that are fake and have been produced using incorrect potency, or incorrect ingredients used to manufacture these drugs. Solving the drug counterfeiting problems by identifying the most effective and innovative technologies for protecting people's health is of essence these days for the world. Drug serialization is essential concept for drug traceability in the pharmaceutical supply chain. Blockchain is the latest stringent technology that makes drug distribution more secure in the supply chain. The blockchain-based drug traceability is a distributed shared data platform that shares information that is irreversible, reliable, responsible, and transparent in the PSC. Blockchain uses two powerful module, Hyperledger Fabric and Besu to satisfy important criteria for medication traceability, such as privacy, trust, transparency, security, authorization and authentication, and scalability. Researchers in Health informatics can use blockchain designs as a useful road map to develop and implement end-to-end pharmaceutical drug traceability in the supply chain to prevent drug counterfeiting. Industrial IoT is also a key component for the pharmaceutical industry. IoT systems in pharmaceutical drug traceability can be beneficial as they are based on automation and computational methodologies.

For conducting advanced analytics initiatives to acquire in-depth data into usage habits, regional access, sales, and promotional success, etc., unique identification of packaged pharmaceuticals will be a fantastic enabler. The main objective of this study is to prevent and reduce the production of erroneous and counterfeit drugs using the enterprise system, which has become a serious threat because it damages the reputation of legitimate drug manufacturers by trying to produce and market placebo medications that are identical to the real thing. Due to federal government procedures and priorities that frequently change over time, the majority of implementation takes time. To achieve compliance with numerous federal regulatory authorities, including drug traceability for patient safety, the pharmaceutical industry must implement a systematic procedure in an ERP environment. The goals would be to guarantee medical drug traceability and provide real-time warnings to supply chain stakeholders and regulatory bodies to maximize the benefit of integrating a drug traceability system into an ERP environment. Additionally, manufacturers are compelled to maintain product costs on the higher side due to a heavy burden of unchecked manufacturing cost spikes. As a result, innovative marketing schemes must be introduced in order to increase the reach to consumers by putting into practice successful strategies.

The paper analyses the extent to which crude oil price shocks impact GDP growth, exchange rate, interest rate and inflation of an emerging oil exporting economy, Ghana. The Structural Vector Autoregressive model is used to analyse the quarterly data from 2009q1 – 2020q4. The results showed that exchange rate and GDP growth respond positively but temporal to the impulse of crude oil price. In contrast, inflation and interest rate respond negatively to crude oil price shock. Specifically, the exchange rate appreciates in the initial quarter and begins to depreciate, whereas GDP growth experiences an increase in the first two quarters and also reduces afterwards. Crude oil price shocks to the Ghanaian economy follow the conventional behaviour of the impact of crude oil on macroeconomic indicators. The positive impact of the price shock on GDP growth and exchange rate is not much reflecting the fact that Ghana is an emerging oil-producing country with low production and export level. Ghana’s prospects in the oil and gas sector should not just be a mere hoax. Policies should be directed toward petroleum exploration and production efforts since the energy transition endanger benefits for future exploitation. Policies should be implemented to attract competitive players locally and internationally in the oil industry. The shock of crude oil prices is beginning to show evidence based on this study. Therefore government must consider recognising the importance of other economic sectors in order not be become heavily dependent on oil.

One of the current issues facing humanity is ensuring sustained global food security in the face of devastating effects of climate change; this challenge is particularly pressing on the African continent. Here, I present an opinion piece identifying local animal genetic resources as "African leverage point" that provide the highest chances to cushion rural fork to climate change, enhance environmental sustainability and food security in Africa. When it comes to boosting food production, coping with climate change, or bolstering the delivery of a wide range of ecosystem services, I believe that African animal genetic resources are essential alternatives for the sustainable growth of the livestock industry and its contribution to food security. Africa needs to address the support and development of indigenous animal genetic resources in order to meet the basic food needs of more than 1 billion people, address numerous environmental issues with continental implications, and focus on more effective and resilient food systems with the greatest impact on food security. The indigenous animal resources diversity and support actions to this unique group could provide a boost in protein that is lacking to constitute healthy diets in Africa. The priorities of nonprofit organizations, foundations, governments, citizens' groups, and companies can be influenced by this leverage point in the African food system. Due to continuous food insecurity, which appears to be becoming worse with climate change and makes it even harder to accomplish the SDGs on the continent, Africa has paid a hefty price for being misled about the worth of its own animal genetic resources. To the contrary, it is highly improbable that a strategy to improve food security and rural livelihoods that undermines the utilization of indigenous animal genetic resources will be viable in long-term. If Africa makes an effort, is committed, and fully commits resources to putting indigenous animal genetic resources at the forefront of combating food insecurity and accelerating the achievement of SDGs, it can achieve more under the adverse prevailing climate change induce environmental conditions. Our personal opinion is that we would not have had the ongoing food problems, even in the face of climate change, if Africa had over the years implemented the necessary mechanisms to develop and promote local animal genetic resources. What lies ahead in terms of climate change effect on food security in Africa is anyone's guess – but whatever it is, promoting continental adapted indigenous animal genetic resources portfolio is ready to handle it. Development and promotion of African animal genetic resources should be part of a continental strategy to transform smallholder animal production by 2050, in line with the goals of achieving the SGDs, to improve rural household food security, and bringing rural economy prosperity, resilience, sustainability, and all other desired animal related food outcomes for rural healthy diets. African animal genetic resources are the most important but underutilized resource to address the issue of ongoing food insecurity. The responsible use of local animal genetic resources through climate smart animal husbandry practices also contributes to food security, rural development and increased employment opportunities. African genetic improvement programs involving indigenous animal genetic resources must be considered as regards to local agriculture and livestock development aspirations, appropriateness to local reality and livelihood security, as well as environmental friendliness. Animal agriculture will fill in the enormous gaps in the continent's food supply if this animal group receives adequate attention and is used integrated properly in crop and livestock systems which characterize smallholder farming sector in Africa. Because they have evolved over time to accommodate the various climatic conditions and environmental pressures on the continent, Africa's native animal genetic resources are particularly resilient. Indirectly, the impact of climate change offers a chance to use native animal genetics from Africa. The use of local animal genetic diversity has the potential to substantially improve Africa's food security landscape hence should be given special consideration for sociocultural, environmental, and economic aspects, and with regard for smallholder farmer-specific factors of interest. African animal genetic resources have contributed significantly to the food and nutrition security aspects of the millions of people in their communities of origin and custody in Africa. The purpose of the perception piece is to educate the reader about the fundamental mechanisms that control the use of continental animal genetic resources and how the outlook for these mechanisms can be manipulated in the future for the benefit of improving food security in Africa. The discussion provides in-depth insight into the pertinent literature in understanding the significance of local animal genetic resources in terms of their contribution to food security in Africa.

Purpose: The model of CSR devised by the American scientist ″Archie B. Carroll in 1991″ - which became well- known in academic circles as Carroll′s CSR pyramid, has been criticized by academics and researchers. The researcher firmly believes that one of the most important reasons that led to the emergence of these criticisms was Carroll's adoption of the idea of the pyramid as a form to illustrate his idea of CSR. The content of the pyramid does not match the accompanying explanations given by Carroll. Carroll's CSR pyramid does not reflect the concept of simultaneous CSRs. Also, Carroll's CSR pyramid does not reflect the logical interaction and interrelationships among CSRs. It is also noted that, Carroll's pyramid reflects the expectations of stakeholders and does not reflect the expectations of companies from their commitment to their social responsibilities. This study aimed to design a model for CSR, in which the content of its figure matches the explanations attached to it, and reflects the concept of simultaneous CSRs, in addition to the logical interaction and the interrelationship among those responsibilities. Method: The researcher used the concept of precedence as an approach to explain the logical interaction and interrelationships among CSRs. The idea of precedence is clearly found only in the railway industry. The interaction of CSRs with each other can be likened to the interaction of the components of a classic train (a cockpit and coaches). Therefore, the researcher adopted the idea of the classic train as an innovative model to address some of the shortcomings of Carroll ′s pyramid. Battal's CSR train as an innovative model- in this study, can reflect the notion of simultaneous CSRs, the logical interaction and interrelationships among CSRs, and expectations and aspirations of both stakeholders and companies. Result: By adopting the idea of the classic train work and its components, this study was able to produce a model of CSR as an alternative model for Carroll′s pyramid (1991). Originality/Value: Battal ′s CSR train is an educational model that is designed to address some of the shortcomings of Carroll's CSR pyramid "as a figure and content." The content of Battal′s CSR train matches the attached explanation, and this will help to understand how CSRs interact with each other, as well as the concept of simultaneous CSRs. It is an enrichment of the thought of CSR.

The Blockchain technology uses different type of database that transact data in blocks and then chained each data blocks. When data fills one block, it is chained the same data onto previous block therefore, all the data blocks are stored in chronological order. The blockchain technology can help to ensure that data in supply chain is reliable, accurate and can be shared with all stakeholders without any manipulation. It makes supply chain more transparent and end-to-end digital visibility of data source. Blockchain is stringent technology which fill the gap between stakeholders by providing transparent unmanipulated and truthful data in supply chain. There are many initiatives has been taken by multiple regulatory bodies with partnering with supply chain stakeholders including pharmaceutical manufacturer, distributors and dispensers. In USA, Food and Drug Administration’s regulatory body Drug Supply Chain Security Act (DSCSA) initi-ated pilot program in 2019 with the partnership of leading pharmaceuticals distributors to au-thenticating saleable returns to distributor from supply chain partners. DSCSA used MediLedger blockchain interoperable technology for process evaluation and it robustness for mitigating risk of drug counterfeiting possibilities in supply chain. The DSCSA found that pilot project was suc-cessful and results were astonishing. Later it was declared that blockchain technology can also be used for meeting DSCSA’s 2023 Act for unit level traceability.

The healthcare access is fundamental rights for every human being. It is Governments responsibility to provide good healthcare services and infrastructure to its citizen. Since last few decades, Government and healthcare industries are struggling to minimize the adverse events impacting people health due to fake medicine. The world health organization also predicted that 4 out of 10 medicines in developing and poor countries are either fake or potentially adulterated. Counterfeit drugs cost billions of dollars deficit to world economy and reduce research and development (R&D) funds allocation from organizations. Stopping counterfeit medicine into supply chain is main challenge for Government and regulatory authorities. The Government and regulatory authorities are now making stringent guidelines to prohibit criminals and counterfeiters to supply fake medicine in markets. Healthcare industry need stringent regulations and secure technologies provide sage and authentic drugs to patients. The FDA has published the 10 years roadmap to implement the drug traceability in United States. The Healthcare Distribution Alliance (HDA) has also mandated to print several barcodes and human readable data in product packaging hierarchy. The FDA is participating in pilot project with leading pharmaceutical drug manufacturer and wholesales to use blockchain technology in interoperable digital network for securing digital traceability data transfer between authorized trading partners.

The purpose of the study was to examine the Motives of Tourists; Socio-Economic and Challenges of tourism in Kwahu in the Eastern Region of Ghana The study adopted a descriptive survey research design. The population of the study comprised six (6) communities (Mpraeso, Atibie, Obomeng, Obo Oworobong, and Nketepa in Kwahu South District Assembly (KSDA) in the Eastern Region of Ghana. Purposive and convenient sampling techniques were employed to select two hundred (200) respondents for the study. The main instrument used for data collection was questionnaires. The study employed the statistical package for social sciences (SPSS) to code and process the collected data. Descriptive and relational statistical techniques involving frequencies, percentages, summations, diagrams, and tables were employed in analysing the data. The Chi-square test analysis was used to explore the relationships and differences in perceptions. The study indicated that every tourist, whether local (Ghanaian) or foreign, had at least one of the following motives in mind for participating in the festival; To socialize; For relaxation; For education to participate and witness the paragliding festivals; To take photographs of festival scenes; Other motives like to sell items, especially souvenirs. The study also revealed that the KEF has had some positive socio-economic impact or implications on the area. These among others include: job creation, income generation for locals of the area, infrastructural development, and projection of the image of the area as the festival has become one of the biggest gatherings of revellers in the country, drawing people from all walks of life, nationally and internationally as a result of the introduction of paragliding since 2005, socialization enhancement, medium for cultural exchange and education, and finally serves as a medium for portraying the cultural identity of the people of Kwahu. The study also indicated that the major challenges encountered by tourists during the event were listed in order of degree of intensity: High cost of living, poor road network in the area, intermitted electricity and water supply, poor sanitary conditions in the area, poor health facilities, and unwelcoming attitude of some local residents of the festival area. It is recommended that, residents must be educated about the potential benefits of tourism as an industry helping to achieve sustainable community development. It is also recommended that Ghana tourism authority and Kwahu District Assembly should collaborate to improve on social amenities in the municipality to attract more foreign and local tourists during the festivity.

Electronics and computer engineering is a scientific and technical field in which electronic and computer systems are studied and their use. They are related to the technical professions within which electronic devices, systems, and computer programs are realized. The past half-century has been greatly marked by the development of electronics, computing, and their application in communications, medicine, and industry. This field has become the fundamental technical infrastructure of modern society. Today, there is almost no electronic device that does not use an electronic computer, and there is also not a single computer that is almost exclusively made of electronic components. This article explores the need for electronic components in digital computing.

The duck industry makes an important contribution to the availability of animal protein sources in Egypt, little known about the genetic parameters, particularly the heritability and genetic correlations of body weight and egg production in ducks. Body weight is the most essential feature for genetic improvement due to its ease of selection, high heredity, and large impact on meat production costs. The target of this study was to evaluate and explain genetic parameters such as the heritability, the genetic and phenotypic correlations, and sire breeding value in Domyati (local) and Khaki-Campbell (foreign) ducks in order to improve body weight and egg traits. A total of 160 (80 Domyati and 80 Khaki-Campbell ducks utilized to measure body weight at 16 and 20 weeks g), as well as 7000 eggs (2500 Domyati and 4500 Khaki-Campbell) to measure egg traits (the egg number, egg weight, and egg mass are all measured throughout the first 90 days of laying). In Domyati and Khaki-Campbell ducks, the heritability estimated for body weight was moderate to high, ranged from 0.35 to 0.40, and 0.21 to 0.30 for egg production. The genetic correlations among body weight and egg traits were all positive and had high values, also among BW16 and BW20 were stronger (0.90); (0.99). So the genetic improvement in BW16 could be followed by an increase in BW20 weeks. It concluded that, the relatively high value of genetic heritability for body weights and egg traits in Domyati and Khaki-Campbell ducks, indicates that it is possible to genetically increase body weight and egg traits through selection and subsequently inbreeding to divide the herd into groups that are selected among themselves to keep their sons.

A survey on food choices with a randomized sample population of individuals using various social media in Nigeria was conducted during the COVID1-19 pandemic. The data generated was subjected to basic standard statistical analysis. The parameters indicated that 94% of the population is young adults, 58.9 % percent are city dwellers, 63.6% are students, 23.4 % are into business, 86.9% are graduates; 73.8% consume various diets, 23.4% are vegetarians and only 2.8% fed only on proteins, 30.8% of them go on two meals per day. The most choices on influence on food purchases decision are hunger (26.2%), mood (26.2%), past experience (45.8%), quality of the food products (66.7%), cost of the food products (50.5%) and government approval (28%). Also,other most preferred choices are for self-prepared food (40.21%), enhanced local diets (36 %), and a blend of foreign and local diets purchases (24%). Other highest choices include: easy preparation (37.4%), shelf life (29%); cute packaging (23.4%), swelling property preference (20.6%), minimal cooking time and energy preference (37.4%). The weighted sum, index and rank on factors influencing food choices showed that the influence of quality of food product ranked highest, followed by influence on cost. Also preference for enhanced local healthy diets to foreign ranked highest, minimal cooking time and energy costs ranked highest. These nutritional adaptations have implications to individuals, food scientists, manufacturers in the food industry, food regulatory agencies, government and other decision bodies.

In this paper, we propose to design an indoor sign detection system for industry 4.0. In order to implement the proposed system, we proposed a lightweight deep learning-based architecture based on MobileNet which can be run on embedded devices used to detect and recognize indoor landmarks signs in order to assist blind and sighted during indoor navigation. We apply various operations in order to minimize the network size as well as computation complexity. Internet of things (IoT) presents a connection between internet and the surroundings objects. IoT is characterized to connect physical objects with their numerical identities and enables them to connect with each other. This technique creates a kind of bridge between the physical world and the virtual world. The paper provides a comprehensive overview of a new method for a set of landmark indoor sign objects based on deep convolutional neural network (DCNN) for internet of things applications.

Like electricity, hydrogen is an excellent energy carrier, as it can be produced from many different and abundant precursors, such as natural gas, coal, water, and renewable energy. The use of hydrogen in fuel cells, particularly in the transport sector, will make it possible to diversify the energy supply, take advantage of domestic resources, and reduce oil imports dependence. Unlike other fuels, hydrogen (H₂) can be generated and consumed without emitting carbon dioxide (CO₂). This results in great ecological benefits and fundamental challenges. Hydrogen can operate in a closed and inexhaustible cycle based on the cleanest, most abundant, and elemental substances: water, oxygen, and hydrogen. If hydrogen is generated using light, heat, and electricity produced from solar, wind, or nuclear energy, hydrogen becomes a versatile and universal means of storing and transporting energy and a necessary element for future energy systems that operate without environmental pollution, CO₂, and other gases that contribute to the greenhouse effect. Hydrogen is necessary to eliminate environmental pollution and stabilize the composition of the planet’s atmosphere and climate. This paper investigates different methods of hydrogen production in the term of their technological and economic aspects. This paper shows that thermochemical methods dominate the hydrogen market while emerging electroreduction methods are developing fast, which might turn the tide in the future.

This article describes the optimization models recently applied to the design and operation of power systems towards forming smart grids and identifies trends, barriers, and possible gaps in this area. Models are described to optimize the design and operation of power systems considering renewable energies, distributed generation, microgrids, demand management, and energy storage systems. It was concluded that it is necessary to validate many of the models formulated recently to optimize the operation through tests with real data and on a large scale. Furthermore, demand management and microgrids are aspects in which it is necessary to develop models for optimal power flow. Finally, it is necessary to predict stochastic variables with greater precision so that these models adapt to the real behavior of the system.

In this study, the effect of energy cost liberalization policies on the cost-effectiveness of wind farms compared to gas power plants has been investigated. To calculate the cost of electricity generation from various sources, including wind energy and fossil fuels, the “levelized cost” method has been used as the most acceptable economic method to compare different types of electricity generation technologies. In addition, calculations related to the cost of electricity, different fuel prices, different technologies of wind farms, and different prices of different types of fuel are considered. Based on the results of this research, it was found that by targeting fuel prices in the country, wind farms are quite cost-effective, and considering the cost of the opportunity to use diesel in the country, the construction of these power plants can provide the possibility of exporting this fuel. This cost also increases with the production of valuable equipment in the country. The important point is that the macro policy should localize the wind turbine industry with priority to its valuable parts and components due to fuel price changes. This policy will continue to make this type of power plant cost-effective in the country and the employment of specialists in the country.

We can’t stop breathing, but we can do something about the quality of air that we breathe. Clean fresh air is indispensable ingredient for a good life quality. Individuals poses the right towards expecting that the breathed air will not harm people. Thus, fighting air pollution will not only improve health outcomes, productivity, and well-being, it’s also essential toward reducing the emissions of greenhouse gas as well as fighting climate change. For examples, a third of the global population is at risk from unhealthy of ambient air pollutants concentrations, with the loss of approximately 6.4 million healthy-life-years attributed specifically to chronic exposure to ambient particulate matter. Expert panels have consistently rated air pollution as a greater health hazard than water pollution. Pollution of air is the leading source of unexplained and undiagnosed diseases, besides have remained associated with a variety of serious human health risks, and in fact, a threshold has not been established under which these pollutants exert no adverse effects. This study evaluates ambient air quality at major sawmill sites in Ilorin Metropolis, Kwara State, Nigeria. “Measurements of Air pollution were accurately carried out using direct reading, automatic in situ gas monitors; Hand held mobile multi-gas monitor with model AS8900 [Combustible (LEL), and Oxygen (O₂)], BLATN with model BR – Smart Series air quality monitor (PM₁₀, Formaldehyde) and air quality multimeter with model B SIDE EET100 (Dust (PM_2.5), VOC, Temperature and Relative Humidity)”. The outcomes disclosed among others, the average concentrations of CO, O₂ as well as other measured parameters for instance formaldehyde (HcHo) etc., they are also consistently low as well as within acceptable range in terms of National as well as Global monitoring standards for air quality indices. However, there are few exceptions for instance the average volatile organic compounds (VOCs) concentrations, PM_2.5, PM₁₀ as well as Combustible (LEL) respectively, which are higher when compared to National and Global standards. This high figure is due to pollutant amount existing in the sawmills air environment resulting from input of influents from activities of the sawmill. However, as a result, air pollution in the city of Ilorin is found to be increasingly polluted and are of major health concern because of their synergistic action. Due to the high evidences and values, it can lead to a remarkable rise in over-all figure of hospital visits/ patients’ admissions with acute respiratory illnesses as soon as air pollutants level remained high. Hence, there is the need for an aggressive control of ambient air pollution.

The manufacturing industry has embraced modern technologies such as big data, machine learning, and artificial intelligence. This paper examines AI and machine learning developments in the manufacturing industry, comparing current practices and data-driven projects. It aims better to understand these technologies and their potential benefits and challenges. The research identifies opportunities for innovative business solutions and explores industry practices and research results. The paper focuses on implementation rather than technical aspects, aiming to enhance knowledge in this area.

The advancement of modern manufacturing is synonymous with the growth of automation. Automation replaces human operators, improves productivity and quality, and reduces costs. However, the initial financial cost and knowledge requirements can be barriers to embracing automation. Manufacturers are now seeking smart manufacturing, known as the fourth industrial revolution. Smart manufacturing goes beyond automation and utilizes IoT, AI, and big data for optimized production. In a smart factory, production can be linked and controlled innovatively, leading to increased performance, agility, and reduced costs.

The unrelenting proliferation of data, entwined with the prevalence of mobile devices, has given birth to an unprecedented growth of information obscured by noise. With the Internet of Things and myriad endpoint devices generating vast volumes of sensitive and critical data, organizations are tasked with extracting actionable intelligence from this deluge. Governments and enterprises alike, even under pressure from regulatory boards, have strived to harness the power of data and leverage it to enhance safety and security, maximize performance, and mitigate risks. However, the adversaries themselves have capitalized on the unequal battle of big data and artificial intelligence to inflict widespread chaos. Therefore, the demand for big data analytics and AI/ML for high-fidelity intelligence, surveillance, and reconnaissance is at its highest. Today, in the cybersecurity realm, the detection of adverse incidents poses substantial challenges due to the sheer variety, volume, and velocity of deep packet inspection data. State-of-the-art detection techniques have fallen short of detecting the latest attacks after a big data breach incident. On the other hand, computational intelligence techniques such as machine learning have reignited the search for solutions for diverse monitoring problems. Recent advancements in AI/ML frameworks have the potential to analyze IoT/edge-generated big data in near real-time and assist risk assessment and mitigation through automated threat detection and modeling in the big data and AI/ML domain. Industry best practices and case studies are examined that endeavor to showcase how big data coupled with AI/ML unlocks new dimensions and capabilities in improved vigilance and monitoring, prediction of adverse incidents, intelligent modeling, and future uncertainty quantification by data resampling correction. All of these avenues lead to enhanced robustness, security, safety, and performance of industrial processes, computing, and infrastructures. A view of the future and how the potential threats due to the misuse of new technologies from bandwidth to IoT/edge, blockchain, AI, quantum, and autonomous fields is discussed. Cybersecurity is again playing out at a pace set by adversaries with low entry barriers and debilitating tools. The need for innovative solutions for defense from the emerging threat landscape, harnessing the power of new technologies and collaboration, is emphasized.

The equipment used in the construction industry is usually associated with a high impact on the environment. Although sustainable design has shown to be a main player among the initiatives focused on reducing environmental impact, it has been driven by the workers and processes, leaving the equipment endeavors in more restrictive and later stages. The equipment industry has been a constant target of environmental standards and economic pressure, but the increasing technological development allows it to respond to sustainability and safety expectations while enhancing its performance. However, there are still several limitations that lead this sector to be one of the last to reach upgrading levels in terms of development. A study identified some gaps in the equipment design that require a greater effort to effectively support the workers and companies towards sustainable construction. This chapter is based on a study aiming to understand the consolidated knowledge of technologically sustainable equipment design and to identify the challenges left for its full development. The findings support the development of innovative eco-friendly equipment, taking into consideration sustainable materials and product guidelines, as well as green economy initiatives. It also supports complex system approaches and safety by design specificities to establish a corporate knowledge of sustainable equipment and align it with the new regulations of the construction industry. The chapter introduces the context of construction equipment in terms of new challenges when faced with the need to provide construction work with a greater capacity for safety, from an environmental and energy efficiency perspective, and within the paradigm of sustainability. Then, it presents the concept of sustainable equipment considering its principles, followed by a characterization of the agents involved in its life cycle.

Factory owners and vehicle manufacturers increasingly opt for predictive analytics to inform their decisions. While predictive analytics have been proven to provide insights into the initiation of maintenance measures before a machine actually fails, the right models and features could have a significant impact on the budget spent and resources allocated. This means that financially oriented questions need to at least partially guide the decisions in the planning phase of data science projects. Data-driven approaches will play an increasingly important role, but only a few of the firms that were confident performed logistic regression models for predictive maintenance. Also, from the available knowledge, data-driven classification models connecting vehicle component failures and the occurrence of delays at the assembly line have not been published. This paper utilizes a real-world data-driven approach using classification models in predictive analytics by vehicle manufacturers and thereby links the financial implications of such data science projects to their results. We expand the existing literature on predictive maintenance and possess a unique dataset of newly launched series of vehicles, presented as-is. Our research context is of interest to researchers and practitioners in the automotive industry that manage and plan the final vehicle assembly with just-in-time principles, factoring the consequences of component failures on the assembly process. Key findings of this paper highlight that while minor tweaking of the models is possible, their potential input in decision-making processes for budget optimization is limited.

Progress in the development and adoption of technological innovations is instrumental in enhancing the efficiency of production systems across the globe. Through the introduction of cost-efficient and high-performing technologies, countries can both reduce the resource use intensity of their economies and boost the global supply of essential products. The focus of this study is to analyze the application of advanced machinery and mechanisms within the agricultural sector, a primary industry that acts as a major contributor to the gross domestic product (GDP) of many nations. Specifically, this paper provides an in-depth review of the latest impact assessments based on analytical and modeling tools conducted on agricultural machinery and production technologies. Our findings highlight the positive role played by scientific progress and innovation in driving the competitiveness, growth and improved sustainability of the agricultural sector. Over the years, advanced technologies have accelerated the development and modernization of machinery, equipment, and processes in farming. Typically, modern machinery and equipment have enabled large-scale production on farms, enhancing the cost-efficient use of both land and labor, as well as the capacity and timeliness in performing essential agricultural operations. The rapid diffusion of technical advancements has further contributed to resource savings, productivity growth, and the overall transformation of agricultural value chains. Accordingly, the implementation of appropriate enabling conditions is of vital importance in encouraging the widespread integration of technologies in agriculture, not only boosting productivity along the agri-food chain but also yielding widespread social, economic, and environmental benefits.

This charge-ahead paper suggests that transitioning the automotive industry towards a zero-carbon ecosystem from material to end-of-life can be accomplished through disruptive zero-carbon manufacturing in the broad area of all-electric vehicle production technology. To accomplish zero carbon emission automotive manufacturing in the vehicle assembly domain, future paradigms must converge on the decoupling of carbon dioxide emissions from automobile manufacturing and use the design, processing, and manufacturing conditions. The envisioned zero carbon emission vehicle manufacturing domain consists of two complementary components: (a) making more efficient use of energy and (b) reducing carbon in energy use. This paper presents the status of key scientific and technological advancements to bring the manufacturing model of today to a zero-carbon ecosystem for the entire automotive industry of tomorrow. This paper suggests the groundbreaking application of dynamic and distributed predictive scheduling algorithms and open sensing and visualization technology to meet the zero carbon emission vehicle manufacturing goals. Power-aware high-performance computing clusters have recently become a viable solution for sustainable production. Advances in scalable and self-adaptive monitoring, predictive analytics, timeline-based machine learning, and digital replica of cyber-physical systems are also seen co-evolving in the zero carbon manufacturing future. These methods are inspired by initiatives to decouple gross domestic product growth and energy-related carbon dioxide emissions. Stakeholders could co-design and implement shared roadmaps to transition the automotive manufacturing sector with relevant societal and environmental benefits. The automated mobility sector offers a program, an industry-leading example of transforming an automotive production facility to carbon neutrality status. The conclusions from this paper challenge automotive manufacturers to engage in industry offsetting and carbon tax programs to drive continuous improvement and circular vehicle flows via a multi-directional zero-carbon smart grid.

The growing complexity of the operating environment urges pharmaceutical innovation. This essay addresses the need for the integration of advanced technologies in the pharmaceutical supply chain. It justifies the value proposition and presents a concrete use case for the integration of deep learning insights to make data-driven decisions. The supply chain has always been a priority for the pharmaceutical industry; research and development recognizes companies' increasing investment in big data strategies, with plans for a CAGR in big data tool adoption. The work presented herein has a preliminary explorative character to recuperate and integrate evidence from partly overlooked practical experience and know-how. The practical relevance of the essay is directed toward practitioners in pharmaceutical production, supply chain management, logistics, and regulatory agencies. The literature has shown a long-term concern for enhanced performance in the pharmaceutical supply chain network. This essay demonstrates the application of deep learning-driven insights to reveal non-evident flow dependencies. The main aim is to present a comprehensive insight into deep learning-driven decision support. The supply chain is portrayed in a holistic manner, seeking end-to-end visibility. Implications for public policy are discussed, such as data equity: many countries are protecting their populations and economic growth by building resilience and efficiency to ensure the capacity to move goods across supply chains. The implementation strategy is covered. The combined reduction of variability, efficiency as matured richness, reliability (on stochastic flows and their understanding through deep learning and data), and system noise (increased dampening through the inclusiveness of all stakeholders) results in increased responsiveness of supply chains for pharmaceutical products. Future work involves the integration of external data, closing the loop between planning and its application in reality.

Managing supply chains efficiently has become a major concern for organizations. One of the important factors to optimize in supply chain management is logistics. The advent of technology and the increase in data availability allow for the enhancement of the efficiency of logistics in a supply chain. This discussion focuses on the blending of analytics with innovation in logistics to improve the operations of a supply chain. An approach is presented on how predictive analytics can be used to improve logistics operations. In order to analyze big data in logistics effectively, an artificial intelligence computational technique, specifically deep learning, is employed. Two case studies are illustrated to demonstrate the practical employability of the proposed technique. This reveals the power and potential of using predictive analytics in logistics to project various KPI values ahead in the future based on the contemporary data from the logistics operations; sheds light on the innovative technique of employing deep learning through deep learning-based predictive analytics in logistics; suggests incorporating innovative techniques like deep learning with predictive analytics to develop an accurate forecasting technique in logistics and optimize operations and prevent disruption in the supply chain. The network of supply chains has become more complex, necessitating the need for the latest technological advancements. The sectors that have gained a fair amount of attention for the application of technology to optimize their operations are manufacturing, healthcare, aerospace, and the automotive industry. A little attention has been diverted to the logistics sector; many describe how analytics and artificial intelligence can be used in the logistics sector to achieve higher optimization. Currently, significant research has been done in optimizing logistics operations. Nevertheless, with the explosive volume of historical data being produced by the logistics operations of an organization, there is a great opportunity to learn valuable insights from the data accumulated over time for more long-term strategic planning. To develop the logistics operations in an organization, the use of historical data is essential to understand the trends in the operations. For example, regular maintenance planning and resource allocation based on trends are long-term activities that will not affect logistics operations immediately but can affect the business’s strategic planning in the long run. A predictive analysis technique employed on historical data of logistics can narrow down conclusions based on the future trends of logistics operations. Thus, the technique can be used to prevent the disruption of the supply chain.

The pharmaceutical industry relies extensively on Active Pharmaceutical Ingredients (APIs) as essential components in the production of drugs. The supply chain supporting these APIs is complex, encompassing multiple stages from raw material sourcing to distribution to pharmaceutical manufacturers worldwide. This manuscript explores the intricate challenges encountered within the pharmaceutical API supply chain, focusing on regulatory compliance, quality control, supply chain disruptions, and global dependencies. Regulatory compliance poses a significant hurdle, with varying standards across regions necessitating meticulous adherence to ensure market access and product safety. Quality control and assurance are paramount to maintaining consistency and purity in APIs, yet they present ongoing challenges such as batch variability and contamination risks. Supply chain disruptions, ranging from natural disasters to geopolitical tensions, highlight vulnerabilities in global sourcing strategies, underscoring the need for resilient supply chain management practices. Global dependencies on a limited number of suppliers or regions expose the industry to supply shortages and pricing pressures, exacerbated by geopolitical events and trade policies. These dependencies necessitate strategic diversification and risk mitigation efforts to ensure continuity in API availability and affordability. By addressing these challenges collaboratively, stakeholders can enhance the resilience and reliability of the pharmaceutical API supply chain, thereby ensuring uninterrupted access to essential medications and improving global healthcare outcomes.

The pharmaceutical industry is fundamental to global healthcare, providing essential medicines that improve health outcomes and quality of life. However, the demand and supply dynamics within this sector are highly complex, shaped by various factors including demographic changes, evolving disease burdens, technological advancements, regulatory challenges, and economic pressures. This manuscript explores the intricate relationship between pharmaceutical medicine demand and supply, focusing on key strategies that can help companies effectively navigate these challenges. The demand for pharmaceutical products is driven by several factors, such as population growth, the aging population, the rise of chronic diseases, and the emergence of new health threats. Additionally, healthcare accessibility, affordability, and policy changes significantly impact the consumption of medicines, while innovations in medical technologies and therapies create new treatment needs. On the supply side, pharmaceutical companies face challenges related to manufacturing capacity, raw material availability, distribution logistics, and compliance with ever-evolving global regulatory frameworks. To address these challenges, the manuscript discusses strategic approaches to managing both demand and supply in the pharmaceutical sector. Key strategies include advanced demand forecasting through data analytics, optimizing supply chains for efficiency and resilience, implementing just-in-time inventory models, and investing in flexible manufacturing systems. Furthermore, global collaboration and partnerships, as well as effective risk management practices, are highlighted as essential to ensuring the availability of medicines, particularly in times of crisis or global health emergencies. This manuscript also delves into the role of policy advocacy and regulatory harmonization in stabilizing the pharmaceutical market, ensuring that medicines are accessible to all populations. In conclusion, the pharmaceutical industry must continually adapt to meet the evolving challenges of demand and supply, embracing innovation and collaboration while maintaining a focus on patient access and global healthcare equity. Through strategic planning and adaptive solutions, the pharmaceutical sector can ensure the continuous availability of critical medicines worldwide, meeting both current and future health needs.

The global pharmaceutical supply chain plays a crucial role in ensuring the timely and safe delivery of medicines to patients worldwide. However, the increasing presence of counterfeit drugs within this supply chain poses a significant and growing risk to public health, patient safety, and the integrity of the pharmaceutical industry. Counterfeit drugs—medications that are fraudulently manufactured, mislabeled, or contain incorrect or harmful ingredients—are a major concern as they can lead to ineffective treatments, adverse health effects, and even death. Despite stringent regulatory frameworks and advanced technological solutions, counterfeit drugs continue to infiltrate legitimate supply chains due to factors such as the complexity of the distribution system, global trade practices, and inadequate enforcement in certain regions. This manuscript explores the primary causes behind the proliferation of counterfeit drugs in pharmaceutical distribution, the associated risks, and the multifaceted approaches required to address this growing threat. It discusses the importance of regulatory measures, including international cooperation and stronger compliance frameworks, as well as the role of emerging technologies like serialization, blockchain, and RFID in ensuring traceability and product authenticity. By focusing on the integration of these technologies, the paper also highlights the potential of innovative solutions to enhance transparency, reduce vulnerabilities, and protect the integrity of pharmaceutical supply chains. Additionally, it emphasizes the importance of public awareness campaigns and collaboration between key stakeholders, including pharmaceutical manufacturers, distributors, regulators, and healthcare providers, in creating a more secure and trustworthy pharmaceutical distribution ecosystem. Through a comprehensive exploration of these strategies, this manuscript aims to provide a roadmap for mitigating the risks posed by counterfeit drugs and ensuring the safety and efficacy of medicines for consumers worldwide.

In today's fast-changing world, digital has become a way of life in every single field, and it is affecting all industries by providing multi-channel connectivity with people. In the banking industry, moving to the digital age allows for more improvements in customer-related operations and transaction-related operations within a day. These studies are from the perspective of customers. Customers prefer the flexibility of using digital financial services. Banking clients are commonly given technology-related services, whether they are online or not. Now, banks are focused on providing instant credit card issuance and personalized financial solution services to their clients. They are responsible for managing mass affluent clients who conduct transactions approximately the same as mass retail clients. Providing personalized services on time to individual end users will significantly enhance customer value with the banks. Customers who use the bank digitally perform more operations than those who go to the branch. Thus, they become more valuable clients for the banks. This strategic approach to the digitization process takes place in this fast-changing environment, and the major steps of this journey will be explained in the next chapters [1].

We investigate and analyze the trends and behaviors in credit card fraud attacks and transactions. First, we perform logical analysis to find hidden patterns and trends, then we leverage game-theoretical models to illustrate the potential strategies of both the attackers and defenders. Next, we demonstrate the strength of industry-scale, privacy-preserving artificial intelligence solutions by presenting the results from our recent exploratory study in this respect. Furthermore, we describe the intrinsic challenges in the context of developing reliable predictive models using more stringent protocols, and hence the need for sector-specific benchmark datasets, and provide potential solutions based on state-of-the-art privacy models. Finally, we conclude the paper by discussing future research lines on the topic, and also the possible real-life implications. The paper underscores the challenges in creating robust AI models for the banking sector. The results also showcase that privacy-preserving AI models can potentially augment sharing capabilities while mitigating liability issues of public-private sector partnerships [1].

Deep learning approaches are very useful to enhance cybersecurity protocols for industry-integrated big data enterprise resource planning systems. This research study develops deep learning architectures of variational autoencoder, sparse autoencoder, and deep belief network for detecting anomalies, fraud, and preventing cybersecurity attacks. These cybersecurity issues occur in finance, human resources, supply chain, and marketing in the big data integrated ERP systems or cloud-based ERP systems. The main objectives of this creative research work are to identify the vulnerabilities in various ERP systems, databases, and the interconnected domains; to introduce a conceptual cybersecurity network model that incorporates variational autoencoders, sparse autoencoders, and deep belief networks; to evaluate the performance of the proposed cybersecurity model by employing the appropriate parameters with real-time and synthetic databases and simulated scenarios; and to validate the model performance by comparing it with traditional algorithms. A big data platform with an integrated business management system is known as an integrated ERP system, which plays an instrumental role in conducting business for various organizations in society. In recent times, as uncertainty and disparity increase, the cyber ecosystem becomes more complex, volatile, dynamic, and unpredictable. In particular, the number of cyber-attacks is increasing at an alarming rate; the resultant security breaches have a disruptive and disturbing effect on businesses around the world, with a loss of billions of dollars. To combat these threats, it is essential to develop a conceptual cybersecurity network model to secure systems by functioning as a mutually supporting and strengthening network model rather than working in isolation. In this dynamic and fluid environment, introducing a deep learning approach helps to support and prevent fraud and other illicit activities related to human resources and the supply chain, among others. Some cybersecurity vulnerabilities include, for example, database vulnerabilities, service level vulnerabilities, and system vulnerabilities, among others. The proposed methodology focuses only on database vulnerabilities, with the main aim of detecting and mitigating new potential vulnerabilities in other dependent domains as a future initiative.

Neural networks are bringing a transformation in wearable healthcare technology analytics. These networks are able to analyze a vast amount of data to help in making decisions concerning patient care. Advancements in deep learning have brought neural networks to the forefront, making data analytics a straightforward process. This study will help in unveiling the use of ICT and AI in medical healthcare technology, crawling through some industry giants. Wearable Healthcare Technologies are becoming more popular every day. These technologies facilitate collecting, monitoring, and sharing every vital aspect of the human body necessary for diagnosing and treating an ailment. At the advent of global digitization, health data storage and systematic analysis are taking shape to ensure better diagnostics, preventive, and predictive healthcare. Healthcare analytics powered by neural networks can significantly improve health outcomes, maximizing individuals' potential and quality of life. The breadth and possibilities of connected devices are getting wider. From personal activity monitoring to quantifying every bit of health statistics, connected devices are making an impact in measurement, management, and manipulation. In healthcare, early diagnosis could be a lifesaver. Data analytics can help in a big way to make moves and predictions to save lives. We are in another phase of the digitization era, "Neural Network and Wearable Healthcare Technology Analytics." A neural network could be conceived as an adaptive system made up of a large number of neurons connected in multiple layers. A neural network processes data in a similar way as the human brain does. Using a collection of algorithms, for many neural networks, objects are composed of 'input' and 'output' layers along with the layers of the neural network.

Biopharmaceuticals, or biologics, are a burgeoning sector in the pharmaceutical industry, predicted to reach $239.4 billion by 2025. This unparalleled growth is often attributed to the enhanced specificity offered by large molecules over small molecules. The large size of the constituent proteins necessitates the continuous implementation of big data predictive analytics to elucidate the most effective candidates in the lead optimization process. These same methodologies can be applied, and with the advent of machine learning and automated predictive analytics, this is becoming an increasingly facile task, to the augmentation and optimization of the downstream production processes that comprise the majority of the development cost of any biologic. In this work, big data from cell line generation, product and process design, and large-scale lead validation studies have been used to compare the applicability of simple statistical models against these black-box approaches for the rapid acceleration of enzymes to the pilot plant stage. This research can be expanded upon to exploit the big datasets generated as part of the progression of biologics through the development pipeline to further optimize production outcomes. Over the coming months, data from the project will be used to probe which approaches are amenable to which processes and, as a result, more amenable to various economic simulations. The computed optimization objective for the HIT must include the cost of acquiring, storing, and analyzing data to construct these predictive models, alongside the expected commercial reward of choosing an optimally ranked candidate. In this vein, perspective must be taken in the probable future price, capability outputs, and ownership issues of increasingly sophisticated data analysis software as superstructures become more frequent. It is frequently stated that decisions made to reduce production costs are data-driven, but that is not because more economically or energetically costly experiments or production methods are employed; to truly evaluate production steps, dynamic energy, and economic models need to become more commonplace. Conversion of process quality approaches from large questionnaires, risk analysis, and expert opinion-driven methods to statistical and thus more reliable approaches is an area of future research in analytics used herein.

This work details how the integration of cloud computing and advanced data engineering can innovate and reshape patient care and digital infrastructure. In the healthcare sector, cloud services offer the necessary support to generate digitally-oriented services and service kits. These services can contain high levels of availability, low levels of latency, and on-demand scaling capabilities, while following the strictest data protection laws and regulations. On the other hand, these services can be combined with data engineering techniques to construct an ecosystem that enhances and adds an optimized data layer on any cloud environment. This ecosystem includes technologies to acquire, process, and manage healthcare data while respecting all regulatory obligations and institutions and can be part of a comprehensive digitalization strategy. The objective is to augment the healthcare services that the industry offers by leveraging healthcare data and AI technologies. Designed services, processes, and technologies can be described either as industry-agnostic services or healthcare-specific services that process and manage electronic healthcare records (EHR). Industry-agnostic services offer a set of tools and methodologies to conduct optimized data experiments. The goal is to exploit any variety, velocity, volume, and veracity of medical data. Healthcare-specific services offer a set of tools and methodologies to connect to any common EHR vendor in a privacy-preserving manner. Participating companies are thus able to hold, share, and make use of healthcare data in real-time. The proposed architecture can be transformative for the healthcare industry, opening up and facilitating experimentation on new and scalable service models. The transition to a more digital health approach would help overcome the limits encountered in traditional settings. Limitations in the availability of healthcare facilities and healthcare professionals have underpinned the increasing share of telemedicine in the care process. However, the record-keeping of the patients that undergo care outside of traditional healthcare facilities is often missing and can severely influence the continuity of treatment. Identifying new methods to implement disease prevention and early intervention processes is crucial to avoid more extensive treatment and to support those on multiple line therapies. For chronic patients, having a service available that monitors the state of health and intervenes when parameters go off the wanted range is crucial. However, the same patients are the most under the influence of the decision of care providers; a second opinion might be given remotely which the patient can access at any time on-demand. To address these different kinds of services, an ecosystem composed of a dictionary's worth data layer is outlined, able to live and operate seamlessly in any cloud environment. This future work's envisioned outcome is the rapid evolution and re-definition of the European healthcare landscape.

Innovations in computing and communication technologies are reshaping finance. The seismic changes are casting uncertainty about the future of financial services. On one hand, fintech evangelists project a rosy future, asserting that the fast-moving algorithms can deliver low-cost financial services intuitively, customized to meet robust consumer expectations. On the other hand, many finance veterans fret that the traditional banking model could disintermediate, bleeding banks via a ‘death by a thousand cuts’, reducing them to passive portfolio holders with no direct customer relationship, eclipsed by digital giants which use their enormous treasure troves of customer data to offer banking as an added service with nearly free cost. Amidst the upbeat technological promises and apocalyptic forebodings, there are two constant, mostly agreed-upon, truths. The first is the vital importance of data. Advances in the internet, cloud computing, and record-keeping technologies are producing an ‘exponential growth in the volume and detail of data’. Some of this big data are personal information. Smartphones are deployed in almost all developed and emerging economies, serving as little spies; tracking, recording location histories, social networks, and app usage of their unsuspecting owners; often with a great degree of precision. ‘People are walking data-factories’ in this ‘mobile digital society’. Data are the fermentation of these global exchanges, electronic commerce and communication, and financial transactions. To just take Facebook as an example, it shares 30 million people a day through updates and posts, hosting personal information on 2.23 billion users. To the alarm of the uninformed public, much of this information is available for commercial harvest. The second constant is the rise of intelligent solutions. Consumers today—be it disclosed or not—are fed tailored clothes, music, film, holiday packages—almost anything you like, notably dynamic pricing, varying in accordance with individual profiles, or personalized search results. The availability of powerful computers has enabled comparable applications that are intended to make the system more responsive to their customer profiles and desires, or to capitalize competitive business possibilities. Such changes will transform the financial industry and occupy a prominent position among the mechanisms of policy competition, reshaping the way in which financial services are bestowed and led on the demand side.

Advances of wearables, sensors, smart devices, and electronic health records have generated patient-oriented longitudinal data sources that are analyzed with advanced analytical tools to generate enormous opportunities to understand patient health conditions and needs, transforming healthcare significantly from conventional paradigms to more patient-specific and preventive approaches. Artificial intelligence (AI) with a machine learning methodology is prominently considered as it is uniquely suitable to derive predictions and recommendations from complex patient datasets. Recent studies have shown that precise data aggregation methods exhibit an important role in the precision and reliability of clinical outcome distribution models. There is an essential need to develop an effective and powerful multifunctional machine learning platform to enable healthcare professionals to comprehend challenging biomedical multifactorial datasets to understand patient-specific scenarios and to make better clinical decisions, potentially leading to the optimist patient outcomes. There is a substantial drive to develop the networking and interoperability of clinical systems, the laboratory, and public health. These steps are delivered in concert with efforts at enabling usefully analytic tools and technologies for making sense of the eruption of overall patient’s information from various sources. However, the full efficiency of this technology can only be eliminated when ethical, legal, and social challenges related to reducing the privacy of healthcare information are successfully absorbed. Public and media are to be informed about the capabilities and limitations of the technologies and the paramount to be balanced is juvenile public healthcare data privacy debate. While this is ongoing, the measures have been progressed from patient data protection abuses for progress to realize the full potential of AI technology for hosting the health system, with benefits for all stakeholders. Any protection program should be based on fairness, transparency, and a full commitment to data privacy. On-going innovative systems that use AI to manage clinical data and analyzes are proposed. These tools can be used by healthcare providers, especially in defining specific scenarios related to biomedical data management and analysis. These platforms ensure that the significant and potentially predictive parameters associated with the diagnosis, treatment, and progression of the disease have been recognized. With the systematic use of these solutions, this work can contribute to the realization of noticeable improvements in the provision of real-time, personalized, and efficient medicine at a reduced cost [1].

Emerging smart medical instruments combined with advanced smart industrial equipment facilitate the collection of vast volumes of critical data. This data not only enables significantly more accurate and cost-effective diagnosis and maintenance but also enriches the datasets available for AI algorithms, leading to improved insights and outcomes. The integration of high-speed and ultra-reliable telecommunications infrastructure is crucial, as it supports the cloud model. This model allows for off-device aggregation in the cloud, which effectively offloads infrastructure demands and provides an extended runway for future technological improvements before the deployment of the next generation of devices. However, in certain scenarios, latency and bandwidth limitations present significant challenges. These limitations require that a substantial amount of AI and machine learning processing is conducted directly on the transmitted data, which places rigorous demands on both the processing subsystems and the communications links themselves. The current project directly addresses the accelerator side of this multifaceted issue. It will carry out comprehensive end-to-end demonstrations leveraging pilot 5G networks and telemedicine facilities, collaborating closely with major industry participants to showcase the capabilities and potential of this innovative technology. This collaborative effort is essential to pushing the boundaries of what is possible in smart medical instruments and industrial applications [1].

This paper includes the impacts of the Internet of Things (IoT), Big Data, and other emerging technologies in the vehicle production sector, digital connectivity, and sustainable transport system. Automated and intelligent transportation for safe, efficient, and sustainable transport systems will be stressed. Key factors to promote automated or connected vehicles including connected environment, integration of all transport modes, advanced cooperative systems, and policy enforcement will be discussed. This paper contains the Axiomatic Categorisation Framework (AFS) for the dynamic alignment in a collection of disparate functions in cyber-physical systems (CPS). Developed system is enhanced for breaking the rules within autonomous vehicles (AV). It means the human personal injury is inevitable while the vehicle does not do any rules. Especially in complicated traffic situations, many of the constraints are mutually exclusive, and there is no way to obey all of them at a time. Also, there is no way to ensure that the self-driving vehicle has priority in all situations [1]. Public distrust in AV systems has to be increased and the investment in this technology has to slow down. Instead, a human driver should be partially responsible for operation. The development of a driver-behavior assistant (DBA) system is proposed, which should be able to break the rules for the distances of such slow development. It is intended to be effective in non-deterministic situations while maintaining the safety of the AV and those involved in the event. A driver's actions would not only be acceptable as a driving strategy but also would be predictable, and therefore other road users could unambiguously react.

Technological advances have had a transformative impact on the retail landscape. Challenges arise with guaranteeing technological changes lead to, rather than detract from, increased efficiency and positive experiences. First, integrating technology into the supply chain in an aggressive way is costly. It requires vast changes to existing systems and developments of cross-industry communication protocols. Secondly, the public is often quick to reject technological changes or slow to become users. Finally, ensuring that technological advancements do not only benefit the top few retailers and are accessible to those of any size poses a challenge, as has been seen in the fate of only a handful of radical changes in retail technology. On the other hand, an integral aspect of technology, particularly that used for big data collection and processing, is that it can account for these and other variables. It can predict the success of ventures into modernizing or developing new systems and can identify more effective and efficient ways to do so. Of course, the concerns of job loss or technological monopoly still loom. But, it would seem, the continued advancement of technology in the retail landscape is inevitable.

The supply chain ecosystem plays a very important role in the success or failure of organizations, markets, and economies. Supply chain ecosystems are broadly defined as supply chain organizations and their collaborators. Today's combined challenges of pandemic shutdowns, rising internet usage, and skyrocketing climate change concerns demand that the supply chain ecosystem better connect with customers, when and how they want, to provide products and services with high levels of availability and zero defects, yet collaboratively do this to reduce transportation and production risks, often at the same time reducing operational costs and carbon footprints. Addressing these challenges, this work explores the cloud delivery capabilities of cloud-native architectures to enable the big data integrations and analytics that are needed to grow smarter supply chain ecosystems. This work describes what smart supply chain ecosystems are and how they are planning to grow their technology and integration capabilities. Discussing the industry-leading advanced and manufacturing technology producer ecosystems, it is explained how their technology collaboration and investment plans are driven by climate change and job creation goals. With these background models, the work examines the new digital reality of customer-driven experiences and economies that are demanding cloud-native and intelligent technology partnerships to deliver climate objectives, operational responsiveness, and compatibility to avoid trading economies of scale for economies of integration. The final objectives of this paper are to share key ideas about the need to balance the growing customer service direct-to-consumer business models with those for collaborative investment by market and industry. In doing this, it hopes to promote an intelligent supply chain ecosystem foundation for helping its different participating countries survive and thrive in the digital economy.

Migrating enterprise policy systems to .NET Core is a key objective of digital transformation in the Insurance IT ecosystem. This change directly addresses strategic drivers: enabling adoption of cloud-first development, resisting market pressure for more flexible and usable enterprise solutions, and preparing for changing demands from regulation and compliance. Phases of operational benefit aligned with risk mitigation form the basis of the migration roadmap, with a strong focus on engaging all relevant stakeholders. Market pressure for a SEAMLESS user experience across ALL applications is a fundamental driver for Investment in digital transformation. Gaps remain in enterprise Operations, where Legislative and regulatory accountability Demand rigid and complex solutions that Liberty has not yet been able to provide. New risk-based capital requirements, Data-Sovereignty controls, Controls for sensitive Data in the Cloud, and new Audit requirements create a long list of challenges for the ecosystem that can no longer be Deferred. At the same time, Cross-organisational integration is becoming more important and integrating partners from the insurance supply-chain requires a much more flexible approach to development and Deployment. These factors combine to generate a credible case for accelerated digital investment with a focus on Migration to Cloud Platforms, with related Risk mitigation, Quality Improvements, and flexibility benefits that close Industry gaps.

Key business objectives for digital infrastructure cloud adoption are often framed in terms of reducing cost, improving fault tolerance and resilience, simplifying scale, and enabling innovation. Given the critical nature of the financial sector, however, where timeliness and price can significantly determine an outcome, cloud migration in delivery environments demands greater throughput on the critical path and, in many enterprise-scale settings, forgoes hybrid complexity and multi-cloud risks. Nevertheless, slack in system designs does exist; financial institutions enable market functionality—trading, clearing/best execution—despite potentially being able to meet such sets with lower service levels than other verticals. A cloud multi-account structure for sensitive data, for example, naturally limits exposure when combined with observed risk. Fulfilling predictions of elasticity during periods of high demand usually requires support from a dedicated environment (or environments) located nearer to the operations. Components can consequently be allocated on a per-account basis or maintained as shared sink systems to which the dedicated streams write. The automation code can similarly be targeted for dedicated accounts, avoiding the resource constraints that beset such operations during industry events like emergency triage/contact desking.

The global banking industry is subject to ever-growing regulatory requirements, designed to prevent financial tour de force repeats tearing through the world economy. The changes are incomplete and new rules being enacted each year. Implementing and executing these rules and regulations requires the guiding principles from senior management to reach the product desks in a clear and efficient way. Technical systems must implement these rules. Differences in interpretation, implementation, and warnings must be addressed during normal operations. Most importantly, systems must provide warning alerts to management and the business as early as possible, to allow for proper handling. History has shown that the importance of early warnings has been overlooked repeatedly. Real-time capabilities are essential to meet these business needs. Organizations must therefore be ready to embrace a next-generation architecture that enables real-time alert and warning generation. Systems based on a streaming architecture, combined with systems enabling the real-time flow of events between domains supported by orchestration, provide a solid foundation to meet these requirements.

Healthcare is increasingly recognized as a data-intensive industry. Multi-hospital networks, among other organizations, face mounting operational and governance challenges because of rigid data-integration pipelines that support all data sources and destinations in the network. These pipelines have become difficult to modify, causing them to lag behind the changing needs of the clinical operation. Scalable data-pipeline architectures better support clinical decision making, optimize hospital operations, ease data quality and compliance concerns, and contribute to improved patient outcomes. Meeting scalability goals requires breaking up monolithic data-integration pipelines into smaller decoupled components and aligning service-level agreements of pipeline components and source systems. Parallelization and adoption of distributed data-warehouse technology mitigate the burden of ingesting data into a multi-hospital network. However, latency requirements still warrant the construction of separate pipelines for data ingress from clinical devices, electronic health records, and external laboratory-information systems. Healthcare associations recommend near real-time data availability for a growing list of clinical and operational applications. Mishandling the real-time ingestion of data from clinical devices, in particular, compromises availability and performance. Scalable architectural patterns for real-time streaming Ingestion from heterogeneous data sources, transport processes, and back-end processing structures are detailed.