Articles

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.

Active Pharmaceutical Ingredients (APIs) serve as the cornerstone of pharmaceutical development, driving therapeutic efficacy and safety in drug formulations. This article provides a comprehensive overview of the lifecycle of APIs, starting from their discovery and development, through to manufacturing processes and regulatory oversight. The development of APIs begins with intensive research and discovery efforts, where medicinal chemists and pharmacologists identify and optimize potential compounds through computational modelling, high-throughput screening, and structure-activity relationship studies. Promising candidates undergo rigorous preclinical testing to assess pharmacological properties, safety profiles, and potential adverse effects in animal models. Upon successful preclinical outcomes, APIs progress to clinical trials, involving phases of testing in human subjects to evaluate efficacy, dosage regimens, and safety profiles under controlled conditions. Clinical trial data are meticulously analyzed to support regulatory submissions, demonstrating the API's therapeutic benefits and safety for eventual patient use. Manufacturing APIs involves complex chemical synthesis or biotechnological methods, ensuring precise control over reaction conditions, purity, and yield. The scale-up from laboratory synthesis to industrial production demands adherence to Good Manufacturing Practices (GMP), where stringent quality control measures verify consistency, potency, and stability throughout production batches. Regulatory oversight by authorities such as the Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) in Europe ensures that APIs meet stringent standards of safety, efficacy, and quality before market approval. Manufacturers must submit comprehensive Chemistry, Manufacturing, and Controls (CMC) data, detailing manufacturing processes, analytical methods, and stability studies to support regulatory filings.

An increasing number of adverse events are raising concern in the pharmaceutical supply chain due to contaminated active pharmaceutical ingredients (APIs). Most of the active pharmaceutical ingredients are not currently under the scope of environmental regulations, despite their negative impact on human health and the environment. API's life cycle plays a significant role in identifying potential supply chain sources and determining their impact on the environment. The Covid-19 pandemic's intermittent manufacturing interruptions and the increase in the frequency of drug shortages over the past ten years have sparked worries about how resilient the world's drug supply chains are. Many clinical trials were conducted on patients with COVID-19 during the SARS-CoV-2 pandemic and resulted in millions of deaths globally by 2022.

The operational performance of Active Pharmaceutical Ingredients (API) supply chains often suffers from significant disruptions attributed to inherent vulnerabilities. Despite theoretical discussions, empirical evidence validating these vulnerabilities remains sparse. This study endeavours to empirically substantiate the vulnerabilities arising from dynamic disruptions within the pharmaceutical supply chain. Its primary goal is to discern actionable insights that can inform the development of robust resilience strategies capable of effectively mitigating such disruptions. This study investigates vulnerabilities within the active pharmaceutical ingredient (API) supply chain in response to disruptions. Despite theoretical insights, empirical evidence validating these vulnerabilities remains limited. Through empirical analysis, this research aims to identify and elucidate the specific vulnerabilities exacerbated by dynamic disruptions in the API supply chain. The findings are intended to inform the development of resilient strategies capable of mitigating the impact of disruptions on pharmaceutical supply chains.

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.

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.

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 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.

Studies on the use of pesticides in southwest Nigeria have revealed a substantial rise in a variety of pesticide-related illnesses, including mental impairment and reproductive problems. Those who work in agriculture and are regularly exposed to pesticides are the most impacted. The World Health Organization (WHO) predicts that three million severe pesticide poisoning episodes occur globally each year, with at least 300.000 deaths and 99% of cases occurring in low- and middle-income nations. The effects of longer-term exposure to pesticides on health are not yet precisely estimated. <b>Objectives</b><b>: </b>To this end, the objective of this study is to assess the knowledge of pesticide residues and common pesticides in grain-based food (brown and white beans, yellow and white maize, brown millet and rice) about pesticide use in selected markets of Southwest Nigeria. <b>Methods</b><b>: </b>A total of 240 respondents were selected from four states (Ado-Ekiti, Ibadan, Osun and Ondo) with the aid of structured questionnaire and interview guide using purposive sampling technique. The markets were Oja-titun (market) Ile-Ife, Osun; Alesinloye Market Ibadan, Oyo; Oja Oba, Ado-Ekiti, Ekiti; and Oja Oba, Akure Ondo State. The identification and quantification of pesticide residues was done using a Varian 3800/4000 gas chromatograph mass spectrometer while 60 structured questionnaires were administered to respondents including food merchants, buyers/consumers as well as food vendors. Data were analysed using descriptive statistics. <b>Results</b><b>: </b>The experiment was conducted from November 2020 to November 2021. Up to 50.4% never read instructions on pesticide application while a shared 24.8% read them always and occasionally. The number of respondents who never read the instructions were particularly high in Ekiti and Ondo, up to 52.5% of the respondents in Ekiti do not know whether or not they are exposed to pesticides during application. Also, majority of the respondents never attended any professional training on pesticide application, this amounted to 79.3%. Most people were not aware that unsafe pesticide application is harmful to human health. Similarly, a wide variety of symptoms were reported by respondents following pesticide application or consumption of grains, these included headaches and dizziness, itching and redness of the eyes, skin allergy, diarrhea, and stomach disorder, vomiting and loss of appetite, weakness of the body, asthma, permanent skin patches, shortness of breath, excessive sweating. Millet recorded the highest number of OCP residues while maize had the lowest. On the contrary, maize had the highest number of OPP and carbamate residues while millet had the lowest number of OPP residues. Meanwhile, maize, rice and beans had only one carbamate pesticide residue. <b>Conclusions</b><b>: </b>According to the study's conclusions, farmers who can only get information from agricultural extension officials should receive urgent and immediate attention for raising awareness. Additionally, extensive IPM training programs must be created with the intention of disseminating precautions for protecting human health and a healthy agro-ecosystem. In order to find more effective pest management methods that utilize less pesticides, it is crucial to reevaluate the pesticide residues and common pesticides found in grains in the targeted markets. To reduce farmers' exposure to pesticides, it is also required to establish personal protective measures, special educational initiatives, and legislation promoting the use of safer pesticides. <b>Significance and novelty</b><b>: </b>This study gives policymakers a comprehensive understanding of the ways that may be utilized to close the significant knowledge gap on pesticide residues in grains and provides an insight into the knowledge of pesticide residues and common pesticides in grains.

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 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.

Many attempts have been made to improve heat transfer for thermally integrated microchannel reforming reactors. However, the mechanisms for the effects of design factors on heat transfer characteristics are still not fully understood. This study relates to a thermochemical process for producing hydrogen by the catalytic endothermic reaction of methanol with steam in a thermally integrated microchannel reforming reactor. Computational fluid dynamics simulations are conducted to better understand the consumption, generation, and exchange of thermal energy between endothermic and exothermic processes in the reactor. The effects of wall heat conduction properties and channel dimensions on heat transfer characteristics and reactor performance are investigated. Thermodynamic analysis is performed based on specific enthalpy to better understand the evolution of thermal energy in the reactor. The results indicate that the thermal conductivity of the channel walls is fundamentally important. Materials with high thermal conductivity are preferred for the channel walls. Thermally conductive ceramics and metals are well-suited. Wall materials with poor heat conduction properties degrade the reactor performance. Reaction heat flux profiles are considerably affected by channel dimensions. The peak reaction heat flux increases with the channel dimensions while maintaining the flow rates. The change in specific enthalpy is positive for the exothermic reaction and negative for the endothermic reaction. The change in specific sensible enthalpy is always positive. Design recommendations are made to improve thermal performance for the reactor.

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.

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.

The major automated plants have produced large volumes of high-quality products at low cost by introducing various technologies, including robotics and artificial intelligence. The code of many defects on the surface of products is embedded with economic loss and sometimes functionality loss because products are rarely found with defects. Therefore, most items’ production is done based on prediction and has an invisible fluctuation in production. The detection process for hidden defect images requires a lot of costs and needs to be supported for better progress and quality enhancement. Paint shop defects should be analyzed from color changes to detect defects effectively by preventing the variability of product demand over time. It is not easy to take visible light images without noise because the paint surfaces are glossy. A few parts of illumination and shadows remain in images, even in larger size and high-resolution images. The various painted surfaces are also needed to reflect both color and texture information in computer vision models to classify defects precisely. Several automated detection systems have been applied to paint shop inspections using lasers, infrared, x-ray, electrical, magnetic, and acoustic sensors. The chance of paint shop defects can be low, unnecessarily low, compared to clouds in the sky, but those chances impact defect functionalities. Thus, they are called as “lessons learned.” Lately, artificial intelligence has been introduced to the field of factory automation, and many defect detection feeds have found footsteps in machine learning and deep learning. Recent attempts at deep learning-based defect detection are proposing simple techniques using specific neural network architectures with big data. However, big data is still in its early stages, and significant challenges exist in normalizing and annotating such data. To get cost-efficient and timely solutions tailored to automotive paint shops, it might be a better consideration to combine deep learning solutions with traditional computer vision and more elaborate machine learning methods.

The banking sector has shown a strong interest in scaling out and utilizing the microservices architectural pattern within their payments domain, not only to manage increased transaction volumes, but also for compliance and risk-related control. Financial organizations are adopting containerization technologies like Kubernetes and Docker to align with the microservices paradigm. Containerization provides the foundation for automation and operational excellence of microservice-based applications by enabling continuous deployment and automated build-test-release cycles. However, deploying a Kubernetes cluster and the services it hosts in production is not sufficient to guarantee a secure and compliant operating environment. Kubernetes itself should be secured to protect workloads, and risks associated with the services being deployed must be managed continuously.

Magnetic iron nanoparticles (Fe<SUB>3</SUB>O<SUB>4</SUB>) were synthesized and characterized using Fourier Transformed Infrared ((FT-IR), UV-Visible spectrophotometer, Scanned Electron Microscopy (SEM) equipped with an Energy Dispersive X-ray spectrometer (EDX), and X-ray Diffraction (XRD). The synthesized nano catalyst was used in the transesterification of mahogany seed oil with methanol. The optimized reaction conditions gave a reaction yield of 88% at a catalyst concentration of 1.5% wt., a volume ratio of methanol to oil of 5:1, a reaction temperature of 60 °C, and a reaction time of 120 minutes. The Fe<SUB>3</SUB>O<SUB>4</SUB> nanoparticles was regenerated from the mixture and reused for various circles by applying the optimum conditions obtained during the present study. The results showed that the biodiesel yield decreased by increasing the number of cycles when the regenerated catalyst was used. However, good conversion (81.9%) was obtained up to the 5th cycles. The elemental analysis of the synthesized magnetic iron nanoparticles Fe<SUB>3</SUB>O<SUB>4</SUB>) revealed the highest proportion of iron with 64.37 and 74.40% for atomic and weight concentration respectively, followed by oxygen with 34.27 and 24.50% for atomic and weight concentrations respectively. It could be concluded that the synthesized nano catalyst would serve as an excellent catalyst for the transesterification of vegetable oils.

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.