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.

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.

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.

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 pharmaceutical supply chain environment has undergone tremendous change in recent decades due to technology, and this shift is intensifying. One of the main concerns of business practitioners is how to cost-effectively integrate, implement, and manage technologies across the supply chain of an organization. Pharmaceutical organizations that produce, ship, and supply goods have trouble tracking their goods, which makes it easier for counterfeiters to get fake medications into the system. The creation and implementation of a stringent technological system might be a significant step in the arduous battle against the prevalence of fake medications and other healthcare items. In supply chain management, digital technologies have a number of potential advantages. The usage of the Internet of Things in supply chains can make every component visible and create a visible supply chain, making it possible to identify the position and specifications of all the components and materials in the supply chain at any given time.

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.

Calotropis procera, a medicinally important plant found in Asia, was explored for its anticancer and antibacterial properties in this study. The leaves of C. procera were extracted using methanol and FTIR and UV-VIS spectrophotometry were used to characterize them. Using the MTT assay and the disc diffusion test, the extract was examined for anticancer activity against the MCF7 breast cancer cell line and antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA). The methanolic fraction of C. procera was found to be efficient against the MCF7 cell line and dramatically suppressed MRSA growth. The metabolic fraction of C. procera leaves is important in suppressing the growth of the MCF7 cell line, and it has the potential to be an effective antibacterial agent, according to our findings. The implications of Calotropis procera for all healthcare professionals including oncologists, physicians, pharmacists, nurses, and nutritional therapists are significant. With the increasing incidence of cancer and antibiotic-resistant bacterial infections, there is a growing need for new, effective, and safer herbal treatments.

This article seeks to relate the narrative strategies used by video channels on YouTube with the advertising of toys and electronic games intended for the Alpha generation. Based on the principle that we live in a society composed of different age groups, the main theoretical references that make it possible to understand generation as a local and global phenomenon will be exposed. After such concepts, the rise of the image and its importance for the new generations will be approached, to support the selection of the audiovisual platform YouTube and its growing fascination by the public. Finally, concepts from semiotics will be applied to verify the narrative and discursive power of video channels for contemporary children, also known as the Alpha generation.

The present article seeks to provoke a discussion into how video games can be used in anxiety treatments and social stimulation tools among middle-year students and children of that same age. To do so, we initially start this article by reflecting on how the covid-19 pandemic disparate all anxiety alerts in our society including mental health issues such as depression and special anxiety disorders. Then we compare how the numbers of anxiety among children and young adults were already alarming before the lockdowns imposition and the social distance measures, especially in urban centers. This rising anxiety condition can be felt especially in the years that followed the social isolation of children especially because their social connection and recognition were just starting to grow and to establish important connections between their peers in-person and in virtual environments, throwing light on how to screen media and children`s homes are related with the anxiety increase and how we can investigate that phenomenon without succumbing to excessive positivism to today`s technology or to a deconstructive pessimism that leads us to distrust those media that are already in contact with middle-year students and children`s in that same age. To do so, we will resort to philosophical tools such as Edusemiotics and Cultural Studies to understand how games such as Minecraft and Roblox can be used in school environments to help students and teachers manage anxiety levels and surpass socialization issues past covid-19 lockdowns. To finish our reflection we also bring some data related to how those same media and games helped middle-year students to surpass social isolation and family disconnection during the pandemic while their kept exercising school content in those games, sharing and debating with their peers on virtual platforms.

The initial omicron (B.1.1.529) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) subvariants, BA.1 and BA.2 (BA.1/2), were progressively displaced by BA.5 in Japan, which showed not only higher transmittivity and less pathogenicity, but also differences in antibiotic use according to the difference in the clinical course of BA.5 compared with BA.1/2 infections. BA.5 patients received more antibiotics, especially ampicillin/sulbactam, although ceftriaxone and meropenem were used significantly in the BA.1/2 period. These data suggest an increased incidence of aspiration pneumonia in elderly patients in the BA.5 period, and we should consider changing the management tactics for COVID-19.

Probiotics, prebiotics, and synbiotics modify various aspects of local and systemic immune function in multiple experimental models. However, their impact and mechanisms of action are not known across all products or noticed in every population studied, and impacts on in vitro, ex vivo, or other measures of immune function do not necessarily result in an impact on infection and illness in vivo. Studies have discussed that intestinal microbiota has an essential role in enhancing the immune system against viruses. The regulatory impact of the intestinal microbiota on viral infection is connected with local and systemic immune responses and plays a part in congenital and adaptive immune responses. The microbiota composition critically modulates the production of virus-specific CD4 and CD8 T cells and antibody responses following influenza virus infection. The intestinal microbiota has an important role in the stabilizing of immune homeostasis by augmenting the integrity of the barrier functions of the gut mucosa, which is a crucial aspect of systemic immunity. In conclusion, the intestinal microbiota can influence organismal immunity locally and systemically, proximally, and distally. Studying the possible mechanism by which the intestinal microbiota maintains host immunity can provide a clearer understanding of the occurrence and development of diseases.

Coronavirus infection (COVID-19) has quickly emerged as a global pandemic leading to critical health complications. The possibility and risk of vertical transmission of this virus is still unclear, particularly as it relates to pregnant women and their fetuses. Regarding scientific data, there is insufficient research focused on COVID-19 infection complicated with hydrops fetalis and intrauterine fetal demise. In this study, we report an intra uterus fetal death due to hydrops fetalis and twin-to-twin transfusion syndrome (TTTS) in monochorionic–monoamniotic (MCMA) twin pregnancy associated with mild COVID-19 infection in a 33-week-pregnant woman.

Psoriasis is a multi-systemic chronic autoimmune inflammatory disorder affecting 125 million people worldwide. The most common type of psoriasis is plaque psoriasis affecting up to 90% of the patients and is characterized by well-demarcated, symmetric, and erythematous plaques with overlying silvery scales that may be painful or itchy. Psoriasis may also affect the joints; increase the risk of developing metabolic syndrome, diabetes, Crohn’s disease, ulcerative colitis, uveitis, certain cancers and an increase in the risk of cardiovascular diseases. Both the skin and the gut microbiome can modulate the development and progression of psoriasis. A connection between the microbiome and immunological mechanisms are antimicrobial peptides, which regulate the microbiome at interfaces and, as antigens, can trigger psoriasis. Few studies were conducted to demonstrate the effect of probiotics on different diseases, as they are living microorganisms that confer a health benefit when administrated in adequate amounts. The effects of administering probiotics include the stabilization of the gut bacterial community and the restoration of “signature” of bacterial microbiota, which is a result of lowering the pH, producing bacteriocins, altering microRNA (miRNAs), competing with pathogens for certain nutrients and improving the gut barrier function. Probiotics counter weight aggressive commensals in the body and reinforce the barrier function of the epithelium while also contributing to the regulation of innate and adaptive immune responses of the host under healthy or pathogenic conditions. Several clinical trials were conducted based on those findings to examine the role of probiotics in psoriasis, but till now there is no evidence of their efficacy.

Chemical solvents are commonly used to prevent microbial growth; dangerous to human health and have limited antibacterial properties. On the other hand, Nanoparticles made of metallic elements (such as copper, silver, and gold) have several uses in the field of biotechnology. Silver nanoparticles are more efficient in their antimicrobial, antibacterial, anti-inflammatory and anti-cancer properties. The current study aimed to determine the green synthesis of silver nanoparticles and their antibacterial activity from the aqueous extracts of leaves of Couroupita guianensis, Punica granatum, Vitex negundo, Cirtrus maxima. AgNPs of plant extracts were prepared using silver nitrate with the respective plant extract. Then they were characterized by FTIR analysis. The respective functional groups in the synthesized silver nanoparticles were confirmed with FTIR Spectra. The antibacterial activities of the synthesized nanoparticle extract were observed by zone of inhibition. From the results, the nanoparticles synthesized from the plants extract could pave a way to formulate a drug to treat microbial infection.

Probiotics as an intestinal microbe regulator, not only improve the ability of the gastrointestinal microbiota to modulate immune activity, but also strengthen the body's immune system, inhibit allergic reactions and has a significant role especially in the anti-viral immunomodulation. Therefore, in patients with COVID-19, the intestinal micro-eco-regulator, represented by probiotics, may be a therapeutic choice. However, there is still a lack of evidence-based studies to support probiotic treatment of patients with COVID-19. New cohort studies and randomized controlled clinical trials to assess the effectiveness of probiotics in the management of COVID-19 are strongly and urgently needed.

Caesarean section is the most common surgical procedure in obstetrics, but also in pregnant women with dystocia. The purpose of this study is to identify factors that contribute to post-caesarean complications in emergency of gynecology-obstetrics service of Saint Joseph Hospital in Kinshasa. This is a descriptive cross-sectional study and was conducted at Saint Joseph Hospital in Kinshasa city for a month (1^st and 30^th July 2021). A questionnaire was used to collect different data namely socio-demographic characteristics and different variables important for the study. Independent variables were factors related to the parturient and the current pregnancy, the surgical procedure and the operational room. While the dependent variable was post-caesarean complications in gynecology-obstetrics service. Descriptive statistical analyses namely frequency and percentage were done to describe the sample profile. To measure the strength of association between different variables were estimated using Pearson's Chi-Square (X²) test and the p-value was 0.05. Data analysis was performed using SPSS version 20 software. The majority of participants are over 33 years of age, have secondary school skills, married and have 3 to 4 children. It was observed that 88.2% of participants have acute respiratory distress prior to caesarean section, 76.6% have a personal history of thromboembolic disease prior to caesarean section. All the participants who underwent caesarean section were victims of external ventilation through the windows of the operating room, which remained open before, during and after the surgery and benefited from antibiotic treatment without any para-clinical analysis before the caesarean section. Furthermore, 82.4% of participants had benefited from the caesarean section with sterilized care materials 48 hours before and after the intervention. While 47.1% of participants had caesarean sections with defective equipment for the operation (use of dry heat sterilization etc.). A better identification of these risk factors can reduce significantly rate of complications and can consequently improve the maternal-infantile prognosis. The obstacles to effective management are related to the provision of quality services.

Background: Food borne outbreaks have been associated with sourcing unsafe food. Therefore, the first preventative strategy should be to source safe food. Even if the sourced food is safe, measures need to be put in place to ensure that it remains safe during the transfer, storage, preparation and serving activities that follow. An understanding of the ship food supply and transfer chain will help to illustrate the points at which the food can become contaminated en route to the point of consumption. Objectives: The study was conducted in selected sea port in the core Niger Delta to assessed the microbiological quality of food served at different ship galley to crew and passengers and compered it to standard. Methods: Samples of food were taken from three (Port Harcourt Area one (PHSP), Warri (WSP) and Koko (KSP)) seaports within the South-South zone for laboratory analysis to uncover food spoilage microorganisms capable of causing disease outbreak among ship which could result to Trans border diseases. Eleven samples of different ready to eat food were collected from the locations, which included cooked rice; fried fish, irish potato porridge, vegetable soup, griki, pepper soup, fried irish potato, salad and bread were collected randomly. The samples were prepared and analyzed using standard procedures. Mean viable counts of aerobic and anaerobic bacteria were determined, ranging from (13×103cfu/g to 78×104 cfu/g) for ready to eat food. Results: Based on the finding KSP I, KSP J and KSP K food samples had the highest bacterial contamination on food while WSP F, WSP G and WSP H food samples had the least with the following isolates Salmonella spp, Nocardia spp, Shigella spp, Listeria spp, Bacillus cereus, Leuconostoc spp, Acinetobacter spp, Acetobacter spp, campylobacter spp, Clostridium spp and Vibrio spp which revealed that the isolates were susceptible to any of these antibiotics Septrin, Chloramphenicol, Gentamycin, Tarvid, Streptomycin, Reflacin, Augumetin, Ceporex, Nalidixic acid, Ampicillin, Ciproflox, Penicillin and Erythromycin. Conclusion: Thus, ships operators and regulatory body are expected to take all practicable measures to ensure that they do not receive unsafe or unsuitable food and maintain adequate food temperature at all time.

Background: From centuries of evolution, knowledge and technological progress for mankind to one day rediscover nature. Currently, the control of bacterial infections is becoming complex due to the concern of antibiotic resistance, which has been a significant global health problem. The aim was to determine and compare phytochemical constituents and in the in vitro evaluation of antimicrobial and antioxidant activities of aqueous, methanol, acetate, dichloromethane extracts from Cola acuminata nuts grown in the Nord Ubangi Province, DRC. Methods: The nuts of Cola acuminata were harvested in April 2016 at Yakoma city, Nord-Ubangi, DRC. The microscopic features of this species were performed in order to identify specific histological structures. Three bacterial strains notably Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 8739 and Pseudomonas aeroginosa ATCC 9027 were used for the assessment of the antibacterial activity. The qualitative and quantitative phytochemical screening were used for compound identification using different fractions and fractions which presented a good extraction yield was used for further analysis. The antioxidant activity was evaluated using ABTS and DPPH scavenging tests while the antibacterial activity was performed using the diffusion method. Findings: The micrography of C. acuminata revealed the presence of following histological elements of which: fibers, spiral vessels, trichomes, ovoid starch grains, sclerenchyma and the fragments of endosperm. Only the methanol and aqueous extracts presented a good extraction yield. The phytochemistry shows the presence of flavonoids, anthocyanins, terpenes, iridoids and tannins. All fractions showed IC₅₀ values lower than 10 µg/mL in the ABTS test and lower than 100 µg/mL in the DPPH test. The antibacterial activity of this plant was low against the three strains used. Conclusion: Seeing the potency of C. acuminata and different biological activities displayed, further analysis are required in order to identify and purify the active ingredients, to study the toxicity of cell lines in vitro, to perform the in vivo experiments and to test for other activities such as the anti-hypoglycemic and anti-inflammatory.

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.

The aim of this study was to determine the phytochemical composition and micrographic charac-teristics of the plants consumed by Okapia johnstoni. The results indicate that each plant species has characteristic microscopic elements for its identification. These plants are rich in phenolic acids, anthocyanins, flavonoids, anthraquinones, coumarins, terpenoids and iridoids. Alchornea cordifolia is richer in total polyphenols (198.53±3.39 mg GAE/g DM) followed respectively by Musanga cer-copioides (91.87±6.71 mg GAE/g DM), Macaranga spinosa (59.65±6.54 mg GAE/g DM), Ficus val-lis-choudae (46.37±2.43 mg GAE/g DM), Cola acuminata (38.83±4.04 mg GAE/g DM), Pycnanthus angolensis (31.96±3.45 mg GAE/g DM), Alstonia boonei (31.55±1.60 mg GAE/g DM) and Trilepisium madagascariensis (25.18±0.99 mg GAE/g DM). As for flavonoids, the highest content is obtained in T. madagascariensis followed respectively by A. boonei, Pycnanthus angolensis, Cola acuminata, M. spinosa, F. vallis-choudae, M. cercopioides and A. cordifolia. The difference in the content of secondary me-tabolites is justified by the fact that their expression in the plant is a function of both abiotic and biotic factors and the specificity of each plant species linked to its genetic make-up. The charac-terization of these chemical compounds is necessary for the formulation of herbal medicines for the management of Okapi ex situ or for human health. Also, the microscopic profiles of the leaves powder of the studied plant species provide relevant information, which may be helpful for the plant authentication and for quality control of raw material.

A 16-year-old female patient had influenza and was treated by inhalation of an anti-influenza agent. However, two days later, she showed slight dyspnea, and massive atelectasis of the left lung with hypoxia was found. She was admitted to our hospital (Day 0) and started on antibiotics and steroids, but the atelectasis was worsend on Day 3. Therefore, bronchoscopy was performed on Day 5. After the pus in the left bronchus was removed, the atelectasis was significantly improved. Atelectasis after influenza may be a rare, but critical complication, and bronchoscopy should be considered to release the obstructive lesions.

The first visualizations of the structures of the sheep's reproductive system, whether of the uterus or ovaries, were performed through exploratory laparotomies, laparoscopies or through materials obtained in slaughterhouse, being possible to evaluate only what was happening at that moment in the estrous cycle. With the emergence of ultrasound from the 90s, it became possible to dynamically monitor what happened at each moment of the estrous cycle at different times of the year, without having to slaughter the females. Because it is a non-invasive technique and the stress caused by its use is very low, it is increasingly necessary to use ultrasound in sheep, either for pregnancy diagnosis or for multiplying herds using biotechnologies, which the ultrasound examination becomes indispensable to obtain the best results. This review aims to describe the main advances in the study of the estrous cycle of sheep with the help of ultrasonography and its influence on the productive efficiency of the herd.

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.

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.

Time series data are increasingly prevalent across domains such as finance, healthcare, manufacturing, and IoT, making accurate forecasting and anomaly detection critical for decision-making and system reliability. Traditional statistical methods (e.g., ARIMA, Holt-Winters) often fail to capture complex temporal dependencies and high-dimensional interactions inherent in modern time series. Recent advances in artificial intelligence particularly deep learning architectures such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), temporal convolutional networks (TCNs), graph neural networks (GNNs) and Transformers have demonstrated marked improvements in modeling both univariate and multivariate series, as well as in detecting anomalies that deviate from learned norms (Darban, Webb, Pan, Aggarwal, & Salehi, 2022; Chiranjeevi, Ramya, Balaji, Shashank, & Reddy, 2024) [1,2]. Moreover, ensemble techniques and hybrid signal-processing + deep-learning pipelines show enhanced sensitivity and adaptability in real-world anomaly detection scenarios (Iqbal, Amin, Alsubaei, & Alzahrani, 2024) [3]. In this work, we provide a unified survey and comparative analysis of AI-driven time series forecasting and anomaly detection methods, highlight key industrial application domains, evaluate performance trade-offs (e.g., accuracy vs. latency, supervised vs. unsupervised learning), and discuss emerging challenges including interpretability, data drift, real-time deployment on edge devices, and integration of causal reasoning. Our findings suggest that while AI approaches significantly outperform classical techniques in many settings, careful consideration of data characteristics, evaluation metrics and deployment environment remains essential for effective adoption.