From 2020 to 2022 the Covid-19 was spread and many people died. Death is near to everyone. The aim of the study was to investigate differences of concept of good death and view of life and death cross cultural. Participants were 92 Japanese, 110 Korean and 100 American who were ordinary people. We used the Good Death questionnaire to measure desirable death and the Death Attitude Inventory to measure view of life and death. Participants completed both questionnaires. As for Good Death, Japanese and Korean regarded “not being burden to others” as important the most, and American regarded “feeling that one’s life is worth living” as important. The scores of “unawareness of death” and “religious and spiritual comfort” of Korean were the highest, following American, and Japan. As for the Death Attitude Inventory, the score of American was higher than Japanese and Korean totally. The scores of “Death as release” or “Death Avoidance” of American were the highest. These results suggest that although Japanese and Korean regard “Not being burden to others” as important in common, Korean regard religious and spiritual comfort as important. American may avoid to think of death and regard it as release. We can make use of these cultural differences in clinical situation.

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.

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.

This study investigates the benefits of forgiveness among adolescents in Junior High Schools (JHS) in Bimbilla, Ghana, focusing on the influence of age on the effectiveness of forgiveness interventions. The study adopted a mixed-method experimental design, a purposive selection of eight JHSs within the Nanumba North Municipality, from which 60 adolescents were randomly chosen to participate. The study employed the Enright Forgiveness Inventory, Depression Mood Scale, and Anger Self-Report items to assess participants' emotional states before and after the intervention. The interventions were structured around the REACH model of forgiveness, which included sessions aimed at helping participants identify sources of hurt, understand the concept of forgiveness, and recognise the emotional costs of holding onto grievances. Qualitative data were analysed into themes using an interpretative lens. A two-way Analysis of Covariance (ANCOVA) was used to analyse the data. The findings revealed that exposure to forgiveness therapies significantly reshaped participants' negative emotions, leading to a marked decrease in feelings of anger and depression. Post-intervention assessments indicated that participants developed a more positive outlook towards their offenders, highlighting the transformative power of forgiveness in fostering emotional well-being. The study's results align with previous research, indicating that forgiveness interventions can effectively reduce negative emotional states and promote psychological resilience. The implications of these findings suggest that integrating forgiveness education into school curricula could be beneficial for enhancing the mental health of adolescents. By fostering an environment that encourages forgiveness, educators and mental health professionals can help mitigate the adverse effects of unresolved emotional conflicts, ultimately contributing to healthier interpersonal relationships and improved overall well-being among young individuals.

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.

The purpose of the study was to examine the effect of REACH and Process Models on forgiveness, anger and depression among 11- to 19-year-old adolescents in junior high schools in Bimbilla in the Northern Region of Ghana. The study employed a mixed-method design. The population for the study comprised all junior high school students totalling 3632. Of this number, 1,888 (55%) of the students were males, while 1744(45%) were females. The accessible population was 1,636 from eight (8) JHSs with 952(55%) of them being boys and the remaining 684(45%) being girls. The participants were selected based on their low levels of forgiveness and high levels of anger and depression determined by the Depression Mode Scale and Anger Self-Report. Purposive and simple random sampling techniques were used to select 60 participants for the study, with each group having 20 participants. The main instruments used for the study are questionnaires (Enright Forgiveness Inventory (EFI). Anger self-report questionnaire (ASR), and Depressed Mood Scale (DMS) and semi-structured interview guide. One-way Analysis of Covariance (ANCOVA) was used to test the hypotheses. The study indicates that both the REACH model and PROCESS model have the efficacy in enhancing forgiveness among adolescents. The study also revealed that the REACH model and Process model have efficacy in reducing levels of depression among adolescent students. It is recommended that Counselling Centres should be set up by District Education Offices and the District Assemblies in the community so that students can visit the centre anytime they feel hurt. Regular seminars, lectures and symposia should be organized regularly by Counsellors and Psychologists using the efficacy of forgiveness therapies (Process and REACH Therapies) for students to be sensitized on the need to patronise forgiveness interventions. It is also recommended that the Government should provide adequate funds and support to encourage the conduct of research in forgiveness counselling since it is a new concept in Africa and Ghana in particular.

The extensive globalization of services and rapid technological advancements driven by IT have heightened the competitiveness of organizations in introducing innovative products and services. Among the noteworthy innovations is enterprise resource planning (ERP). An integral field in computer science, known as artificial intelligence (AI), is undergoing a transformative integration into various industries. Grasping the concept of artificial intelligence and its application in diverse business applications is crucial, given its broad and intricate nature. The primary focus of this paper is to delve into the realm of artificial intelligence and its utilization within enterprise resource planning. The study not only explores artificial intelligence but also delves into related concepts such as machine learning, deep learning, and neural networks in greater detail. Drawing upon existing literature, this research examines various books and online resources discussing the intersection of artificial intelligence and ERP. The findings reveal that the impact of AI is evident as businesses attain heightened levels of analytical efficiency across different ERP domains, thanks to remarkable advancements in AI, machine learning, and deep learning. Artificial intelligence is extensively employed in numerous ERP areas, with a particular emphasis on customer support, predictive analysis, operational planning, and sales projections.

Background: Depression occurs in one-third of stroke patients, in what is known as post-stroke depression. The lesion sites in stroke have been associated with the degree of depression. However, studies have provided different perspectives, and this necessitates further clarification. This study investigates the relationship between the lesion sites and the severity of depression in ischemic stroke patients. Methods: This cross-sectional study was conducted between January and April 2020. All samples were obtained from admitted patients with ischemic stroke who agreed to participate in the study. Data were collected using Beck’s Depression Inventory-II (BDI-II), which was used to determine the severity of depression, and the lesion sites were based on radiological imaging interpretation. Results: The study showed a significant association between the lesion site and the degree of depression (OR = 5,368, p-value = 0,013). Lesions in the frontal lobe demonstrated stronger associations with the severity of depression. Conclusion: The location of the lesion, especially in the frontal lobe, was associated with more severe post-stroke depression.

Parental anxiety and depression are factors that significantly affect the practice of parental role and have an impact on the upbringing of children and their quality of life. The purpose of this study is to investigate whether the counseling intervention that parents received during their participation in parent school programs reduced stress and depressive symptoms and improved their quality of life. A sample of 160 parents was collected from two independent groups: the experimental group consisting of 80 parents who participated in the program and the control group consisting of parents who after being informed about the implementation of the programs in their schools decided not to participate. The psychometric tools have been used are: The Spielberger Stress Trait Anxiety Inventory (STAI), the Beck depression Inventory (BDI), and the Health and well-being Questionnaire (The RAND 36-Item Healthy Survey, SF-36, Version 1.0). The results of the research confirmed the research hypothesis according to which the effect of the counseling intervention on the parents who participated in the parent schools affected the reduction of the stress index, state anxiety (Ζ=-2,882, p=0,004) and trait anxiety (Ζ=-3,776, p<0,001), the reduction of the depression index (Ζ=-6,876, p<0,001) and the improvement of their quality of life(Ζ=-5,364, p<0,001) ) especially of physical health (Ζ=-4,529, p<0,001) and emotional health (Ζ=-4,529, p<0,001).

The present study examines a sample of 140 juveniles. Study aimed to explore the relationship between interparental conflicts, irrational beliefs and mental health. Sample of the study was taken from borstal jail Faisalabad and district jail Lahore. Perceived interparental conflicts scale (CPIC), Irrational beliefs inventory (IBI) and mental health inventory (MHI) was use Results indicated that psychological distress has significant positive relationship with interparental conflicts and mental health. Furthermore, it was found that interparental conflicts significantly predict irrational beliefs. The study will give insights into as what type of interparental conflicts predicts irrational beliefs and different mental health problems in juveniles. It may help clinical psychologists/mental health practitioners to develop appropriate ways to manage interparental conflicts, irrational beliefs and mental health problems. Findings of the study may help mental health practitioners to develop appropriate assessment and treatment programs.

Sports injuries among student-athletes in high schools have become prevalent in most developing countries such as Ghana. In identifying the prevailing injuries among senior high student-athletes in the Akuapem municipality, a descriptive cross-sectional survey design was used. A total of 610 student-athletes were purposely selected for the study to respond to the DEMASS inventory questionnaire regarding their opinion on the prevailing sports injuries. The results indicated that wounds (67.8%), knee injury (65.1%), muscle cramps (56.1%), and thigh injury (55.1%) were prevailing in S.H.S tournament than sprain (37.8%), strain (32.0%), dislocation (30.0%), fracture (31.3%), nose bleeding (31.3%) and groin injuries (23.1%). Participants who played more games professed that the number of games played contributed to sustaining sprain, strain, dislocation, and nose bleeding. Further studies should be carried out at other places and districts to uncover more about injuries sustained during high school sports competitions to minimize their occurrence.

The impact of narcissism and self-esteem on academic achievement has long been an important issue in developmental research. The study aimed to explore the students’ narcissism and self-esteem as correlates of academic achievement in Mathematics in Anambra State. Five research questions and five null hypotheses guided the study. The study adopted a correlational approach. The population of the study comprised of 21204 SS2 students from which a sample of 630 was drawn. Multi-stage procedure was used to select the sample. Two standardized research instruments namely; Narcissistic Personality Inventory (NPI), and Self-esteem Questionnaire (SQ), as well as score from students’ promotional examination were used for data collection. Cronbach’s alpha was used to determine the reliability of the items in the instruments. The overall reliability coefficient was 0.75 which shows that the instrument was reliable and good for the study. The Pearson Product Moment Correlation was used to answer research questions 1 to 4 and to test hypotheses 1 to 4, while the research question 5 and hypothesis 5 were answered and tested with multiple correlations. The findings showed that students’ power narcissism recorded a very low positive relationship with academic achievement in mathematics. Findings also revealed that the multiple correlation of these variables is positively non significant with academic achievement in mathematics. Based on these findings, it was recommended that as narcissistic individuals believe strongly that they are better than others, teachers and counsellors should develop a strategy to enhance the confidence and ability in the students as these will help them to become life long learning individuals thereafter.

This research is based on a retrospective analysis of medical records filed in the archives of the emergency departments of Kalembe-lembe Hospital in Kinshasa city in the Democratic Republic of the Congo. The study involved 324 records of patients aged 0-71 months admitted to the emergency departments and hospitalized for acute diarrhea from January 1 to December 31, 2015. The aim was to inventory the cases of rotavirus diarrhea and/or other germs (individually or in combination) to study their epidemiological and clinical aspects. Thus, the epidemiological and clinical parameters (age, sex, season, symptoms, frequency and physical aspects of stools, dehydration status and duration of hospitalization) of diarrheic children diagnosed as positive for rotavirus were compared with those infected with other germs (individually or in combination with rotavirus or other viruses). The search for the etiological agents of the diarrhea was performed in 56.48% of the cases. The results of this work allowed us to show: (i) a predominance of infections by viruses (69.94%) including rotavirus (48.08%), (ii) high rates of infections by etiological agents of diarrhea including rotavirus in children under 12 months, (iii) a high proportion of vomiting, fever, physical asthenia and restlessness or frequent and liquid stools or moderate dehydration in children infected with rotavirus, (iv) specific clinical pictures according to the etiological agents of diarrhea or their combinations.

This study aimed to assess the floristic composition, vegetation structure and species associations of Cashew agro-ecosystems in the North region of Cameroon. The data were collected in 100 m x 20 m transect methods. An analysis of the data collected in the field was conducted around the diversity and richness of the flora, but also the structure of Cashew agro-ecosystems. For the analyzes of vegetation structure, tree density, basal area and biovolume were calculated. The inventory included trees with a dbh greater than or equal to 5 cm on an area of ​​1 ha per plot. The experimental device installed is a complete random Fisher block with five repetitions. A total of 7849 individuals in 17 families, 20 genera and 38 species were recorded. Species Important value of vegetation cover indicated that Diospyros mespiliformis, Anogeissus leiocarpus, Annona senegalensis and Terminalia laxiflora are the most represented. Combretaceae are the most ecologically important families. Additionally, the grouping of species into four subdivisions showed that the Shannon diversity index varies from 2.18 ± 0.09 - 3.47 ± 0.17 bits, Pielou index vary from 0.98 ± 0.01 - 0.99 ± 0.01, Simpson's index ranges from 0.70 ± 0.18 - 0.93 ± 0.11, density ranges from 102 ± 1.92 - 128 ± 6.36 indiv/ha, basal area ranges from 2.02 ± 0.31 - 12.68 ± 2.01 m²/ha, biovolume ranges from 11.58 ± 1.29 - 68.03 ± 13.21 m³/ha. The Sörensen similarity index is very high with floristic affinities greater than or equal to 90%. The diameters and heights structure show a predominance of young individuals for the diameter and height classes on all plots. The most represented species associations are legumes (Arachis hypogeae: 40.54%) and native fruit trees (Vitellaria paradoxa: 15%). These results show the contribution of Cashew agro-ecosystems in biodiversity conservation.

Due to influence of Internet, this e-commerce sector has developed rapidly. Most of the online retailing or selling businesses are seeking for way for predicting their products demand. Sales forecasting may help retailers develop a sales strategy that will enhance sales and attract more money and investment. The current research work puts forward a machine learning framework to forecast E-commerce sales for strategic management using a dataset of E-commerce transactions. With 70 percent of the data for train and 30 percent for test, three models were produced, namely, Random Forest, Decision Tree, and XGBoost. In order to evaluate the models, performance measures inclusive of R-squared (R²) and Root Mean Squared Error (RMSE) were employed. Thus, the XGBoost model was the most accurate in marketing predictive capabilities for E-commerce sales with the R² score of 96.3%. This has demonstrated the increased capability of XGBoost algorithm to forecast E-commerce monthly sales more accurately than other models and can assist decision makers for managing inventory and arriving smart and quick decisions in this rapidly growing E-commerce market. The findings reiterate the importance of using advanced analytics in order to drive effectiveness and customer experience within E-commerce sector.

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 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.

This is a comprehensive, multi-year study designed to explore proactive security technologies implemented in cloud-driven retail technology architectures. Deploying cloud technologies in the retail environment creates a need for more comprehensive and proactive security technologies that protect both the psychological estate and fiscal estate. This work contributes to cloud-driven retail research by investigating anticipatory security technologies across numerous case studies. These case studies offer best practice models for elevating proactive cybersecurity in retail environments. The academic and professional communities currently lack security information and practices that apply to the retail environment. It is anticipated that the final results of this project will have value in shaping the next set of research in cybersecurity in retail environments. Many retail organizations are restricted to reactive security operations. Advanced security technologies operate on piloted activations that require the intervention of security analysts. In actuality, basic security products and security operations are now piloted by automation and machine learning. In one case study, a retail CTO shares a forensics example using a proactive security technology aimed at both psychological estate and fiscal estate. In another case study, direct discussions provide a retail university lecturer with insight into the use of driven intelligence for inventory management. The use of card technology for a model is used as an example that can be implemented as security technology which can be offered as a service to retail organizations.