The energy transition is a complicated process that involves changing production and consumption habits, boosting the use of renewable energy sources, and reducing greenhouse gas emissions. This article examines the management of risks associated with this shift, focusing on investment, commercial, technological, regulatory, and environmental challenges. The paper proposes an effective way for dealing with new difficulties by assessing risks and the management techniques that accompany them. Furthermore, as a critical component of the energy revolution, the concept of Nearly Zero Energy Buildings (NZEB) is given, with a focus on the ambitions of INGV – Irpina Headquarters and the EU regulatory framework. Finally, the actions and methods employed to achieve the NZEB objectives are discussed, including the utilization of cutting-edge technologies and renewable energy sources. Furthermore, the concept of Nearly Zero Energy Buildings (NZEB) is introduced as an important component of the energy transition, with an emphasis on the European regulatory framework and the ambitions of the INGV – Irpina Headquarters. Finally, the interventions and techniques employed to achieve the NZEB targets are highlighted, such as the employment of advanced technologies and renewable energy resources. This study contributes significantly to understanding the processes and issues associated with the energy transition and the implementation of NZEB, hence boosting sustainability in the construction sector.

This article explores the application of machine learning techniques, specifically focusing on ensemble methods like Random Forests, for detecting fraudulent activities in digital financial transactions. Highlighting the evolution from traditional statistical approaches to modern machine learning models, it underscores the effectiveness of Random Forests in handling the inherent challenges of imbalanced datasets typical in fraud detection scenarios. Using a Kaggle dataset of credit card transactions, the study optimizes Random Forest parameters through rigorous parameter tuning, achieving significant improvements in model performance metrics such as Area Under the Curve (AUC). The findings underscore the critical role of machine learning in enhancing fraud detection capabilities, emphasizing the ongoing evolution and future potential of these methodologies in financial risk management.

The integration of deep learning models into financial risk prediction and analysis has significantly transformed traditional approaches. While conventional quantitative methods often rely on simplistic metrics like maximum drawdown, the advent of deep learning necessitates a more nuanced evaluation, emphasizing the model's generalization ability, especially during market crises such as stock market crashes. This paper explores the critical aspects of evaluating deep learning models' risk control capabilities in finance, underscoring the importance of understanding both statistical metrics and generalization abilities, particularly in adverse market conditions. By examining deep learning models' performance in scenarios like stock market crashes and highlighting the significance of cross-validation techniques, this study aims to offer practitioners insights into constructing robust risk management systems. It advocates for a comprehensive approach integrating quantitative analysis with macroeconomic factors to enhance financial risk prediction and analysis in volatile markets. The experimentation reveals that different deep generative models excel in various aspects of financial time series analysis, with generative adversarial networks (GANs) demonstrating superior performance in predicting Value at Risk (VaR) and variational autoencoders (VAEs) excelling in return rate prediction. Moreover, integrating multiple models further enhances predictive performance, leveraging the strengths of each model to compensate for individual weaknesses. Overall, this paper underscores the potential and significance of deep generative models in financial time series analysis, offering a roadmap for improved risk management and decision-making in financial markets.

This article examines the integration of artificial intelligence (AI) and robotic process automation (RPA) in financial accounting and management, underscoring their role in driving the digital transformation of corporate finance. It discusses the shortcomings of traditional financial processes and highlights the potential of AI and RPA technologies to enhance efficiency, accuracy, and cost-effectiveness. The paper also explores the limitations of RPA, such as its challenges in processing unstructured data and handling complex decision-making scenarios. Looking forward, it considers the future trends in AI and RPA, emphasizing the benefits of cloud technology in scaling automated systems and addressing associated challenges.

The paper explores using generative artificial intelligence (AI) in financial market data management and forecasting. By integrating multiple data sources and feature extraction techniques, such as fundamental analysis, technical indicators, global economic data, and sentiment analysis, generative AI constructs a comprehensive deep learning framework that significantly enhances financial data management efficiency and market forecasts' accuracy. Specifically, technologies like generative adversarial networks (Gans) and variational autoencoders (VAE) demonstrate substantial data augmentation and model optimisation potential. The application value of the model in real-time market prediction and trading strategy optimization is further amplified through reinforcement learning methods.

Building a social security system to ensure Singapore residents have peace of mind in funding for retirement has been at the top of Singapore Government’s policy agenda over the last decade. Implementation of the Lifelong Income For the Elderly (LIFE) scheme in 2009 clearly shows that the government spares no effort in improving its pension scheme to boost its residents’ income after retirement. Despite the recent modifications to the LIFE scheme, Singapore residents must still choose between two plans: the Standard and Basic plans. To enhance the flexibility of the LIFE scheme while maintaining its simplicity, we propose some plan modifications such that scheme members do not face a dichotomy of plan choices. Instead, they select two age parameters: the Payout Age and the Life-annuity Age. This paper provides an actuarial framework for determining members’ payouts and bequests based on the proposed age parameters. We analyze the net cash receipts and internal rate of return (IRR) for various plan-parameter configurations. This information helps members make their plan choices. To address cost-of-living increases we propose to extend the plan to accommodate an annual step-up of monthly payouts. By deferring the Payout Age from 65 to 68, members can enjoy an annual increase of about 2% of the payouts for the same first-year monthly benefits.

In recent years, the digital economy has played an increasingly important role in enhancing productivity, fostering growth, and facilitating the emergence of digital banking. With the widespread adoption of digital financial services for financial inclusion, the banking sector is experiencing a demand for digital transformation. It is important to note that despite the many advantages of this change, it also disrupts the familiar banking experience that customers have grown to expect, as well as introducing potential risks to maintaining customer protection at previous levels. Many critics have raised concerns regarding customer protection and potential exposure to various risks that may arise from digital financial services, which may result in both financial and non-financial losses. These risks can impact customer retention as dissatisfied customers may switch to alternative service providers. Additionally, reputation can be affected since merely offering digital financial services may not sufficiently safeguard customers. To remain competitive, financial institutions must deliver a secure experience that aligns with customer expectations while considering economic factors and societal needs. This is why it is essential to comprehend the severity of the risk factors that influence the protection of customers in the realm of digitalized banking services and products. In this study, five factors have been identified that influence customer protection when using digital financial services in Pakistan. Based on the analysis conducted using SmartPLS, it appears that all the factors proposed in this study have a significant and positive impact on the protection of customers. In particular, the information provided remains of paramount importance when it comes to determining the level of protection that a customer is entitled to within digital financial services, followed by responsiveness, privacy, authentication, and encryption mechanisms. Due to these findings, the implementation of enhanced information security management principles becomes imperative for the development and progress of the Pakistani digital banking industry.

Diabetic foot is a serious complication that poses significant risks for diabetic people. The resulting reduction in protective sensitivity in the plantar region requires early detection to prevent the imminent ulceration and amputation. The primary method employed for evaluating this sensitivity loss is the 10 g f Semmes-Weinstein monofilament test, commonly used as a first-line procedure. However, the lack of calibration in existing devices often introduces decision errors due to unreliable feedback. In this paper, a new equipment designed to address these limitations and enhance the assessment of the sensitivity loss using the 10 g f monofilament is introduced. The proposed system automates metrological verification and evaluation processes, ensuring accurate and reliable results. Furthermore, it simulates the practitioner’s procedure, serving both as a valuable training tool and a means to provide force-feedback information. An analytical model of the monofilament buckling and deflection is also presented. The obtained results showed that the tested monofilaments had a very high error compared to the 10 g f declared by the manufacturers. It is aimed, by introducing this advanced equipment, to improve the precision and reliability of assessing the sensitivity loss. The integration of automated verification, simulation capabilities, and precise measurements shows great promise for diabetic patients, reducing the likelihood of adverse outcomes such as ulceration and amputation.

This paper utilizes the 2020 financial data of A-share listed companies, both ST and non-ST, as the data sample. The model is constructed employing the Factor-Logistic fusion algorithm. Eight key indicator factors were selected from the frameworks of profitability, solvency, operating capacity, development potential, shareholder retained earnings, cash flow and asset growth. An independent indicator system was subsequently established to address the issue of collinearity among risk indicators. The results of model are presented in the form of probabilities, enhancing the interpretability of model. The weights of the features indicate the influence of different features on the final outcome. The model achieves a prediction accuracy of over 89%, with an AUC score exceeding 95%. Finally, by applying the principles of interval estimation theory in statistical hypothesis testing, the risk levels are categorized as A-level representing significant risk, B-level representing moderate risk, C-level representing minor risk and D-level representing no risk. This paper aims to provide a comprehensive definition of a universal financial risk management warning model for enterprises, applicable to all enterprises in China.

Enterprise risk management (ERM) was introduced in the 1990s and has become an expectation by boards of directors and regulators as a sign of good management and good corporate governance. However, many organizations struggle to implement ERM, and still seek practical ad-vice on ERM implementation. This article explains many of the reasons why organizations are unsuccessful in their efforts at implementation and provides practical solutions by an experienced risk manager and consultant, an ex-Chief Risk Officer, and an academic, all who has writ-ten extensively in the subject. This article should be of interest to practitioners involved in implementing ERM, to consultants in ERM, and to academics teaching courses on ERM, risk management, and related topics. This article also provides a base against which further future re-search can be done as ERM best practices continue to evolve.

This paper aims to develop a hierarchical risk assessment model using the newly-developed evidential reasoning (ER) rule, which constitutes a generic conjunctive probabilistic reasoning process. In this paper, we first provide a brief introduction to the basics of the ER rule and emphasize the strengths for representing and aggregating uncertain information from multiple experts and sources. Further, we discuss the key steps of developing the hierarchical risk assessment framework systematically, including (1) formulation of risk assessment hierarchy, (2) representation of both qualitative and quantitative information, (3) elicitation of attribute weights and information reliabilities, (4) aggregation of assessment information using the ER rule and (5) quantification and ranking of risks using utility-based transformation. The proposed hierarchical risk assessment framework can potentially be implemented to various complex and uncertain systems. A case study on the fire/explosion risk assessment of marine vessels demonstrates the applicability of the proposed risk assessment model.

The use of artificial intelligence technologies may help companies obtain the optimal use of resources and accomplish more activities with an increase in productivity by 40%. With access to real-time data and the ability to learn from patterns and behaviors, AI can perform these routine tasks more efficiently. Ambient intelligence, Ami, is one of the applications of artificial intelligence, which is called artificial environmental intelligence, which is a concept that focuses on using technology to create an environment that is intelligent and responsive to the needs of its users. This technology has the potential to revolutionize disaster response and management, which in turn facilitates access to necessary information and resources. necessary vitality in the event of a crisis. It may use ambient intelligence to create a network of sensors that can detect and respond to changes in the environment. So that this network can be used to monitor the environment for potential disasters, such as floods, fires and earthquakes. Sensors can also be used to detect changes in temperature, humidity, and other environmental factors that may indicate a potential disaster. This information can then be used to alert emergency responders and provide them with the information needed to respond quickly and effectively. Ambient intelligence can also be used to create a comprehensive database of resources and information that can be accessed in times of crisis. This database may include information about the location of shelters, medical facilities, and other resources that can be used to assist those affected by a disaster. It may also provide information about the condition of roads, bridges, and other infrastructure that may be affected by a disaster. This information can be used to inform response and management decisions, helping to ensure that resources are used in the most efficient way possible. Ambient intelligence can be used to create a communication platform between emergency responders and those affected by a disaster. This platform can also be used to provide updates on the emergency situation, in addition to providing information about available resources to those affected. This can help ensure that those affected by a disaster can access the resources they need in a timely manner.

Seismic vulnerability assessment in urban areas would in principle require the detailed modeling of each single building and the implementation of complex numerical calculations. This procedure is clearly difficult to apply at urban scale where many buildings have to be considered and therefore it is essential to have simplified, but at the same time reliable, approaches to vulnerability assessment. Among the proposed strategies, one of the most interesting concerns the application of machine learning algorithms, able to classify buildings according to their vulnerability on the basis of training procedures applied to existing datasets. In this paper machine learning algorithms will be applied to a dataset which collects and catalogs the structural characteristics of a large number of buildings and reports the damage observed in L’Aquila territory during the intense seismic activity occurred in 2009. A combination of a trained neural network and a random forest algorithm allows to identify an opportune “a-posteriori” vulnerability score, deduced from the observed damage, which is compared to an “a-priori” vulnerability one, evaluated taking into account characteristic indexes for building’s typologies. By means of this comparison an inverse approach to seismic vulnerability assessment, which can be extended to different urban centers, is proposed.

The dynamic and unpredictable nature of modern supply chains necessitates the development of resilient systems capable of withstanding various disruptions. This qualitative study examines the impact of collaborative practices on supply chain resilience, providing an in-depth analysis of their contributions to the robustness and adaptability of supply chains. Through extensive interviews with industry professionals, including supply chain managers, logistics coordinators, and strategic planners, the study identifies key collaborative practices—such as information sharing, joint decision-making, and coordinated response strategies—that significantly enhance resilience. These practices facilitate proactive risk management, improve response times during disruptions, and foster innovation through shared knowledge and resources. Thematic analysis of the collected data reveals that strong relationships among supply chain partners and integrated approaches that leverage each participant's strengths are crucial for effective risk mitigation and recovery. Additionally, the study underscores the role of technology in enabling real-time information sharing and decision-making, which are essential for effective collaboration. The findings highlight the importance of investing in robust collaborative networks and adopting supportive technologies to enhance communication and coordination. This research contributes to the existing body of knowledge by offering a nuanced understanding of how strategic implementation of collaborative practices can build more resilient supply chains. It provides practical recommendations for businesses seeking to improve their resilience, emphasizing the qualitative aspects of collaboration and its impact on supply chain management. Overall, this study presents a comprehensive exploration of how collaboration can bolster supply chain resilience, offering valuable insights for practitioners and scholars focused on creating stable and sustainable supply chain operations in the face of growing uncertainties.

Supply chain risk management (SCRM) is a critical aspect of contemporary business operations, necessitated by the complex and interconnected nature of global supply chains. This qualitative research delves into the intricacies of SCRM through in-depth interviews with industry experts, aiming to unravel the multifaceted nature of supply chain risks and the strategies employed for mitigation. Key themes emerged, including the diverse sources of risks encompassing operational, financial, environmental, and geopolitical factors, highlighting the need for a comprehensive approach to risk management. Challenges such as limited visibility, resource constraints, and organizational silos were identified, underscoring the importance of addressing these barriers to enhance SCRM effectiveness. Strategies for risk mitigation encompassed technological investments for enhanced visibility and predictive capabilities, collaboration and information sharing among supply chain partners, and the integration of sustainability principles into risk management practices. Leadership, organizational culture, and continuous learning emerged as critical factors in driving effective SCRM practices, emphasizing the need for proactive and adaptable approaches to navigate evolving risks. Overall, this study contributes to the existing body of knowledge on SCRM by providing valuable insights for practitioners and academics, and underscores the importance of holistic and proactive approaches to enhance supply chain resilience and agility in today's dynamic business environment.

In today's globalized economy, supply chain disruptions present significant challenges, impacting businesses' operational continuity and efficiency. This study delves into the ways businesses navigate these disruptions, with a particular emphasis on qualitative insights into risk management practices. The complexity and interconnectivity inherent in modern supply chains make them vulnerable to a broad spectrum of disruptions, ranging from natural disasters and geopolitical conflicts to technological failures and pandemics. Effective supply chain risk management requires a structured approach to identifying potential risks, evaluating their likelihood and potential impact, and formulating robust mitigation strategies. Employing a qualitative research methodology, this study gathers in-depth insights from semi-structured interviews with supply chain managers, industry experts, and executives. The findings underscore the necessity of proactive risk identification and comprehensive assessment processes. Advanced technologies, including artificial intelligence (AI), the Internet of Things (IoT), and blockchain, are highlighted as crucial tools for enhancing visibility and responsiveness within supply chains. Strategies such as diversifying suppliers and maintaining safety stock emerge as vital components of risk mitigation, ensuring supply continuity in the face of disruptions. Strong relationships with suppliers are pivotal, facilitating better information sharing and collaborative problem-solving. Leadership commitment to risk management and fostering a culture of resilience within organizations is also critical. Training programs and simulation exercises are identified as effective means of preparing employees to handle disruptions. Furthermore, adherence to regulatory compliance and a focus on sustainability are integral to maintaining long-term stability and reducing the risk of future disruptions.

This study explores the multifaceted role of culture in supply chain collaboration, focusing on how cultural differences shape communication, trust-building, negotiation, conflict resolution, risk management, and ethical standards. Utilizing a qualitative methodology, semi-structured interviews were conducted with 25 supply chain professionals from diverse cultural backgrounds and industries. The findings reveal that high-context cultures favor indirect communication and nuanced information exchange, which can lead to misunderstandings with partners from low-context cultures who prefer direct and explicit communication. Trust-building practices vary significantly, with high uncertainty avoidance cultures emphasizing formal agreements and procedural rigor, while low uncertainty avoidance cultures prioritize personal relationships and demonstrated reliability. Negotiation strategies also diverge, reflecting the underlying values of collectivism versus individualism, impacting the tactics and outcomes of supply chain negotiations. Conflict resolution approaches differ, with high power distance cultures favoring hierarchical and mediated solutions, and low power distance cultures preferring direct and egalitarian methods. The study highlights the need for adaptability to cultural changes and the development of cultural intelligence as essential competencies for managing cross-cultural supply chains. Additionally, cultural differences in risk management approaches and governance mechanisms affect the coordination and resilience of supply chains, while varying ethical standards influenced by cultural norms necessitate culturally sensitive strategies for promoting corporate social responsibility. The insights gained from this research underscore the importance of understanding and leveraging cultural differences to build stronger, more productive supply chain relationships, contributing to the broader literature on culture and supply chain management and offering practical implications for enhancing global supply chain collaboration.

The effects of climate change health impacts on urban health care systems and vulnerable populations are a researched topic with foundations on urban risk management, urban health methodologies, and sustainable urbanization. As climate change is a dynamic and transformative global issue the gap between adaptation and mitigation efforts and urban planning relies, among other things, in the limitations of previous studies of risk management structures tailored to the existing strategy to calculate the uncertainties and predict their influence on urban systems and subsystems. Moreover, as climate and health is a well-researched issue, it is also complex as the methodologies and strategic approach to minimize health risks of climate change is intrinsically contingent to risk methodologies and policy-making processes that are efficient with support of knowledge-based information. This paper emphasis the need for focusing on urban health risk management methods that serves to identify potential opportunities, and then manage and take action to prevent adverse health effects. It also emphasizes the probability of events and their consequences, which are measurable both qualitatively and quantitatively.

Teledermatology, the dermatological consultation and diagnosis via digital communication technologies, has become increasingly important in modern healthcare. The integration of teledermatology into clinical practice has gained significant interest, especially during the COVID-19 pandemic. Numerous studies have shown the effectiveness and reliability of teledermatology, with high levels of patient satisfaction and diagnostic accuracy. Pathpoint® eDerma is a cloud-based dermatology platform that aims to streamline the documentation and management of dermatological conditions. It offers several clinical benefits, including faster diagnosis, reduced unnecessary procedures, improved access to care, and cost savings. The platform follows a comprehensive risk management plan and complies with safety regulations. It undergoes regular recertification and reviews to ensure its safety and effectiveness. The clinical evaluation of eDerma confirms its ability to deliver significant clinical benefits while effectively mitigating risks. The benefit-risk ratio of eDerma is highly acceptable, supporting its use in clinical settings to enhance dermatological care.

The distribution of zakat, which is still carried out directly through mosques or zakat amil bodies, can hinder the payment process due to inaccurate calculations and difficulties in finding zakat amil institutions. In the digital era, information technology is very important in the management of zakat, alms, and infaq. Therefore, a zakat information management system was created. The e-Zakat program, developed using the waterfall method, plays an important role in five main aspects of zakat management: muzaki and mustahiq data collection, zakat collection, zakat distribution, mustahiq development, and reporting. The use of information technology in this program is expected to increase efficiency, accountability, transparency, as well as the reach of zakat and muzaki involvement.

Risk assessment management have been a hot topic for the researchers since a very long time. Software risk management is an important part of project management as it contains the identification, analysis, estimation and monitoring of different risks present in the system. This helps developers in decision making while assessing the problems that could arise in the software systems. Risk management is very complex in large scale system as these systems have very complex development. The paper describes risk management techniques for large scale system. Furthermore we have provided a detailed comparative analysis of these techniques with commonly identified risks in software systems and have provided a systematic order for risk management process to ensure risk mitigation.

This qualitative research explores the intricate relationship between cultural influences on supply chain practices and their implications for marketing strategies. Cultural dimensions such as individualism versus collectivism, uncertainty avoidance, and contextuality significantly shape decision-making processes, risk management strategies, and relationship dynamics within global supply chains. Through semi-structured interviews with supply chain managers, marketing executives, and cultural experts across diverse industries and regions, this study investigates how cultural values impact supply chain operations and consumer engagement strategies. Key findings highlight that collectivist cultures prioritize consensus-building and long-term relationships, fostering trust and collaboration among supply chain partners. In contrast, individualist cultures emphasize efficiency and accountability but may face challenges in relationship building. Risk management practices vary with cultural orientations towards uncertainty, influencing the adoption of structured versus flexible approaches to mitigate disruptions. In terms of marketing implications, the study underscores the importance of cultural sensitivity in crafting effective strategies. High-context cultures require marketing messages that resonate emotionally through implicit communication and cultural symbolism, while low-context cultures prefer clear, direct messaging focusing on product benefits. The study concludes by advocating for cultural competence as a strategic imperative for global businesses, enabling them to leverage cultural diversity for innovation and competitive advantage. It emphasizes the need for continuous adaptation to cultural nuances and market dynamics to foster inclusive collaboration and achieve sustainable growth in a globalized economy.

This qualitative research explores the intricate relationship between cultural influences on supply chain practices and their implications for marketing strategies. Cultural dimensions such as individualism versus collectivism, uncertainty avoidance, and contextuality significantly shape decision-making processes, risk management strategies, and relationship dynamics within global supply chains. Through semi-structured interviews with supply chain managers, marketing executives, and cultural experts across diverse industries and regions, this study investigates how cultural values impact supply chain operations and consumer engagement strategies. Key findings highlight that collectivist cultures prioritize consensus-building and long-term relationships, fostering trust and collaboration among supply chain partners. In contrast, individualist cultures emphasize efficiency and accountability but may face challenges in relationship building. Risk management practices vary with cultural orientations towards uncertainty, influencing the adoption of structured versus flexible approaches to mitigate disruptions. In terms of marketing implications, the study underscores the importance of cultural sensitivity in crafting effective strategies. High-context cultures require marketing messages that resonate emotionally through implicit communication and cultural symbolism, while low-context cultures prefer clear, direct messaging focusing on product benefits. The study concludes by advocating for cultural competence as a strategic imperative for global businesses, enabling them to leverage cultural diversity for innovation and competitive advantage. It emphasizes the need for continuous adaptation to cultural nuances and market dynamics to foster inclusive collaboration and achieve sustainable growth in a globalized economy.

This qualitative research study delves into the intricate dynamics of how organizations adapt to supply chain disruptions during crises, offering profound insights into the strategies, practices, and challenges encountered in enhancing supply chain agility. Through in-depth interviews with supply chain managers across diverse industries, key themes such as proactive risk management, technological integration, collaboration with supply chain partners, and adaptive leadership have emerged as crucial determinants of agility and resilience. Additionally, the study highlights the significance of organizational culture, effective communication, and external factors such as regulatory requirements and market dynamics in shaping supply chain agility. By embracing a holistic approach that integrates these multifaceted factors, organizations can bolster their capacity to anticipate, detect, and respond to disruptions, thereby maintaining or enhancing overall performance even in the face of uncertainty and complexity. The insights gleaned from this research are poised to inform theory development, guide managerial practice, and influence policy-making in the realm of supply chain management, equipping organizations with the knowledge and tools needed to navigate future disruptions successfully.

This qualitative research explores the influence of supply chain disruption management on brand resilience and consumer perception. Amidst increasingly complex global dynamics, effective strategies are crucial for organizations to mitigate risks, maintain operational continuity, and preserve consumer trust. The study employs semi-structured interviews with stakeholders across diverse industries, focusing on strategies employed, outcomes observed, and challenges faced in managing disruptions. Key findings underscore the significance of proactive measures such as risk assessment, contingency planning, diversification, technology integration, and collaboration in enhancing organizational resilience. These strategies not only enable businesses to navigate disruptions effectively but also contribute to sustaining competitive advantage and stakeholder confidence. Consumer perception during disruptions emerges as pivotal, shaped by brands' transparency, responsiveness, and ethical considerations in managing supply chain challenges. Transparent communication and reliable service delivery during disruptions enhance brand reputation and foster consumer loyalty. However, challenges including information gaps, resource constraints, coordination issues, and regulatory compliance complexities highlight the need for adaptive strategies and robust partnerships across the supply chain. Overall, the study provides insights into the interconnectedness of disruption management, brand resilience, and consumer perception, offering practical implications for organizations seeking to strengthen their resilience strategies and consumer relationships in dynamic market environments.

Supply chain disruptions pose significant challenges to organizations, highlighting the critical importance of resilience in contemporary supply chains. This qualitative research explores the implications of supply chain disruptions on organizational resilience, drawing insights from interviews with supply chain managers and executives across various industries. The study identifies key factors that contribute to resilience, including agility, collaboration, risk management, strategic planning, technology integration, leadership, and organizational culture. Through thematic analysis, the research elucidates how these factors interact to enable organizations to manage and recover from disruptions effectively. Findings underscore the importance of agility in adapting to changing circumstances, collaboration with stakeholders to coordinate responses, and proactive risk management to anticipate and mitigate disruptions. Strategic planning and technology integration emerge as vital enablers of resilience, along with effective leadership and a resilient organizational culture that fosters adaptability and continuous improvement. The implications of supply chain disruptions extend beyond operational and financial impacts to include reputational and relational dimensions, emphasizing the importance of transparent communication and stakeholder engagement. The practical insights offered by this study provide guidance for organizations seeking to enhance their resilience and ensure continuity of operations in an increasingly complex and dynamic supply chain environment.

Abstract: Integration of Medical Cyber Physical Systems (MCPS) and Internet-of-Medical Devices (IoMT) with conventional hospital networks have facilitated easy and speedy data collection, vertical and horizontal connectedness and collaborations among healthcare providers. Federated identity management (FIM) provides a solution towards the identity management challenge arising from this integration of millions of MCPSs to get personalized care and frictionless experiences for the patients, doctors, and employees. FIM protocols such as OAuth, Security Assertion Markup Language (SAML) are highly susceptible to cyber attacks like theft of barrier token, replay attack, message insertion, and Man-in-Middle attacks. IoMT devices have vulnerabilities in their firmware, operating systems, data encryption, data at store and data transmission. Combined vulnerabilities of FIM framework and IoMT devices creates major cyber risks for the current digital healthcare system. Therefore, a comprehensive and evidenced based cyber risk assessment is an urgent need for a cyber-safe digital health care system that can avoid frequent life threatening situations. This paper proposes a comprehensive and evidenced based cyber risk assessment approach for FIM and IoMT based collaborative digital healthcare systems. The novelty of the proposed approach is that it considers three dimensional vulnerabilities arising from existing IT communication protocols and infrastructure, IoMT and MCPS medical devices, protocols of FIM and their combined impact on hospitals and provides corresponding recommendations of security controls. The proposed approaches combine two industry standards including Cyber Resilience Review (CRR) asset management, NIST SP 800-30 to take advantage of both approaches. We have used a large number of IoMT and MCPS devices from multiple providers for threat modelling and produced evidence based cyber-risks using attack trees and detailed attack sequence diagrams to validate proposed approaches. The corresponding recommendation of security controls will support healthcare professionals and providers significantly for improving both the patient and medical device safety management within the FIM enabled healthcare ecosystem.

The qualitative research on the influence of supply chain risk management (SCRM) on marketing strategies during economic uncertainty explores the complex interplay between these two critical areas. Conducted through in-depth interviews with industry professionals across various sectors, the study examines how organizations manage supply chain risks and adapt their marketing strategies in response to challenges such as natural disasters, geopolitical tensions, economic downturns, and pandemics. The findings reveal that these risks significantly impact marketing efforts by causing supply shortages, delays, increased costs, and demand fluctuations, necessitating rapid adjustments in promotional activities, pricing strategies, and communication with customers. Key strategies for integrating SCRM with marketing include leveraging technology for real-time supply chain monitoring, building strong supplier relationships, diversifying supply sources, and enhancing communication and collaboration between supply chain and marketing teams. These practices enable organizations to enhance their resilience and responsiveness, ensuring that marketing strategies are aligned with the evolving realities of the supply chain. The study also identifies challenges such as organizational silos, lack of cross-functional collaboration, resistance to change, and limited technological capabilities, which can hinder effective integration. Addressing these barriers through fostering collaboration, investing in technology, and promoting change management is essential for achieving successful integration. The outcomes of this integration include improved supply chain resilience, enhanced customer satisfaction, increased marketing agility, and a stronger competitive position. The research underscores the importance of a holistic and adaptive approach to integrating SCRM with marketing strategies, providing valuable insights for organizations seeking to navigate economic uncertainty and sustain their growth in a rapidly changing environment.

Supplier Relationship Management (SRM) is a critical aspect of supply chain management, particularly in today's dynamic and interconnected business environment. This qualitative research study explores SRM practices within the context of supply chain dynamics, aiming to understand how organizations manage their supplier relationships to achieve strategic objectives. Through semi-structured interviews, document analysis, and participant observation, key themes emerge, including collaboration, trust, risk management, performance measurement, digital technology integration, sustainability, cultural alignment, supplier development, strategic alignment, emotional intelligence, and marketing perspectives. The findings underscore the importance of collaboration and trust in fostering strategic partnerships with suppliers, mitigating risks, and enhancing supply chain resilience. Effective risk management practices, performance measurement systems, and digital technology integration are essential for improving transparency, efficiency, and responsiveness in SRM. Sustainability considerations highlight the need for aligning SRM practices with corporate social responsibility goals and stakeholder expectations. Cultural alignment, supplier development programs, strategic alignment, emotional intelligence, and marketing perspectives further enrich the understanding of how organizations navigate their supplier relationships. This study contributes to the body of knowledge on SRM by providing empirical insights into the practices, challenges, and opportunities associated with managing supplier relationships in dynamic supply chains. The findings offer valuable guidance for practitioners and policymakers seeking to enhance their SRM practices and achieve competitive advantage in today's complex business landscape.

We investigated and quantified bacterial accumulation in facemasks used by the general population, using 16S rRNA (Sanger Sequencing), culture and biochemical analysis along with Rose Bengal staining. Additionally, a systematic overview of the literature on face mask contamination was undertaken. We found an average bacterial load of 4.24 × 10⁴ CFU recovered/mask, with a maximum load of 2.85 × 10⁵ CFU. This maximum is 310 times higher than the limit value for contamination of ventilation system outlet surfaces specified by the German standard VDI 6022. Biochemical and molecular identification predominantly found Staphylococcus species (80%), including Staphylococcus aureus, along with endospore-forming Bacillus spp. Literature reports also indicate contamination of masks by bacterial and fungal opportunists of the genera Acinetobacter, Aspergillus, Alternaria, Bacillus, Cadosporium, Candida, Escherichia, Enterobacter, Enterococcus, Klebsiella (including K. pneumoniae), Micrococcus, Microsporum, Mucor, Pseudomonas, Staphylococcus and Streptococcus. Bacterial counts increase linearly with wearing duration, and prolonged use may affect the skin and respiratory microbiomes, promoting consequential eye, skin, oral and airway conditions. These aspects underscore the urgent need for further research and a risk-benefit analysis in respect of mask use, particularly given their unproven efficacy in disrupting the transmission of respiratory viruses and their adverse social consequences.

The Internet of Things (IoT) continues to grow at a rapid pace, becoming integrated into the daily operations of individuals and organisations. IoT systems automate crucial services within daily life that users may rely on, which makes the assurance of security towards entities such as devices and information even more significant. In this paper, we present a comprehensive survey of papers that model cyber risk management processes within the context of IoT, and provide recommendations for further work. Using 39 collected papers, we studied IoT cyber risk management frameworks against four research questions that delve into cyber risk management concepts and human-orientated vulnerabilities. The importance of this work being human-driven is to better understand how individuals can affect risk and the ways that humans can be impacted by attacks within different IoT domains. Through the analysis, we identified open areas for future research and ideas that researchers should consider.

This study investigates the complex role of financial risks in the context of public–private partnerships (PPPs) in India and their relationship to the amount and extent of investments made by private partners (s). We hypothesize that subsidies, bid criteria, and risk have a positive relationship with investments made by private partners. A project’s leverage moderates these relationships. Regression was used to analyze the hypotheses using SPSS 24 and PROCESS Macro. Data from the World Bank were collected from 2009 to 2019 for the study variables. Interestingly, our research finds support for these relationships that may seem contrary to some commonly-held notions of risk relationships, such as – the government using ‘viability gap’ funding to attract private investment and ‘leverage’ does not moderate the relationship between risk assumed and private investment. This study is among the first to recognize and elaborate on financial risk relationships, specifically in the context of Indian PPPs. These findings are significant for both private and public participants in terms of financial considerations in PPP projects, especially in emerging markets.

The aim of the article is to verify differences in the perception of selected factors of sustainability among small and medium-sized enterprises (SMEs) in Central Europe. The subjects of analysis are factors such as corporate social responsibility (CSR), business risk sources (market, financial, personnel, and operational), reputation and social media, national support, social changes, and crisis events in business. The questionnaire was completed by 1,090 SMEs from the business environments of Visegrad Group countries. Statistical hypotheses were verified using Z-scores for two population properties. The most negatively perceived factors among owners and managers in the SME segment are national support and legislative changes. The most positively perceived factor is reputation and social media. The country of doing business is a significant factor. The perception of crisis events in business is most negative among Czech SMEs. More than 80% of Hungarian SMEs perceive their companies as sustainable. In comparison with Slovak and Polish SMEs, Hungarian perceptions are more positive in the context of sustainability. The country of doing business is the strongest factor in evaluating CSR indicators. The positive perception of CSR in Czech SMEs (43.9%) is lower than in other countries.

In the Republic of Serbia, no comprehensive scientific multimethod research has been conducted to identify weaknesses and opportunities for improving the disaster risk management system. Regarding that, in the paper, author presents project description ,,Strengthening Integrated Disaster Risk Management System in Serbia - DISARIMES” which shall enable strengthening the disaster risk management system through research, development and innovative solutions implementation in the preparation, mitigation, response, remediation and post-disaster phases. The scientific importance of the projected research is reflected in the creation of assumptions for the advancement of theoretical and empirical knowledge in the scientific field of disaster risk management, bearing in mind that it is a relatively young scientific discipline in Serbia. The results of the project research will make it easier for decision makers in Serbia to understand the shortcomings of the system, but also provide innovative opportunities to improve their functioning in conditions of increasingly frequent and serious disasters. Establishing a scientific and professional society in the field of disaster risk management will create sustainable and necessary conditions for the transfer and improvement of knowledge and experience of importance for raising the level of operability of the system of protection and rescue of the Republic of Serbia in the event of disasters. A key contribution of the research findings will be to create a sustainable knowledge base that will be supported by the relevant amount of information regarding innovative capabilities and solutions identified as necessary to raise social resilience to a much higher level. In order to achieve the set goals, DISARIMES makes it possible, through a large number of SWOT analyses and other multimethod studies, to clearly identify and systematize the objective deficiencies and barriers encountered by the disaster risk management system in Serbia in all its stages before, during and after disasters, to identify and implement the appropriate solutions based on this. The objectives of the project are: to assess and identify strengths (advantages), weaknesses (disadvantages), opportunities (innovation potential) and threats for the disaster risk management system in Serbia; to develop and update RDI (research, development, innovation) Roadmap – knowledge databases with innovative solutions and other relevant information for improvement of the disaster risk management system; to fully deploy the DISARIMES scientific-professional network involving a broad range of the disaster risk management scientists and civil protection professions and organisations; to prepare the ground for the disaster risk management policy innovations.

Current trends in systemic risk literature provide new insights into multi-hazard risks that interact and compound with built environments, creating more significant impacts on socio-economic and human systems. Recurrent natural hazards and extreme weather events are more likely to compound and cascade into more impactful events, especially in vulnerable societies. In small-island communities (SICS), including large archipelagos and Small Island Developing States (SIDS), the long-term impact of multi-, compounding-, and cascading-hazards (MCC hazards) can result in persistent vulnerability and residual risks. This is due to delayed responses, limited resources, poverty, fewer evacuation options, and inadequate markets and infrastructure. Using Ternate, a densely populated small volcanic island in North Maluku, Indonesia, as a case study, this paper assesses the impacts of seven types of natural hazards: flash floods, landslides, extreme weather, extreme waves and abrasion, earthquakes, tsunamis, and volcanic eruptions. The research focuses on the multi-hazard impact on population, land use, and infrastructure exposures in 60 villages in Ternate. The findings highlight population density, land use, and infrastructure exposure to multi-hazard risks, providing valuable information on potential losses in future hazard events.

The brain being arguably the most complex organ in the human body, its detailed functioning is yet to be fully deciphered, let alone accurately modeled. Nonetheless, the medical community has gathered quite a remarkable amount of studies about the consequences of illnesses and injuries on its development and functioning. This bibliographic review aims to cover mostly the findings related to the changes in the physical distribution of neurons and their connections - the connectome -, both structural and functional, as well as their modelling. It does not intend to offer an extensive medical description of all injuries and diseases affecting the brain, rather presenting the most common ones succinctly so the need for accurate brain modelling can be fully understood and pondered, offering propositions to this aim.

Abstract This study focuses on the toxicity assessment of various flavonoids, with particular emphasis on Hesperidin. Among the investigated compounds, Hesperidin exhibits notable potential and low toxicity in both short and long-term evaluations. The toxicity parameters for Hesperidin include a low Max. Tolerated Dose (Human) (0.525 log mg/kg/day), indicative of its minimal potential harm to humans. Additionally, its Oral Rat Acute Toxicity (LD50) (2.506 mol/kg) suggests a relatively low acute toxicity level. The Oral Rat Chronic Toxicity (LOAEL) (3.167 log mg/kg_bw/day) signifies a higher dose threshold for observing adverse effects over the long term. Furthermore, Hesperidin demonstrates low Minnow Toxicity (7.131 log mM), suggesting minimal impact on aquatic organisms. Overall, this comprehensive assessment positions Hesperidin as a promising flavonoid with favorable properties and minimal toxicity.

This paper presents MADS (Multi-Agents for Data Science), an innovative and simple open-source multi-agent framework designed for systemic pipeline execution in applied supervised machine learning. By leveraging the capabilities of multi-agent systems (MAS), we introduce a universal approach to optimize and streamline machine learning pipelines. Our framework highlights the differences between various types of agents, such as reinforcement learning (RL) agents and large language model (LLM) agents, and their distinct contributions to the process. While we currently employ LLM agents to automate and enhance machine learning tasks, we acknowledge the potential of incorporating RL agents in future iterations to further improve performance and adaptability. The primary objective is to enhance the efficiency, scalability, and adaptability of supervised learning applications across various domains. This integration addresses the complexity and manual effort typically associated with machine learning workflows, paving the way for more automated, robust, and scalable solutions. Our approach demonstrates significant improvements in task automation, reduced human intervention, and enhanced model performance. The MADS framework, which will soon be available as an open-source implementation, represents a pivotal contribution to the field of machine learning, promising to facilitate broader adoption and collaborative advancement.

Stroke is a global leading cause of death and disability. Knowledge of related guidelines is crucial for Emergency Department (ED) staff, influencing early diagnosis and timely treatment. We investigated Greek-Cypriot ED healthcare professionals' (nurses and physicians) knowledge in recognizing and managing stroke. A descriptive cross-sectional study spanned November 2019 to April 2020, encompassing four private and seven public EDs in the Republic of Cyprus. The data were collected through a self-reported questionnaire developed by the research team, consisting of 37 questions. Eight questions focused on sociodemographic and employment characteristics, 28 assessed knowledge in stroke recognition and management (each item was equally weighted without deliberate prioritization), and one question addressed self-assessment of knowledge in stroke care. 255 nurses [Response Rate (RR): 74.1%] and 26 physicians (RR: 47.3%) completed the questionnaire. The average correct response rate was 12.9 out of 28 statements (SD: 4.2), with nurses and physicians scoring 12.6 (SD: 4.1) and 15.7 (SD: 4), respectively. Work experience significantly influenced stroke knowledge, with all groups demonstrating superiority over those with less than one year of experience. Participants with previous training scored an average of 1.45 additional correct answers while educational attainment did not significantly influence stroke knowledge. Investigating stroke knowledge among Emergency Department nurses and physicians in the Republic of Cyprus revealed significant deficits. The study stresses targeted interventions, including, education, yearly examinations, workshops with hands-on training, and repeated training, to address these gaps and enhance the overall stroke care capabilities of the healthcare professionals.

Electromobility has gained significant attention in the auto- motive industry, with car manufacturers striving to improve the charging experience for their users. In this study, we compare multiple realistic charging strategies in order to quantify the benefits arising from better planning of charging sessions. We base our evaluation on electricity price data from three regions: Sweden (SE3 region), Germany (DE region), and Denmark (DK2 region). This data is obtained from the ENTSO-E Transparency Platform, covering the two full years 2021 and 2022. We enhance this dataset with commercial price forecasts from the company StormGeo and benchmark it against a naive forecasting method. The primary aim of our work is to compare how the strategies of varying levels of complexity perform across the three different countries in terms of minimizing energy costs for electric car users.

In this work, several random Boolean networks (RBN) are generated and analyzed from two characteristics: their time evolution diagram and their transition diagram. To do this, its randomness is estimated using three metrics, of which algorithmic complexity is the only one capable of revealing both transitions towards the chaotic regime and the algorithmic contribution of certain states to the transition diagram and their relationship with the order they occupy in the temporal evolution of the respective RBN.

This paper presents quantitative research results regarding the influence of demographic factors on the earthquake risk perception of the citizens of Belgrade. This research aims to determine how much the citizens of Belgrade are aware of the risk and prepared to react in the event of an earthquake. The relationship between gender, age, level of education, and facility ownership with risk perception was examined. T-test, One-way ANOVA, and Pearson correlation coefficient were used to examine the relationship between the variables and the earthquake risk perception. The survey was conducted using a questionnaire that was given and then collected online among 235 Belgrade respondents during September 2020. The questions were divided into three categories. The first part of the questionnaire was consisted of general questions about the demographic characteristics of the respondents, then the questions that would determine the level of awareness of the respondents about earthquakes, and finally, the questions for determining the respondents' preparedness. The results of the research show that women have a higher perception of risk. It has been proven that the youngest respondents from the age category of 18-30 have the lowest risk perception. The influence of education level in no case showed a statistically significant correlation with risk perception.

In the construction business, which is known for its intricate and strategic nature, it is crucial to have efficient risk management in place. This is because various internal and external elements can cause disruptions to projects. Although the industry has had a historically negative reputation for risk analysis, the methodical process of identifying, analyzing, and managing risks has been more widely recognized and respected. This paper examines the historical progression of construction risk management, highlighting the need to identify and classify risks promptly. The study examines various risk factors related to finance, law, environment, and logistics. The study explores various risk management strategies, such as contract management, risk distribution, evaluation, mitigation, and sharing. The article discusses the significant impact of risk management software, such as Procore, PlanGrid, e-Builder, Autodesk BIM 360, and Oracle Primavera, on improving project results. The study finishes by highlighting the importance of proactive risk management in protecting against financial losses and maintaining a reputation in the construction business.

The aim of this study was to determine the impact of information technology in management of risks in the capital market-listed company is in Tehran Stock Exchange. The purpose of the present study is an applied descriptive approach. The target population for the survey, companies that from 2009 to the first half of 2015 have been a member of the Tehran Stock Exchange, through Cochranʼs sample size of 140 companies, respectively. We used cluster sampling method. In order to collect data from two questionnaires: risk management questionnaire Foakeh (2013) has 38 items and a standard questionnaire Chanvyas (2006) has 40 items, the whole five-item Likert scale questionnaire is above has been used. Data gathered through the questionnaire, sign the application was 21spss. For inferential analysis of the variables and to analyze the data from different statistical tests and regression was used Kolmogorov-Smirnov test. The results showed that information technology on risk management and its dimensions (primary market risk, market risk and the risk of secondary non-financial) impact.

We prove through path integral Monte Carlo computer experiments that the affine quantization of the $\varphi_4^4$ scaled Euclidean covariant relativistic scalar field theory is a valid quantum field theory with a well defined continuum limit of the one- and two-point-function. Affine quantization leads to a completely satisfactory quantization of field theories using situations that involve scaled behavior leading to an unexpected, $\hbar^2/\varphi^2$ which arises only in the quantum aspects.

The Internet-of-Things (IoT) triggers new types of cyber risks. Therefore, the integration of new IoT devices and services requires a self-assessment of IoT cyber security posture. By security posture this article refers to the cybersecurity strength of an organisation to predict, prevent and respond to cyberthreats. At present, there is a gap in the state-of-the-art, because there are no self-assessment methods for quantifying IoT cyber risk posture. To address this gap, an empirical analysis is performed of 12 cyber risk assessment approaches. The results and the main findings from the analysis is presented as the current and a target risk state for IoT systems, followed by conclusions and recommendations on a transformation roadmap, describing how IoT systems can achieve the target state with a new goal-oriented dependency model. By target state, we refer to the cyber security target that matches the generic security requirements of an organisation. The research paper studies and adapts four alternatives for IoT risk assessment and identifies the goal-oriented dependency modelling as a dominant approach among the risk assessment models studied. The new goal-oriented dependency model in this article enables the assessment of uncontrollable risk states in complex IoT systems and can be used for a quantitative self-assessment of IoT cyber risk posture.

The Semantic-Enhanced Tree-LSTM (SeT-LSTM) network, a novel advancement in the realm of linguistic modeling, marks a significant step forward from traditional Tree-based Long Short Term Memory (LSTM) networks. By intricately weaving in the nuances of typed grammatical dependencies, SeT-LSTMs offer a more nuanced understanding of language semantics. Traditional models often overlook the semantic shift caused by variations in word or phrase roles, a gap this paper aims to fill by focusing on the types of grammatical connections, or typed dependencies, within sentences. Our proposed architecture, dubbed the Semantic Relationship-Guided LSTM (SRG-LSTM), leverages a control mechanism to model the interplay between sequence elements. Additionally, we present a novel Tree-LSTM variant, the Semantic Dependency Tree-LSTM (SDT-LSTM), which integrates dependency parse structures with dependency types for more robust sentence embedding. The SDT-LSTM demonstrates superior performance in Semantic Relatedness Scoring and Sentiment Analysis compared to its predecessors. Qualitatively, it shows resilience to changes in sentence voice and heightened sensitivity to nominal alterations, aligning well with human intuition. This research underlines the pivotal role of grammatical relationships in sentence understanding and paves the way for further exploration in this domain.

The novel coronavirus disease (COVID-19) pandemic has resulted in over 720 million confirmed cases and 7 million deaths worldwide, with insufficient treatment options. Innumerable efforts are being made around the world for faster identification of therapeutic agents to treat the deadly disease. Postacute sequelae of SARS-CoV-2 infection or COVID-19 (PASC), also called Long COVID, is still being understood and lacks treatment options as well. A growing list of drugs are being suggested by various in silico, in vitro and ex vivo models, however currently only two treatment options are widely used: the RNA-dependent RNA polymerase (RdRp) inhibitor remdesivir, and the main protease inhibitor nirmatrelvir in combination with ritonavir. Computational drug development tools and in silico studies involving molecular docking, molecular dynamics, entropy calculations and pharmacokinetics can be useful to identify new targets to treat COVID-19 and PASC, as shown in this work and our recent paper that identified alendronate as a promising candidate. We have now investigated all bisphosphonates which can bind competitively to nidovirus RdRp-associated nucleotidyl (NiRAN) transferase domain, and systematically down selected seven candidates (CHEMBL608526, CHEMBL196676, CHEMBL164344, CHEMBL4291724, CHEMBL4569308, CHEMBL387132, CHEMBL98211), two of which closely resemble the approved drugs minodronate and zoledronate. This work and our recent paper together provide an in silico mechanistic explanation for alendronate and zoledronate users having dramatically reduced odds of SARS-CoV-2 testing, COVID-19 diagnosis, and COVID-19-related hospitalizations, and indicate that similar observational studies with minodronate could be valuable.

Presence detection is essential in many applications, including smart homes, office automation, and public space management. Traditional technologies like infrared sensors and cameras frequently encounter obstacles due to privacy concerns, high costs, and complexity. The study provides a novel way to detect presence based on Wi-Fi signals and machine learning algorithms, utilizing the ESP8266 microcontroller. The suggested system attempts to reliably detect the presence of people in a given place by evaluating fluctuations in Wi-Fi signal strength and patterns. The theoretical basis of this strategy is investigated, including the ESP8266’s capabilities for Wi-Fi data gathering and the use of machine learning methods for data analysis. The methodology includes data collecting with the ESP8266, preprocessing, feature extraction, and training and evaluating machine learning models. This system’s potential uses include smart home automation, office occupancy tracking, and crowd management in public settings. The paper also discusses potential issues such as signal interference, data privacy concerns, and the limitations of Wi-Fi detection. Future work is also mentioned, including the suggested system’s installation and testing and enhancement proposals to improve accuracy and scalability. This research aims to provide a cost-effective, non-intrusive, and scalable presence detection system by harnessing the power of Wi-Fi signals and machine learning, enhancing intelligent environments

The space exploration landscape has transformed dramatically over the past several decades, with a significant rise in the number of annual launches. Key factors driving this increase include technological advancements, the miniaturization of satellites, and the surge in commercial satellite deployments for communication, Earth observation, and remote sensing. This paper examines the evolution of space activities from the launch of Sputnik 1 in 1957 to recent developments in commercial spaceflight and international collaborations. The paper discusses notable milestones in space exploration history, including early space missions, Apollo Moon landings, and the development of space stations like the International Space Station. It highlights the shift from state-led space programs to a more diverse landscape, characterized by private sector involvement and international partnerships. The "Space 4.0" phase, beginning around 2006, has seen the entry of commercial players like SpaceX, leading to a significant upsurge in orbital launches. Statistical data reveals an upward trend in launch activity, with over 200 launches in 2023 alone, driven by large satellite constellations like SpaceX's Starlink and the steady growth of China's space program. The increasing launch rate poses challenges such as space debris, emphasizing the need for mitigation strategies. Looking ahead, the paper projects a continued increase in launch activity, with over 150 launches expected annually by 2026 and 200-300 launches by 2040. Commercial space ventures, deep space exploration, lunar activities, and space tourism drive this growth. The paper concludes by emphasizing the importance of addressing space debris and promoting the long-term sustainability of space activities, which is crucial as humanity's presence in space continues to expand.

In small animal models of severe acute respiratory syndrome coronaviruses (SARS-CoV and SARS-CoV-2) infection, ferrets (Mustela putorius furo) have been used to investigate the pathogenesis. Podoplanin (PDPN) is an essential marker in lung type I alveolar epithelial cells, kidney podocytes, and lymphatic endothelial cells. Monoclonal antibodies (mAbs) against ferret PDPN (ferPDPN) are useful for the pathological analyses of those tissues. We previously established an anti-ferPDPN mAb, PMab-292 using the Cell-Based Immunization and Screening (CBIS) method. In this study, we determined the critical epitope of PMab-292 using flow cytometry. The N-terminal ferPDPN deletion mutants analysis revealed that the Val34 is located at the N-terminus of the PMab-292 epitope. Furthermore, the PA tag-substituted analysis (PA scanning) showed that Asp39 is located at the C-terminus of PMab-292 epitope. The epitope sequence (₃₄-VRPEDD-₃₉) also exists between Val26 and Asp31 of ferPDPN, indicating that PMab-292recognizes the tandem repeat of the VRPEDD sequence of ferPDPN.

The Western Balkans region is persistently vulnerable to natural hazards, inadequate infrastructure, and limited institutional capacities, necessitating a collaborative approach to disaster risk management. This paper presents a comprehensive project led by the Scientific-Professional Society for Disaster Risk Management (NSD-URVs) in Belgrade, aiming to establish and strengthen a regional network of experts in disaster risk management. The project focuses on building expertise, facilitating knowledge exchange, promoting expert coordination, and enhancing cross-border collaboration. Key activities include developing a web platform, organising an international conference, and advocating for regional policies that integrate best practices and innovations in disaster risk management. The expected outcomes are the establishment of a cohesive network, enhanced expertise and coordination, and improved preparedness and responsiveness to disasters. By leveraging collective expertise and resources, this initiative aims to fortify the region's resilience against potential disasters. Addressing the identified gaps and implementing the proposed policy recommendations can significantly enhance the region's resilience to natural hazards. Future research should focus on the implementation and impact of these recommendations to ensure continuous improvement in disaster preparedness and response.

This article seeks to strengthen efforts in technological developments based on the classification of PET plastic that positively impacts sustainable development and contributes to suitable solutions in collection centers in Mexico. Three experiment designs and machine learning tools for data processing were developed. For the experimentation, three factors were considered: the bottle's size, the amount of liquids inside the bottle, and the label on the bottle. The first experiment was to identify the distance of the sensor from post-consumer PET bottles. The second experiment was to determine the detection ability of the sensor with different levels of liquids inside the bottles, and the third was to determine the detection ability on the bottle labels. A digital lux meter on a microcontroller was developed to monitor illuminance on post-consumer PET plastic when it contains liquid as it passes through a conveyor belt for processing at an average rate of three bottles per second. With the implemented methodology, liquids were satisfactorily detected inside the transparent PET bottles when they had beverages between 25% and 100% of their capacity. Finally, this paper highlights that it is possible to implement an affordable design to identify bottles with liquids for collection centers.

The ordinary 3D wave equation for nondissipative, homogeneous, isotropic media admits solutions where the point sources are permitted to move, but it does not admit solutions where the receiver is allowed to move. To overcome this limitation, the new wave equation is derived, permitting both the receiver and the source to move. This new wave equation is a generalization of the standard wave equation, and it reduces to the standard wave equation when the receiver is at rest. To derive this new wave equation, we first mathematically define a diverging spherical wave caused by the stationary point source. From this purely mathematical definition, the wave equation for the stationary source and the moving receiver is derived together with the corresponding free-space Green's function. Utilizing the derived Green's function, it was shown that this new wave equation also permits the solutions where both the receiver and the source are permitted to move. In conclusion, this paper demonstrates that, instead of an ordinary wave equation, the wave equation for the moving source and the moving receiver governs the waves emitted by the moving point sources and received by the moving receivers.

The future index prices are viewed as a critical issue for any trader and investor. For this craving, various models have shown up in the literature. The Geometric Brownian motion (GBM) is one of the popular models. This work examines four types of GBM as per the presence of memory or kind of volatility. These models include classical GBM with memoryless and constant volatility assumptions, SVGBM with memoryless and stochastic volatility assumption, GFBM with memory and constant volatility assumption, and SVGFBM with memory and stochastic volatility assumption. These models are utilized in an empirical study to forecast the future index price of Energy Sector in the Saudi Stock Exchange Market. The assessment was led by utilizing two error standards, mean square error (MSE) and mean absolute percentage error (MAPE). The outcomes showed that the SVGFBM occupies the highest level of accuracy according to smallest values of MSE and MAPE. While the accuracy of GBM come in the tail of the list models under study. These results have affirmed the positive affection of combining memory and stochastic volatility assumption into the GBM model, which agreed findings of numerous earlier works. Furthermore, the findings showed the GFBM models are more accurate than GBM models regardless of the type of volatility. While, under the same type of memory, the models with stochastic volatility assumption are more accurate than the corresponding models of constant volatility assumption. In general, all models considered in this work uncovered high accuracy through the value of MAPE ≤ 10%. This indicates of the ability of applying these models in a real financial environment. Through this empirical study, we can say that the Energy Sector in Saudi Arabia is predictable and stable and afterward we urge financial investors and stockholder to trade and invest in this sector.

New advances in creating effective and affordable copper-based catalysts for water splitting represent a promising strategy for driving sustainable energy technologies. However, ma- terials containing copper are susceptible to corrosion and agglomeration. In this study, we describe a synthesis route to produce olamine-functionalized CuO nanoparticles using the Hot Injection method. Nanoparticles were obtained and subsequently deposited on FTO substrates, undergoing thermal treatment at 500°C and 600°C in atmospheres containing O2 and N2, aiming to enhance the adhesion of the active material to the substrate and modify its oxidation state. Microstructural properties were analyzed using XRD, SEM, and TEM, while electrochemical properties were studied using CV, LSV, and EIS. Electrodes treated thermally in an N2 atmosphere at 600°C exhibited higher ESCA, indicating a greater area of active sites exposed to surface reactions and, therefore, superior catalytic activity in Hy- drogen Evolution Reaction (HER). These findings show an interesting strategy for avoiding rapid electron-hole recombination and, consequently, CuO corrosion. For Oxygen Evolution Reaction (OER), samples treated thermally in O2 atmosphere at 500°C and 600°C appear to be more effective.

Over the last few decades, advancements in astrophysics have been closely linked to the development of powerful machine-learning models that can accurately classify celestial bodies. At the same time, however, many astronomical datasets are filled with new features collected by increasingly powerful telescopes. These features can cause overfitting, clouding predictive abilities, and dampening the ability of many models to classify images. Therefore, motivation to design more efficient models has skyrocketed—aiming to optimize for lower run times and high accuracies, even with fewer provided features. In our project, we seek to optimize a convolutional neural network model using a technique known as wavelet analysis. This technique allows us to pick the key features of an astronomical image and accentuate niche details, saving time and boosting accuracy. We applied it to the MiraBest Dataset, a dataset compiled from the FIRST sky survey using a virtual telescope. In the end, after training our neural network on the original images and the five filters (approximation, horizontal, vertical, diagonal, and combined), we found that with fewer features and less overfitting, the vertical Daubechies-family wavelet filter outperformed the original runs with the unaltered images by over 10%. Our findings suggest that wavelet analysis can help harvest the most valuable features in images of celestial bodies–leading to enhanced predictions in astronomical applications and perhaps bolstering modern astrophysical theory.

Background: Pulmonary embolism (PE) is a common multifactorial disease, but high-risk PE s a life-threatening condition associated with high mortality and morbidity and a heavy prognostic burden. Systemic thrombolysis is the first-line treatment approach for severe high-risk PE, but only few agents are indicated for use by international guidelines. Tenecteplase, a thrombolytic agent, is proven to reduce mortality and right ventricle dysfunction in high-risk PE, but is not approved yet by major recommendations. Aim: We present a series of cases of high-risk PE treated with Tenecteplase, with satisfying hemodynamic and clinical outcomes. Our goal is to encourage the usage of Tenecteplase with secure safety and efficacy in situations of unavailability of guidelines-approved fibrinolytic agents. Cases : Patients were included from January 2022 to January 2024. We report 3 cases of patients with high-risk PE treated with Tenecteplase where rtPA and other second-generations agents were not available. Clinical, biological and imaging data were detailed for each case. Conclusion: Tenecteplase was effective in our patients, without bleeding complications, and should be suggested in settings of unavailability of first-line recommended agents.

Telemedicine has been increasingly considered an effective strategy to support patients with several diagnosis and therapies, avoiding the on-site visit and drastically reducing the risks to share infections. In the recent years, a number of different technologies have been developed and applied to clinical studies, with the aim to investigate new ways for increase the early diagnoses and monitor the clinical evolution of the most predictable diseases directly from the patients’ home. Telemedicine refers to all those technological interfaces used to remotely perform clinical procedures. Telemedicine applications could be useful in several medical fields, such as preventive medicine, home patient care, education for healthcare professionals and patients, research, public health, and health management. On the other hand, the synergy of these different technologies has a significant impact on the clinical risks analysis, particularly useful in frail patients. Although Telemedicine shows to have important advantages, undoubtedly important issues still remain. The present work aims to shed light on the main advantages and disadvantages related to of each application’s features, which may serve as a useful tool for researchers.

Identifying and assessing risk is one of the most important processes in managing complex systems and requires careful consideration. The need for an effective, efficient approach to risk management is considerably more important for defense industries, because they are exposed to risk already in early stages of development. This paper uses Heterogeneity and Homogeneity analysis between risk factors with Cochran’s Q test and Multidimensional scaling in order to present the complexity of the risk factors relevant to defense SoS, and proposes a methodology for identifying, analyzing and monitoring the risks that they face. Findings from an in-depth analysis of 46 classified defense SoS shows a need to focus on three main risks faced by defense projects: insufficient human resources, changes in the original specifications, and lack of other (non-human) resources. The paper also presents some recommendations for minimizing risk factors in defense SoS.

Identifying and assessing risk is one of the most important processes in managing complex systems and requires careful consideration. The need for an effective, efficient approach to risk management is considerably more important for defense projects based on systems of systems (SoS), because they are exposed to risk already in early stages of development. This paper uses advanced data science tools to present the complexity of the risk factors relevant to defense systems, and proposes a methodology for identifying, analyzing and monitoring the risks that they face. Findings from an in-depth analysis of 46 classified defense projects based on SoS shows a need to focus on three main risks faced by defense projects: uncertainty, the lack of clearly defined goals, and managing a system under constrained conditions. The paper also presents some recommendations for minimizing risk factors in SoS for defense projects.

This study employs molecular docking techniques to investigate a spectrum of targets within Escherichia coli (E. coli), with a specific focus on four key proteins: Beta-lactamase, MccE protein, Penicillin G acylase, and DNA polymerase II. The research explores the binding interactions of various natural compounds, emphasizing Hypericin. Through computational analyses, the study aims to unveil the potential interactions between these compounds and essential proteins in E. coli. Notably, Hypericin demonstrates remarkable binding energy scores of approximately -10.5 kcal/mol within the Ligand Binding Site of the specified proteins. This observation suggests Hypericin's potential as a potent modulator of E. coli targets, prompting further investigation into its antimicrobial properties and therapeutic applications.

The assessment of Histone deacetylases in conjunction with Curcumin, conducted through the Mcule Database employing the Autodock Vina algorithm, involves an examination of molecular interactions. This analysis includes the calculation of binding energies, revealing the strength and stability of the interactions between Curcumin and various Histone deacetylase targets. The comparison encompasses diverse aspects such as binding energies, specific binding sites, interactions across different targets, structural details, and potential implications for biological activity. The results imply that Curcumin shows heightened affinity for Histone deacetylase 4 in comparison to other investigated histone deacetylases.

The escalation of mining activities in the karst regions of Guizhou Province has heightened the occurrence of water-inrush incidents in deep coal mines. This study focused on water-inrush phenomena within the Xinhua mining area of Jinsha County, Guizhou Province, aiming to investigate the sources of these incidents. The findings indicated that the overlying limestone of the Changxing Formation in the coal seam served as a vulnerable aquifer under certain conditions, leading to water inrushes. The analysis of the spatiotemporal distribution patterns of water-inrush incidents at the working face indicated that previous mining operations damaged the shallow Changxing Formation limestone, resulting in the accumulation of goaf water and the formation of numerous mining-induced fractures. These fractures served as rapid conduits for water inrushes from both atmospheric precipitation and underground sources at the deep working face. The examination of surface water and mine water quality demonstrated that both exhibited similar characteristics, predominantly featuring bicarbonate, sulfate, and sodium compositions. Investigation into the relationship between mine water inflow and atmospheric precipitation established that atmospheric precipitation influenced the mine water supply cycle, with a replenishment period of ~10 months during the operational phase of the Jinyuan Coal Mine and about one month post-closure. The fractures induced by mining activities within the Changxing Formation limestone facilitated water flow, with atmospheric precipitation serving as the primary water source for the mine. This study offered a valuable scientific foundation for addressing water-related damage resulting from atmospheric precipitation in mines susceptible to water inrushes under analogous hydrogeological conditions.

Cities are complex systems requiring urban design models that balance order and disorder. Collective creativity initiatives engage citizens in these processes, empowering bottom-up approaches that prioritize people and social well-being within urban development. This paper investigates an 'Urban Laboratory' as a case study, examining the potential of collective creativity to address urban complexity. The successful and ongoing project 'El Campo de Cebada' in Madrid, Spain, demonstrates how a community transformed a vacant lot into a vibrant social hub. The phases of this study include case selection, data collection, data analysis, and presentation of the results. This study identifies key enabling factors, including agents, management, social dynamics, infrastructure, and actions. These insights offer a methodological framework for designing future collaborative, resilient, and inclusive urban spaces, addressing the complex needs of communities within our cities.

This research introduces a hybrid feature extraction approach that combines Linear Discriminant Analysis (LDA) and Multilayer Perceptron (MLP) methods to address the challenges of reducing feature vector dimensionality and accurately classifying smartphone-based human activities. Moreover, to refine activity classification accuracy, Support Vector Machine (SVM) optimization with Stochastic Gradient Descent (SGD) is employed. LDA, a statistical tool, is leveraged to derive a new feature space for data projection, enhancing class separation and test feature label prediction. The proposed approach, named LMSS, was evaluated using the UCI-HAR dataset and compared with state-of-the-art models. The results demonstrate that the proposed approach outperformed the best-performing method over this dataset. It achieved an accuracy rate of 99.52%, precision of 99.55%, recall of 99.53%, and an F1-score of 99.54%, highlighting the effectiveness of the proposed method in accurately classifying the data.

Sublingual vaccines offer the benefits of inducing mucosal immunity to protect against respiratory viruses, including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and influenza, while also enabling needle-free self-administration. Previously, we developed a sublingual vaccine formulated with the SARS-CoV-2 receptor binding domain antigen and Poly(I:C) adjuvant in nonhuman primates, cynomolgus macaques. This study examined the immune and inflammatory responses elicited by the sublingual influenza vaccine containing hemagglutinin (HA) antigen and Poly(I:C) adjuvant and assessed the safety of this vaccine in nonhuman primates. The Poly(I:C)-adjuvanted sublingual vaccine induced both mucosal and systemic immunities. Specifically, the sublingual vaccineproduced HA-specific secretory IgA antibodies in saliva and nasal washings, and HA-specific IgA and IgG were detected in the blood. This vaccine appeared to be safe, as judged from the results of blood tests and plasma C-reactive protein levels. Notably, sublingual vaccination neither increased the production of inflammation-associated cytokines—IFN-alpha, IFN-gamma, and IL-17—in the blood, nor upregulated the gene expression of proinflammatory cytokines—IL12A, IL12B, IFNA1, IFNB1, CD69, and granzyme B—in white blood cells. Moreover, DNA microarray analyses revealed that sublingual vaccination evoked both enhancing and suppressing expression changes in genes associated with immune-related responses in cynomolgus monkeys. Therefore, the sublingual vaccine with Poly(I:C) adjuvant is safe and creates a balanced state of enhancing and suppressing the immune-related response.

The work contains a detailed analysis of the electromagnetic field originating from a point source placed above the layered structure.The surface impedance of the multilayer dielectric structure was determined. Based on the boundary conditions, a method for determining the surface impedance of a layered structure with a perfectly conductive screen and or a magnetic screen is presented. The resonance conditions and the conditions for the excitation of surface waves are derived. The integral expression defining the generalized impedance was analyzed with a view to adapting it to numerical implementation. This allowed the development of a computer program based on which calculations were made of the effect of excitation of waves on multilayer dielectric substrates.

Conventional text style transfer (TST) methodologies primarily utilize style classifiers to segregate the content and stylistic elements of text for effective style transformation. Despite the pivotal role of these classifiers, their influence on TST techniques remains largely unexplored. This study embarks on a detailed exploration of the limitations inherent in style classifiers within current TST frameworks. We reveal that these classifiers often inadequately comprehend sentence syntax, leading to diminished performance in TST models. In response, we introduce the Syntax-Enhanced Style Transfer (SEST) model, a groundbreaking approach incorporating a syntax-sensitive style classifier. This classifier ensures that the extracted style representations robustly encapsulate syntax nuances, enhancing TST effectiveness. Rigorous evaluations across diverse TST benchmarks demonstrate that SEST significantly surpasses contemporary models in performance. Additionally, our case studies highlight SEST's proficiency in producing syntactically coherent sentences that aptly retain original content.

Controlling and managing risk is always a critical point for the managers, investors and even consultants. Having clear and better percepts about future can reduce the risk of fail. In addition it is important for a risk analyzer to consider the stakeholders views and senses about the risk. This research aimed at framing a comprehensive risk management model which builds up on stakeholder requirements and forecasting techniques. This conceptual model attempts to use the stakeholder’s points of view to write prefer and probable scenarios to neglect critical risks and increase the chance of success in long-term plannings. Using futures studies methods prepare decision makers for the possible upcoming events and donate them a powerful tool to handle the uncertainties cover the future.

We propose a novel framework for understanding quantum phenomena through the lens of a modified electromagnetic spacetime governed by Einstein field equations. By assuming the existence of an electromagnetic spacetime that adheres to these modified geodesic and Einstein field equations, provided the field equations satisfy the Coulomb force law in weak-field approximations, we explore the derivation of Maxwell’s equations and highlight the necessity of incorporating spin to validate these equations. We derive the wave equation for the normalized transverse metric and establish its equivalence with the de Broglie wave proposition, suggesting a relationship between this metric and the particle's wavefunction. This approach offers a classical explanation for several quantum phenomena, including the double-slit experiment, entanglement, tunneling, non-locality, and wavefunction collapse. Additionally, it posits a method to visualize atomic orbital structures in line with quantum mechanical predictions. These findings suggest that the foundational principles of quantum mechanics can be interpreted as linearized approximations of general relativity applied to electromagnetic spacetime. While this theory does not yet extend to the correlations between general relativity, quantum electrodynamics (QED), and quantum chromodynamics (QCD), it provides a valuable visualization tool for physicists and advances our understanding of the unresolved challenges linking quantum mechanics with general relativity.

The study assessed the risk perception, attitude, and management tools of 260 randomly selected farmers and processors in the rice value chain using data elicited via a structured questionnaire. Descriptive statistics, Equally Likely Certainty Equivalent (ELCE) technique, Binary logistic regression, and a 5-point Likert scale were employed in the data analyses. Descriptive statistics show that the respondents are young, active, and in their productive age with an intermediate education level. Majority of the farmers and processors were risk averse. Risk aversion was common among farmers and processors. Farmers' risk attitudes and perceptions of risk are significantly influenced by a number of factors, including age, access to extension services, farm size, marital status, household size, education, income, training, and experience. Majority of rice farmers consider price risk as the most important risk while majority of processors perceive production risk as the most important risk. Pests and diseases, price fluctuation, drought, and lack of input were the most important source of risk to farmers, while processors perceived price fluctuation, technology adoption, and poor storage facilities as the most important source of risk. Off-farm investment, spraying for pests and diseases, cash contribution, market information, and cooperative were the important risk mitigating management strategies employed by the farmers/processors. The study suggests improvements in the identified risk sources, which could influence the attitude and perception of the farmers and processors.

We introduce a novel algebraic structure, termed algebra B, which generalizes Lie algebras through the definition of a non-associative and unital algebra. We demonstrate that algebra B retains essential properties such as bilinearity and antisymmetry, and satisfies both the Jacobi and Malcev identities; this occurs when the product operations of Lie and Malcev algebras are derived from the product operation defined in our algebra. By examining the connections between algebra B, Malcev algebras, and Lie algebras, we establish that algebra B effectively generalizes these structures. Furthermore, we show that algebra B can generate complex entities with a novel structure distinct from those currently known. Our findings lay the groundwork for future investigations into the practical applications and further theoretical development of this new algebraic framework.

Postpartum haemorrhage remains a significant contributor to maternal mortality and morbidity worldwide, particularly in low- and middle-income countries. PPH continues to pose challenges in both developed and developing nations despite being preventable. This review examines the current state of knowledge regarding PPH within the context of Vietnam, a country with its unique healthcare landscape. This review identifies critical areas for improvement and future research directions based on a comprehensive literature analysis, including epidemiological insights, risk factors, prevention strategies, and management approaches. Discrepancies in PPH definitions and prevalence rates across regions highlight the need for tailored interventions and policies. Understanding the specific challenges and opportunities related to PPH in Vietnam is crucial for informing evidence-based interventions and policies to improve maternal health outcomes. This review contributes to the broader global effort to reduce maternal mortality by addressing gaps in knowledge and proposing strategies to enhance PPH prevention and management in Vietnam.

Neisseria gonorrhoeae is a bacterium that causes the sexually transmitted infection gonorrhea. This study employed molecular docking simulations to investigate potential interactions between natural molecules and the Fe(3+) ions import ATP-binding protein fbpC in Neisseria gonorrhoeae FA 1090. Among the screened molecules, Hypericin exhibited a remarkable binding energy of approximately -13.2 kcal/mol, indicating a robust and favorable binding affinity with the target protein. This promising outcome suggests that Hypericin could serve as a potential candidate for further exploration as a therapeutic agent against Neisseria gonorrhoeae infections. However, it is imperative to emphasize that these in silico findings necessitate experimental validation through in vitro and in vivo studies to ascertain the actual efficacy and safety of Hypericin as a treatment option.

The study focused on Molecular Docking simulations using Autodock Vina in conjunction with the Pyrx program to investigate potential drugs and natural compounds against the Bcr-Abl oncoprotein. From initial virtual screening of 200 compounds, 11 potential candidates were selected for further evaluation. Additional investigations included toxicity prediction using the pKCSM server. Among the compounds, Diosmin and Hesperidin showed high maximum tolerated doses in humans and low toxicity. Conversely, only Eltrombopag among the selected drugs exhibited excellent parameters, albeit with a potential risk of liver damage at high dosages. These findings underscore the need for comprehensive evaluation of efficacy and safety in the development of potential treatments targeting the Bcr-Abl oncoprotein.

For the first time, this study identified seven potential ligands with high binding energies through the docking approach against the Crystal Structure of human Melanoma-associated antigen B1 (MAGEB1). The ligands—Imatinib, Bafatinib, Diosmin, Ginkgetin, Bilobetin, Amentoflavone, and Hypericin—showing strong binding energies around -10 kcal/mol, indicate a potentially robust interaction with the target protein. Moreover, the additional step of predicting toxicity properties using the pKCSM server provides valuable insights into the safety profiles of these ligands. From these results, the parameters for Diosmin: —Max. tolerated dose (human),- Oral Rat Acute Toxicity (LD50), and - Oral Rat Chronic Toxicity (LOAEL)—suggest a favorable safety profile, making it a potential candidate for further consideration against Melanoma. Moving forward, it's advisable to conduct further experimental validations, including in vitro and in vivo studies, to confirm the efficacy and safety of Diosmin and other potential ligands. Consideration of pharmacokinetics, metabolism, and specific mechanisms of action will contribute to a comprehensive understanding of these compounds' potential as therapeutic agents.

Multi-stakeholder based construction projects are subject to various risk factors due to dynamic business environments. These risks affect project activities which indirectly impact construction costs, resulting in delays and poor building quality. So, managing these project risks requires suitable risk mitigation strategies to evaluate and analyse their severity. Hence, risk evaluation and assessment of construction projects is a multi-criteria decision making (MCDM) problem. In present real-life problems, evaluation of project risks is often uncertain and even incomplete, and the prevailing methodologies fail to handle such situations. To address the problem, this paper extends the analytical network process (ANP) methodology in the D number domain to handle three types of ambiguous evaluations, viz. complete, uncertain, and incomplete, and assesses the weight of risk criteria. The D number based approach overcomes the deficiencies of the exclusiveness hypothesis and completeness constraint of Dempster-Shafer (D-S) theory. Here, preference ratings of the decision matrix for each decision-maker are determined using a D number extended consistent fuzzy preference relation (D-CFPR). An extended multi-attributive border approximation area comparison (MABAC) method in D number is then developed to rank and select the best alternative risk response strategy. Finally, an illustrative example from construction sector is presented to check the feasibility of the proposed approach. For checking the reliability of alternative ranking, a comparative analysis is performed with different MCDM approaches (D-COPRAS, D-ARAS, D-MABAC, and D-TOPSIS). Based on different criteria weights, a sensitivity analysis of obtained ranking of the hybrid D-ANP-MABAC model is performed for verify the robustness of the proposed method.

Diversification is an important strategy for managing risk in agricultural systems. Risk analysis can help to support farmers’ diversification strategies, but existing analytical methods are complicated and little used. The minimum regret model helps to fill this gap. It provides a simple, transparent calculation procedure that can be executed with existing spreadsheet software. Regret is an important heuristic in the behavioural sciences and regret-based models are used in finance. The article presents the model with a numerical example. It also presents a framework to compare minimum regret portfolios with two limit cases (maximum utility and minimax regret). A case study illustrates the use of the model and the comparative framework.

The quadruple burden of diseases, early discharge from hospital and hospital at home have resulted in home-based care services becoming a requirement in South Africa. The home-based care services generate a significant amount of health care risk waste that is mismanaged. However, more attention is given to the health care risk waste generated in hospitals and clinics than to health care risk waste generated by home-based caregivers. Therefore, this study investigates the health care risk waste management practices by home-based caregivers. The study adopted a mixed research approach, qualitative and quantitative methods, using a literature review, interviews, and questionnaires as means of data collection. Results show that there are different types of health care risk waste generated as a result of different activities performed by home-based caregivers, but that the waste was found to be managed in an unsafe manner. The majority of households receiving home-based care did not have basic sanitation facilities such as toilets, running water and waste removal services, aggravating the issue of health care risk waste mismanagement. The study recommends a new policy framework that will lead to safe management practices of generated health care risk waste to be adopted by home-based caregivers.

With the Sendai Framework (2015-2030) and the Paris Agreement (2015), the UN has been promoting at a global level, the priority objectives to protect the life and well-being of people, these documents of global consensus have scheduled in several countries, an extensive list of actions of responsibilities and commitments to the environment, to address natural disasters among those of greatest impact earthquakes and floods and among earthquakes (seismic activity). The materialization of the disaster is a natural or human-induced event, it manifests itself in permanent changes in society and by its origin it is classified as natural, anthropogenic (anthropogenic) and mixed, creating a worldwide problematic situation of immediate reaction. The attention and alert in the scientific community have been adding efforts, but in a disorganized way, with limited human and material resources and with globalized strategies still to be improved. The literature review of disaster risk management due to seismic activity, from 2008 to 2023, allows us to have updated information to identify the factors and deepen them, with the understanding, comprehension, and multidisciplinary knowledge. The selected researches that mention the factors (elements) that influence risk management of natural disasters due to seismic activity (RMNDDSA) allow us to know the advances and spaces to approach them, which constitutes the motivation to study the twenty-four (24) factors present in this type of disasters. The selected research articles indicate important advances that associate disaster risk management (DRM) and communication technology (ICTs). The technological means mentioned are well received and accepted by society, capable of detecting and communicating in real time seismic activity worldwide, with the help of social mobile networks, technological platforms, Twitter, Facebook, and others.

This study employs molecular docking simulations using Autodock Vina with the Pyrx program to investigate the binding interactions between Human prostate-specific antigen (PSA) and natural compounds, namely Amentoflavone, Ginketin, Rutin, and Hypericin. The evaluation reveals excellent binding energy values, averaging around -10 kcal/mol, suggesting robust and favorable interactions between the selected compounds and PSA. The results provide valuable insights into the potential therapeutic applications of these natural compounds in the context of prostate cancer treatment or management. Further experimental validation is warranted to confirm these findings and explore the viability of these compounds as promising candidates for innovative therapeutic strategies. This research contributes to the growing body of knowledge in the field of molecular docking and its applications in drug discovery and bioinformatics.

To proactively manage information security, enterprises often employ information security risk assessment techniques. Asset value - which is used to calculate the financial impact of possible threats - is one of the key parameters of information security risk. However, assets in a system are rarely independent, and their values are typically interdependent. Asset owners and IT teams may hold different views as regards these values, and there is thus the need to reduce subjectivity in a qualitative risk assessment. The research entails the development of a conceptual framework derived from the literature to minimize subjectivity, and the design of a system based on those concepts. The study uses the Unified Modeling Language as a design tool and puts forward an object-oriented model for defining asset values, in which the relationships between assets, vulnerabilities and related threats are identified. A “segregation of duties” approach is integrated into the risk management system to mitigate against subjectivity and better determine asset values. Survey responses from 16 practitioners working in the private and public sectors confirm the validity of the approach, but suggest it may be more workable in larger organisations where resources allow dedicated risk professionals to operate.

Atrial fibrillation (AF) is a common arrhythmia associated with various risk factors and significant morbidity and mortality. This article presents findings from a study involving 115 patients with permanent AF. The study examined demographics, risk factors, associated pathologies, complications, and anticoagulant therapy. The results showed a slight predominance of AF in males, with the highest incidence in individuals aged 75 and above. Common risk factors included arterial hypertension, dyslipidaemia, diabetes mellitus type 2, and obesity. Comorbidities such as congestive heart failure, mitral valve regurgitation, and pulmonary disorders were prevalent among the patients. Major complications included congestive heart failure, stroke, and myocardial infarction. Thromboembolic and bleeding risk assessment using CHAD2DS2-VASc and HAS-BLED scores demonstrated a high thromboembolic risk in all patients. The majority of patients were receiving novel oral anticoagulants (NOACs) before admission, while NOACs were also the most prescribed antithrombotic therapy at discharge. The study highlights the importance of risk factor management and appropriate anticoagulant therapy in AF patients to reduce complications and improve outcomes.

My innovative entropy measure, Ismail entropy, namely, (IE) is an ultimate generalization to numerous generalized entropies within the literature. The fractal dimension of IE is identified in this paper. On another applicative note, this paper offers some potential applications of fractal dimension to smart cities. The paper ends with closing remarks combined with some challenging open problems and the next phase of research.

Worldwide, there is a significant increase in the use of unmanned aerial vehicles (UAVs) by emergency services. They offer a lot of possibilities during the rescue operation. Such a wide application for various purposes and environments causes many threats related to their use. To minimize the risks associated with conducting air operations with UAVs, the SORA methodology will be important. Due to its level of detail, it is a methodology adapted to civilian use. In this article, the authors' team will try to develop guidelines and directions for adapting SORA to the requirements of the operational work of emergency services. Thus following article aims to present the most important risks related to conducting operations with the use of UAVs by First Responders (FRs) and to show the sample risk analysis performed for this type of operation on the example of the ASSISTANCE project. The paper describes, on the one hand, possibilities offered by UAVs in crisis or disaster management and step-by-step Specific Operations Risk Assessment (SORA) and on the other hand, presents possible threats, consequences and methods of their mitigation during FRs missions.

This study investigates how the determinants including risk preference affect farmers’ decision to adopt SLM practices in upland areas of Vietnam. Empirical data collected through in-depth interviews with 200 farmers in Na Ri district, Bac Kan province were used. The factors affecting SLM adoption of farmers in Na Ri district were examined by the 2SLS or IV-Probit model. The estimated 2SLS regression indicated that there is a set of factors affecting SLM adoption, namely, relative risk aversion, farming experience, farm size, knowledge of SLM, membership in farmers’ organization, number of labors, and slope of farm land. Specifically, relative risk aversion had a negative effect on SLM practices adoption. The farmers who are less risk averse are more likely to adopt more SLM practices. This implies that reducing farmer’ risk exposure could promote SLM practices. This result is also helpful for policymakers to understand farmers’ behaviors and promote the diffusion of SLM practices across regions on a large scale.

Risk management of electric power transmission lines requires knowledge from different areas such as environment, land, investors, regulations, and engineering. Despite the widespread availability of databases for most of those areas, integrating them into a single database or model is a challenging problem. Instead, in this paper, we use a single source, the Brazilian National Electric Energy Agency’s (ANEEL) weekly reports, which contains decisions about the electrical grid, comprising most of the areas. Since the data is unstructured (text), we employed NLP techniques such as stemming and tokenization to identify keywords related to common causes of risks provided by an expert group on energy transmission. Then, we used models to estimate the probability of each risk. Our results show that we were able to estimate the probability of 97 risks out of 233.

ABSTRACTCaffeine is known to impair both growth and development in the model social amoeba Dictyostelium discoideum. However, little is known about the transcriptional regulation in response to caffeine treatment in Dictyostelium. In yeast cells, caffeine mimics the effect of rapamycin in inhibiting mTORC1 activity. Here, we investigated whether changes in growth and development of Dictyostelium in the presence of caffeine or rapamycin are mediated by mTORC1 and/or mTORC2. Results: Treatment of wild type cells with caffeine or rapamycin resulted in severely impaired growth and in the formation of small aggregates in comparison to untreated cells. rip3‾ cells (lacking a mTORC2 component) and lst8‾cells (lacking a component of both mTOR complexes) showed higher and slower growth rates, respectively, in comparison to parental AX3 cells. Parental AX3 and rip3‾ cells displayed similar sensitivity to rapamycin and caffeine, while lst8‾ cells were more sensitive to these compounds. When developed in the presence of caffeine or rapamycin, AX3 cells formed aggregates after a long delay. Caffeine did not alter the morphology of small rip3‾ mounds and lst8‾ cells continued to remain solitary. However, in the presence of 1µM rapamycin rip3‾cells failed to form mounds while AX3 cells streamed and formed aggregates. Complementation of lst8‾ cells with the wild type lst8 gene rescued the aggregation phenotype. cDNA microarray analysis to uncover the spectrum of differentially regulated genes in response to caffeine treatment revealed 104 and 193 differentially expressed genes at 3-and 6-hours of development, respectively, in comparison to control cells. After 6 hours of starvation in the presence of caffeine, 25 genes encoding ribosomal proteins and 4 encoding elongation factors were up regulated. Furthermore, we identified that upon caffeine treatment at the 3- and 6-hours’ time points, 12 (10 up and 2 down) and 21 (13 up and 8 down) genes, respectively, encoding proteins involved in early development. Conclusion: We conclude that caffeine and rapamycin have similar effects in decreasing the growth rate and in reducing the aggregate size of D. discoideum. Studies with lst8‾ and rip3‾ cells suggest that these compounds affect cell growth and aggregation by acting on both mTOR complexes, mTORC1 and mTORC2. Analysis of the differentially expressed genes after caffeine treatment suggests a key role of these genes in growth and aggregation.

Optimizing warehouse operations and leveraging data insights are driving forces behind the rise of automation. This research explores the development of the SWIF warehouse management system (WMS) using the Agile methodology, a departure from traditional software development approaches. Agile’s iterative and step-by-step approach facilitated a streamlined development process. It allowed for quick adjustments based on evolving needs and feedback from stakeholders. This resulted in a substantial decrease in development time and a noticeable improvement in the final product’s quality. The study emphasizes the positive effect of Agile on teamwork and customer satisfaction. Developers reported enhanced communication and collaboration, leading to a stronger sense of ownership and engagement. Customers expressed greater satisfaction with the features and functionalities delivered in stages. The successful implementation of Agile in SWIF’s development showcases its effectiveness in managing complex and ever-changing software development projects. The study encourages the adoption of Agile methodology for similar projects to boost responsiveness, quality, and overall project success.

The Estimation of systemic risk in India is still in its infancy stage. There are several methods which are available but none of the methods are fully compatible to forecast the systemic risk since under different circumstances the factors responsible for the risk differs. In this paper the systemic risk estimation in India being carried out based on spread in daily stock market price(Difference between the bid and ask price of a share) of the top 100 firms in India according to market capitalization for the period of July2007 to March 2016. The results were compared with the Financial Stability Report published by Reserve Bank Of India for the period of March 2010 to June 2016.The results clearly indicates that there exits relationship between market illiquidity represented by spread and risks associated with the Financial System. In most of the cases the Z score (deviation from the mean/Standard Deviation) of the spread has become negative which provides the spread which is farther from the mean, also a good indicator of volatility in market and risk to financial system. It is also seen that the Systemic Risk Survey conducted by Reserve Bank of India which started during October 2011 has supported the results.

Even though the rules for free movement of machinery within the European Union market have existed for more than 30 years, accidents related to their activities have constantly been achieving significant value. When designing the machine, a designer must stem from risk assessment, whereas all stages of its life cycle and ways of its use must be taken into consideration. In industrial operations, there is old machinery, which, although fulfilling its function reliably, the safety level is not in accordance with the developing requirements for their safe operation. The proposed methodology of assessment of the machinery safety condition comes out from the presupposition of the right application of steps of risk assessment and their reduction mainly by means of implementation of both effective and efficient preventive measures. The aim of the research applied in 3 operations, was to verify the method of machinery safety management. The created methodology based on 19 requirements for safety evaluates the level of the actual measures by means of the so-called criterion of current status and total efficiency of measures. Its output is the assessment of the efficiency level of implemented safety of each machine as well as of the whole operation.

In the rapidly evolving FinTech sector, risk management is paramount for dealers and brokers. This paper underscores the significance and progression of risk management within this realm. Initially, it reviews the traditional risk management technologies and their inherent limitations. The discourse then shifts to contemporary solutions, specifically emphasizing the role of artificial intelligence, machine learning algorithms, and big data analytics in forecasting and mitigating potential risks. The paper also presents case studies of companies effectively utilizing these advanced techniques, highlighting their benefits in real-world scenarios. Additionally, it addresses forthcoming challenges, including regulatory compliance, data privacy issues, and the imperative for strengthened cybersecurity measures. The conclusion acknowledges that the adoption of these innovative technologies not only boosts operational efficiency but also markedly diminishes financial risks, thereby contributing to a more stable and reliable financial market

Owners and managers of small and medium-sized enterprises (SMEs) have changed their perspective on risk management due to the current global negative threats in the business environment. If they want to be successful and ensure the financial performance of their business, they must accept a proactive approach to reducing strategic risks in connection with operational risks. The aim of the article is to identify and quantify the differences in operational risk management and its effect on the financial management of SMEs between four Central European countries. The statistical sample of SMEs consisted of 1,090 owners and top managers. Data collection was carried out through a questionnaire in the period December 2022-January 2023. Statistical hypotheses were evaluated with the application of correlation analysis and linear regression modelling. Empirical findings have confirmed that operational risk management has a significant positive impact on improving the profitability of the enterprise; on reducing the enterprise's indebtedness as well as in the context of reducing the enterprise's inability to pay its obligations. However, there are disparities in the perception of this influence between owners/managers with regard to the country in which they carry out their business activities. The findings are important for both national and multinational organizations and entities dealing with risk management in the business environment of small and medium-sized enterprises.

Integrated disaster risk reduction in schools represents a key component of safety strategies within the educational sector of every country. In this study, a series of 850 face-to-face interviews (650 with students and 200 with teachers) were conducted throughout 2023 in 10 out of the total 18 municipalities in the Western Morava Basin of the Republic of Serbia. The paper proposes two central hypotheses regarding school-based disaster risk reduction. Firstly, it suggests that gender, age, parent's employment, academic achievement, living situation, paternal and maternal education levels, and engagement with social media collectively influence students' perspectives on this matter. Secondly, it posits that gender, age, marital status, parenthood, and educational background significantly impact teachers' viewpoints on school-based disaster risk reduction. Multivariate linear regression was used to explore predictors of students' and teachers' insights on school-based disaster risk reduction. Various statistical tests including Chi-square, t-tests, one-way ANOVA, and Pearson's correlation were employed to investigate the influence of demographic and socioeconomic factors on these insights. The results of multivariate regression analyses indicate that age, gender and marital emerge as the primary predictors across various facets of students' and teachers' insights on school-based disaster risk reduction (awareness of disasters, disaster education activities, attitudes on disaster risk reduction education, enhancement of disaster information accessibility, etc). The results of this study provide insight into the key factors influencing students' and teachers' perceptions of disaster risk reduction in schools. As such, they can serve as a basis for improving educational programs, developing policies and strategies, normative educational frameworks, teacher training, and further research in disaster education.

This paper is a brief review of our own work on the Planck quantized version of general relativity theory. It demonstrates several other straightforward methods to simply rewrite the same equations that we have already presented.

Background: While the clinical factors of the post-COVID-19 condition (PCC) are becoming clearer, the economic implications remain uncertain, posing a challenge to healthcare professionals in terms of understanding and managing this emerging phenomenon. This article aims to investigate the demographic and clinical characteristics of PCC patients and quantify the economic impact of their healthcare resource utilisation. Methods: A retrospective and case-control observational study was conducted, comparing the case group of PCC population before and after diagnosis with a control group. Demographic and clinical variables were analysed with the objective of describing the population. Economic analysis was performed to evaluate the resource costs in procedures and primary, secondary (both outpatient and inpatient) and emergency care. Findings: PCC patients (N=341) exhibited older age and elevated cardiovascular risk factors compared to controls (N=49,078). There were differences in the socio-economic distribution between male and female patients in the PCC patients. Hypertension and diabetes mellitus type 2 were the most common chronic diseases observed among the case patients. Economic analysis revealed that PCC patients were approximately four times as costly as control patients, with increased utilisation of healthcare resources. However, post-diagnosis PCC patients showed a reduction in costs, primarily driven by decreased primary care visits and hospitalisations. Conclusion: Coordinated care management for PCC patients is associated with reduced costs and improved healthcare resource utilisation. Further research is warranted to investigate long-term health outcomes and establish causal relationships between COVID-19 sequelae and healthcare resource utilisation.

The first member of the family, visual arrestin-1, was discovered in the late 1970s. Later the other three mammalian subtypes were identified and cloned. The first described function was regulation of GPCR signaling: arrestins bind active phosphorylated GPCRs, blocking their coupling to G proteins. It was later discovered that receptor-bound and free arrestins interact with numerous proteins, regulating GPCR trafficking and various signaling pathways, including those that determine cell fate. Arrestins have no enzymatic activity, they function by organizing multi-protein complexes and localizing their interaction partners to particular cellular compartments. Today we understand the molecular mechanism of arrestin interactions with GPCRs better than the mechanisms underlying other functions. However, even limited knowledge enabled the construction of signaling-biased arrestin mutants and extraction of biologically active monofunctional peptides from these multifunctional proteins. Manipulation of cellular signaling with arrestin-based tools has research and likely therapeutic potential: re-engineered proteins and their parts can produce effects that conventional small molecule drugs cannot.

C-C motif chemokine receptor-8 (CCR8) belongs to class A of G protein-coupled receptors (GPCRs). CCR8 interacts with the specific chemokine ligand CCL1/I-309 in humans, which is produced by various cells, including tumor-associated macrophages and regulatory T cells (Treg). CCR8 is highly expressed on Treg and T-helper 2 (T_H2) cells recruited to the inflammation site and is implicated in allergy, asthma, and cancer progression. The CCR8⁺Treg has been suggested to be an important regulator in the immunosuppressive tumor microenvironment (TME); therefore, it has been desired to develop sensitive monoclonal antibodies (mAbs) for CCR8. This study developed a specific mAb for human CCR8 (hCCR8), which is useful for flow cytometry by employing the Cell-Based Immunization and Screening (CBIS) method. The established anti-hCCR8 mAb, C₈Mab-21, (mouse IgM, kappa) reacted with hCCR8-overexpressed Chinese hamster ovary-K1 (CHO/hCCR8) cells, TALL-1 (acute T lymphoblastic leukemia), CCRF-HSB2 (human T-lymphoblastic leukemia), and natural killer cells, which express endogenous hCCR8 by flow cytometry. Furthermore, C₈Mab-21 demonstrated a moderate binding affinity for CHO/hCCR8 and TALL-1 with a dissociation constant of 6.5×10^-8 M and 2.0×10^-8 M, respectively. C₈Mab-21, which was established by the CBIS method, could be a useful tool for analyzing the hCCR8-related biological response using flow cytometry.

Studies have established the crucial role of sleep among the students which plays a significant role in their mood. This study aimed to examine the mood disorders and sleep quality among undergraduate students at the University of Georgia (UG) and comparison between students’ nationality.This cross-sectional study was a self-reported questionnaire comprised of demographics including age, gender, year of study, current location, lifestyle data (Exercise and smoking), the Depression, Anxiety, and Stress Scale (DAS21) and the Pittsburgh Sleep Quality Index (PSQI). We performed a descriptive analysis, and the Chi-square was statistically significant at p<0.05. The prevalence was at a 95% confidence interval (95% CI) as multivariate analysis examined the multicollinearity. The female students were most of the respondents, below the age of 20 years. The student's mean age was 20.20 (SD± 3.0). DAS report presented 72.7% of students with depressive symptoms, 77.8% with anxiety symptoms, and 62.2% had stress. Georgian students were more at risk of having depressive complaints (95% Cl[1.567-3.788]), anxiety (95% Cl[1.612-4.285]), and stress symptoms (95% Cl[1.743-3.831]). There was a strong relationship between the students who experienced poor sleeping patterns and depressive complaints (aOR 0.10). The students who were smokers (aOR 0.39) were more likely to report anxiety symptoms than the students that do not exercise (aOR 1.68). It was observed that students with depressive symptoms, anxiety, and complaints of stress had a significantly high risk of poor sleep quality. Further studies are recommended to curb psychological symptoms of mood changes in association with sleep disorders among students.