The integration of Customer Relationship Management (CRM) systems within Small and Medium Enterprises (SMEs) is increasingly recognized for its potential to enhance business performance. While CRM adoption is on the rise, the specific impacts on operational efficiency, customer satisfaction, and financial outcomes in SMEs require further investigation. This systematic review evaluates the impact of CRM systems on SME performance, with a focus on operational improvements, sales growth, and customer retention. The study aims to identify key determinants of successful CRM implementation and assess the long-term effects on SMEs' competitive advantage. The review encompasses studies published between 2014 and 2024, sourced from Google Scholar, Web of Science, and SCOPUS. A total of 46 studies were included after screening 18,306 records. The mixed-methods approach involves quantitative analysis of survey data from SME owners and managers, along with qualitative synthesis of case studies. Metrics evaluated include customer acquisition, retention rates, revenue growth, and organizational efficiency. The analysis indicates that CRM adoption leads to a 25-40% improvement in customer retention and a 15-30% increase in sales across SMEs. Operational efficiency gains range from 20-35%, primarily driven by process automation and enhanced data management capabilities. Successful CRM implementation is strongly linked to managerial support, system customization, and user training. However, barriers such as limited financial resources and technical expertise hinder the full realization of CRM benefits. CRM systems significantly contribute to SME growth by optimizing customer relationships and enabling data-driven decision-making. The review underscores the importance of strategic CRM implementation tailored to the specific needs of SMEs to maximize its impact. Future research should focus on overcoming adoption barriers and refining CRM strategies for sustained performance improvements.

This qualitative research explores Supplier Relationship Management (SRM) within the context of subscription-based e-commerce models. Through in-depth interviews and thematic analysis, the study investigates key elements such as trust, collaboration, technology integration, sustainability, risk management, cultural alignment, innovation, cost management, globalization, supplier selection, and performance measurement. Findings reveal that trust is foundational in fostering reliable interactions and open communication between e-commerce businesses and suppliers. Collaboration emerges as crucial for innovation and operational efficiency, facilitating co-development and shared risk management. Technology integration, including AI and blockchain, enhances supply chain visibility and decision-making capabilities. Sustainability considerations drive businesses to engage with suppliers adhering to environmental standards, enhancing brand reputation and consumer loyalty. Effective risk management strategies mitigate disruptions and ensure supply chain resilience. Cultural alignment fosters harmonious partnerships and mutual understanding across diverse global markets. Innovation through supplier collaboration drives product development and market responsiveness. Cost management practices optimize operational efficiency and strategic supplier relationships. Globalization necessitates adaptive strategies to manage diverse cultural and regulatory landscapes. Strategic supplier selection and continuous performance measurement drive ongoing improvement and alignment with business objectives. This study contributes empirical insights and practical implications for enhancing SRM in subscription-based e-commerce, informing strategies to navigate challenges and leverage opportunities in a competitive marketplace.

This qualitative research explores the impact of Supplier Relationship Management (SRM) on e-commerce brand reputation, focusing on the strategic imperatives, operational challenges, and economic implications within digital supply chains. Trust emerges as a foundational element in effective SRM practices, facilitating consistent product quality, timely delivery, and consumer trust, thereby enhancing brand credibility and loyalty. Strategic alignment of SRM with organizational goals enables e-commerce brands to leverage supplier capabilities for mutual growth and innovation. Operational challenges such as technological constraints and logistical complexities underscore the importance of digital integration and proactive risk management strategies to optimize SRM effectiveness. Digital technologies, including AI and blockchain, play a transformative role in enhancing transparency, efficiency, and resilience in SRM strategies, enabling e-commerce firms to mitigate risks and capitalize on market opportunities. Emotional intelligence (EI) competencies among supply chain professionals facilitate effective communication, conflict resolution, and sustainable supplier relationships that bolster brand reputation resilience. Economically, effective SRM practices yield cost efficiencies, improved financial performance, and resilience against supply chain disruptions, supporting sustainable growth in competitive digital markets. This study contributes valuable insights for practitioners and scholars aiming to enhance SRM strategies, optimize supplier relationships, and strengthen brand reputation management in e-commerce. By prioritizing trust, strategic alignment, digital innovation, EI competencies, and proactive risk management, e-commerce brands can navigate complexities, capitalize on opportunities, and foster positive brand reputations in the evolving landscape of digital commerce.

Social customer relationship management (SCRM) is a new philosophy influencing the relationship between customer and organization, where the customer gets opportunity to control relationship through social media. The paper aims on (a) identification of current level of SCRM and also on (b) influence of basic organizational characteristics on SCRM level. The data were gathered through the questionnaire distributed in 362 organization headquartered in the Czech Republic. The questionnaire comprised 54 questions focusing on the significance of marketing and CRM practices, establishing a relationship with the customer, online communities, the use of social media in marketing, and acquiring and managing information. The majority of questions were scalable and used typical five-level Likert scale. Results showed that larger firms more often set up their own online communities and manage them strategically, moreover they are able to manage information better. Contrariwise, small sized organizations use social networks as a way to establish communication with the customer more than large sized entities. Use of social media for marketing purposes is significantly higher in organizations oriented on consumer markets than on business markets.

This qualitative study explores the relationship between supply chain responsiveness (SCR) and customer loyalty within the e-commerce sector. E-commerce has revolutionized global commerce, driven by technological advancements and shifting consumer behaviors. Central to e-commerce success is the ability to meet customer expectations swiftly and reliably, necessitating agile supply chain management (SCM) practices. SCR in e-commerce encompasses the capacity to adapt to dynamic market demands, minimize disruptions, and enhance customer satisfaction through efficient logistics, inventory management, and last-mile delivery. Through in-depth interviews with customers, e-commerce platform managers, and supply chain practitioners, this study investigates key themes, challenges, strategies, and impacts associated with SCR and customer loyalty. Findings reveal that customers prioritize fast order fulfillment, reliable service quality, personalized experiences, and trustworthy interactions, all of which significantly influence their loyalty and advocacy behaviors. Challenges identified include inventory management complexities, logistical hurdles in last-mile delivery, and the need for effective supplier communication. Strategic approaches to enhance SCR include leveraging data analytics for demand forecasting, adopting agile SCM practices, integrating customer feedback into service improvements, and fostering collaborative relationships with suppliers. These strategies not only optimize operational efficiency but also cultivate enduring customer relationships based on satisfaction, trust, and loyalty. The study concludes that improved SCR practices lead to increased customer satisfaction, repeat purchases, and positive word-of-mouth, thereby strengthening competitive advantage and sustainability in the e-commerce marketplace.

This qualitative research explores the impact of Artificial Intelligence (AI) on Customer Relationship Management (CRM), providing a comprehensive analysis of how AI technologies are transforming CRM practices. AI integration in CRM has revolutionized customer interactions, enabling enhanced personalization, efficiency, and customer engagement. By leveraging machine learning, natural language processing, and predictive analytics, organizations can create highly tailored customer experiences, automate routine tasks, and anticipate future customer needs with precision. The study identifies the significant benefits of AI, including improved customer insights, faster response times, and proactive engagement strategies. However, it also highlights the challenges associated with AI adoption in CRM, such as data privacy and security concerns, algorithmic bias, high implementation costs, and resistance to change. The findings emphasize the need for robust data protection measures, strategies to mitigate biases, significant investments in technology and training, and effective change management to overcome these challenges. The thematic analysis reveals key areas where AI is making an impact, including customer service, customer segmentation, and personalized marketing strategies. Additionally, the study discusses the organizational implications of AI adoption, such as enhanced decision-making, increased productivity, and the necessity for staff reskilling. Future directions for AI in CRM suggest further advancements in AI algorithms, greater integration with other business systems, enhanced customer experiences, and a focus on ethical AI practices. This research underscores the transformative potential of AI in CRM and provides insights for organizations aiming to leverage AI for improved customer relationship management.

The integration of supply chain analytics and Customer Relationship Management (CRM) systems represents a critical strategy for organizations seeking to enhance operational efficiency and customer-centricity in today's competitive business environment. This qualitative study investigates the motivations, strategies, challenges, and outcomes associated with integrating supply chain analytics with CRM systems across diverse industries. Motivations for integration include gaining a unified view of customer data, improving decision-making capabilities, enhancing operational efficiency, and ensuring strategic alignment across organizational functions. Strategies employed by organizations include cross-functional collaboration, investment in scalable technology platforms, and leadership commitment to drive successful integration initiatives. Despite the potential benefits, the integration process presents challenges such as data integration complexities, technical interoperability issues, and organizational resistance to change. Addressing these challenges requires robust technological solutions, effective change management strategies, and a proactive approach to data governance and security. The study identifies significant outcomes from integration efforts, including improved operational efficiency through streamlined processes and optimized resource allocation, enhanced customer insights leading to personalized experiences, and strengthened strategic decision-making supported by real-time analytics.

In today's highly digitized and globalized financial environment, the need to protect payment systems from risk is more urgent than ever. Artificial intelligence (AI) technology is rapidly becoming a key tool in this space, with machine learning algorithms, big data analytics, and real-time monitoring enabling AI to effectively detect and prevent fraudulent activity, optimize risk management processes, and deliver intelligent services. AI not only improves the security and efficiency of payment systems, but also significantly improves customer satisfaction and loyalty. With the continuous development of AI technology and financial payment technology, the financial payment industry will usher in more innovations and changes in the future, further optimizing the payment process, improving payment efficiency, and ensuring payment security.

Brand trust holds significant importance in the Mongolian cashmere industry. This study examines the primary predictors of customer brand trust among Mongolian cashmere users. Our research includes a survey with 800 participants and 20 individual interviews conducted among both Mongolian cashmere users and non-users. We utilized an online platform to reach participants, employing convenience sampling. The study investigates the relationship between various factors and brand trust within the context of Mongolian cashmere brands. Through a combination of in-dividual interviews and statistical analysis, we test six hypotheses. The findings indicate that higher brand quality, a strong reputation, greater transparency, effective issue resolution, commitment to environmental and social responsibility, and pre-purchase research on such commitments positively impact brand trust. Key insights gleaned from the interviews emphasize the importance of quality, reputation, transparency, customer service, and environmental responsibility in shaping consumer trust. These results offer valuable implications for cashmere brands aiming to bolster consumer trust and foster loyalty.

The rapid expansion of social media and instant messaging applications, such as WhatsApp, has transformed how individuals and organizations communicate and engage. This think piece explores the potential of integrating WhatsApp as a customer relationship management (w-CRM) tool within the education sector to foster and sustain long-term relationships among students, teaching staff, and non-teaching staff. By leveraging real-time features and the widespread use of WhatsApp, educational institutions can create a more interactive and dynamic learning environment, benefiting all stakeholders involved. The literature highlights the importance of establishing clear frameworks for mutual engagement and interaction and the potential of w-CRM to improve communication between students' families and educational institutions. There are potential risks associated with the pervasive use of WhatsApp, and call for future research to explore strategies for mitigating these risks while maximizing the benefits of w-CRM in the education sector. Longitudinal studies are recommended to evaluate the long-term impact of w-CRM on stakeholder relationships, student outcomes, and overall institutional performance.

The dynamics involving market competition and the challenges of dealing with empowered customers, means that creating and delivering relevant customer experience (CX) of service, is as important as creating product or services. Several studies have treated customer experience as though a front-desk, sales-point affair with the customer in the retailing environment, negating the critical role of organization-wide efforts in the overall customer experience management sequence. This review, however, adopts customer-centricity, as a theoretical lens to underscore the [re]configuration of organizational-level factors that are critical to adopting a high customer-experienced centred organization. Based on the review, we highlight conditions for adopting high customer experience management organization: (1) developing an integrated CX strategy, (2) CX-based knowledge management, (3) organizational re-design that supports CX-management, (4) top management commitment, (5) integrated CX IT systems, and (6) CX-oriented HR policies. These practices are only necessary conditions, but not sufficient, for creating, delivering, relevant and sustainable customer experience. However, more robust empirical studies are needed to advance the application of organization-wide customer experience management, which vary across industry, products/services, and sector.

This study explores the dynamic interplay between supply chain integration and marketing strategies in achieving customer satisfaction. Through a qualitative approach involving semi-structured interviews with supply chain and marketing professionals, the research delves into how these traditionally distinct functions can be aligned to enhance operational efficiency and deliver superior customer experiences. Findings reveal that strategic alignment between supply chain management (SCM) and marketing fosters better coordination of product availability and service delivery, thereby meeting customer expectations consistently and enhancing brand loyalty. Information sharing and technological integration emerge as critical enablers, facilitating real-time data exchange and collaborative decision-making. The study also identifies challenges such as organizational silos, cultural resistance, and technological constraints that impede seamless integration, highlighting the need for proactive strategies to foster cross-functional collaboration. Additionally, the evolving roles of sustainability and customer-centricity are emphasized as key drivers for differentiating brands and achieving competitive advantage. By integrating sustainable practices and adopting a customer-centric approach, organizations can build stronger customer relationships and enhance their market position. The research underscores the importance of leadership support, investment in technology, and a collaborative organizational culture in overcoming integration barriers. The study’s findings provide actionable insights for organizations aiming to optimize their SCM and marketing strategies, drive innovation, and sustain growth in an increasingly complex and competitive business environment. This research contributes to the growing body of knowledge on supply chain and marketing integration, offering practical recommendations for achieving customer satisfaction and long-term success.

In today’s business landscape, customer retention has become a critical factor for sustainable growth. Many companies continue to focus on acquisition, which leads to high costs and constant customer turnover. This article highlights the importance of retention, proposing a shift in focus toward loyalty as the key to maximizing customer lifetime value (LTV). Improving retention not only increases LTV but also reduces acquisition costs, enhances virality, and provides financial stability. This paper introduces a scientific approach that allows companies to identify the underlying causes of low retention through observation, hypothesis formulation, and controlled experimentation. Using tools such as data analysis and A/B testing, companies can iteratively optimize their retention strategy, adjusting interventions in real-time based on the results obtained. Continuous experimentation emerges as an indispensable tool for navigating market complexity, enabling organizations to learn and adapt quickly. This data-driven approach not only improves retention but also drives long-term growth by building strong relationships with customers. In an increasingly competitive environment, companies that prioritize retention and experimentation are better positioned to achieve resilient and profitable growth.

Purpose: This research investigates the influence of supplier relationship management methods on the operational efficiency and customer satisfaction of Italian restaurants.Design/Methodology/Approach: Employing a qualitative research methodology, data was gathered via interviews and questionnaires from a sample of 200 individuals across many Italian restaurants throughout Italy.Findings: The investigation uncovered robust relationships between supplier relationship management approaches and other operational factors, highlighting the interdependence of management tactics. Efficient management of supplier relationships not only improves the efficiency of the supply chain, but also has an impact on the procurement of ingredients, the variety of menu options, and the integration of technology.Research Limitations/Implications: Although technology developments were shown to enhance operational efficiency, their influence on customer satisfaction was minimal. The research highlights the complex nature of factors that influence customer happiness, indicating the need of adopting a well-rounded strategy that combines technology with personalised care. Identified continuous improvement opportunities provide valuable insights for restaurant owners to enhance strategy and better meet consumer expectations.Practical Implications: This research provides useful insights on the management of Italian restaurants, but it has limitations due to its qualitative approach and dependence on self-reported data.Social Implications: However, the results add to the current knowledge and provide practical consequences for restaurant owners who want to improve their operational efficiency and customer pleasure.Originality/Value: Italian restaurants may achieve long-term success in a highly competitive culinary industry by acknowledging the interdependence of management strategies and using customer-focused techniques.

In recent years of technological advancements, the digitization of information has become a crucial factor for the growth and sustainability of small and medium-sized enterprises (SMEs), particularly in developing countries. These enterprises face a growing problem in accessing IT resources due to financial and lack of qualified IT personnel in their regions. This led to a growing interest in database and data warehouse technologies, which serve as foundational tools for organizing, storing, and analyzing vast amounts of data. This systematic review proposes to evaluate the impact of database and data warehouse technologies on organizational performance by employing the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this review thoroughly synthesizes existing literature to identify the key benefits and challenges associated with the adoption of these technologies in SMEs. The proposed inclusion criteria were bounded by (1) publication date between 2014 and 2024, (2) research written in English, (3) research work focusing on evaluating the impact of database and data warehouse technologies on organizational performance, (4) research with a clear evaluation analysis of database and data warehouse technologies research framework. Following these guidelines, 150 eligible research studies were included. The analysis reveals a wide range of findings concerning the impact of database and data warehouse technologies. Key results include the identification of cost-efficiency trends, improvements in decision-making processes, enhancements in customer relationship management, and gains in operational efficiency. The findings provide actionable guidance for SMEs, policymakers, and IT professionals seeking to leverage these technologies to enhance organizational performance. To the best of our knowledge, this systematic review offers a comprehensive analysis that fills a gap in the existing literature on the impact of these technologies on organizational outcomes.

The advent of social commerce (SC) has transformed the landscape of online consumer behavior, emphasizing the significance of customer-to-customer relations in shaping sustainable relation-ships with customers. This research investigated the intricate relationships between social com-merce dimensions, particularly suggestions and recommendations, forums and communities, and ratings and reviews, and their influence on customer-to-customer value co-creation (C2CVCC) and sustainable customer relationships (SCRs). A questionnaire was designed and administered to 635 respondents. We examined the psychometric properties of the measurements and subse-quently applied Partial Least Squares as a Structural Equation Modeling method (PLS-SEM) for hypothesis testing. The findings reveal that the dimensions of SC significantly impact C2CVCC, with ratings and reviews playing a pivotal role. Furthermore, C2CVCC emerges as a substantial mediator in the path between SC and SCRs. The empirical analysis showcases strong support for the proposed model, with robust path coefficients (β) and p-values confirming direct and indirect effects. These findings offer valuable insights for businesses seeking to leverage SC and customer interactions to enhance SCRs in the digital era. Understanding the dynamics of C2CVCC within the context of SC has become essential for marketers and businesses aiming to thrive in today's competitive online marketplace.

This study examines the development of effective social media strategies for businesses, focusing on how small and medium-sized enterprises (SMEs) can leverage these strategies to boost revenue and sustain growth. In the context of a dynamic digital environment, SMEs face challenges in maximizing the benefits of social media platforms due to limited resources and expertise. Using the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), this study analyzed sixty-nine research papers to identify key strategies that contribute to successful social media marketing. The review considered factors such as content quality, engagement techniques, platform selection, and targeted advertising. The findings suggest that tailored strategies that align with business objectives and audience needs, coupled with consistent engagement and content relevance, lead to more significant revenue growth and customer retention. However, the lack of data on long-term impacts and industry-specific insights limits the generalizability of these findings. This study provides a foundation for SMEs to develop strategic social media marketing plans, but further research is needed to explore long-term effects and industry-specific variations.

Despite the demonstrated benefits of data monetization initiatives in achieving competitive advantages, many of these efforts struggle to realize their potential. Companies often find it challenging to sustain even initially successful data monetization initiatives due to significant data quality issues. This reflects a disconnect between advancements in data monetization research—spanning digitization, digitalization, and digital transformation—and practical implementation within companies. Consequently, misguided approaches to data monetization are relatively common. A critical prerequisite for successful data monetization is the establishment and maintenance of clean, high-quality data. This study underscores the importance of data quality by conducting an in-depth analysis of Medical Inc., a company engaged in preparing pristine customer master data for advanced customer analytics. The investigation aims to elucidate Medical Inc.’s approach to addressing data cleanliness challenges and to distill a general framework for the customer master data cleansing process. This framework illuminates a relatively unexplored aspect of data monetization supplementing existing literature on digitization, digitalization, and digital transformation.

With the advances in industry and commerce, passengers have become more accepting of environmental sustainability issues; thus, more people now choose to travel by bus. Government administration constitutes an important part of bus transportation services as the government gives the right-of-way to transportation companies allowing them to provide services. When these services are of poor quality, passengers may lodge complaints. The increase in consumer awareness and developments in wireless communication technologies have made it possible for passengers to easily and immediately submit complaints about transportation companies to government institutions, which has brought drastic changes to the supply-demand chain comprised of the public sector, transportation companies, and passengers. This study proposed the use of big data analysis technology including systematized case assignment and data visualization to improve management processes in the public sector and optimize customer complaint services. Taichung City, Taiwan was selected as the research area. There, the customer complaint management process in public sector was improved, effectively solving such issues as station-skipping, allowing the public sector to fully grasp the service level of transportation companies, improving the sustainability of bus operations, and supporting the sustainable development of the public sector-transportation company-passenger supply chain.

This paper systematically discusses the application and development of federated learning in data privacy protection and data value sharing. With the rapid development of global information technology, especially the explosive growth of data from Internet of Things devices, data security and privacy protection are facing unprecedented challenges. This paper first analyzes the growth trend of global data volume and its importance to next generation technologies such as artificial intelligence technologies such as deep learning. Second, the paper provides an in-depth look at the impact of current data privacy regulations on data flows and value creation, particularly the EU's GDPR and China's Data Security and Personal Information Protection Law. Then, this paper introduces in detail federated learning, as a new distributed machine learning paradigm, which effectively solves the contradiction between existing data sharing and privacy protection by protecting individual data privacy and realizing global model collaborative construction. Finally, this paper discusses the combination of blockchain technology and federated learning, and proposes BeFL architecture as a new secure, decentralized and trusted federated learning system, which is expected to provide a comprehensive solution for large-scale data processing and value creation in multi-party scenarios. The research in this paper not only deepens the understanding of federation learning in theory, but also provides important reference and enlightenment for future research and application in related fields.

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.

Purpose: To evaluate prognostic factors for visual outcome in patients with diabetes who have undergone vitrectomy (PPV) for severe proliferative diabetic vitreo-retinopathy (PDVR) in at least one eye in the past 15 years. Methods: Medical records of 132 eyes of 66 patients were analyzed (median age 52ys 21-80; patients with type 1/2 diabetes 40/26, median follow-up 38 months 9-125). Correlation between final good visual outcome defined as 0.5≤ best corrected visual acuity (BCVA) and prognostic factors (age, sex, type and duration of diabetes, metabolic status, BCVA, diabetic retinopathy status, data of preoperative management, data of vitrectomy, postoperative complications) were analyzed. Results: BCVA improved significantly in the entire study cohort (from median 0.05 min-max 0.001-1 to 0.32 0.001-1, p <0.000). Visual stabilization was achieved in the majority of patients, and relatively good visual acuity (0.5≤BCVA) was maintained in more than one third of eyes. Multivariable GEE statistics showed that in addition to duration of diabetes and stable HbA1c values, only preoperative tractional macular detachment proved to be an independent significant predictor of visual outcome. Conclusions: Pars plana vitrectomy is a useful tool when performed early before tractional macular detachment. However, long-term visual stability can only be achieved with good metabolic control.

The integration of supply chain data analytics with marketing strategies has become increasingly critical for organizations seeking to enhance customer insights and competitive advantage in today's dynamic business environment. This qualitative research explores the intersection of these two disciplines, focusing on implementation strategies, benefits, challenges, and future directions. Through semi-structured interviews with industry experts and stakeholders, key themes emerged, highlighting the importance of cross-functional collaboration, technological integration, and robust data governance in achieving effective integration. The study identifies significant benefits, including enhanced customer insights, improved operational efficiencies, and competitive advantage through personalized marketing strategies and optimized supply chain management. However, challenges such as data silos, technological complexities, and regulatory compliance issues pose barriers that require strategic solutions and organizational commitment. Future directions point towards the continued evolution of predictive analytics, AI-driven solutions, and sustainability integration, shaping the future landscape of supply chain management and marketing. Proactive engagement with regulatory frameworks and ethical considerations will be crucial in navigating these challenges and building trust with consumers. Ultimately, this research contributes to a deeper understanding of how organizations can leverage data analytics to drive strategic decision-making, enhance customer relationships, and achieve sustainable growth in an increasingly data-driven economy.

Social media platforms have become pivotal tools for small and medium-sized enterprises (SMEs), offering vast opportunities for enhanced organizational engagement. However, these platforms also present challenges such as data privacy concerns, feedback management, and content saturation. This systematic review critically evaluates the existing literature on the dual role of social media in fostering organizational engagement while addressing key barriers faced by SMEs. We systematically assessed 104 peer-reviewed articles sourced from Scopus, Web of Science, and Google Scholar, focusing on the impact of social media on marketing strategies and organizational outcomes in SMEs. The Newcastle-Ottawa Scale was used for quality assessment, and effect measures, including mean difference and odds ratio, were employed to evaluate performance metrics such as customer engagement, business performance, and long-term organizational impact. Key findings indicate that 74% of SMEs reported improvements in brand visibility, with customer engagement increasing by 65%. However, significant concerns were identified, with 45% of studies highlighting privacy issues and 52% addressing challenges in managing negative feedback. The review emphasizes that while social media can enhance market reach and customer interaction, its effectiveness largely depends on strategic content management and planning. This review provides actionable insights for SMEs aiming to optimize social media use, highlighting the need for future research to address privacy management and feedback strategies for sustained success.

Inflammation and insulin resistance are recognized as central features in the pathology of psoriasis, profoundly affecting the lives of those affected by this condition. The relationship between lifestyle choices and psoriasis is well-documented, highlighting the potential benefits of dietary modifications and physical activity in managing the condition during exacerbations. Furthermore, growing evidence emphasizes the significant impact of nutritional interventions on various skin disorders, particularly those characterized by inflammation and insulin dysregulation. Notably, the role of dietary fats is of particular interest, with their influence on inflammation and insulin resistance in psoriasis garnering increasing attention. Moreover, the association between excessive adiposity and heightened psoriatic severity underscores the importance of weight management strategies in disease management. This review explores the intricate relationship between specific dietary fats and inflammation in psoriasis, offering a comprehensive analysis of their implications for disease progression and management.

This study investigates the role of sustainable inventory management within supply chains, exploring strategies, benefits, challenges, and implications for organizations. Through qualitative research involving semi-structured interviews with supply chain professionals from diverse industries, key themes emerged. Sustainable practices such as green logistics, waste minimization, and ethical sourcing were identified as critical strategies to reduce environmental impact and enhance operational efficiency. Technological advancements, including AI, IoT, and blockchain, facilitate real-time monitoring, predictive analytics, and transparency, supporting data-driven decision-making in inventory management. Benefits include cost reduction, improved brand reputation, regulatory compliance, and risk mitigation, positioning organizations favorably in a competitive landscape shaped by environmental regulations and consumer expectations. Challenges such as cost constraints, regulatory complexities, and organizational inertia necessitate strategic investments, collaborative efforts, and leadership commitment to overcome. The study underscores the importance of integrating environmental, social, and economic considerations into supply chain practices to foster sustainability and resilience. Looking forward, the study advocates for a holistic approach to sustainable supply chain management, emphasizing innovation, stakeholder engagement, and capacity building. By aligning organizational values with societal expectations and regulatory frameworks, businesses can navigate complexities, drive positive environmental and social impacts, and achieve sustainable growth. This research contributes insights into current practices and future directions for sustainable inventory management, informing strategies that balance profitability with environmental stewardship and societal responsibility.

The modern world is gradually moving towards a global civilisation that is automated and propelled by technology. Artificial intelligence (AI) is a highly advanced technology that has been acknowledged as a major innovation. The incorporation of this technology into the banking sector has enhanced operational procedures in relation to customer satisfaction, virtual assistance, and risk reduction. This machine is employed to streamline activities and improve how decisions are made in this sector. This study investigates the potential benefits and challenges of implementing artificial intelligence in the banking sector. The methodology ensures a comprehensive understanding of the implementation of artificial intelligence in banking by showcasing its effectiveness, precision, and efficiency, as well as the opportunities it presents. The study data were collected from secondary sources, such as recent publications that demonstrate applications of artificial intelligence from five different banks, as well as the examination of industry reports. The main findings of this study demonstrate the substantial improvement of administrative operations through AI-driven technology, which effectively automates activities that include inputting data and handling information. As a result, the frequency of mistakes caused by people has reduced, leading to a 30% decrease in operational expenditures for all evaluated institutions. However, the banking industry admits the challenges it encounters, such as issues with confidentiality and a scarcity of skilled employees. In the financial services industry, the integration of artificial intelligence into the banking sector is perceived as an advantageous chance instead of a harmful danger, with a greater potential for beneficial impact.

In today's volatile and competitive business landscape, achieving synergy between supply chain agility and marketing flexibility is imperative for organizations striving to enhance resilience and maintain competitiveness. This qualitative study explores the integration of these strategic elements and their impact on organizational performance in turbulent markets. Semi-structured interviews were conducted with key stakeholders from diverse industries to capture insights into adaptation strategies, challenges, and outcomes associated with aligning supply chain agility and marketing flexibility. Findings reveal that supply chain agility, characterized by rapid response capabilities to disruptions and fluctuating demands, is essential for optimizing operational efficiency and maintaining continuity in uncertain environments. Meanwhile, marketing flexibility enables organizations to adjust strategies swiftly based on real-time market insights, enhancing customer engagement and market responsiveness. The study identifies organizational silos, resistance to change, and technological limitations as primary barriers to achieving seamless integration. Benefits of synergy include improved customer satisfaction, accelerated time-to-market for new products, and enhanced competitive advantage through personalized marketing strategies and optimized resource allocation. Moreover, organizations that effectively integrate supply chain agility with marketing flexibility reported greater innovation, employee satisfaction, and sustainable growth.

Metabolic syndrome, obesity, and type 2 diabetes are closely related. They are characterized by chronic inflammation and oxidative stress. Obesity is the most important risk factor for metabolic syndrome and type 2 diabetes. Metabolic syndrome is characterized by insulin resistance and elevated blood glucose levels, among other conditions. These disorders contribute to the development of type 2 diabetes, which can exacerbate other metabolic disorders. Numerous studies indicate that diet and nutrients can have a major impact on preventing and treating these conditions. One such ingredient is lycopene. It exhibits antioxidant and anti-inflammatory effects and can affect metabolic processes in the body. According to the research presented, lycopene supplementation may have significant potential in the prevention and treatment of metabolic syndrome, obesity, and type 2 diabetes.

Tourism Geographers in Anglo-American geographical departments migrate to teach and research tourism in applied schools and departments such as business, resource and environment, and even cultural and creative industry. Under national research assessment and funding pressures, tourism geographers are not encouraged to keep on undertaking serious theoretical researches related to their mother discipline and transferred to research applied researches and publish in hybrid journals. Such changes in tourism geographers` research behavior have not been tested and reported in Asian academic contexts, especially Taiwan while frequently being discussed internationally. This research aims to investigate the differences in the research behavior of tourism geographers between those who are staffed in geography departments and those who migrate to applies departments such as tourism and recreation, resource and environment, and others in terms of publishing journals, paradigmatic approaches, research types(qualitative, quantitative, and mixed), research methods and geographical scales conducted based on content analysis of articles published by tourism geographers in geography and tourism and recreation departments. This study revealed that there are significant differences in the research behavior of tourism geographers between different department. Finally, this paper explains the differences in terms of Taiwan changing social and economic conditions, academic traditions and politics of geography and applied departments, research habits and preferences of tourism geographers among departments and other aspects. This study can help international tourism geography academic circles to understand the complexity and diversity of tourism geography academic landscape of the Chinese language.

The aim of this study was to evaluate the nature of relationship between service quality and customers’ satisfaction thorough SERVQUAL model. This study can be considered as an applied research, from purpose point of view and descriptive-survey, with regards to the nature and method. Passengers of Kaveh and Sofeh terminal in Isfahan have been considered as research population. Sample size included 200 passengers witch was determined by Cochran formula. Spss 19 was used to analyze collected data. Results show that there is a significant positive relationship between service quality and customers’ satisfaction. It is also proved that in terms of the importance of satisfactions’ dimensions, assurance is the most important aspect and then reliability, empathy, equipment appearance and responsiveness in sequence are the most important dimensions.

In this study, we explored the application of deep learning techniques for credit card fraud detection, aiming to improve the performance and reliability of anomaly detection methods in financial transactions. We first utilized the Isolation Forest algorithm, achieving a detection accuracy of 26% for the top 1000 transactions. Subsequently, we experimented with the Autoencoder algorithm, an unsupervised deep neural network model, which enhanced the detection accuracy to 33.6% in the best case despite some fluctuations. The results demonstrate deep learning models' strong feature extraction capability and adaptability, highlighting their potential to surpass traditional methods. However, the high imbalance in the dataset, with only 0.17% of transactions being fraudulent, poses a significant challenge. This study underscores the necessity for further experimentation and optimization of network structures and hyperparameters to achieve more stable and efficient fraud detection. The findings provide valuable insights and reference points for future research in financial fraud detection using deep learning methodologies.

Motivated by South Africa's need in the transition to a net-zero economy; this study 10 investigates the integration of renewable energy sources (RES) into oil refineries, considering 11 therein unique challenges and opportunities. The research focuses on optimizing RES allocation 12 using Particle Swarm Optimization (PSO), a data-driven approach that adapts to real-time 13 operational conditions. Traditional energy management systems often struggle with the inherent 14variability of RES, leading to suboptimal energy distribution and increased emissions. Therefore, 15 this study proposes a PSO-based renewable energy allocation strategy specifically designed for oil 16 refineries. It considers factors like the levelized cost of energy, geographical location, and available 17 technology. The methodology involves formulating the optimization problem, developing a PSO 18 model, and implementing it in a simulated oil refinery environment. The results demonstrate 19 significant convergence of the PSO algorithm, leading to an optimal configuration for integrating 20 RES and achieving cost reductions and sustainability goals. The optimization result of 4,457,527.00 21 Rand achieved by iterations is much better than the result of 4,829,638.88 Rand acquired using 22Linear Programming as the baseline model. The mean cost, indicating consistent performance, has 23 remained at its original value of 4,457,527.00 Rand, highlighting the convergence. Key findings 24 include the average distance measure decreasing from 4.2 to 3.4, indicating particle convergence; 25 swarm diameter decreasing from 4.7 to 3.8, showing swarm concentration on promising solutions; 26 average velocity decreasing from 7.8 to 4.25, demonstrating refined particle movement; and the 27 optimum cost function achieved at 4,457,527 Rand with zero standard deviation, highlighting 28 stability and optimal solution identification. This research offers a valuable solution for oil 29 refineries seeking to integrate RES effectively, contributing to South Africa's transition to a 30sustainable energy future.

Accurate forecasting and efficient management of commodities are crucial for stakeholders in the industry, given the volatile nature of these markets. This study addresses these needs by leveraging advanced forecasting techniques and machine learning models to predict prices and enhance supply chain efficiency. TThe focus is on utilizing ARIMA, SARIMAX, and LSTM models to analyze historical trading data for commodities such as cocoa, coffee, and sugar sourced from Kaggle’s comprehensive dataset. The research applies ARIMA and SARIMAX models to forecast price trends, overcoming initial challenges related to data index frequency and seasonality. LSTM models are employed for more nuanced demand forecasting, particularly for random-length lumber, demonstrating the model's capability to predict future market trends accurately. The study highlights significant improvements in prediction accuracy and supply chain management through meticulous feature engineering and model optimization, offering valuable insights for strategic decision-making in the supply chain sector.

This exploratory study investigates the role of wearable technologies in fostering emotional connections between human brands and customers. Given the rapid growth of the wearable technology market and the increasing importance of emotional connections in marketing, this research aims to uncover the potential of these technologies in strengthening the affective bond between brands and consumers. The mixed-method approach included semi-structured in-depth interviews with 25 marketing experts and an online survey of 300 consumers. Data analysis was conducted using thematic coding and statistical tests including Pearson correlation and multiple regression. Results indicated that wearable technologies can enhance emotional connections through personalized interactions (r=0.72, p<0.001), creating a sense of proximity (β=0.65, p<0.01), and facilitating two-way communications (β=0.58, p<0.01). These findings offer new perspectives for marketing strategies and customer relationship management in the digital age, highlighting the potential of wearable technologies as a powerful tool for building and maintaining emotional bonds between human brands and their audiences. The study contributes to the growing body of literature on technology-mediated brand relationships and provides practical implications for marketers seeking to leverage wearable technologies in their brand communication strategies.

This systematic review examines the role of Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Human Resource Management (HRM) systems in enhancing managerial decision-making and employee productivity in small and medium-sized enterprises (SMEs) between 2014 and 2024. It explores how these systems streamline operations, improve workflow automation, and support real-time communication, helping SMEs overcome resource limitations and enhance operational efficiency. Additionally, the review considers how these integrated technologies, particularly when combined with advancements such as artificial intelligence (AI) and machine learning (ML), contribute to better resource allocation, strategic planning, and long-term business performance. In addressing the challenges SMEs face in system adoption—such as costs, training, and implementation—the review offers actionable insights into how ERP, CRM, and HRM systems can be strategically employed for sustainable growth and competitive advantage.

In today’s fast-paced technological landscape, businesses must adapt to evolving business models and harness the power of data to thrive. Small and Medium-sized Enterprises (SMEs) face significant challenges in aligning IT infrastructure with business objectives while navigating digital transformation. This systematic review, conducted using PRISMA 2020 guidelines, investigates the role of Enterprise Architecture (EA) and Information Management (IM) in driving IT growth and sustaining competitive performance in SMEs. Through a synthesis of academic research, industry analysis, and case studies from the last decade, this review identifies key frameworks—such as TOGAF, Zachman, and FEAF—that support the alignment of IT capabilities with organizational goals. The integration of IM within EA frameworks is found to enhance decision-making, resource allocation, and operational efficiency. Emerging technologies like Artificial Intelligence (AI) and cloud computing are highlighted for their transformative impact, enabling real-time data analysis, improved scalability, and enhanced agility. Our review reports that 43.44% of the studies focused on cloud-based solutions, while 24.59% adopted hybrid models, offering SMEs scalable and flexible IT infrastructures. The findings emphasize the necessity of strong governance frameworks to ensure compliance, adaptability, and long-term sustainability in a dynamic environment. This research contributes to a comprehensive roadmap for SMEs, enabling them to leverage EA and IM for sustained growth and competitive advantage in the digital era.

Pakistan is an agricultural country, and an exporter of crops in many countries. The crop production is an important factor to earn revenue for the country. But the agriculture of Pakistan is still not revolutionized and no modern system has been deployed commonly. Whereas the farmers face difficulty in growing some crops because those crops needs more care and calculated steps. Moreover the farmers face difficulty at different stages of the crop growth like to check fertility of soil and if the soil is non-fertile then which chemical property is lacking. The objective of this research is to provide digitize the agriculture of Pakistan. An emerging technology IoT and Deep learning based system will be developed. The IoT part contains some devices like sensors,gateways and communication technologies whereas the software part consists of Deep learning models that can make predictions about the fertility of the soil. The motive of this system is to provide best results in term of accuracy. Whereas the dataset used is publicly available on kaggle but it was of non-fertile values, so data augmentation is also performed to generate data for fertile values. We have implemented LSTM,RNN,CNN among them LSTM performed best for the prediction of soil fertility.

Loyalty is one of the greatest intangible assets that any organization can possess and improving client loyalty is a primary marketing goal that can have a significant financial impact on any business. This quantitative study examined the mediating role of communication on the relationship between satisfaction and loyalty (attitudinal and behavioral) in veterinary clinics, along with the moderating roles of trust, commitment, perceived value, and relational characteristics. Responses collected from 351 pet-owners through social media were analyzed using descriptive and inferential statistics. The results show that attitudinal loyalty (AL) has a strong positive relationship with communication at multiple points in a veterinary clinic whereas the relationship with behavioral loyalty was not as clear. Additional findings suggest that AL, which is influenced by trust in the veterinarian, communication from staff members and commitment, has a strong positive relationship with behavioral intentions, increases the number of products and services that a pet-owner consumes at his or her primary veterinary clinic, and attenuates the role of cost in receiving veterinary care. These findings can help veterinary clinic owners and managers in developing and implementing relationship strategies that improve pet-owner loyalty. The article that follows is a synopsis of the author’s dissertation.

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.

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.

In the dynamic and evolving tourism industry, engaging of stakeholders is essential for fostering innovation and improving service quality. However, tourism companies often struggle to meet expectations for customer satisfaction through interactivity and real-time feedback. While new digital technologies can address the challenge of providing personalized travel experiences, they can also increase the workload for travel agencies due to the maintenance and updates required to keep travel details current. Intelligent chatbots and other Generative Artificial Intelligence (GAI) tools can help mitigate these obstacles by transforming tourism and travel-related services, offering interactive guidance for both tourism companies and travellers. In this study, we explore and compare the main characteristics of existing responsive AI instruments, applicable in tourism and hospitality scenarios. Then, we propose a new theoretical framework for decision-making in the tourism industry, integrating GAI technologies to enable agencies to create and manage itineraries, and tourists to interact online with these innovative instruments. The advantages of the proposed framework are as follows: 1) providing a comprehensive understanding of the transformative potential of new generation AI tools in tourism and facilitates their effective implementation; 2) offering a holistic methodology to enhance the tourist experience; 3) unifying the applications of contemporary AI instruments in tourism activities and paves their further development. The study contributes to the expanding literature on tourism modernization and offers recommendations for industry practitioners, consumers, and local, regional, and national tourism bodies to adopt a more user-centric approach to enhancing travel services.

Artificial intelligence (AI) shows great potential in medical diagnosis and drug analysis. Through deep learning technology, AI can automatically extract features from large amounts of complex medical data, significantly improving diagnostic accuracy and drug development efficiency. Especially in drug target discovery and antiviral peptide classification, AI technology can accelerate data processing and prediction, helping researchers identify potential therapeutic molecules more quickly and optimize the drug development process. This study proposes and validates a Deep learn-based model, deep-Avpiden, to improve the classification and discovery efficiency of antiviral peptides (AVPs). By using sequential convolutional networks (TCNs), the model outperforms traditional recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) in capturing long-term dependencies and parallel computing capabilities. In terms of dataset, we used AVPs and non-AVPS samples from multiple databases, totaling 5,414 cleaned and de-weighted peptide sequences for model training after data preprocessing and embedding. The Deep-AVPiden model has been shown to outperform existing advanced classifiers in experiments, and its effectiveness has been verified by accuracy, precision, recall rate, and area under the ROC curve (OC-ROC). In addition, to accommodate computing resource constraints, we propose an optimized version of Deep-AvPIDen (DS), which utilizes Deep separation convolution technology to significantly reduce computing resource consumption. Through the online application platform, researchers can efficiently classify antiviral proteins and discover new AVPs. Future research could further optimize the model's computational efficiency, handle larger data sets, and expand its potential for biomedical problems such as drug combination prediction and new drug discovery.

The aim of this study is to investigate and determine the key factors guiding customer attitudes towards social media influencers, and on that base to explore their effects on purchase intentions towards advertised products or services. A total of 376 fulfilled questionnaires from an online survey were analysed. The main characteristics of digital influencers’ behaviour, affecting consumer perceptions have been systematized and categorized through a combination of both traditional and advanced data analysis methods. Structural Equation Modelling (SEM), machine learning and multi-criteria decision-making (MCDM) methods were selected to uncover the hidden dependencies between variables from the perspective of social media users. The developed models elucidate the underlying relationships that shape the acceptance mechanism of influencers’ messages. The results obtained provide specific recommendations for stakeholders across the social media marketing value chain. Marketers can make informed decisions and optimize influencer marketing strategies to enhance user experience and increase conversion rates. Working collaboratively, marketers and influencers can create impactful and successful marketing campaigns that resonate with the target audience and drive meaningful results. Customers benefit from more tailored and engaging influencer content that aligns with their interests and preferences, fostering a stronger connection with brands and potentially affecting their purchase decisions. As the perception of customer satisfaction is individual and evolving process, stakeholders should organize regular evaluations of influencer marketing data and explore the possibilities to ensure continuous improvement of this e-marketing channel.

Background:Tourette syndrome (TS) is a characterized by motor and vocal tics, often accompanied by obsessive-compulsive disorder (OCD) and attention-deficit/hyperactivity disorder (ADHD). Cannabis-based medicines (CBM) present a promising avenue for future therapeutic intervention owing to their interaction with the endocannabinoid system, potentially mitigating tics and associated symptoms. Unlike conventional treatments such as antipsychotics, CBM exhibits improved tolerability and reduced side effects. While the available evidence is still limited, ongoing research underscores the potential of CBM to enhance the quality of life for patients, establishing it as a pivotal area for future therapeutic advancements in TS. Aim: In our systematic review and meta-analysis, we aim to assess the effectiveness of cannabis-based medicine (CBM) in the treatment of Tourette Syndrome. Methods: We searched in electronic databases (PubMed, Google Scholar, Science Direct, Cochrane Collaboration Database of Randomized Trials) was performed to identify cohort studies and randomized controlled trials (RCTs) up to 18th June 2024. Data extraction focused on baseline characteristics of the included studies and efficacy outcomes, including scores on the Yale Global Tic Severity Scale (YGTSS) and Premonitory Urge for Tics Scale (PUTS). We conducted the meta-analysis using Review Manager version 5.4. software. We compared the measurements before and after CBM intake using mean difference (MD) and 95% confidence interval (CI). Result: In total, 1105 articles were identified for screening, with eight studies included in the systematic review and seven in the meta-analysis. These studies involved 306 adult patients with TS treated with cannabis. YGTSS revealed a significant reduction in total scores (MD = -13.29, 95% CI [-21.67 to -4.91], P = 0.002) and PUTS revealed a significant decrease in scores (MD = -4.09, 95% CI [-7.24 to -0.93], P = 0.01). Conclusion: The research emphasizes the potential of cannabis-based medication in alleviating tics and premonitory urges associated with Tourette syndrome. While the initial results are promising, they are based on limited-scale studies. It is imperative to conduct larger, placebo-controlled trials encompassing diverse demographic groups to substantiate the effectiveness, ensure safety, and optimize dosages for long-term therapeutic advantages.

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.

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.

Spectacular shifts have been led to by The COVID-19 crisis in consumer behavior. Retailers will have to work hard to meet ever-evolving customer service experience with respect to the ways in which it may be differently affected by offline or online transactions in order to win and stay relevant. We suggest an integrative framework and construct customer service experience hypotheses, based on its antecedents and consequences that will contribute to academic study as well as managerial implications. The hypotheses are tested by a simultaneous equation model employing two data sets of the retail industry's offline and online customers. In this study, 571 samples of these businesses, 319 and 252 respondents from offline and online retail channels, respectively, were collected by means of an online web survey of consumers. The results show that the impact of consequences and antecedents of CSX differs based on the media utilized. The integrative framework of CSX in its online medium is far more effective than its explanatory power offline. The outcomes are reasonably counterintuitive in so far as they demonstrate that while most elements of CSX where a service is selected offline is the same in terms of customer loyalty and value equity, the emotional element related to the service provider is higher when the service is selected offline rather than online. These outcomes indicate that, contrary to popular fears, the online medium enables firms to develop a loyal customer foundation. These findings offer perceptivity into how an online channel could be used to better complement the offline channel, contributing towards new knowledge and understanding on CSX and how it may be utilized for managerial decision-making.

Low dropout (LDO) regulators are widely used in portable electronic devices because they 1 occupy small chip and printed circuit board (PCB) areas. This work presents the design of a low 2 noise low dropout (LDO) linear voltage regulator using folded cascode operational amplifier and 3 source follower buffer for frequency compensation. The proposed design achieves low output noise 4 with feedback loops, which makes it possible to improve load and line regulations as well as the 5 transient response for voltage applications in comparison with the literature. The designed LDO 6 linear regulator works under the input voltage of 2.8-4.8 V and provides up to 100 mA load current 7 for an output voltage of 2.6 V. 8 9 Moreover, the operational amplifier design contributes to the major reduction in the frequency 10 noise, and make sure the stability of LDO under different conditions. Furthermore, the novel LDO 11 design topology has very less dropout voltage, fast transient response and minimized power dissi- 12 pation under maximum load current, worst process corner, and maximum temperature to achieve 13 high power efficiency, maintain output voltage in the presence of fast load changes, and to lower the 14 power loss across the pass element especially at high load. Design criteria are addressed in detail. 15 16 This LDO topology is able to achieve ultra-low noise i.e. 2 /muv/√Hz for frequency >1kHz. The 17 proposed LDO is implemented in the cadence virtuoso H9A with CMOS 130 μm technology.

Introduction : In today’s competitive business landscape, organizations strive to maintain a highly committed workforce to enhance productivity, reduce turnover, and achieve long-term success. Organizational commitment, defined as the psychological attachment of employees to their organization, is crucial in fostering employee loyalty and engagement (Meyer & Allen, 1991). However, identifying the factors that influence organizational commitment requires a deep understanding of employee attitudes and behaviors.

Background: The last-mile delivery phase, the final stage where goods move from a distribution center to customers, is pivotal but faces significant inefficiencies and high costs due to its complexity. Recent advancements in Edge AI or Edge Intelligence (EI) offer promising solutions to these challenges. Methods: This study explores how AI-driven technologies and real-time data processing, combined with EI, can enhance last-mile delivery operations. A thorough literature review was conducted to assess technological advancements, and a Delphi method was used to systematically and empirically assess the impact of EI solutions on both operational efficiency and customer satisfaction. Results: Although EI technologies offer substantial benefits, EU companies are hesitant to adopt these innovations due to high implementation costs. However, firms that have embraced these technologies report significant improvements, including better route optimization, reduced delivery times, and enhanced service reliability. These findings highlight the need for a culture of innovation and the recruitment of experts with advanced qualifications to drive technological advancement in last-mile logistics. Conclusions: The integration of EI represents a significant step towards more efficient, cost-effective, and customer-focused last-mile delivery solutions. Future research should aim to refine these technologies and explore their long-term impacts on the logistics industry.

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.

This study assessed the financial management practices and challenges of food cart businesses in Cabanatuan City, Nueva Ecija. The study used a descriptive research design and structured surveys and questionnaires to gather quantitative data from 20 food cart businesses. The data were analyzed using statistical tools such as weighted means, frequencies, and percentages to provide a comprehensive overview of these small enterprises' financial practices and challenges. Key findings highlight that while many businesses demonstrate basic financial literacy, significant gaps exist in financial record-keeping, budgeting, and revenue management. The study emphasizes the importance of improving these practices to ensure the sustainability and growth of food cart businesses. The research contributes to the broader discourse on small business management and economic development by identifying common challenges and offering targeted recommendations. The findings underscore the need for targeted interventions, such as financial training and policy support, to enhance the financial acumen of food cart owners. This study adds to the existing body of knowledge as it provides practical insights that can benefit business owners, policymakers, and community stakeholders in fostering a more resilient and economically vibrant local economy.

Background and Aim: Patients with Type 2-diabetes condition tend to have multiple medications in various dosage which leads to complex medication regimens. Complexity of regimen results in poor adherence, uncontrolled glycemic value and treatment failure. The research aimed to investigate medication regimen complexity index, medication adherence, and impact of medication regimen complexity on glycemic control and medication adherence.Materials and Methods: A Crossectional Study methods was conducted at Bheri Hospital Nepalgunj, among 145 Diabetes patients. Medication adherence was assessed by using a General Medication Adherence Scale (GMAS Nep-Tool). Medication regimen complexity was assessed by using a Medication Regimen Complexity Index (MRCI) tool. Chi-square test, One-way ANOVA, Paired t-test, and binary logistic regression were used for inferential statistics. Results: This study shows that most of the participants were female and aged group 50-59 years. Hypertension was the most common comorbidity, followed by hypothyroidism. The majority of participants exhibited partial adherence 90 (62.1%) to their medication regimen. It was seen that medications prescribed for diabetes were more complex than non-diabetes medications (Mean±SD MRCI: 8.28±3.155 vs. 4.57±2.107). The negative correlation was seen with diabetes MRCI and medication adherence (r=-0.090). There was statistically significant association between diabetes MRCI and glycemic control, (p<0.05). Binary logistic regression revealed that females had lower odds of high MRCI compared to males ( multivariate p-value: 0.014, AOR: 0.403, 95% CI: 0.195-0.832), while sedentary lifestyle showed high Diabetes MRCI (COR: 4.709, 95% CI: 0.962-23.05) and abnormal BMI were associated with higher MRCI (binary p-value: 0.067, COR: 2.30, 95% CI: 0.942-5.65; multivariate p-value: 0.092, AOR: 2.323, 95% CI: 0.878-5.67). Conclusion: Diabetes medications are more complex, reducing adherence and worsening A1C outcomes. Simplifying regimens improves results, with MRCI being valuable for healthcare providers. The medication adherence and therapeutic outcomes can be improved by the improved by the involvement of clinical pharmacist using MRCI.

Observing customers is one of the methods to uncover their needs. By closely observing how customers use products, we can indirectly experience their interactions and gain a deep understanding of their feelings and preferences. Through this process, companies can design new products that have the potential to succeed in the market. However, traditional methods of customer observation are time-consuming and labor-intensive. In this study, we propose a method that leverages the analysis of online customer reviews as a substitute for direct customer observations. By correlating Customer Journey Map (CJM) with online reviews, this research establishes a verb-centric analysis that produces a CJM based on online review data. Various Text analysis techniques were utilized in this process. Through empirical data analysis, we validated that indirect observation based on customer reviews can be performed more efficiently than traditional methods. Additionally, we observed that the customer behavior VOC (Voice of Customer) identified during the CJM mapping process offers unique insights that are distinct from traditional product feature-centric review analyses. To verify the usefulness of these behavior VOC, we evaluated the quantitative analysis results of the same reviews through product development experts. Experts recognized these customer behavior VOC as useful information for developing new products.

This study explores the transformative impact of e-commerce technologies on supply chain management within the retail sector. Through a thematic analysis of current literature and qualitative research findings, the study examines how technologies such as real-time monitoring, predictive analytics, AI, blockchain, and IoT are reshaping inventory management, procurement, logistics, and customer service. The research highlights significant improvements in operational efficiency, decision-making, and customer experience driven by these technologies. However, challenges such as system integration complexities, cybersecurity risks, and regulatory compliance issues are also identified. The study emphasizes the strategic adoption and integration of e-commerce technologies to enhance supply chain resilience, sustainability, and competitiveness. Insights from the analysis underscore the evolving role of technology in driving innovation and efficiency across supply chain functions, while also necessitating careful management of risks and complexities. Looking forward, the study discusses future trends including AI advancements, IoT connectivity, and blockchain applications, which are poised to further revolutionize supply chain dynamics. By addressing these trends and challenges strategically, retailers can leverage e-commerce technologies to optimize operations, mitigate risks, and meet evolving consumer demands in a digital-first marketplace.

Costa Rica, widely acknowledged for its environmental leadership, is now facing a significant challenge in managing waste, a situation that could potentially compromise both its environmental and public health achievements. This analysis delves into the waste management issues in Costa Rica, particularly in urban areas such as San José, responsible for nearly half of the country's waste. The study identifies several pressing concerns, including the heavy burden on landfill capacities, a recycling rate of just 9.6%, and substantial regional differences in waste management practices.To address these challenges, the research employs a combined methodology, utilizing Linear Regression (LR) and AutoRegressive Integrated Moving Average (ARIMA) models to project future trends in CO2e emissions and waste minimization from 2024 to 2050. This method underscores the vital need for public involvement and the introduction of innovative strategies in waste management. The research also introduces a Waste Management Innovation Index to compare Costa Rica’s practices with those of leading nations like Germany and South Korea, identifying key areas for improvement.Furthermore, the study incorporates insights from stakeholders, revealing the intricate balance of cultural, economic, and regulatory factors that shape waste management policies. Despite efforts by the government, such as the Environmental Health Route and the National Circular Economy Strategy, results show limited progress, particularly in rural regions. The study highlights the urgent need for better infrastructure, targeted public awareness campaigns, and the use of advanced technologies like Artificial Intelligence (AI) and blockchain to enhance sustainable waste management practices. The research provides strategic recommendations to help Costa Rica improve its waste management systems in line with international standards, ensuring long-term environmental sustainability and protection of public health.

This research aimed to discover how to create a customer experience that includes FEEL, SENSE, THINK, ACT, and RELATE to increase satisfaction with the Subscription Video on Demand (SVoD) application. A descriptive quantitative method was used, and the sample was 423 respondents in Indonesia who had used the application for at least 1 month. Furthermore, this research used probability sampling using a technique for random sampling. Based on the data available, experience accounted for 0.509, or 50.9%, of the application satisfaction. With a beta value of 0.295, the FEEL variable was the only one in this study to significantly and positively affect customer satisfaction; the other variables, SENSE, THINK, ACT, and RELATE, had no discernible effects. It is anticipated that the theoretical foundation provided by this research will be beneficial to the entertainment industry, particularly the SVoD application sector. This includes the product and service development division that focuses on customer inner feelings and emotions.

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.

In the dynamic landscape of the telecommunications industry, understanding and mitigating customer churn is paramount for maintaining competitive advantage and sustaining revenue streams. This comprehensive literature review delves into the intricacies of customer churn prediction, a crucial aspect in fortifying the bond between telecom businesses and their customers. The review methodically analyzes 11 articles published between 2000 and March 2024, spotlighting the strategic role of Customer Relationship Management and the prevalence of high churn rates that necessitate a shift from customer acquisition to retention strategies. Furthermore, the review navigates through the challenges inherent in churn prediction, including the complexities of managing large, imbalanced, and noisy datasets and the intricacies involved in model selection and optimization. Despite these challenges, the paper highlights the superior capability of machine learning techniques in handling complex datasets to provide accurate predictions, albeit acknowledging their practical complexities. The main aim of the study is to provide an overview, serving as a knowledge base for researchers interested in machine learning techniques within the realm of telecommunications.

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.

This manuscript reviews the relationship between customer perception factors and AI-enabled customer experience in the Ecuadorian banking industry. The study employs a self-designed online questionnaire with five factors for customer perception (convenience in use, personalization, trust, customer loyalty, and customer satisfaction) and two categories for AI-enabled customer experience (AI-hedonic customer experience and AI-recognition customer service). The final valid dataset consisted of 226 questionnaires. The data analyses and the hypotheses tests were done using SPSS 26 and structural equation modeling, respectively. The main findings displayed that all five customer perception factors have a positive and significant effect on AI-enabled customer experience in the Ecuadorian banking industry. Study results are aligned with previous findings from other countries, particularly the banking environment in the United Kingdom, Canada, Nigeria, and Vietnam. The AI techniques involved in the financial sector increase the valuation of customer experience due to AI algorithms recollecting, processing, and analyzing customer behavior. This study contributes with a complete statistical and econometric model for determinants of AI-enabled customer experience. The main restriction of the study is the particular analysis of the most demanded AI financial services (not all services and products are included) and the inexistence of a customer perception index. For upcoming research, the authors recommend performing a longitudinal study using quantitative data to measure the effect of AI-enabled customer experience on the Ecuadorian banks’ performance.

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.

Covariant scalar field quantization, nicknamed $(φ^r)_n$, where $r$ denotes the power of the interaction term and $n=s+1$ where $s$ is the spatial dimension and $1$ adds time. Models such that $r<2n/(n-2)$ can be treated by canonical quantization, while models such that $r>2n/(n-2)$ are trivial, or, if treated as a unit, emerge as `free theories'. Models such as $r=2n/(n-2)$, e.g., $r=n=4$, again using canonical quantization also become `free theories', which must be considered quantum failures. However, there exists a different approach called affine quantization that promotes a different set of classical variables to become the basic quantum operators and it offers different results. It is well-known that the canonical quantization of $\varphi^4_4$ fails. Here is how to fix it along with many other problems.

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.

In today’s competitive landscape, achieving customer-centricity is paramount for the sustainable growth and success of organisations. This research is dedicated to understanding customer preferences in the context of the Internet of Things (IoT) and employs a two-part modeling ap-proach tailored in this digital era. In the first phase, we leverage the power of the Self-Organizing Map (SOM) algorithm to segment IoT customers based on their connected device usage patterns. This segmentation approach reveals three distinct customer clusters, with the second cluster demonstrating the highest propensity for IoT device adoption and usage. In the second phase, we introduce a robust Decision Tree methodology designed to prioritize various factors influencing customer satisfaction in the IoT ecosystem. We employ the Classification and Regression Tree (CART) technique to analyze 17 key questions that assess the significance of factors impacting IoT device purchase decisions. By aligning these factors with the identified IoT customer clusters, we gain profound insights into customer behaviour and preferences in the rapidly evolving world of connected devices. This comprehensive analysis delves into the factors contributing to customer retention in the IoT space, with a strong emphasis on crafting logical marketing strategies, en-hancing customer satisfaction, and fostering customer loyalty in the digital realm. Our research methodology involves surveys and questionnaires distributed to 207 IoT users, categorizing them into three distinct IoT customer groups. Leveraging analytical statistical methods, regression analysis, and IoT-specific tools and software, this study rigorously evaluate the factors influencing IoT device purchases. Importantly, this approach not only effectively clusters the IoT Customer Relationship Management (IoT-CRM) dataset but also provides valuable visualizations that are essential for understanding the complex dynamics of the IoT customer landscape. Our findings underscore the critical role of logical marketing strategies, customer satisfaction, and customer loyalty in enhancing customer retention in the IoT era. This research makes a significant contri-bution to businesses seeking to optimize their IoT -CRM strategies and capitalize on the oppor-tunities presented by the IoT ecosystem.

Smart technology is essential for integrating circular economy principles. This study examines how conflict management strategies including collaboration, accommodation, avoidance, compromise, and competitions impact smart technology adoption, particularly blockchain, in Jordanian SMEs, a context that has been under-explored. Additionally, the research examines the moderating role of customer-centric green supply chain management in the relationship between blockchain adoption and the implementation of circular economy principles. Data were collected from 421 senior managers, department heads, and executives of Jordanian SMEs. Primarily, this study employed robust methodological approach, a structural equation modeling (SEM) technique, to test the hypotheses and analyze complex relationships. This research provides valuable insights into the adoption of blockchain technology in Jordanian SMEs, highlighting the importance of strategic conflict management, a context that is rarely explored. Incorporating customer-centric green supply chain management as a moderating factor enhance the sustainability and competitiveness of SMEs through blockchain technology. This approach offers new insights into how blockchain technology aligns with circular economic principles, highlighting the strategic role of conflict management in driving technological innovation and sustainability. The study results indicate that conflict management strategies have a significant impact on the adoption of blockchain technology among Jordanian SMEs. The results also revealed a unique finding that collaboration and compromise are the most influential strategies. Furthermore, the study found that blockchain adoption positively impacts the implementation of circular economy principles. This study provides theoretical and practical insights for SMEs, especially in the context of Jordan.

Artificial Intelligence (AI) is poised to revolutionize the architectural design and energy management of green buildings, offering significant advancements in sustainability and efficiency. This paper explores the transformative impact of AI on improving energy efficiency and reducing carbon emissions in commercial buildings. By leveraging AI algorithms, architects can optimize building performance through advanced environmental analysis, automation of repetitive tasks, and real-time data-driven decision-making. AI facilitates precise energy consumption forecasting and integration of renewable energy sources, enhancing the overall sustainability of buildings. Our study demonstrates that AI can reduce energy consumption and CO2 emissions by approximately 8% and 19%, respectively, in typical mid-size office buildings by 2050 compared to conventional methods. Further, the combination of AI with energy efficiency policies and low-emission energy production is projected to yield reductions of up to 40% in energy consumption and 90% in CO2 emissions. This paper provides a systematic approach for quantifying AI's benefits across various building types and climate zones, offering valuable insights for decision-makers in the construction industry.

Cardiofaciocutaneous (CFC) syndrome, linked to mutations in genes including KRAS, BRAF, MEK1, and MEK2, leads to dysregulated RAS-MAPK signaling with limited therapeutic options. This study utilized molecular docking simulations to screen natural compounds against mutated KRAS, BRAF, MEK1, and MEK2 structures. Promising candidates such as Amentoflavone and Hypericin displayed strong binding affinities and favorable interactions with mutated sites, suggesting their potential as lead molecules for targeted therapies in CFC syndrome. This research offers novel insights into therapeutic interventions for this rare genetic disorder.

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 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.

Historically, the military and non-religious, sub-continent’s intellectual elite Muslim men crafted state ideologies before partition, by actively contributing to sociocultural constructs prevalent to this day, in service of colonial patriarchal concepts. The purpose of this ideological collaboration has been to mould a Pakistani cis-gendered female child primarily into the ideal “Pakistan-Muslim” bride for marriage, carried out through her family and society. This has shaped the lives of cis-gendered female children in Pakistan through coerced, gendered control, implemented as prescriptive societal norms. These norms are rooted in colonial, patriarchal ideologies and are functions of capital and coercion intentionally perpetuated to erase her pluralistic identity, reducing her to a central resource within a family unit. To the military concept of nationalism, her mind and her body symbolize the unification and centralization of a statist ideology. Thus, the military state plays an essential role in shaping her purpose around the concept of nationalism. The intellectual elite, via the state, uphold colonial, patriarchal ideologies, where the primary identity of a cis-gendered female child is actualized through her marriage. The cis-gendered female child’s life is a vehicle to unify the state’s two-fold “Pakistani-Muslim” agenda, by implementing a ‘singular statist narrative’. A narrative that attempts to resolve two major tensions: First, it seeks to wipe out the possibility of a nation connecting with and exploring its own immense, internal ethnocultural diversity. It does this by imposing an imagined “homogenous Muslim” identity — which doesn’t exist — and is instead imported from multiple countries to reinforce a ‘singular statist narrative’, Second, it aims to control the mixing of local, sociocultural narratives by bridging any disjuncture between them by labelling colonial institutional patriarchy concepts as inherently “Pakistani”; in service of its preferred ‘singular statist narrative’.

Background Obesity is a major healthcare challenge worldwide. Recently, glucagon-like peptide-1 receptor agonists (GLP-1RAs) have emerged as a promising pharmacological option for managing type 2 diabetes mellitus (T2DM) and obesity in diabetic patients. Objective This systematic review aims to evaluate the efficacy and safety of GLP-1RAs in nondiabetic patients. Methods We systematically reviewed 2356 published articles from the past ten years (2013 to November 22, 2023). After rigorous screening, 11 articles were selected for an in-depth review. The analysis focused on the efficacy and safety of GLP-1RAs in nondiabetic patients, considering various baseline characteristics and subgroups. Results The review found that GLP-1RAs, including exenatide, liraglutide, semaglutide, and the dual glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonist (GIP/GLP-1RA) tirzepatide, significantly promoted weight loss in obese/overweight nondiabetic patients. Additionally, these agents positively affected cardiovascular risk factors such as blood pressure, glycemic control, nephropathy, lipid levels, serum insulin levels, and C-reactive protein (CRP) profiles. However, GLP-1RAs, including tirzepatide, were associated with gastrointestinal adverse drug reactions such as nausea, diarrhea, vomiting, and constipation. These adverse events were typically transient, mild-to-moderate in severity, and subsided over time. Conclusion GLP-1RAs are effective and relatively safe for weight management and improving cardiovascular risk factors in nondiabetic patients, despite the occurrence of some gastrointestinal side effects. Further research is warranted to optimize the use of these agents in nondiabetic populations.

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.

This paper aims to explore the potential application of Extreme Learning Machines (ELM) in stock price prediction and optimise the ELM model by combining the Crown Porcupine Optimisation Algorithm and the Adaptive Bandwidth Kernel Function Density Estimation Algorithm. Specifically, the model's root mean square error (RMSE) on the test set is 0.018, which shows its good prediction accuracy. Meanwhile, the correlation coefficient R reaches 0.968, proving the model's effectiveness in predicting stock prices. In addition, the coefficient of determination R² of the training set and test set are 0.987 and 0.954, respectively, which are not much different, indicating that the model has good generalisation ability and is not prone to overfitting. This is crucial for practical application, as it implies that the model fits the existing data well and makes accurate predictions for future data. In terms of other evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), the performance is excellent in both the training and test sets. Together, these results indicate that after optimising the ELM by introducing the crown porcupine optimisation algorithm and the adaptive bandwidth kernel function density estimation algorithm, the model can effectively capture the changing stock price pattern, thus achieving efficient and accurate stock price prediction. In summary, this study not only demonstrates the great potential of ELM in stock price prediction but also highlights the importance of improving the model performance through advanced algorithm optimisation, which provides a new way of thinking and methodology for financial market analysis. This strongly supports investors' decision-making and lays the foundation for related research.

This study explores the use of Generative Artificial Intelligence (GAI) in assessing pilot fatigue risk by integrating facial recognition and physiological signals with Inertial Measurement Units (IMUs). By leveraging IMU technology's precise, real-time data on movement and combining it with GAI's advanced data analysis capabilities, the study aims to enhance the accuracy of fatigue prediction models. The analysis reveals that while traditional classifiers like Extreme Random Trees and Random Forests offer modest performance, advanced models such as Support Vector Machines and Naive Bayes demonstrate superior recall rates, highlighting their potential to identify true positives. This integration of AI and IMUs offers a promising approach to developing comprehensive, real-time fatigue monitoring systems, improving safety and efficiency in aviation by providing actionable insights and facilitating more effective fatigue management.

The study addresses the challenge of efficiently peeling pineapples, which have a distinct elliptical form, thick skin, and small eyes that are difficult to detect with conventional automated methods. This results in significant flesh waste. To improve the process, the researchers developed an integrated system combining an enhanced BlendMask method, termed SAAF-BlendMask, and a PCP method. SAAF-BlendMask improves the detection of small pineapple eyes, while PCP ensures accurate posture adjustment for precise path planning. The system uses 3D vision and deep learning technologies, achieving an average precision (AP) of 73.04% and a small object precision (APs) of 62.54% in eye detection, with a path planning success rate reaching 99%. The fully automated electromechanical system was tested on 110 real pineapples, demonstrating a reduction in flesh waste by 11.7% compared to traditional methods. This study highlights the potential of advanced machine vision and robotics in enhancing the efficiency and precision of food processing.

In this paper, we investigate the proftability of investing in movies for retail investors, focusing on the return on investment (ROI) metric, which is calculated as revenue divided by budget. Our analysis encompasses 5000 movies from the movie database TMDB, examining factors such as production scale, genres, key actors, directors, and more. Additionally, we employ textual analysis techniques like Latent Semantic Analysis (LSA) on movie overviews and tags to incorporate movie themes. We then evaluate various supervised classifcation models including Logistic Regression, Random Forest, and Light Gradient Boosting (LightGBM), comparing their performance. Our fndings highlight the signifcance of production scale, team structure, and movie themes in identifying potential high-return opportunities for investors.

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.

This paper explores the application of machine learning in financial time series analysis, focusing on predicting trends in financial enterprise stocks and economic data. It begins by distinguishing stocks from stocks and elucidates risk management strategies in the stock market. Traditional statistical methods such as ARIMA and exponential smoothing are discussed in terms of their advantages and limitations in economic forecasting. Subsequently, the effectiveness of machine learning techniques, particularly LSTM and CNN-BiLSTM hybrid models, in financial market prediction is detailed, highlighting their capability to capture nonlinear patterns in dynamic markets. The study demonstrates the advancements in predictive accuracy and robustness achieved by deep learning methods through empirical analysis and model validation. The findings contribute significantly to academic discourse and offer practical insights for investors, financial analysts, and policymakers navigating market volatility and optimizing investment strategies. Finally, the paper outlines prospects for machine learning in financial forecasting, laying a theoretical foundation and methodological framework for achieving more precise and reliable economic predictions.

It is difficult to automatically recognize defects using digital image processing method in X-ray radiograph that tested from lap joint of unequal thickness plates. The continuous change of the wall thickness for the lap joint workpiece brings about the very different gray levels in X-ray background image. Besides, due to the shape and fixturing of the workpiece, the distribution of the weld seam in the radiograph is not vertical and results in an existence of a certain angle with the vertical direction. This makes it difficult for automatic defect detection and localization. In this paper, a method of X-ray image correction based on invariant moments is presented to solve the problem. In addition, an automatic defect detection method combined of image noise suppression, background removal, image segmentation and mathematical morphology is introduced. The results show that the proposed method can effectively recognize the gas pores in lap joint of unequal thickness and it is suitable for automatic detection.

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.

Wireless network technologies, including 3G, 4G, and 5G, are transforming telecommunications infrastructure globally. However, the adoption and effectiveness of these technologies vary significantly across regions and industries, posing unique challenges and opportunities for Small and Medium Enterprises (SMEs). Understanding the critical factors influencing network deployment and optimization in different contexts is essential for telecom companies and business leaders. This systematic review aims to evaluate the infrastructure, performance, and strategic implications of wireless network technologies (3G, 4G, and 5G) across multiple industries and geographic regions, providing insights for SMEs and telecom companies on adopting these technologies to enhance operational efficiency and competitiveness. A comprehensive search of academic databases, including Google Scholar, Web of Science, and SCOPUS, was conducted using keywords such as “wireless network,” “3G,” “4G,” “5G,” “evaluation,” and “infrastructure.” Studies were selected based on pre-established eligibility criteria, and a risk of bias assessment was performed using the Newcastle-Ottawa Scale. Statistical synthesis and sensitivity analyses were conducted to identify key trends and challenges. A total of 121 studies were included, with the majority focusing on 5G technology (42%) and its infrastructure. Key findings highlight the importance of network densification, high-speed connectivity, and low-latency applications, particularly in urban regions. The analysis also revealed significant regional disparities in infrastructure deployment, with developing countries facing challenges in expanding coverage and integrating advanced technologies. Industry-specific customization of wireless networks is essential for sectors such as manufacturing, healthcare, and retail. Wireless network technologies present vast opportunities for SMEs, but their successful implementation requires addressing regional infrastructure gaps and tailoring solutions to industry-specific needs. Telecom companies must prioritize strategic investments in network densification, scalability, and security to fully leverage the benefits of 5G. The findings of this review provide actionable insights for business leaders and policymakers aiming to optimize wireless technology deployments for enhanced performance and competitiveness.

This paper comprehensively explores the integration of cloud computing and advanced recommendation systems, emphasizing their pivotal roles in enhancing user experiences and operational efficiencies across digital platforms. It reviews the evolution of recommendation algorithms, highlighting their application in diverse domains such as e-commerce and media. The study evaluates the performance of advanced models like UniLLMRec against traditional counterparts using datasets from news and e-commerce domains. Additionally, the paper discusses the infrastructure architecture of cloud computing, demonstrating its capability to support scalable and efficient data processing. Through experimental insights and methodology, the research underscores the transformative impact of cloud technologies on optimizing recommendation system performance, thereby advancing digital engagement and competitiveness.

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.

first_pagesettingsOrder Article ReprintsOpen AccessArticleThe Carbon Footprint of Spanish University Websitesby Sonia Sanchez-Cuadrado ¹ andJorge Morato ^2,*1Department of Library and Information Science, Complutense University of Madrid, 28010 Madrid, Spain²Department of Computer Science, Carlos III University of Madrid, Leganes, 28911 Madrid, Spain^*Author to whom correspondence should be addressed.Sustainability 2024, 16(13), 5670; https://doi.org/10.3390/su16135670 (registering DOI)Submission received: 31 May 2024 / Revised: 22 June 2024 / Accepted: 23 June 2024 / Published: 3 July 2024(This article belongs to the Special Issue Sustainable Education in the Digital Society: Applying Communication Technologies and Digital Tools to Education) Downloadkeyboard_arrow_down Browse Figures Versions NotesAbstractThe consumption of digital content is essential for economic and scientific development, leading to a high dependence on information and communication technologies, which significantly impacts the environment. Quantifying this ecological impact is challenging due to its intangible nature and the diversity of factors. This research focuses on assessing the energy consumption and CO₂ production of Spanish university websites to evaluate their levels of digital pollution and environmental impact. The calculation of the carbon footprint considers factors related to energy consumption and the use of green or renewable energies. The findings indicate that only 17% of university websites can be considered environmentally friendly. There are significant differences in CO₂ consumption and emissions depending on whether the websites implement environmentally sound practices. This study highlights the lack of initiatives aimed at reducing energy consumption and greenhouse gas emissions associated with web information management. The study shows that Spanish universities have the potential to contribute to the reduction in CO₂ emissions by implementing sustainable development practices on their websites. Furthermore, it fosters awareness about technology usage and its environmental impact, promoting the adoption of green and sustainable approaches.

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.

Cybersecurity ecosystem is an important facet in protecting sensitive information and securing critical infrastructure for countering modern cyber threats. With the increasing complexity and frequency of security incidents, there is an escalating demand for development of innovative solutions beyond current human capabilities pertaining to cybersecurity measures. Artificial Intelligence or AI can be utilized in a myriad of areas of cybersecurity. It emerged as a technological innovation to enhance cyber protection by facilitating faster and real-time threat detection for known and unknown threats, automating processes to minimize human error, and optimal decision-making. Harnessing the power of AI in cybersecurity creates formidable defense capabilities against the constantly changing cyber threats of future while empowering the cybersecurity personnel with threat intelligence and proactive foresight to safeguard critical assets and confidential information with unparalleled precision and effectiveness. This research paper aims to investigate the potential of AI-enabled cybersecurity systems and focuses on deducing the benefits of using AI in enhancing cybersecurity processes for organizations seeking to manage their risk profile. Through a comprehensive literature review, the wide-ranging applications of AI in cybersecurity have been analyzed such as intrusion detection, predictive simulation, and automated emergency response management. The study examines the benefits of implementing AI-based cyber defenses such as improved promptness and accuracy in vulnerability assessment and threat management, reduced false positives, and recognize patterns. The future potential of AI in cybersecurity will take a leap forward in expanding protection mechanisms to evaluate the strengths and weaknesses of attack vectors to prevent an adversarial attack.

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.