The integration of artificial intelligence into pharmaceutical research has ushered in a transformative era for drug discovery and development. Among various deep learning architectures, Convolutional Neural Networks (CNNs) have emerged as particularly powerful tools for modeling complex biological and chemical data. This paper provides a comprehensive review of CNN-based approaches in drug discovery, examining their applications in drug-target interaction prediction, adverse drug reaction forecasting, and de novo drug design. Through a systematic analysis of recent literature and experimental studies, we demonstrate that CNN architectures achieve superior performance across multiple pharmaceutical applications, with drug-target interaction prediction accuracies reaching 93-95% and adverse drug reaction detection rates of 78%. The review synthesizes findings from 2018-2025, highlighting how CNNs effectively extract spatial patterns from molecular representations, identify critical features from protein sequences, and integrate with complementary architectures such as graph neural networks and long short-term memory networks. We also address key challenges including data quality limitations, model interpretability concerns, and regulatory integration pathways. This paper concludes by outlining future research directions, emphasizing the potential of hybrid architectures, multi-modal learning, and explainable AI in advancing CNN-driven drug discovery toward clinical implementation.

In the current era of rapid urbanization and digital transformation, the efficient use of electrical energy has become an essential factor in promoting sustainable living and industrial development. The Intelligent Energy Meter with Alerting System using Arduino Uno and IoT technology is designed to provide users with real-time monitoring, control, and management of electrical power consumption. The proposed system continuously measures the power usage of household or industrial appliances and transmits the data to an IoT-based cloud platform for analysis, visualization, and reporting. The Arduino Uno serves as the central processing unit that collects data from voltage and current sensors such as the ACS712current sensor and a voltage divider circuit, calculates the power consumption, and communicates with an IoT Wi-Fi module (ESP8266) to upload the data online. When the energy usage exceeds a predefined threshold, the system automaticallygenerates alerts to the user through IoT-based notifications or buzzer indications. This threshold-based cost management mechanism helps prevent unnecessary energy wastage and high electricity bills. The system can also control or automate appliances based on user-defined parameters, thereby improving energy efficiency. Furthermore, users can remotely access detailed energy consumption analytics through the IoT platform, which provides insights into load patterns, daily or monthly usage reports, and anomaly detection. Overall, the proposedsystem enables proactive energy management, encourages conservation, and contributes to the vision of smart homes and smart grids. It represents an economical and scalable solution adaptable to both residential and industrial environments.

Abstract In the era of digital transformation, organizations increasingly rely on Business Analytics Capabilities (BAC) to enhance decision-making and improve customer-centric strategies. Customer Relationship Management (CRM) effectiveness is critical for achieving customer satisfaction, retention, and long-term profitability. This study examines the predictive impact of Business Analytics Capabilities on CRM effectiveness, using primary data collected from employees in analytics and customer management functions. A structured questionnaire was administered to 120 respondents across the service and retail sectors Customers. Regression analysis was employed to evaluate the relationship between analytics capabilities and CRM outcomes. The findings reveal that data management capability, analytical skills, and technology infrastructure significantly predict CRM effectiveness. The study contributes to the existing literature by providing empirical evidence of the strategic role of analytics capabilities in strengthening CRM performance. Managerial implications and future research directions are discussed. Keywords: Business Analytics, CRM Effectiveness, Predictive Modelling, Data Analytics Capability, Customer Management, Primary Data

The rapid advancement of technology has significantly transformed the hospitality industry, necessitating the development of innovative solutions that enhance customer experience and operational efficiency. This research report presents the design and development of an Online Hotel Reservation System (OHRS), aimed at revolutionizing the booking process for both consumers and hotel operators. The study begins with a comprehensive analysis of existing reservation systems, followed by a clear outline of the functional and non-functional requirements derived from user feedback and market trends. The design phase encompasses the formulation of a user-centric interface that facilitates easy navigation, real-time availability checks, and streamlined booking processes. Through the application of agile development methodologies, the system was iteratively developed, allowing for continuous testing and refinement. Key features of the OHRS include integrated payment processing, customizable booking options, and a robust backend management system for hotel staff, which collectively enhance user engagement and operational oversight. This report also examines the testing procedures employed to ensure system reliability, usability, and performance under various conditions. Findings indicate that the OHRS significantly improves the efficiency of hotel operations while providing customers with a seamless and satisfying booking experience. Furthermore, the anticipated impact on customer satisfaction and business growth underscores the relevance of digital solutions in the contemporary hospitality market. Ultimately, this research contributes valuable insights into the design principles and technological considerations essential for developing effective online reservation systems in the future. In the era of digital transformation, the hospitality industry is increasingly leveraging technology to improve customer service and operational efficiency. This research report details the design and development of an Online Hotel Reservation System (OHRS), aimed at streamlining the reservation process for guests and enhancing management capabilities for hotel operators. The study begins with an identification of key challenges within traditional reservation systems, including limitations in accessibility, user experience, and data management. Through a structured methodology, the project establishes a robust framework for the OHRS encompassing user interface design, database management, and integration of payment systems. The system was developed following modern software engineering practices, ensuring scalability, security, and responsiveness across various devices. Notable features include real-time room availability updates, user authentication, customizable booking options, and comprehensive backend analytics tools for hotel management. The effectiveness of the system was evaluated through rigorous user testing and feedback collections, which demonstrated significant improvements in user satisfaction and booking efficiency compared to existing frameworks. Findings indicate that the OHRS not only enhances the booking experience for customers but also equips hotel administrators with vital insights to optimize operations and marketing strategies. This research serves as a vital contribution to the ongoing evolution of hospitality technology, providing a reference point for future developments in online reservation systems. By addressing the needs of both consumers and service providers, the OHRS aims to set a new standard in hotel reservation practices.

The longitudinal relationships between Internet adaptability and usage behavior on AI-driven healthcare platforms: A cross-lagged panel network analysis.

Green innovation in a digital transformation context: implications of green social capital and green creativity.

Complex global food chains and rising demand for high-quality halal product requires better and more efficient management. Traditionally, the Halal Assurance System (HAS) and Quality Management System (QMS) have been implemented separately, resulting in overlapping processes and redundant documentation. In the era of digital transformation and Industry 4.0, integrating these systems digitally improves traceability, compliance, and overall organization’s performance. This review aimed to examine current trends, technologies, and research gaps related to the digitalization of integrated Halal and Quality Management Systems (HAS–QMS). A comprehensive literature analysis highlights the role of emerging technologies—such as blockchain, the Internet of Things (IoT), artificial intelligence (AI), cloud computing, and big data analytics—in supporting halal integrity and quality assurance across the food value chain. The findings indicated that digital integration facilitates real-time monitoring, automated documentation, and improved audit readiness. However, challenges persist in areas such as data interoperability, standard harmonization, regulatory acceptance, and the digital readiness of small and medium enterprises. The paper proposed a conceptual framework for the digitalized integration of HAS and QMS, aligning technological capabilities with halal and quality compliance requirements. To align with industry 5.0, future research should standardize models, test performance, and integrate sustainability and ethical standards.

In the era of digital transformation, the utilization of information technology in business activities has become an important factor in improving operational efficiency and effectiveness. Dzikri Baby Shop, a business engaged in the sale of baby equipment, still faces challenges in managing sales transactions and inventory manually, which may lead to recording errors, process delays, and less optimal customer service. This study aims to design and develop a sales information system for Dzikri Baby Shop to support digital transformation in its business processes. The system development method used in this research is Rapid Application Development (RAD), as it enables software to be developed in a relatively short time through an iterative approach and active user involvement. The system developed includes product data management, sales transaction processing, inventory control, and report generation. The testing results using the Black Box Testing method show that all system features function in accordance with user requirements. Therefore, the implementation of this sales information system is expected to improve operational efficiency, accelerate transaction processes, minimize errors in data management, and enhance the quality of customer service.

In the era of digital transformation, vocational high school students must possess strong learning motivation to master essential technical competencies. However, fluctuations in motivation are often driven by how effectively students manage digital literacy, utilize their learning environment, and receive moral support from their families. This study aims to analyze the levels of digital literacy competence, learning environment utilization, parental support, and learning motivation among vocational high school students, while examining the partial and simultaneous influences of these factors on motivation. Employing a quantitative approach with an ex post facto design, the research sampled 150 11th-grade Computer and Network Engineering students from three public vocational schools in Makassar City, selected via proportional random sampling. Data were collected using validated Likert-scale questionnaires and analyzed through descriptive and associative regression techniques. Findings indicate that all variables—digital literacy, learning environment, parental support, and learning motivation—fall within the high to very high categories (65%–80%). Partially, digital literacy (R2=0.585), learning environment (R2=0.262), and parental support (R^2=0.575) significantly affect motivation. Simultaneously, these variables contribute 71.7% to learning motivation (F_count=123.423 > F_table=2.67), with parental support emerging as the most dominant factor (β=0.781). This study underscores that enhancing vocational motivation requires a synergy between digital proficiency and active parental involvement. Consequently, schools should develop integrated digital literacy programs that foster stronger parental participation to optimize student engagement.

With the acceleration of digital transformation, venture companies are emerging as key economic actors capable of responding agilely to technological innovations and market changes. In this environment, entrepreneurial orientation, centered on the founders of venture companies, serves as a crucial driver for securing competitive advantage and achieving sustainable growth. Therefore, this study aims to empirically examine the effects of the core components of entrepreneurial orientation—innovativeness, proactiveness, and risk-taking—on sustainable business performance, encompassing economic, social, and environmental dimensions. In particular, this study analyzes how the level of utilization of government support programs provided in the era of digital transformation moderates this relationship. To achieve this, survey data were collected from executives and key officers of domestic venture companies affiliated with organizations such as the Venture Business Association and the Gyeonggi Venture Business Association. The data collection period was from September 1, 2025, to December 31, 2025. A total of 2,300 surveys were distributed, and 310 were retrieved. After excluding 19 insincere responses, 291 valid samples were used for the final analysis. Frequency analysis and exploratory factor analysis were conducted using SPSS 28.0 to verify the reliability and validity of the data. Subsequently, structural equation modeling (SEM) was performed using SmartPLS 4.1 to derive the research results. The results of the empirical analysis are as follows: First, the sub-dimensions of entrepreneurial orientation— innovativeness, proactiveness, and risk-taking—were found to have a statistically significant positive (+) effect on sustainable business performance. In particular, risk-taking showed the greatest influence, followed by proactiveness and innovativeness. This proves that bold execution capabilities are a key factor for performance creation in the uncertain Digital Transformation environment. Second, the verification of the moderating effect revealed that the level of government support utilization positively (+) moderates the relationship between risk-taking and sustainable business performance.

Lecturer innovation is a strategic factor in improving the quality of learning, research productivity, and competitiveness of higher education institutions in the era of digital transformation. However, empirical studies on the determinants of lecturer innovation are still scattered and have not been comprehensively integrated. This study aims to systematically synthesize international literature on the role of information technology (IT) literacy, self-efficacy, achievement motivation, and knowledge management in enhancing lecturer innovation. The study used a Systematic Literature Review (SLR) approach with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Articles were obtained from the Scopus database for the period 2010–2025. After going through the processes of identification, screening, eligibility, and inclusion, a total of 46 empirical articles were analyzed thematically. The results of the study indicate that IT literacy increases the capacity for digital pedagogical innovation; self-efficacy strengthens the courage to experiment and adapt to change; achievement motivation encourages innovative performance orientation; while knowledge management acts as an institutional reinforcement that integrates individual innovation into the organizational system. This study produces an integrative conceptual framework that positions knowledge management as a structural enabler and psychological factors as proximal drivers of lecturer innovation. These findings provide theoretical contributions and strategic implications for the development of higher education policies based on sustainable innovation.

In recent healthcare settings, digitally based smart environments—such as electronic medical records (EMRs), smart bed systems, and remote monitoring—have been rapidly expanding. As a result, nurses’ digital competencies have emerged as critical factors influencing work efficiency and patient safety. In response to these environmental changes, this study aimed to examine the effects of hospital smart environments and nurses’ digital literacy on nursing performance. Based on a review of prior studies, a research model and hypotheses were developed. The hospital smart environment was conceptualized as comprising a physical environment and an operational environment, while nurses’ digital literacy was examined in terms of technical utilization capability and technology-based collaborative competence. This study not only investigated the direct effects of the hospital smart environment on nursing performance but also further examined the moderating effects of nurses’ customer orientation on the relationship between digital literacy and nursing performance. To conduct this research, a survey was administered to nurses working in hospitals. The results indicated that higher levels of an operational smart environment significantly improved nursing performance. In addition, nurses with higher levels of digital literacy demonstrated enhanced nursing performance. Furthermore, the findings partially confirmed that nurses’ customer orientation strengthened the positive effects of the smart environment and digital literacy on nursing performance. This study provides both practical and academic implications for improving nursing performance in the era of digital transformation.

With the increasing deployment of 5G private networks in smart manufacturing, transportation, and IoT environments, the high performance of these networks has also introduced new cybersecurity challenges. The convergence of Information Technology (IT), Operational Technology (OT), and Communication Technology (CT) significantly expands the attack surface and exposes systems to cross-domain threats such as rogue base station infiltration, distributed denial-of-service (DDoS) attacks, and lateral movement. These attacks threaten data integrity, service continuity, and user privacy. To address these issues, this study applies the MITRE FiGHT threat model to depict representative attack stages—including reconnaissance, intrusion, lateral propagation, and service disruption—and integrates CVSS 3.1 scoring with corresponding mitigation strategies. Based on these components, we construct an analytical workflow capable of quantifying risks, identifying governance deficiencies, and guiding targeted improvements. A prototype Cybersecurity Governance Maturity Model (CSGMM) tailored for 5G private networks is subsequently proposed. The proposed framework incorporates six governance domains—policy, asset management, risk defense, incident response, control practices, and supply chain management—and formalizes a three-tier structure covering strategic, tactical, and operational layers. It also defines twenty-five practice objectives aligned with international standards such as ISO/IEC 27002 and NIST CSF 2.0, improving both applicability and interoperability. Experimental validation was conducted using a Free5GC and UERANSIM testbed to simulate practical attack scenarios, including traffic-based DDoS and endpoint-level exploitation. The results show that the implementation of the governance framework, together with FiGHT-based mitigation strategies, leads to a measurable reduction in CVSS risk scores and attack success probability. These findings demonstrate that integrating governance mechanisms with technical defense measures enhances incident response, strengthens network resilience, and supports continuous security management throughout the lifecycle of 5G private network deployment. The proposed model provides a structured reference for both industry and government seeking to advance 5G cybersecurity strategies. It offers practical value for smart manufacturing and other mission-critical applications, supporting organizations in addressing increasingly complex cyber threats in the era of digital transformation.

This study seeks to identify the combined impact of the dimensions of digital governance (technology infrastructure and operational processes, management and authority, accountability and transparency, and digital risk management) on digital transformation. The digital transformation era represents the "application of digital capabilities to processes, products, and assets" with the aim of "improving efficiency, enhancing customer value, managing risks, and unlocking new revenue opportunities." Similarly, digital transformation impacts not only life but also individual experiences. This study focuses on the mechanisms necessary to digitally transform the interaction between governments and citizens to ensure greater transparency, accountability, and integrity. It contributes to defining the current state of e-governance and digital maturity in government institutions.

Background In the era of rapid digital transformation, the banking sector faces increasing pressure to integrate ethical values and adaptive organizational behaviors to sustain performance and employee well-being. In Jordan, commercial banks are navigating technological advancements while maintaining moral and operational integrity, making it essential to understand how organizational morality and improvisation shape digital performance and workforce welfare. Objective This study aims to examine the impact of organizational morality and organizational improvisation on digital electronic performance and employee well-being in Jordanian commercial banks. It further explores the moderating role of organizational prestige within these relationships. Method Data were collected from managers, assistant managers, and tellers across 12 Jordanian commercial banks. A total of 261 valid responses were obtained using a purposive sampling technique. The hypotheses were tested using partial least squares structural equation modeling (PLS-SEM). Result Findings indicate that organizational morality and organizational improvisation significantly affect digital electronic performance and employee well-being. Digital electronic performance, in turn, demonstrates a strong positive relationship with employee well-being and serves as a mediating variable between organizational morality, organizational improvisation, and well-being. Furthermore, organizational prestige moderates the relationship between organizational morality, organizational improvisation, and digital electronic performance.

The rapid advancement of digital technology has significantly transformed economic activities and entrepreneurial practices, creating both opportunities and challenges for university graduates. In response to these changes, higher education institutions are expected to foster entrepreneurial attitudes that align with the demands of the digital economy. This study aims to examine the effect of economic education on students’ entrepreneurial attitudes in the era of digital transformation. A quantitative research design was applied using a survey method involving undergraduate students who had completed economic education courses. Data were collected through structured questionnaires that measured learning outcomes in economic education and entrepreneurial attitudes, including creativity, innovation, risk-taking, opportunity recognition, and adaptability to digital technologies. The data were analyzed using descriptive statistics and multiple regression analysis to assess the relationship between economic education and entrepreneurial attitudes. The findings show that economic education has a positive and significant effect on students’ entrepreneurial attitudes. Students with higher levels of economic understanding tend to demonstrate stronger entrepreneurial characteristics that support innovation and adaptability in digitally driven business environments. These results indicate that economic education is important not only for strengthening students’ economic knowledge but also for developing the entrepreneurial mindset needed to respond to the challenges of the digital era. The study also reinforces the link between economic education and entrepreneurship in the setting of digital transformation. In practical terms, the findings may serve as a reference for educators and policymakers in designing economic education programs that are more oriented toward entrepreneurship in higher education.

Access to communication is a fundamental right of every individual, including the speech-impaired community, which often faces limitations in social interaction. This study designs a real-time system that transforms sign language alphabet gestures into speech by utilizing Computer Vision and Deep Learning technologies. A sign language alphabet dataset is processed using a Convolutional Neural Network (CNN) to recognize visual hand patterns representing letters A–Z. The trained model is then integrated with OpenCV and Mediapipe for real-time hand gesture detection and connected to a speech synthesis engine so that the recognized letters can be automatically spoken. The results demonstrate the system’s potential as a basic communication bridge that supports digital inclusion for the speech-impaired community. From a sustainable development perspective, this innovation is relevant to SDG 4 (Quality Education) and SDG 10 (Reduced Inequalities), as it enables more equitable, inclusive, and sustainable social interaction in the era of digital transformation. This innovation can also serve as a foundation for further development toward an automatic sign language translator capable of recognizing full words and complete sentences. Consequently, this system has strong potential to become a practical solution for ensuring equal access to communication across various sectors, including education, public services, and the workplace.

Background: Parkinson’s disease presents unique challenges in healthcare delivery due to its symptom complexity and the progressive nature of the condition, requiring coordinated care across multiple healthcare disciplines. In response to the limitations of traditional, fragmented care models, the healthcare system is increasingly shifting towards integrated, Value-Based Care (VBC), highlighting the need for innovative quality monitoring frameworks and real-world evidence supported by digital health technologies. Effective monitoring and evaluation are thus essential to ensure evidence-based program design that addresses the complex needs of all people with Parkinson's (PwP). Here, we present the process and results of the quality monitoring framework by outlining key domains, indicators and data sources to monitor equity and the quality of person-centred and digitally-enabled integrated care in Parkinson’s. Approach: The quality monitoring and evaluation framework for Parkinson's Disease (Réseaux de Compétences: Maladies Neurodégénératives) was developed in a multiphased approach in collaboration with the Ministry of Health as part of a national initiative to implement integrated care models for various health conditions across Luxembourg. The development of the PD-relevant indicators involved a consultative process within a multidisciplinary team, including neurologists, nurses, allied healthcare professionals, patient representatives, and researchers of various backgrounds and expertise. A core set of indicators was proposed for baseline data collection and annual project monitoring, along with key patient characteristics and various data sources to support real-world monitoring. A feasibility study was conducted to determine the digital readiness among healthcare professionals and a minimal set of indicators that could be collected in the first year. Results: A core set of 20 indicators for digitally enabled integrated care was proposed, directly contributing to the healthcare system's quadruple aim: enhancing population well-being, improving patient and healthcare provider experiences, and reducing costs. These indicators were organised into four primary domains: (1) Patient Outcomes, Experiences, and Capabilities, encompassing clinical data, quality of life, patient-reported outcomes and experience, and empowerment; (2) Process Outcomes, covering healthcare utilisation, adherence to clinical guidance, and digital engagement among patients and healthcare professionals; (3) System Structure and Capacity, which includes healthcare professional training and competencies, operational and coordination costs, and related infrastructure; and (4) Healthcare Professional Work Satisfaction, focused on job fulfilment and well-being. Patient characteristics included measurements of sociodemographic status, digital (health) literacy and disease severity. Real-world data sources included patient and healthcare professional questionnaires via digital apps, electronic health records, health claims, and administrative data. Initial assessment showed challenges in adopting digital health technologies and a need to implement fewer indicators at the first stage. Implications: This framework presents a comprehensive model for quality monitoring in digitally enabled, integrated care for Parkinson’s disease, offering essential indicators to evaluate and optimise VBC. By focusing on patient-centred outcomes and integrating digital health technologies, the framework aligns with healthcare systems' goals to provide effective, efficient, equitable, timely, and safe services in an era of digital transformation. While the model has significant potential, challenges remain in ensuring the feasibility of consistent data collection, integration across various healthcare providers, and maintaining engagement with digital health technologies.

Type of the article: Research ArticleIn the era of digital transformation, organizations are increasingly integrating digitalization and artificial intelligence to enhance employee behavior and organizational outcomes. This study examines the associations among digital leadership, AI-based performance assessment, employee empowerment, work engagement, and organizational performance in a Chennai-based IT company in India. Specifically, the study investigates the direct and indirect effects of digital leadership and AI performance assessment on organizational performance through employee empowerment and work engagement. Data were collected from 373 IT employees using an online survey conducted between June and August 2025 and analyzed using partial least squares structural equation modelling (PLS-SEM). A stratified random sampling technique based on organizational job levels (entry, mid, and senior) was adopted to ensure adequate representation of hierarchical positions within the organization. Hypothesis testing revealed that digital leadership and AI performance assessment significantly enhance employee empowerment and work engagement (β = 0.490, 0.415, 0.527; p < 0.001), which in turn positively influence organizational performance (β = 0.383, 0.477, 0.195, 0.287; p < 0.001, 0.033). Furthermore, employee empowerment and work engagement significantly mediate the relationships between digital leadership, AI performance assessment, and organizational performance (β = 0.135, 0.199, 0.265, 0.244; p < 0.001). In addition, a multi-group analysis was conducted to examine differences across employee hierarchical levels. The findings highlight that transformations in modern workplaces and digitalized HR practices contribute to organizational performance across the workforce, while ensuring that employees from different hierarchical levels are adequately represented in the sample.

This special issue of mediAzioni, titled Advanced Technologies, Methods and Materials for Human Health and Well-Being, emerges from a shared commitment to exploring the human, cultural, and communicative dimensions of healthcare in an era of technological transformation. It reflects the research and dialogue developed within the Tuscany Health Ecosystem (THE) Project “Advanced Technologies, Methods and Materials for Human Health and Well-Being”. Since 2022, the editors and the majority of the contributors to this volume have been conducting research in THE’s Spoke 3, subprojects 3 and 4, which focus respectively on human-centered innovation in health technologies and on communication, mediation, and care practices in intercultural and interdisciplinary contexts. Other contributors collaborated within Spoke 5, which focuses on technology transfer, dissemination of research results and targeted training for personnel involved in the ecosystem of healthcare. The issue brings together perspectives from linguistics, translation and interpreting studies, anthropology, philosophy, ethics, and health communication, aiming to foster an integrated reflection on health as a relational, transcultural, and socially embedded practice. While advanced technologies increasingly mediate interactions between doctors, patients, and caregivers, the human dimension of care, as rooted in language, narrative, and empathy, remains indispensable. This collection thus invites readers to reconsider health and well-being through a transcultural and interdisciplinary lens that bridges scientific, humanistic, and ethical approaches.