This paper proposes a heuristic approach for managing complex construction projects. The tool incorporates with Primavera P6 EPPM and Synchro 4D, enabling proactively clash detection and resolution of spatial conflicts during concurrent tasks. Additionally, it performs resource verification for sufficient allocation before task initiation. This integrated approach facilitates the generation of conflict-free and feasible construction schedules. By adhering to project constraints and seamlessly integrating with existing industry tools, the proposed solution offers a comprehensive and robust approach for construction project management. This constitutes to our knowledge the first dynamic digital twin for the delivery of a complex project.

Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios.

Proposed study aims to investigate the digital transformation of Greek small and medium enterprises (SMEs), with a particular focus on their digital maturity and the strategic and organizational factors contributing to digital transformation. The research issue of digital transformation is attracting considerable interest among academics and business practicians since COVID-19 accelerated the procedure of implementing Industry’s 4.0 principles all over global economies. Quantitative research on 147 Greek SMEs revealed most important issues on how these businesses implement digital transformation, factors accelerating or decelerating the process, barriers and expected outcomes. Research enlights how SMEs face the transition to digital business and reveals the factors that can facilitate the whole process. Results can be valuable for stakeholders enabled to digital transformation process from both business and academic point of view, while there exist aspects that can contribute to policy makers / motivation developers in state’s level as well.

Surface water distribution extracted from remote sensing data has been used in water resource assessment, coastal management, and environmental change studies. Traditional manual methods for extracting water bodies cannot satisfy the requirements for mass processing of remote sensing data; therefore, accurate automated extraction of such water bodies has remained a challenge. The histogram bimodal method (HBM) is a frequently used objective tool for threshold selection in image segmentation. The threshold is determined by seeking twin peaks, and the valley values between them; however, automatically calculating the threshold is difficult because complex surfaces and image noise which lead to not perfect twin peaks (single or multiple peaks). We developed an operational automated water extraction method, the modified histogram bimodal method (MHBM). The MHBM defines the threshold range of water extraction through mass static data; therefore, it does not require the identification of twin histogram peaks. It then seeks the minimum values in the threshold range to achieve automated threshold. We calibrated the MHBM for many lakes in China using Landsat 8 Operational Land Imager (OLI) images, for which the relative error (RE) and squared correlation coefficient (R2) for threshold accuracy were found to be 2.1% and 0.96, respectively. The RE and root-mean-square error (RMSE) for the area accuracy of MHBM were 0.59% and 7.4 km2. The results show that the MHBM could easily be applied to mass time-series remote sensing data to calculate water thresholds within water index images and successfully extract the spatial distribution of large water bodies automatically.

The changing of banking customer preferences after Covid-19 pandemic encourages banking transformation. Neobanks emerges as innovation in banking that enter the market to compete with the incumbent banks by offering new digital experience for customer. This paper analyzes the neobanks which transformed from traditional bank, specifically its efficiency after they implement digital transformation. Efficiency is measured by using Stochastic Frontier Analysis (SFA). This study also uses Pooled Mean Group (PMG) of Panel ARDL (Autoregressive Distributed Lagged) to investigate digital transformation as one of determinants neobank’s efficiency, as well as to examine the existence of short-term and long-term relationship between digital transformation and efficiency. The SFA results show that the efficiency of the neobanks increase after they carry out digital transformation. Furthermore, from PMG Panel ARDL, it can be concluded that digital transformation is one of determinant of efficiency and there is longevity relationship between digital transformation and efficiency. In the short run, digital transformation has a significant and negative correlation with efficiency and in the long run, it has a significant positive relationship because initially digital transformation cost for operational and risk management innovation decreases the profit efficiency, but afterwards it increases the profit efficiency.

Este artigo apresenta o processo de implementação da Hiperautomação no setor público brasileiro, especialmente no âmbito da Procuradoria Geral do Estado de Pernambuco. Devido à atual economia competitiva, a demanda por soluções inteligentes e confiáveis está aumentando. Para atender a essa necessidade, tecnologias inovadoras que abordem a complexidade inerente à transformação digital são desejáveis. Assim, as organizações públicas têm buscado aplicar a automação de processos para atender às necessidades dos cidadãos por um serviço mais eficiente e eficaz. No entanto, podemos observar que a automação pura e direta é apenas parte do processo de transformação. Algumas das razões que explicam a necessidade de ir além da automação de processos e alcançar a hiperautomação é a abundância de dados de várias fontes e tipos. Tudo deve estar dentro dos padrões legais, ressaltando que toda tecnologia deve estar alinhada com transparência e auditabilidade para ser incorporada com segurança. Para a experimentação, foi utilizado o método de pesquisa-ação com a aplicação da Hiperautomação, visando aumentar a arrecadação e a economia do Estado na Procuradoria Geral do Estado de Pernambuco.

In the coming years, digital applications and services that continue to use the country's native cloud systems will be huge. By 2023, that will exceed 500 million, according to IDC. This corresponds to the sum of all applications developed in the last 40 years. If you are the one you answered, yes! This article is for you!

With the exponential growth of internet usage and mobile devices, the volume of user behavior data has surged, offering significant opportunities and challenges for businesses. This paper explores user behavior analysis in the context of big data, highlighting its pivotal role in driving innovation and strategic decision-making. We begin by discussing the importance of user behavior data and reviewing technological advancements such as machine learning, natural language processing, and data mining that enable deep insights into user behavior. Through a case study on linguistic analysis in livestreaming e-commerce, we demonstrate how text-mining techniques can correlate linguistic characteristics with sales performance, providing actionable marketing insights. We further explore the broader business applications, including personalized recommendations, user portraits, product design, and strategic decision-making. Technical and ethical challenges, such as data integration, privacy concerns, and algorithmic bias, are also addressed. The paper concludes with a discussion on future trends, such as AI and ML advancements, edge computing, IoT proliferation, and ethical AI frameworks, poised to shape user behavior analysis. Our findings underscore the transformative potential of user behavior data in enhancing business operations and user experiences, providing recommendations for future research focused on real-time analytics, privacy-preserving techniques, and ethical implications of data-driven decisions.

Digital transformation provides new drivers for economic performance growth in enterprises, but can it further improve ESG performance and support sustainable development? Based on the perspective of resources and capabilities, this paper uses the relevant data of 1588 listed companies in strategic emerging industries from 2011 to 2021 to study the differential impact of digital transformation on enterprise ESG performance, and further tests the mediating role of absorptive capacity and the moderating role of regional digital level. The research results indicate that: firstly, digital transformation of enterprises can positively promote the improvement of ESG performance; Secondly, there are significant differences in the impact of digital transformation on the ESG performance of enterprises in terms of property rights and industry characteristics. The ESG performance of state-owned enterprises and high-end equipment manufacturing enterprises is more sensitive to digital transformation; Thirdly, absorptive capacity plays a mediating role between digital transformation and corporate ESG performance; Fourthly, the positive regulation of regional digital level promotes the ESG performance of enterprises through digital transformation. The research conclusion is based on a digital perspective, providing relevant insights for improving the ESG performance of strategic emerging industry enterprises and expanding their ESG development paths.

Digital transformation of companies is often referred to as a future track and a strategic path towards ensuring the survival and sustainability of companies. In this paper, we aim to outline this transformation process in Tunisian companies and identify its driving factors and finally explain its key success factors. To this end, we examine a sample of 70 companies operating in all economic sectors. The results indicate that such a digital strategy has a significant and a positive effect on the success of digital transformation of companies. Leadership has an effect at a low level of digital maturity. On the other hand, corporate culture does not have a significant effect on digital transformation. These results contribute to explaining this emerging phenomenon by focusing on the driving factors as well as the factors that contribute to its success.

The COVID-19 crisis has emphasized the importance of digital transformation of SMEs and has served as an accelerator. Digital transformation empowers SMEs to rethink the way they make decisions and apply technology in meaningful and sustainable ways. The purpose of this study is to explore the role of digital transformation, fostered by COVID-19, and its potential contribution to the sustainability of SMEs. This paper examines a new conceptual framework designed on resource-based theory perspectives by using a survey data of 246 SMEs in Latvia and employing mediation analysis. The main findings of the study are that digital orientation and digital capability have direct positive effects on digital transformation; higher levels of digital orientation and capability, mediated by digital transformation, lead to higher revenue; as well as a more sophisticated business model in the case of digital orientation. Even if we cannot confirm that higher digital capability, mediated by digital transformation, leads to a more sophisticated business model, we found statistically significant direct effects from digital capability to digital transformation and from digital transformation to business model. These findings can be useful for policymakers, managers and practitioners to clarify how digital orientation, digital capability intermediated through digital transformation affect sustainability of SMEs.

The digital transformation of the global economy presents new challenges and opportunities for businesses and nations. With the growing role of digital technologies and the dynamic changes in the structure of the economy, there is a need for a better understanding of the processes shaping contemporary enterprises. This article analyzes the impact of digital transformation on businesses and the global economy, identifying key areas where these changes are most evident. As part of this discussion, a modification of the “9 Pillars of Industry 4.0” concept is proposed, incorporating solutions that have emerged since its inception. The resulting model, “11 Pillars of Industry 4.0,” enabled the analysis of the “born digital” and “born global” concepts, highlighting their numerous common features in the context of the digital transformation of economies. This became the basis for proposing a new concept: “born digibal” — enterprises that from the outset operate in multiple markets, with their main sphere of activity based on digital solutions.The conclusions of the article provide an assessment of the changes occurring in businesses and the global economy in the digital age. They suggest that the dynamics of globalization in the 21st century will be shaped by complex interactions between digital transformation and the actions of businesses and nations, requiring flexibility and innovation in adapting to new economic realities.

First came the digital banks without offices, and then the “fintech” companies that combined technology and finance to achieve greater efficiency and new business models. Later was the turn of transport and logistics, understood as the sector whose economic activities derived from the transport of people and goods, while on the one hand, it has had a new spring thanks to factors such as online sales. Then, perhaps catalyzed by the pandemic scenario, the turn has reached education, where years ago was only a complement of traditional education, now is a central part with multiple modern methodologies. Numerous studies exist about using or implementing these e-learning procedures, but technical analysis is barely found in references. This work aims to analyze the digital transformation of the main sectors of our society, including finances, transport, and education, together with several examples of how technology was helping to fight the pandemic scenario.

There is no doubt that the COVID-19 pandemic has greatly affected the higher education sector in South Africa. However, academics and students’ preparedness and competencies have been considered a major conundrum concerning the integration of online learning in the classroom. The purpose of this paper is to examine the academics’ perspectives on a blend of a metaverse in the higher education sector in the post-COVID-19 era at one of the South African rural-based universities. A survey was administered in 2022 to ten (10) academics and the reports were consulted from four (4) E-learning practitioners to solicit their perceptions regarding the use of online learning and contact classes in the post-pandemic era. A qualitative research approach was employed to comprehend the challenges encountered in a historically disadvantaged rural-based university in Limpopo Province. The findings of this study show that there are major issues and challenges impeding the establishment of effective e-learning education. Consequently, technological continuity as a new normal in the post-COVID-19 era appears to be unattainable due to the challenges encountered by the students and lecturers. The use of Learning Management Systems among academics seems to have deteriorated due to the adjustments in COVID-19 rules and regulations. Furthermore, the findings revealed that most academics believed that contact learning remains irreplaceable throughout generations and ages as some are technophilic. This paper recommends that the incompetence and technological resistance and reluctance in adopting transformative pedagogies may not be used as a stumbling block to consider e-learning as a viable option to discontinue online education.

Exopolysaccharide (EPS)-producing bacteria were isolated from the intestines of freshwater fish as prebiotics. Among the isolates, Bacillus sp. P1 was the potent EPS producer with a high EPS production and was then identified as Bacillus subtilis P1 based on 16S rRNA and biochemical characteristics. The produced microbial EPS was characterized its functional groups by FTIR, showing a 90.20% correlation to inulin while the EPS molecular weight was approximately 105 Da with a high PDI (>1.5). Moreover, the EPS from B. subtilis P1 was assessed for prebiotic properties by growing probiotic bacteria and the significant cell growth occurred in sugar-free TSB with 0.5% EPS (p<0.05). EPS exhibited approximately 55.37% DPPH radical scavenging at 20 mg/mL and inhibited certain fish pathogens at 400 μg/mL (10-13 mm inhibition zone). EPS cytotoxicity was confirmed (<1% hemolysis) prior to use as immunobiotics in Nile Tilapia (Oreochromis niloticus) diet supplementation. Nile tilapia growth and immune response were monitored after feeding with control (C): Basal Diet (BD) and treatment (T): BD + EPS 5 g/kg. In the T-group, WG, %SGR, and ADG significantly increased compared to control (p<0.05) by week 4th, with no negative effects on blood chemistry parameters. Lysozyme activity and respiratory burst activity in T-groups were significantly higher than control (p<0.05), with a 50% RLP survival rate of Nile Tilapia challenged with Aeromonas veronii in the T-group within two weeks. On the other hand, for the proximate analysis revealed higher protein content in the T-group. These findings suggest that EPS from B. subtilis P1 in fish diets efficiently supports fish growth and stimulates innate immune responses in Nile Tilapia, thus showing potential as the immunobiotics for aquaculture.

This study aims to propose a method for constructing basic digital twin data in South Korea by adhering to international standards and utilizing publicly available data. Specifically, the study focuses on designing and proposing a digital twin data model for buildings, which are the most applicable among the basic digital twin data. To achieve this, the first section provides essential background information, introduces concepts and requirements related to digital twin basic data, and offers a brief overview of CityGML. The second section explains the methodology and data used in this study. The third section presents the main findings: the selection of public data (buildings) for constructing digital twin basic data, the mapping process using CityGML, and the creation of UML diagrams. The fourth section discusses these findings. Finally, the conclusion and suggestions for future research are provided. This approach enhances the accuracy of building-related digital twin data and supports the use of digital twin services in both public and private sectors by enabling various spatial analyses.

Breast cancer remains a global health problem requiring effective diagnostic methods for early detection in order to achieve WHO’s ultimate goal of breast self-examination (BSE). A literature review indicates the urgency of improving diagnostic methods and identifies thermography as a promising, cost-effective, and non-invasive and adjunctive and complementary detection method. This research explores the potential of using machine learning (ML) techniques, specifically Bayesian Networks (BN) combined with Convolutional Neural Networks (CNN) to improve breast cancer diagnosis at early stages. Explainable artificial intelligence (XAI) aims to clarify the reasoning behind any output of artificial neural networks based models. The proposed integration adds interpretability of the diagnosis, which is particularly significant for a medical diagnosis. We have constructed two diagnostic expert models. In research model A, combining thermal images after XAI process together with medical records, an accuracy of 84.07% has been achieved, while model B, that includes also CNN prediction, achieved an accuracy of 90.93%. These results demonstrate the potential of XAI to transform breast cancer diagnosis, increasing accuracy and reducing the risk of misdiagnosis.

There are clear signs that the COVID-19 pandemic has had a straining impact on the higher education sector in South Africa and the far reaches of the world. This chapter examines (Science, Technology, Engineering, Mathematics) STEM lecturers’ experiences with digital assessments and perspectives on the continuity of digital transformation in the higher education sector in the post-COVID-19 era at one of South Africa’s rural universities. In 2022, a survey of ten (10) lecturers was conducted and the reports were also consulted by four (4) e-learning practitioners to obtain their views on the use of online learning and contact courses in the post-pandemic period. A qualitative research approach was used to understand the puzzling issues faced by STEM lecturers at a previously disadvantaged institution in Limpopo Province. The results of this study show that there is a puzzling conundrum that hinders the integration of effective online learning. Furthermore, online education as the new normal in the post-COVID-19 era seems unattainable due to extremely complex and difficult-to-understand mathematical symbols. Software and tools proved to be limitations in terms of establishing an interactive learning environment for students and lecturers on online learning platforms. Lecturers’ use of learning and teaching platforms deteriorated due to numerous unknown challenges, but also the tightening of COVID-19 restrictions. Furthermore, the results showed that science and mathematics lecturers believe that contact learning remains irreplaceable across generations and eras as some are not technology enthusiasts. This paper recommends that the lack of technological systems as well as technological resistance and reluctance to adopt transformative pedagogy and innovative practices, should not be viewed as a thorny issue to end the dominance of online education in STEM courses. Keywords: connectivism, Learning Management Systems, affordances, technological resistance, Moodle, transformative pedagogy

Resource and capacity constraints are common challenges for suppliers in digital transformation. This study examines the impact of government subsidies on supplier digital transformation from the perspectives of supply chain diffusion and the local government competition, with supply chain relationship data from A-share listed companies spanning 2010 to 2021. The findings indicate that: government subsidies promote suppliers to expand their diversified downstream customer base, which will boost supplier digital transformation. However, the spillover effect within the same region is higher than that across regions. The heterogeneity analysis results indicate that the effect of government subsidies becomes increasingly apparent as the opportunistic risk escalates; conversely, the impact of such subsidies is considerably constrained by heightened systemic risk. The higher the human capital of suppliers and the smaller the digital divide between suppliers and customers, the better the effect of suppliers receiving innovation diffusion. Further analysis reveals that local government subsidies to upstream suppliers reduce the proportion of downstream local customer purchases outside the jurisdiction, thereby hindering cross-regional spillover effects. This article enriches the research on the promotion mechanism of enterprise digital transformation and the efficiency loss of market segmentation, providing some reference for optimizing local government subsidy behavior.

Cybercrime Threat Intelligence (CTI) allows us to change our actions from reactive to proactive in the fight against threat actors, and to make more informed, data-driven security decisions. Thus this study proposes the practical implementation of CTI in the Arab world. This study focuses on strengthening CTI by integrating Indicators of Compromise (IoCs) and collecting realistic security alerts from honeypot systems and open source intelligence. The collected data is stored in the Malware Information Sharing Platform (MISP), an open source platform that allows users to create and share IoCs with other organizations while staying informed about new threats. It features an intuitive interface for data analysis and threat identification, facilitating sharing, storage, and correlation of IoCs. Therefore, we leveraged MISP to generate IoCs based on the collected data and analyzed the results to identify potential cyber threats. The implemented platform aims to provide organizations with actionable information to prevent, detect and respond to cybercrime. This study presents a practical approach to strengthening CTI in the Arab world and provides an opportunity to strengthen the region’s security posture.

Digital transformation is revolutionizing supply chain management, profoundly influencing marketing effectiveness across industries. This qualitative study explores the impact of digital transformation on supply chains and its implications for marketing strategies. Through in-depth interviews, case studies, and document analysis, the research elucidates how digital technologies such as IoT, blockchain, AI, and advanced analytics are reshaping supply chain dynamics. Key findings highlight enhanced supply chain visibility, improved decision-making capabilities, and strengthened collaboration among supply chain partners. These technological advancements empower organizations to optimize inventory management, anticipate market trends, and deliver personalized customer experiences, thereby enhancing operational efficiency and customer satisfaction. Furthermore, the study underscores the role of digital transformation in promoting sustainability and ethical practices within supply chains. Technologies like blockchain facilitate transparent and traceable supply chains, supporting initiatives for responsible sourcing and environmental stewardship. Despite the transformative benefits, the study identifies challenges including significant investment requirements, integration complexities, and data security concerns. Strategic approaches to technology adoption, robust cybersecurity measures, and employee training are essential for overcoming these challenges and maximizing the benefits of digital transformation. Overall, this study contributes to understanding how digital transformation drives innovation in supply chain management and enhances marketing effectiveness. By embracing digital technologies and aligning supply chain strategies with marketing objectives, organizations can navigate competitive landscapes, meet evolving consumer expectations, and achieve sustainable growth in the digital era.

Digital transformation systems generate a substantial volume of data, creating opportunities for potential innovation, particularly those driven by artificial intelligence. This study focuses on the intricate relationship between artificial intelligence and innovation as foundational elements in the digital transformation framework for sustained growth and operational excellence. This study provides a holistic perspective on the cultivation and pillars of AI-powered innovation, highlighting their pivotal role in revolutionizing industries, including healthcare, education, finance, manufacturing, transportation, and agriculture. The work emphasizes the key pillars essential for fostering AI-powered innovation, including monitoring performance measurement to use the power of the present, continuous learning and innovation, data analytics and insights, predictive analytics, and innovative product development. This study investigates how these pillars serve as the foundation for groundbreaking advancements, driving efficiency, enhancing decision-making processes, and fostering creativity within organizations. This study explores the significance of continuous learning, interdisciplinary collaboration, and industry partnerships in nurturing a thriving AI-powered innovation ecosystem. By understanding and harnessing these fundamental elements, businesses can navigate the complexities of the digital age, fostering innovation that not only optimizes processes but also enhances the overall human experience, ushering in a new era of technological excellence and societal progress.

The purpose of this study is to bring out a scientometric analysis of the advances of digital transformation in the global banking sector. For this, a background search of the term "Digital Transformation Banking " has been brought out from the Web of Science (Wos) Database, covering records published between 2002 and 2023. A set of 312 publications was identified, which mainly includes a set of 264 academic articles, 29 articles published without cataloging a specific volume of a journal, 9 articles under review, 5 editorial material documents, 4 conference articles, and 1 document is a book chapter. Nowadays the scientific production is low but showing growing development presenting 123 publications and 7,754 citations for the year 2022. Among the scientific production, 43 publications from the Republic of China (8.8%) and 35 publications from Ukraine (7.8%) stand out. ,2%). It is also worth mentioning that the most relevant authors on this topic are Ricardo Palomo, professor and dean of San Pablo University, and Robert J Kauffman, professor at Singapore Management University in terms of influence. Main institutions in terms of scientific production, mainly: the Massachusetts Institute of Technology (MIT) of the United States and the Russian Academy of Sciences of Russia.

This case study explores the transformative effects of emergent digital technologies, particularly augmented reality (AR), on literacy engagement and reading for pleasure. The United Arab Emirates Ministry of Education implemented the UAE Storytime programme, utilising AR to create an interactive literacy experience for young learners. The programme incorporated bilingual episodes, featuring stories based on local and global contexts, with 3D objects and interactive games to enhance understanding. The results indicate a positive correlation between the use of AR and literacy engagement. A high proportion of learners scanned the QR codes to access the AR objects, which enhanced their engagement and understanding of the stories. The programme fostered a learner agency, engaged family members in the learning process, and established a sense of global community. The study recommends the integration of similar initiatives into the curriculum to promote collaboration, creativity, and holistic learning experiences.

The management of a cultural change in higher education is becoming necessary and unavoidable: changes in teaching, changes in research and changes in governance. Digital transformation is an essential part of many countries’ modernisation agendas. The aim of this non-experimental, descriptive, survey-based study was to explore the perceptions of university students toward the digital transformation that took place in university teaching as a consequence of COVID-19. The specific objectives proposed were to: a) analyse the perceptions of students toward digital transformation in university teaching; b) determine the valuation of students about the digital transformation that occurred in university teaching as a result of COVID-19; and c) explore the resources (hardware-software), professional collaboration, digital pedagogy and student empowerment (motivation) with respect to digital education and the recent changes in university teaching due to the pandemic. The results show that a large number of items are positively correlated. In conclusion, further research should delve into motivation, collaboration, reflective experience sharing, self-learning and initiatives that promote the development of competences in future teachers.

Affective computing has emerged as a prominent technology in recent years, driven by the increasing demand for machines capable of understanding and interpreting emotions. The COVID-19 pandemic has intensified this need by reducing physical interactions and increasing reliance on digital communication, which often involves complex emotional exchanges. To enhance social interactions by identifying and addressing negative emotions, an interactive emotion-perception system based on multi-sensing interfaces and affective computing has been proposed. Integration of gesture, voice, and brainwave sensors is employed to provide users with a rich sensory experience and immersive environment. Design principles and system development processes are derived from the affinity diagram method and prototype development approach. The proposed system was constructed, and experiments were conducted with 60 participants. Interpersonal interaction scenarios were explored to understand emotional transmission mechanisms, aiming to enhance user satisfaction and positive interpersonal experiences. Data were collected through indirect observation and questionnaires, followed by evaluation and statistical analysis. Findings include: (1) The emotion-perception system enhances positive interpersonal interactions by identifying 36 distinct emotions, with overall user emotions being positive but exhibiting notable individual variations; (2) The system effectively stimulates positive emotions and strengthens interpersonal interactions, with affective computing technology fostering enjoyable emotional resonance and high-quality relationships; (3) A positive correlation was observed between semantic analysis and brainwave metrics of relaxation and concentration, showing increased concentration and enhanced positive emotional interactions during message transitions; and (4) The multi-sensing and affective computing technology provides a pleasant and relaxing emotional journey, with interactions shifting from enthusiasm to comfort, demonstrating the system’s capacity for a satisfying and enjoyable experience.

Pardosa pseudoannulata is one of the most common wandering spiders in agricultural fields and a potentially good bioindicator for heavy metal contamination. However, little is known about the mechanism by which spiders respond to heavy metals at the molecular level. In this study, high-throughput transcriptome sequencing was employed to characterize the de novo transcriptome of the spiders and to identify differentially expressed genes (DEGs) after cadmium exposure. We obtained 60,489 assembled unigenes, 18,773 of which were annotated in the public databases. Ultimately, 3450 cDNA simple sequence repeats were identified and validated as potential molecular markers in the unigenes. A total of 2939, 2491 and 3759 DEGs were detected among the three libraries of two Cd-treated groups and the control. Functional enrichment analysis revealed that metabolism processes and digestive system function were predominately enriched in response to Cd stress. At the cellular and molecular levels, significantly enriched pathways in lysosomes and phagosomes as well as replication, recombination and repair demonstrated that oxidative damage resulted from Cd exposure. Based on the selected DEGs, certain critical genes involved in defence and detoxification were analysed. These results may elucidate the molecular mechanism underlying spiders' responses to heavy metal stress.

The goal of the study presented in this article is to investigate interconnection issues of the Intelligent Electrical Networks (IENs) with the existing ones, as well as the deployment of the appropriate load forecasting demand techniques. The concept of IENs has recently emerged as a potential solution to the global energy problem, due to the more flexible dispersed production units that are involved. In the same context, additional issues, such as the reduction of production-transfer-distribution costs as well as power losses are analyzed as well, via the penetration of Renewable Energy Sources (RESs). To this end, it is necessary to use appropriate advanced infrastructure, where in combination with modern telecommunication networks will make possible the full exploitation of IENs. Moreover, to make the electricity system more efficient, to avoid voltage and frequency imbalance issues and to implement optimal production and consumption planning, the Electric Load Forecast (ELF) is a vital process and plays a key role in the planning as well as in the management of electricity systems. Therefore, recent state of the art approaches in ELF methods are discussed as well.

The chemical stability of tetramethylammonium (TMA) head groups, both with and without the presence of a choline chloride and ethylene glycol-based deep eutectic solvent (DES), was studied using Density Functional Theory (DFT) calculations and ab initio Molecular Dynamics (MD) simulations. DFT calculations of transition state energetics (ΔEreaction, ΔGreaction, ΔEactivation, and ΔGactivation) for key degradation mechanisms, ylide formation (YF) and nucleophilic substitution (SN2), suggested that the presence of DES enhances the stability of the TMA head groups compared to systems without DES. Ab initio MD simulations across hydration levels (HLs) 1 to 5 indicated that without DES, YF dominates at lower HLs, while SN2 does not occur. In contrast, both mechanisms are suppressed in the presence of DES. Temperature also plays a role: without DES, YF dominates at 298 K, while SN2 becomes prominent at 320 K and 350 K. With DES, both degradation mechanisms are inhibited. These findings suggest DES could improve the chemical stability of TMA head groups in anion exchange membranes.

This qualitative research explores the impact of digital transformation on supply chain collaboration, delving into its implications for organizational dynamics, processes, and outcomes. Through in-depth interviews with key stakeholders in diverse industries, insights are gathered into how digital technologies reshape collaboration within supply chains. Findings reveal a shift towards greater connectivity, visibility, and agility enabled by digital platforms and ecosystems, fostering innovation and value creation. However, challenges such as data security, organizational silos, and trust issues hinder the realization of collaborative potential. The study underscores the importance of embracing digital technologies, fostering a collaborative culture, and prioritizing trust and transparency to unlock the full benefits of digital collaboration. Moving forward, future research should focus on exploring evolving dynamics, identifying best practices, and developing frameworks for effective implementation in diverse organizational contexts. By leveraging digital transformation, organizations can enhance their supply chain capabilities, strengthen competitiveness, and drive sustainable growth in an interconnected, digitalized world.

This qualitative research study explores the multifaceted impacts of digital transformation on supply chain processes within contemporary business environments. Through in-depth interviews with industry professionals, the study investigates the implications of digitalization for visibility, collaboration, resilience, and innovation within supply chains. The findings highlight the opportunities presented by digital technologies for enhancing operational efficiency, mitigating risks, and creating value across supply chain networks. However, the study also underscores the challenges and barriers that organizations face in their digital transformation journey, including integration complexity, data security, and organizational culture. The research emphasizes the importance of strategic investments in digital capabilities, change management, and organizational culture to capitalize on the opportunities presented by digital transformation while addressing the associated challenges. By doing so, organizations can build agile, resilient, and competitive supply chains capable of thriving in an increasingly digitalized and interconnected world.

Accelerating the green transformation of the economic development mode and promoting high-quality economic development is an international trend and the general direction. This article utilizes data from manufacturing companies listed on the Shanghai and Shenzhen stock exchanges from 2011 to 2021, based on dynamic capability theory, to explore the impact of enterprise digital transformation on green technological innovation. Furthermore, dynamic capabilities are further subdivided into absorptive capacity, innovative capacity, and adaptive capacity to investigate their mediating role. The research findings demonstrate that enterprise digital transformation has a significant positive impact on green technological innovation. Moreover, enterprise digital transformation positively influences the absorptive capacity, innovative capacity, and adaptive capacity, with these three factors playing a mediating role in the impact of enterprise digital transformation on green technological innovation. The results of the bootstrap test confirm this mediating effect. The conclusions of this study are validated through a series of robustness tests. This research not only provides theoretical support for the impact mechanism of digital transformation on green technological innovation but also offers practical insights for enterprises to achieve sustainable development.

Pore structure characterization and fractal analysis have great significance for understanding and evaluating tight limestone reservoirs. In this work, the pore structure of tight limestone is characterized combined with low-temperature nitrogen adsorption (LTNA) and low-field nuclear magnetic resonance (NMR), and the fractal dimension of tight limestone pore structure is discussed based on LTNA and NMR data. The results indicate that the pores of tight limestone have H3 and H4 types, the pore size distribution (PSD) of H3 type is a wave distribution ranging from 2 to 10 nm, and the PSD of H4 type is a unimodal distribution ranging from 2 to 10 nm. The transverse relaxation time (T2) spectrum of tight limestone shows single peak (DF), double peak (SF) and three peak (TF), and the T2 spectrums for micropores, mesopores and macropores range from 0.1 to 10 ms, from 10 to 100 ms, and greater than 100 ms, respectively. The LTNA fractal dimension of tight limestone (DL) ranges between 2.4446 and 2.7688, with an average of 2.5729, and the NMR fractal dimensions of micropores (DNMR1), mesopores (DNMR2) and macropores (DNMR3) are distributed between 0.3744 to 1.1293, 2.4263 to 2.9395 and 2.6582 to 2.9989, respectively. Moreover, there is no correlation between fractal dimension and permeability and porosity, a negative correlation between DL and average pore radius, a positive correlation between DL and specific surface area, and a positive correlation between DNMR2 and DNMR3 and micropore content, while DNMR2 and DNMR3 are negatively correlated with the content of mesopores and macropores.

Organisations need dynamic capabilities in the ongoing digital transformation to reconfigure knowledge and learning. There is a need to define new concepts and explain mechanisms of relevant factors to build dynamic capabilities. Especially organisations acting in healthcare are caught in a dilemmatic situation. New, digital processes and business models are promising benefits for cost-containment measures and improved patient-centric care, offering digital services. However, investments are needed to benefit. The important question is: how can individual actors in healthcare be motivated to engage in this transformational process to build and reconfigure relevant competences and establish new learning routines? Founded on the essence of existing literature, we assume sustained learning as a relevant dynamic capability to seize and sense competences and reconfigure human capital. This paper answers the call for deeper investigations into the mechanisms in new digitally transformed environments and sectors with less focus on performance and competitive advantages, like public administration or the healthcare sector. Based on previous research, validated in qualitative interviews and quantitative testing, we define the new construct of sustained learning with its subdimensions. By providing measures, we build the ground for further quantitative research.

The main purpose of this study was to design a workplace in the conditions of a digital factory. The goal of this study is to compile a proposal for improving the layout solution in the selected enterprise to reduce material flows between individual workplaces. In addition, the workplace research study uses methods of material flow analysis with the application of visTABLE software support, which serves as a tool for designing production processes. The main result of the study was the reduction of the production time of the beam by 10 minutes and the creation of proposals for solutions for the company. The study helped with the visualization of processes within the digital factory to simplify and clarify processes at the workplace.

Clarifying the impact mechanism of digital transformation of enterprises on the M&A efficiency of listed companies can provide a factual basis for improving capital market regulatory policies. Taking the mergers and acquisitions of listed companies from 2007 to 2021 as a research sample, the influence mechanism of the digital transformation degree of companies on their M&A efficiency was studied. The research results show that the digital transformation of listed companies will improve their M&A efficiency. Digital transformation will reduce the degree of mispricing of stocks of M&A companies, curb conflicts between managers and agents of M&A companies, and improve their M&A efficiency. Further research finds that the promotion effect of digital transformation on M&A efficiency is more significant in non-state-owned companies, with a higher degree of financing constraint and high analyst attention. In the future, regulatory authorities should actively promote the digital transformation of listed companies, curb mispricing and management agency problems in the capital market with digital governance, and improve the efficiency of mergers and acquisitions in the capital market.

This critical review illuminates the transformative potential of Artificial Intelligence (AI) and sensor technologies in the dairy livestock export industry, an area facing mounting pressure for heightened efficiency and sustainability. We rigorously scrutinize the uptake of these novel technologies in identifying 'shy feeders,' automating weight monitoring of individual livestock, and refining cattle enumeration procedures. The investigation unravels their capacity to bolster animal welfare standards, minimize supply chain discrepancies, and amplify operational productivity. Moreover, the research delves into how these innovations may enhance market access and competitiveness in a swiftly shifting global dairy landscape. We further highlight the challenges encountered and future trajectories, providing a strategic framework for technology integration within the livestock export sector. Ultimately, this review underlines the importance of adopting AI and sensor technologies, indicating a shift towards precision digital livestock farming that amalgamates efficiency, animal welfare, and profitability.

This paper presents a study of the 2D roughness profiles on a flat surface generated on a steel work piece, by ball nose end milling, with linear equidistant tool paths (pick-intervals). The milled surface exploration with a surface roughness tester (on pick and feed directions) produces usually 2D roughness profiles having periodical evolutions. These evolutions can be seen as time-dependent signals, describable as a sum of sinusoidal components (the wavelength of each component being regarded as a period). In order to detect a well approximated description of these components, two appropriate signal processing techniques are used in this work: first technique provides a direct mathematical (analytical) description and is based on computer aided curve (signal) fitting (more accurate), the second technique (providing an indirect and incomplete description) is based on the spectrum generated by fast Fourier transform (less accurate). This study can be seen as a way for a better understanding the interaction between the tool and the work piece or to achieve a mathematical description of the machined surface microgeometry in terms of roughness (e. g. its description being a collection of closely spaced 2D roughness profiles).

The contemporary discourse focuses on the fourth industrial revolution and its impact on compa-nies. An explicative, quantitative, questionnaire survey was conducted with managers of 101 companies in Hungary and 54 in Austria. Our study focused on assessing their preparedness for Industry 4.0 and the key factors they take into account when implementing Industry 4.0 initiatives. We also examined whether there is a correlation between the areas in which companies have made improvements to support Industry 4.0. To complement our quantitative analysis, we conducted a series of four semi-structured interviews with two Hungarian and two Austrian companies. The aim of the interviews was to further investigate the digital readiness of companies and to formulate recommendations for companies venturing into the field of digitalisation. A particular focus was put on comparing the digital maturity of these two neighbouring Central European countries, given the significant differences in their digital maturity as shown by the macro indicators of the Digital Economy and Society Index (DESI). Therefore, we conducted a micro-level analysis to investigate the extent to which DESI scores correspond to the digital performance of companies at the orga-nisational level.

Digital Transformation (DT) encompasses profound technological changes. Businesses face the challenge of adapting their activities to the pace of new technologies to increase the efficiency of business processes, improve the customer experience, and create innovative value propositions. This research focuses on how family businesses, which are a relevant part of global economic activity and job creation, address the challenges associated with Digital Transformation. Based on the case of the automotive retail sector in a small European country characterized by the high acceptance of technologies, we use fuzzy-set Qualitative Comparative Analysis (fsQCA) to understand how critical success factors (causal conditions) combine towards adopting technologies representing different perspectives of the DT. The results confirm the existence of multiple configurations based on the importance of Organizational Culture, Change Management, Knowledge, Human Capital, IT Systems, and DT Strategy towards the adoption of CRM, Digital Marketing, and Social Networks technologies, thus helping family businesses to understand the adoption of these technologies leading to organizational success in the context of the Digital Transformation.

Abstract:The digital landscape is rapidly reshaping under the influence of AI-powered cloud-based e-commerce platforms (PCEPs). These platforms leverage the transformative power of AI to personalize customer experiences, optimize pricing strategies, enhance security, and streamline operations, offering businesses a significant competitive edge. However, challenges surrounding data privacy, algorithmic bias, and potential job displacement raise crucial ethical questions regarding widespread AI adoption in e-commerce.This study delves into the untapped potential and ethical considerations of PCEPs, distinguishing itself by proposing practical strategies for responsible development and ethical implementation.Objectives: Investigate the transformative potential of PCEPs: Personalized product recommendations:Analyze effectiveness and potential bias. Dynamic pricing strategies:Explore impact on consumer fairness and market stability. Enhanced fraud detection:Evaluate accuracy and privacy implications. Optimized supply chain management:Assess efficiency gains and job displacement risks. Critically analyze the ethical considerations of AI in e-commerce: Data privacy:Assess current practices and develop mitigation strategies. Algorithmic bias:Identify potential sources and propose solutions for fairer decision-making. Job displacement:Evaluate impacts and propose reskilling initiatives. Propose comprehensive and practical strategies for responsible PCEP development and implementation: Transparency:Enhance user awareness and control over data usage. Fairness:Design AI systems to mitigate bias and uphold consumer rights. Accountability:Establish clear guidelines and oversight mechanisms for ethical AI development.Conclusion: While PCEPs offer a plethora of transformative benefits, their ethical implementation is paramount. By adhering to principles of transparency, fairness, and accountability, businesses can unlock the full potential of PCEPs while minimizing potential risks and shaping the future of online commerce in a sustainable and equitable manner

The new SARS-CoV-2 coronavirus has brought with it an increase in the use of digital platforms and an exponential increase in the number of scientific papers worldwide. The purpose of this study is to show a global overview of the digital transformation from 1975 to the present (2021). The main collection of the Web of Science database was used to retrieve global scientific production on digital transformation. Bibliometric indicators of production, visibility, impact, and collaboration were analyzed to assess research progress on the topic. The results show that digital transformation is a construct of recent development, increasingly relevant and transdisciplinary, with a clear growth after the declaration of the global COVID-19 pandemic.

The paper is based on the scientific outcome of a PhD Thesis. It introduces the generic, model-based, reusable, and extensible conceptual framework to incorporate Facility Management data based within the three-dimensional model-based design and construction of an asset to enable smart applications, which are introduced. The conceptual framework is composed of empirical data from expert interviews, questionnaires, and factual analysis from 13 projects of varied sizes of public and private clients. It shows which phases need which data, who needs them, and which added value can be generated if intelligent data structuring is used at the beginning of the construction project and bridges the gap between requirement and practice. The term “smart application” is introduced.

The ageing transformation of digital health services faces issues of how to distinguish influencing factors, redesign services, and effectively promote measures and policies. In this study, in-depth interviews are conducted and grounded theory applied to open coding, main axis coding, and selective coding to form concepts and categories. Trajectory equifinality modeling clarified the evolution logic of digital transformation. Based on the theory of service ecology, a digital health service ageing model is constructed from the "macro-medium-micro" stages and includes governance, service, and technology transformation paths. The macro stage relies on organizational elements to promote the institutionalization of management and guide the transformation of governance for value realization, including the construction of three categories: mechanism, indemnification, and decision-making. The meso stage relies on service elements to promote service design and realize service transformation suitable for aging design, including the construction of three categories: organization, resources, and processes. The micro stage relies on technical elements to practice experiencing humanization, including the construction of three categories: target, methods, and evaluation. These results deepen the understanding of the main behaviors and roles of macro-organizational, meso-service, and micro-technical elements in digital transformation practice and have positive significance for health administrative agencies to implement action strategies.

With the increasing concern of environment, the energy-efficiency scheduling of manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time can affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer linear programming model for energy-efficient scheduling of flexible flow shop. Accordingly, a new decoding method is developed by considering of the optimization of spindle speed and scheduling scheme simultaneously, and an estimation of distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistical technique with a simplified practical case. In order to validate the proposed method, a case from practical factories is studied, in which the makespan can be shortened by 25.22%, and the consumed energy can be saved by 5.48%. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

The search for growth and sustainability in any organization largely depends on its ability to carry out digital transformation and agility processes. These processes are essential to adapt to an increasingly changing and competitive business environment, allowing companies to be more efficient and effective in creating an unbreakable bond with their customers. This manuscript addresses the importance of detecting and eliminating anti-patterns in organizational culture for a successful digital and agile transformation. During the early stages of the transformation process, prejudices and paradigms rooted in the organization can become an obstacle to change. The goal of this writing is to offer a practical guide for leaders and teams in organizations seeking successful cultural transformation. Effective strategies are proposed to overcome the challenges faced by organizations during this early stage. In summary, the key steps to be followed in the initial stages of digital transformation with agility are described, including the importance of establishing strong leadership, defining a clear vision and scope of the transformational process, among others. A set of recommendations will be provided to overcome recurring anti-patterns, such as lack of stakeholder participation and commitment, lack of proper planning, and confusion of roles and responsibilities.

This study investigates the factors influencing consumers' intentions to purchase overseas travel packages via social media in Thailand, with a focus on consumer trust and perceived risks. A mixed-methods approach was employed, combining qualitative and quantitative research techniques. In the qualitative phase, the Fuzzy Set Delphi Method was applied to gather expert consensus from 19 participants. For the quantitative phase, a survey was conducted among 600 individuals who had experience purchasing overseas travel packages on social media in Thailand. First-order and second-order confirmatory factor analyses were used to examine the relationships between eight identified factors: social media influencer, e-WOM, trust, perceived risk, brand image, rating review, personal attitude, and destination image. The results indicate that trust and perceived risk are the most significant factors affecting purchase intention. Trust plays a crucial role in customers' decision-making processes, while perceived risk influences their tendency to avoid purchasing packages if the risk is perceived as high. The insights gained from this study contribute to the understanding of factors influencing purchase intentions for overseas travel packages through social media in Thailand, providing valuable information for travel agencies to develop effective marketing strategies.

Digital transformation has become a strategic priority for the enterprises, the public organizations, and the cities lately since it addresses the adoption of cutting-edge information and communication technologies (ICT) for optimizing their business processes and for generating new types of value to their end users. Although the Covid-19 pandemic is claimed to be over, it appears that organizations and cities changed their digital transformation strategies that had defined before the outbreak. The aim of this paper is to investigate the type of changes that were caused both to enterprises and cities due to the pandemic. It investigates whether and in which directions it caused business changes and how the smart city strategic objectives were affected due to pandemic, especially in cases where digital transformation strategies were defined before the outbreak. This study uses bibliometric analysis to determine the effects of Covid-19 on businesses and cities. Findings specify strategic changes and important trends, like the migration of the production supply chains to nearby territories. Moreover, this article utilizes the European Intelligent Cities Challenge initiative, in which cities had defined their priorities for their digital transformation before the outbreak. From the analysis of the strategic plans of the involved cities, it is attempted to determine whether, which cities and in which directions they changed their strategies due to the pandemic.

This qualitative research explores the role of digital transformation in procurement through insights gathered from interviews with industry leaders. Digital technologies such as Robotic Process Automation (RPA), Big Data Analytics, Artificial Intelligence (AI), and Blockchain are examined for their impact on operational efficiency, strategic procurement management, supplier relationships, and sustainability practices. Findings reveal that these technologies streamline processes, enhance decision-making capabilities, and improve transparency, thereby enabling organizations to achieve greater agility and competitive advantage. Strategic factors including leadership commitment, strategic investments, and digital skills development emerge as crucial for navigating challenges such as organizational resistance, data security, and integration complexities. The study underscores the transformative potential of digital transformation in procurement, emphasizing its role in driving innovation, fostering sustainability, and aligning procurement practices with broader organizational goals. Looking forward, the research suggests that organizations must continue to innovate and adapt to technological advancements to maintain resilience and leadership in a digital economy. By integrating digital strategies effectively, organizations can enhance operational efficiencies, strengthen supplier relationships, and promote ethical business practices, ultimately positioning themselves for sustained success.

Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light and thermal infrared images was established for analyses, including morphological, thermographic, comb, and behavioural as-sessments, enabling a comprehensive evaluation of the hens' health, behaviour, and population counts. To address the issue of insufficient data samples in the health detection process for indi-vidual and group hens, a dataset named BClayinghens was constructed containing 61,133 images of visible light and thermal infrared images. The BClayinghens dataset was completed using three types of devices: smartphones, visible light cameras, and infrared thermal cameras. All thermal infrared images correspond to visible light images and have achieved positional alignment through coordinate correction. Additionally, the visible light images were annotated with chicken head labels, obtaining 63,693 chicken head labels, which can be directly used for training deep learning models for chicken head object detection and combined with corresponding thermal infrared data to analyze the temperature of the chicken heads. To enable the constructed deep-learning object detection and recognition models to adapt to different breeding environ-ments, various data enhancement methods such as rotation, shearing, colour enhancement, and noise addition were used for image processing. The BClayinghens dataset is important for ap-plying visible light images and corresponding thermal infrared images in the health detection, behavioural analysis, and counting of caged laying hens under large-scale farming.

The optimal structural design is imperative to minimize material consumption and reduce the environmental impact of construction. Given the complexity in the formulation of structural design problems, the process of optimization is commonly performed using artificial intelligence (AI) global optimization, such as Genetic Algorithm (GA). However, the integration of AI-based optimization together with visual programming (VP) in building information modeling (BIM) projects warrants further investigation. This study proposes a workflow by combining structure analysis, VP, BIM, and GA to optimize trusses. The methodology encompasses several steps including: (i) generation of parametric trusses in Dynamo VP; (ii) performing the finite element modeling (FEM) using Robot Structural Analysis (RSA); (iii) retrieving and evaluating the FEM results interchangeably between Dynamo and RSA; (iv) finding the best solution using GA; and (v) importing the optimized model into Revit, enabling the user to perform simulations and engineering analysis, such as life cycle assessment (LCA) and quantity surveying. The feasibility of the proposed workflow was tested on benchmark problems and compared with open literature. The outcomes of this study offer insight into the opportunities and limitations of combining VP, GA, FEA, and BIM for structural optimization applications, particularly to enhance structural efficiency and sustainability in construction.

The information about the BCoV receptors, co/receptors that might be involved in the BCoV entry to the host cells is inconceivable. In addition to that, the roles of other host cell proteases, such as Furin and TMPRSS2, have not yet been well studied during BCoV replication. The current study’s main objectives are to identify some novel BCoV receptors and host cell proteases that might fine-tune the BCoV replication, tissue tropism, and molecular pathogenesis. We used a combination of some in silico prediction, molecular docking, and gene expression profile tools to predict some new BCoV receptors out of some known other coronavirus receptors and some host cell enzymes that are potentially involved in BCoV replication. To achieve these goals, we applied the in-silico prediction tools to check the potential interactions between eh BCoV/S glycoprotein and some known coronaviruses receptors (ACE2, APN1, NPR1, CECAM-1, AXL, and DPP4). We also established some potential models for the interaction between the BCoV/S and some common host cell proteases proved to play important roles in other coronavirus replication cycle (Furin, TMPRRS2, and Cathepsin-L). To confirm our prediction models, we assessed the gene expression profiles of these potential receptors and proteases in some bovine cell lines infected with BCoV using the next generation sequencing and the qRT-PCR assay. Our results show that the NPR1, AXL1 and the ACE2 have the strongest and firm binding affinities to the BCoV-S protein. Our prediction models and gene expression profiles show the potential bindings of the Furin and TMPRSS2 to the BCoV-S polybasic cleavage site (RRSRR|A). Notably, high expression of the host cell Furin was observed in response to both BCoV/Ent and BCoV/Resp infection, whereas TMPRSS2 expression was upregulated in the case of the BCoV/Ent infection, positioning Furin as a common receptor for different BCoV isolates. Further research is needed to study the roles of these top-ranked receptors and host cell proteases in molecular pathogenesis, and tissue tropism.

Plant leaf area index (LAI) is a key characteristic affecting field canopy microclimate. In addition to traditional professional measuring instruments, smartphone camera sensors have been used to measure plant LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5° angle. To validate further the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that both methods can be used to reveal the conifer leaf-growing trajectory. However, the method with the phone oriented vertically upwards always produced better consistency in magnitude with LAI-2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regard to four aspects that can affect LAI: gap fraction, leaf projection ratio, sensor field of view (FOV), and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5° method to overestimate the gap fraction and hence underestimate conifer LAI, especially when tree height is greater than 2.0 m. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio.

The study sought to gauge the impact of COVID-19 pandemic in unleashing digital transformation in the education sector in South Africa. In order to gauge the impact, the study tracked the rate at which the 4IR tools were used by various institutions during the COVID-19 lockdown. Data were obtained from secondary sources, mainly newspaper articles, magazines and peer-reviewed journals. The findings are that, in South Africa, during the lockdown, a variety of 4IR tools were unleashed from primary education to higher and tertiary education where educational activities switched to remote learning (online learning). These observations point to the fact that South Africa generally has, some pockets of excellence to drive the education sector into the 4IR, which has the potential to increase access. Access to education, particularly at a higher education level, has always been a challenge due to a limited number of spaces available. Much as this pandemic has brought with it massive human suffering across the globe, there is an opportunity to assess successes and failures of deployed technologies, costs associated with them, and scaling these technologies to improve access.

The central aim of this paper is to present the Green Web Meter software which is designed to support the measurement of digital sustainability performance of websites and web apps, as well as providing sound based analytic tools for improving their overall quality. We analyzed scientific literature and specifically selected three quality assessment models (E-S Qual, Sitequal, Webqual 4.0) which we used as a starting point for identifying the most crucial aspects for the monitoring activities. A consistent set of Key Performance Indicators (KPIs) suitable for automated tracking has been provided. We finally developed model-based software able to deliver solutions for real-time website performance analysis and reports. We structured the model to offer accurate references for digital sustainability reporting by aggregating the KPIs into an Environmental, Social, and Governance (ESG) framework and computing a comprehensive score for its pillars.

This research explores the profound impact of digital transformation on marketing strategies within the retail sector. As the retail landscape evolves, digital technologies have become central to understanding and engaging with consumers. The study examines how data-driven decision-making, enabled by advanced analytics, has replaced traditional intuition-based methods, leading to more targeted and efficient marketing strategies. Personalization has emerged as a key trend, with retailers leveraging customer data to offer tailored experiences, fostering greater customer satisfaction and loyalty. The adoption of omnichannel marketing strategies is highlighted as crucial for providing a seamless and consistent customer experience across various touchpoints, integrating both online and offline channels. The role of social media as a powerful platform for brand engagement and community building is discussed, emphasizing its importance in modern marketing. Mobile technologies are identified as transformative, offering convenience and enhancing the shopping experience through mobile apps and payment solutions. The research also explores the emergence of new business models, such as subscription services and direct-to-consumer approaches, which have been enabled by digital technologies. Challenges associated with digital transformation, including the need for continuous technological investment, skill development, and data privacy concerns, are also addressed. The study concludes by highlighting the acceleration of digital adoption due to the COVID-19 pandemic and the potential of emerging technologies like artificial intelligence, virtual reality, and blockchain to shape the future of retail marketing. The findings underscore the necessity for retailers to embrace digital transformation to remain competitive and responsive to changing consumer behaviors.

Digital transformation is revolutionizing inventory management practices within supply chains, offering unprecedented opportunities and challenges for businesses worldwide. This study explores the impact of digital technologies on inventory management, focusing on the adoption of IoT sensors, RFID tags, AI-driven analytics, and cloud-based systems. Through a qualitative research approach encompassing interviews with industry professionals and secondary data analysis, the study examines key themes including enhanced inventory visibility, improved accuracy, advanced demand forecasting, and streamlined supply chain collaboration. Findings reveal that digital technologies significantly enhance inventory visibility by providing real-time tracking and data integration capabilities. This facilitates accurate inventory monitoring and decision-making, reducing errors and optimizing inventory levels to meet fluctuating demand effectively. AI-driven analytics and machine learning models emerge as pivotal tools for predictive demand forecasting, enabling businesses to anticipate market trends and adjust inventory strategies accordingly. Additionally, cloud-based systems and electronic data interchange (EDI) foster improved communication and coordination among supply chain partners, enhancing overall operational efficiency. Despite these benefits, challenges such as system integration complexities, high implementation costs, data quality management, cybersecurity risks, and regulatory compliance issues are prevalent. Successful adoption of digital inventory management solutions requires strategic planning, investment in technology infrastructure, and organizational readiness to navigate these challenges effectively. This study contributes to the understanding of how digital transformation reshapes inventory management practices, offering insights for researchers and practitioners alike to leverage digital technologies for enhanced supply chain performance and competitive advantage

The integration of automation and process optimization within the context of AI-powered digital transformation has emerged as a pivotal strategy for organizations aiming to enhance efficiency, foster innovation, and competitiveness. This paper is devoted to present innovative contribution by providing a comprehensive structured roadmap that outlines the foundational principles necessary for the successful integration of automation and optimizing processes within the con-text of emerging AI technologies. The paper introduce a cohesive framework consisting of es-sential key pillars: Data-Driven Insights, Seamless Automation, Adaptive Learning and Contin-uous Improvement, Human-Centric Collaboration, Ethical and Responsible AI, Strategic Align-ment, Scalability, and Innovation. These pillars function as guiding principles to navigate the in-tricate landscape of automation-driven initiatives within AI-powered digital transformation. By embracing these pillars, organizations can embark on a transformative journey that maximizes the potential of automation, fosters innovation, and positions them as leaders in the ever-evolving landscape of AI-driven business operations.

Keywords: AI Readiness Assessment; Business Processes; and Data Infrastructure.

This study aims to determine the role of Industry 4.0 sensors in digital transformation. Using a systematic literature review with a bibliometric analysis, we examine a sample of 52 studies from the SCOPUS database to identify research activity on this topic until December 2022. We find that 2022 was the year with the highest number of peer-reviewed documents on the subject, with 18 publications. We also examine current themes and find that sensors can connect multiple systems and devices, thus enabling machine-to-machine communication and facilitating great precision during product designing, development, and production, thus enabling customization and improving quality. However, despite these benefits, the current sensor technologies face multiple challenges that undermine their maximum adoption.

Investments in digital infrastructure in marginalised communities are set to increase in the next decade. These are premised on the potential of digital technologies to contribute towards solving societal problems, including the fragility of food value chains in rural areas. Although there are mixed empirical findings on the impact of these digital infrastructure investments, huge investments are continuing amid changing ICT policies in most developing countries. This paper, using a case study of a local livestock value chain in a rural community in Zimbabwe, argues for the application of non-conventional approaches towards digital infrastructure transformation impact assessment. Using selected theories and frameworks (socio-ecological systems framework, choice framework and technology affordances theory) as well as empirical data from a project in a rural community, the paper shows that real-time impact assessment using context-specific metrics may reveal hidden digital infrastructure transformation impacts, positive and negative, that are often overlooked when traditional impact assessment approaches are employed. The findings of this study contribute towards improving approaches towards ICT impact assessment. Practitioners engaging in impact assessment are challenged to move beyond dependence on traditional metrics (e.g. access) to the adoption of participatory processes to decipher context-appropriate metrics.

The increasing focus on sustainability and the adoption of green practices across industries underscore the necessity of equipping future professionals with competencies in implementing sustainable solutions. This study explores the perceptions of former students from two departments within the technological sector towards sustainability practices. A cross-sectional online survey was carried out among alumni from two programs at the University of Thessaly, a university recognized for its commitment to sustainability and green initiatives as a member of the INVEST European University alliance. The survey questionnaire included background inquiries and 16 statements designed to gauge alumni perspectives towards sustainability practices. Specifically, it aims to assess their views through self-assessment and compare differences between the two alumni groups. Responses were collected on a 5-point Likert scale ranging from strongly disagree to strongly agree. A total of 300 graduates from the Computer Science and Telecommunications as well as Electrical Engineering undergraduate programs were encouraged to take part in the survey, achieving a response rate of 69%. Of those who responded, 61% (n = 126) represented the Computer Science and Telecommunications group, while 39% (n = 81) were from the Electrical Engineering program. Our findings indicate a generally positive attitude towards sustainability, with statistically significant differences in perspectives on the implementation of sustainable technologies and stakeholder engagement. These results underscore the necessity of integrating sustainability education deeply within the curricula of technological programs to cultivate a workforce capable of addressing future environmental challenges. This study illuminates the critical role of educational frameworks in shaping the sustainability competencies of technology professionals.

The present study drops the line for the flow characteristics of fly ash-based (FA) geopolymers reinforced with polypropylene (PP) fibers undergoing the extrusion process during three-dimensional printing. Applying to the Herschel-Bulkley rheological model the research is ready with the sound theoretical basis which is used to understand the flow behavior of these materials under various conditions. A combination of experimental and numerical techniques that are based on the Finite Element Method (FEM) in COMSOL has been used in this study. The results of both the experimental and simulation approaches are then compared and the behavior of the material during extrusion is then examined. The experimental results proved PP fiber content has significant effects on rheological properties. The experiments on mixes which are either devoid of fibers or possess a high content of fiber, encountered problems like a high static yield stress and material segregation. Mixes with fibers of moderate content showed smoother extrusion processes, which means that a suitable range of fiber addition exists for the fibers that optimally balance the mechanical properties and extrudability. Numerical simulations typically agreed with experimental data for fiber contents up until it reached a certain level but not above, suggesting the existence of more complex interactions inside the fibers and the necessity for the subsequent model refinements. The investigation outlines a 0.25% and 0.5% fiber content range that leads to enhanced performance without bringing the extrusion process to greater complications and further making the production of proper, architecturally printed structures possible.

The integration of digital technologies in managing technical and design information is trans-forming the fields of architecture and construction. This article presents a website developed to compile detailed information on the construction processes of buildings at Pontificia Universidad Javeriana (PUJ) campus. The site offers comprehensive construction records and interviews with designers, engineers and other professionals who collaborated to align design approaches with technical construction requirements. A chronological record of the construction processes was maintained, from inception to completion of the buildings. Digital tools include interactive modules that facilitate access and data updates, improving coordination and efficiency for man-aging future projects. Gamification elements and social media dissemination boost user engage-ment. The study also assesses the impact of the website on the university community, analyzing usability and user (N=235), among students, teachers, and staff members. Findings indicate that digital technologies optimize construction processes and improve the management of technical and design information, and reveal opportunities for future projects and research, benefiting both internal and external stakeholders.

The technological revolution has contributed significantly to environmental and social issues worldwide. In higher education institutions, there is a theoretical gap in systematic reviews on the integration of digital transformation into sustainability. This systematic review of the literature aims to address this gap by exploring how digital transformation improves sustainability in higher education institutions, identifying emerging trends and best practices for effective strategies. Using the PRISMA guidelines, the study conducted a comprehensive search in the Scopus database, producing 1,686 publications from 2019 to 2024. The findings reveal a widespread awareness of digital technology integration but highlight a lack of consensus on sustainable integration strategies. Furthermore, the results demonstrate that educational achievements can be enhanced through tools and methodologies that align with the Sustainable Development Goals of the United Nations. The analysis identifies three primary domains: sustainability capabilities enabled by digital transformation, innovative sustainable approaches, and the conceptualisation of sustainability in higher education. The incorporation of digital tools, such as AI chatbots, institutions foster innovation and achieves sustainability objectives, thus transforming teaching and learning approaches. This review contributes significantly to understanding the role of digital transformation in promoting a sustainable and digitally enabled future in higher education.

This qualitative study explores Supplier Relationship Management (SRM) in the context of digital transformation within e-commerce businesses, focusing on the implications of emerging digital technologies. Through semi-structured interviews and documentary analysis, the study examines how organizations leverage technologies like artificial intelligence (AI), blockchain, and Internet of Things (IoT) to enhance SRM practices. Findings reveal that digitalization enables improved operational efficiencies, real-time data analytics, and expanded supplier networks through platforms such as Amazon Business and Alibaba. Strategic shifts are noted towards collaborative supplier relationships, emphasizing joint innovation, technology co-investment, and shared risk management strategies. However, the implementation of digital SRM strategies presents challenges, including data privacy concerns, integration complexities, cybersecurity threats, and regulatory compliance issues. Organizations must navigate these challenges by developing robust governance frameworks and cybersecurity protocols to protect sensitive information and ensure regulatory adherence. Key performance indicators (KPIs) such as supplier performance, cost savings, innovation impact, risk management effectiveness, and sustainability integration are critical for evaluating the success of digital SRM initiatives. Strategically, businesses are advised to prioritize leadership commitment, cross-functional collaboration, and continuous learning to optimize digital SRM practices effectively. Future research should explore emerging trends in digital SRM, investigate technological impacts on supplier dynamics and organizational performance, and examine ethical considerations in digitalization. By integrating these insights into strategic decision-making, businesses can enhance supplier relationships, mitigate operational risks, and achieve sustainable growth in the global marketplace.

The rapid evolution of technology has profoundly reshaped supply chain operations across various industries, compelling organizations to adapt swiftly to maintain competitive advantage. This qualitative study delves into the adaptive strategies that organizations employ to navigate the intricate landscape of digital transformation within their supply chains. By conducting comprehensive in-depth interviews with key stakeholders, including supply chain managers, technology officers, and operational staff, this research aims to uncover the multifaceted challenges, opportunities, and best practices associated with integrating digital technologies into supply chain operations. The findings of this study reveal that successful digital transformation in supply chains hinges on several critical factors. Leadership plays a pivotal role in steering the organization through the complexities of technological change, emphasizing the need for visionary and adaptive leaders who can drive strategic initiatives. Organizational culture, characterized by a willingness to embrace change and foster innovation, emerges as a significant enabler of digital transformation. Additionally, strategic planning and a clear roadmap are essential for aligning technological initiatives with business objectives and ensuring seamless integration. Employee training and development are identified as crucial elements in overcoming resistance to change and enhancing digital literacy among the workforce. The study highlights the importance of continuous learning and skill development programs to equip employees with the necessary competencies to leverage new technologies effectively. Furthermore, the research underscores the significance of collaboration and communication across different levels of the organization to foster a cohesive approach to digital transformation. Challenges such as data security concerns, substantial financial investments, and the complexity of integrating diverse technologies are also explored. The insights derived from the interviews provide a nuanced understanding of how organizations can address these challenges by adopting robust cybersecurity measures, securing adequate funding, and leveraging modular and scalable technological solutions. This research contributes valuable empirical insights into the digital transformation journey of supply chain operations, offering practical recommendations for practitioners and scholars. The implications of this study are far-reaching, providing a framework for organizations to navigate the digital landscape successfully. Key recommendations include fostering a culture of innovation, investing in leadership development, prioritizing employee training, and adopting a strategic and holistic approach to digital transformation.

Purpose: The rapid development of technological innovations is causing major transformations in organisations. These changes are facilitating the transition of organisations from traditional to digital cultures and are influencing the positioning and even the survival of many of them. Higher Education Institutions are no exception. This article aims to understand the main factors that facilitate and drive this transformation from the point of view of their external stakeholders. Design/methodology/approach - Due to the complexity of the study, the case study method has been used as an appropriate methodology. Findings - As the results of the study show, the use of new agile methodologies, the development of continuous improvement processes, the automation of tasks with the use of new technologies, data-driven decision making, and the management of new digital talent are enablers. Research limitations/implications - Further research could conduct similar analyses with other stakeholders, to achieve a complete management model. Practical implications - The case study allows researchers to learn from practitioners and business leaders, while providing a theoretical model that can help other managers in the process of transforming their organisations. Originality/value - The main contribution of this study has been to apply the study to a specific Higher Education Institution.

With the continuous development of digital transformation and upgrading of Chinese construction enterprises, it is becoming increasingly important to measure their digital level, find the problems in the enterprise transformation process, and identify the key factors of enterprise digital capacity enhancement. This paper constructs a construction enterprise digital transformation maturity evaluation model from six first-level indicators and 20 second-level indicators, including digital strategy, digital business application, digital technology capability, data capability, digital organization capability, and change management. Digital maturity is divided into five levels: business management, process operation, intelligent construction, intelligent scene application, and industrial ecological collaboration. A detailed process of digital maturity evaluation based on the method of Analytic Hierarchy Process (AHP)-Decision Testing and Evaluation Laboratory (DEMATEL) is then developed. A questionnaire survey of 25 experts is used to weight the various parameters in the model, which is then demonstrated with an example construction enterprise. The model comprehensively reflects digital levels under the background of the digital economy. Its application will help understand the advantages and disadvantages enterprises face in their digital transformation to enable targeted measures to improve their digital transformation capabilities and efficiency, enhance their core competitiveness of enterprises, and promote the development of digital transformation in the construction industry.

Background: The COVID-19 pandemic has sped up digital health transformation across the health sectors to enable innovative health service delivery. Such transformation relies on competent managers with the capacity to lead and manage. However, the health system has not adopted a holistic approach in addressing the health management workforce development needs, with many hurdles to overcome. The objectives of this paper are to present the findings of a three-step approach in understanding the current hurdles in developing a health management workforce that can enable and maximise the benefits of digital health transformation, and to explore ways of overcoming such hurdles. Methods: A three-step, systematic approach was undertaken, including an Australian digital health policy documentary analysis, an Australian health service management postgraduate program analysis, and a scoping review of international literatures. Results: The main findings will guide the formulation of strategies in developing a digitally enabled health management workforce in the digital health era. Conclusions: With the ever-changing landscape of digital health, being able to lead and manage in times of system transformation requires a holistic approach to develop the necessary health management workforce capabilities and system-wide capacity. The evidence would support that this can be achieved with the required system, policy, educational and professional organizational enablers, which drive a digital health focused approach across all the healthcare sectors, in a coordinated and contextual manner.

The management of a cultural change in higher education is becoming necessary and unavoidable: changes in teaching, changes in research and changes in governance. Digital transformation is an essential part of many countries’ modernisation agendas. The aim of this non-experimental, descriptive, survey-based study was to explore the perceptions of university students toward the digital transformation that took place in university teaching as a consequence of COVID-19. The specific objectives proposed were to: a) analyse the perceptions of students toward digital transformation in university teaching; b) determine the valuation of students about the digital transformation that occurred in university teaching as a result of COVID-19; and c) explore the resources (hardware-software), professional collaboration, digital pedagogy and student empowerment (motivation) with respect to digital education and the recent changes in university teaching due to the pandemic. The results show that a large number of items are positively correlated. In conclusion, further research should delve into motivation, collaboration, reflective experience sharing, self-learning and initiatives that promote the development of competences in future teachers.

This paper will discuss Data, Information, Knowledge, and Wisdom, which is commonly referred to as DIKW. The DIKW Pyramid Model that is often referenced in both academics and practitioner circles will be discussed and shown to be faulty on several levels, including. a lack of definitional agreement. A new DIKW framework will be proposed that focuses on what the DIKW elements do in the way humans think, not what they are by definition. Information as a replacement for wasted physical resources in goal-oriented tasks will be a central organizing point. The paper will move the DIKW discussion to the computer-based concept of Digital Twins (DTs) and its augmentation of how we can use DIKW to be more effective and efficient. This will especially be the case as we move to Intelligent Digital Twins (IDT) with Artificial Intelligence (AI).

Our article AI, cultural heritage and bias examines the challenges and potential solutions for using machine learning to interpret and classify human memory and cultural heritage artifacts. We argue that bias is inherent in cultural heritage collections (CHCs) and their digital versions, and that AI pipelines may amplify this bias. We hypothesise that effective AI methods require vast, well-annotated datasets with structured metadata, which CHCs often lack due to diverse digitisation practices and limited interconnectivity. The paper discusses the definition of bias in CHCs and other datasets, exploring how it stems from training data and insufficient humanities expertise in generative platforms. We conclude that scholarship, guidelines, and policies on AI and CHCs should address bias as both inherent and augmented by AI technologies. We recommend implementing bias mitigation techniques throughout the process, from collection to curation, to support meaningful curation, embrace diversity, and cater to future heritage audiences.

In recent years, Chinese manufacturing enterprises compete to chase the wave of "digital revolution", digital empowerment has become an important strategic path of technological reform for many manufacturing enterprises. Based on the micro data of listed companies, this paper investigates the impact of digital transformation of Chinese listed companies on the quality of their export products. It is found that digital transformation can significantly improve the quality of enterprises' export products. After a series of robustness tests, this conclusion is still valid, and there are two ways to improve innovation performance and total factor productivity. The export product quality enhancement effect of enterprise digital transformation also has the heterogeneity of ownership, region and industry. In the further study, this paper also examines the impact of digital transformation on the internal salary gap of enterprises, and finds that digital transformation may increase the internal salary gap of enterprises, and form a "masking effect" on the quality of export products. To a certain extent, this paper deepens the understanding of the study of enterprise digital transformation on the quality of export products and its differences, and provides certain guidance for enterprises to implement the strategy of digital transformation.

Named Entity Recognition (NER) serves as a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries.To address these challenges, a corpus, labeled datasets and a lexicon specific to pig diseases for Chinese named entity recognition were constructed. Subsequently, a Pig Disease Chinese Named Entity Recognition (PDCNER) model was proposed. The model integrates external lexicon knowledge of pig disease by employing Lexicon-enhanced BERT and enhance feature representation by incorporating contrastive learning. Experimental results show that the model achieved the best recognition results, with a precision of 86.92%, a recall of 85.08%, and an F1-score of 85.99% respectively. Furthermore, the model exhibits robustness and generalizability across few-shot and publicly available datasets. Experimental results illustrate the proposed model could effectively identify Chinese named entities of pig diseases, outperforming several existing baseline methods. Moreover, the proposed model can be extended to other animal disease domains, such as chicken and cattle, thereby facilitating seamless adaptation for named entity identification across diverse contexts.

This study examines if enterprises’ digital transformation enhances their sustainable development capabilities and reduces carbon emissions, thus enhancing carbon neutrality performance and implementing the "dual-carbon" strategy. It specifically explores the impact of Chinese firms’ digital transformation on carbon neutrality performance in 30 provinces and municipalities in China from 2011 to 2021. Based on the Difference-in-Difference method and spatial econometric modeling, this study finds a U-shaped relationship between enterprise digital transformation and carbon performance, with spatial spillover effects. It also finds that converting environmental charges into taxes can significantly improve carbon performance. In terms of the mechanism of action, digital transformation affects carbon performance by influencing energy use efficiency and adjusting the level of industrial structure advancement and rationalization. In terms of heterogeneity, this U-shaped relationship exists only in the eastern and central regions, and the significance is more substantial in the east; in addition, the impact of enterprise digital transformation on carbon performance is more evident in regions with higher R&D intensity and higher economic development levels. These findings imply that the government should further encourage and guide enterprise digital transformation, monitor the effects of environmental protection policies and select and implement carbon emission reduction strategies according to local conditions to improve carbon performance.

Digital federated platforms and data cooperatives for secure, trusted and sovereign data exchange will play a central role in the construction industry of the future. With the help of platforms, cooperatives and their novel value creation, the digital transformation and the degree of organization of the construction value chain can be taken to a new level of collaboration. The goal of this research project was to develop an experimental prototype for a federated innovation data platform along with a suitable exemplary use case. The prototype is to serve the construction industry as a demonstrator for further developments and form the basis for an innovation platform. It exemplifies how an overall concept is concretely implemented along one or more use cases that address high-priority industry pain points. This concept will create a blueprint and a framework for further developments, which will then be further established in the market. The research project illuminates the perspective of various governance innovations to increase industry collaboration, productivity and capital project performance and transparency as well as the overall potential of possible platform business models. However, a comprehensive expert survey revealed that there are considerable obstacles to trust-based data exchange between the key stakeholders in the industry value network. The obstacles to cooperation are predominantly not of a technical nature, but of a competitive, predominantly trust-related nature. To overcome these obstacles and create a pre-competitive space of trust, the authors therefore propose the governance structure of a data cooperative model, which is discussed in detail in this paper.

Digital infrastructure is the basic guarantee for digital transformation, it is of practical significance to discuss how to effectively use digital infrastructure construction to promote the digital transformation of tourism enterprises. This paper examines the impact of digital infrastructure construction on tourism enterprises based on the "Broadband China" policy as a quasi-natural experiment. A-share tourism listed enterprises from 2010 to 2021 are studied using the double difference method. Findings indicate that digital infrastructure construction positively and significantly influences the digital transformation of tourism enterprises, driven by digital leadership and innovation capabilities. State-owned, small-scale, and high-growth tourism enterprises benefit more from this transformation. The results suggested that the state should develop differentiated digital infrastructure plans based on the specific conditions of different tourism enterprises. Additionally, tourism enterprises should enhance digital leadership and innovation capabilities through strategic changes and digital applications to amplify the impact of digital infrastructure on their transformation.

The effective macroscopic model describing interactions of a viscous compressible heat-conducting fluid and a two-level fine bristly thermoelastic structure is derived from microstructure by means of the Allaire–Briane multi-scale convergence method. This new model naturally generalizes the isothermal formulation earlier constructed by the authors in 2020, see in Siberian Electronic Mathematical Reports, vol. 17 (https://doi.org/10.33048/semi.2020.17.100). In applications, the established model can be used, for example, in description, with account of heat transfer phenomemon, of airflow near surface of plant’s leaf, in simulation of epithelium surfaces of blood vessels, and in design of biotechnological devices operating in liquids.

In practice, a post-extractivist vision for farmer communities in the Amazon demands the study and demonstration, in the field, of alternative economic activities that are not based on resource extraction but on adding value to currently generated residual biomass. Following principles of bioeconomy, this study presents an experimental analysis of a retort burner and a pilot-scale auger-type pyrolysis reactor used to convert coffee husks generated in a post-harvesting facility of a farmer´s cooperative into thermal energy and biochar, respectively. The study shows that use of coffee husks, whether for combustion or pyrolysis processes, can supply the thermal energy required by the post-harvesting processes. The combustion or pyrolysis of coffee husks avoids its accumulation and decomposition while replacing the fossil fuels used in post-harvesting operations, reducing costs and making the farmers independent of fossil fuel subsidies. Unlike combustion, the CO concentration in the flue gas during the pyrolysis process was below the eco-design standard of 500 mg/Nm3. According to the European Biochar Certificate, carbonized coffee husks can be regarded as biochar for soil amendment and carbon sequestration. A study is required to assess the ability of cooperatives to generate and trade carbon credits linked with the application of biochar in their cropping systems.

As a service business models have been ranked by management consultants the management innovation of 2020, and yet are scarcely theorized. Therein, value stipulations in customer contracts lead to the shift from product to service. However, there is no available research about the nature, formula and decisional process about these value stipulations. The motive for this research is to fill in this gap. The goal is to provide first insight about value stipulations in as a service business models: their formula and decisional methodology. The article is a multiple theory building case study on leading platform as a service providers, which aims to contruct the paradigm of the value decision methodology to invest in this technology by purchasing it from dedicated vendors. The methodology works to identify the management instruments involved in the decisional activities to invest in digital technology, where each management instrument is assumed to match an activity at seller and buyer; the sequence of the activities using the Critical Path Method gives the decisional process. Dependent variables are the elements of the as a service business contracts: the customer offering (product-service solution) and the value created, captured and delivered by the solution. Independent variables are given by empirical data analysis. Findings show a relationship between dependent variable value in the as a service business model contracts and independent variables governance objective, strategy, as a service business model type, activity management techniques (roadmap, program, project, operations, lean start-up), capital allocation techniques (equity, debt, options, venture capital typology). If innovation is exploitative, then the customer offering is known and value is determinable via roadmap – project management techniques (program, project, an operations) and capital allocation techniques for precictable environment, If innovation is radical, then the customer offering is not known and value is a target used to architect the solution via lean start-up innovation techniques funded by venture capital. The contribution is a new theoretical construct and deep insight into the practice of digital transformation considering all inherent issues.

Digital transformation has become the top priority for 80% of sports companies worldwide, but statistics show that between 70% and 95% of all digital transformation projects fail due to signif-icant and varied challenges that sports companies face during the process of digital transfor-mation. This is because strategy, not digital technology, drives digital transformation, and suc-cess is unlikely without a mature digital transformation model. Moreover, the digital transfor-mation model of developing and emerging markets is not the same and cannot be adopted as a blueprint. Therefore, the aim of this research was to propose a framework for the digital trans-formation maturity of esports businesses in developing and emerging markets. Semi-structured interviews were conducted with a sample of 15 stakeholders of esports businesses in Iran. Thematic analysis was used to analyze the interviews, and three main perspectives were identified: (1) enablers, (2) digital resilience and capabilities, and (3) digital transformation ma-turity stages. The enablers were formed by six sub-themes, including governance and leader-ship, strategy, culture and skills, innovation, technology, and data. Digital resilience and capa-bilities were formed by four sub-themes, including business model and ecosystem, digital cus-tomer experience, digital employee experience, and digital processes. Additionally, it was found that digital transformation maturity stages are based on four stages of digital beginners, digital followers, digital conservatives, and digital leaders. The findings indicate that moving towards digital transformation and achieving digital resilience in esports businesses will be impossible without synergy between enablers and digital resilience and capabilities. This framework can be useful for esports businesses to evaluate their current digital status and effectively guide them towards a desirable digital status. Furthermore, esports businesses can prioritize their efforts and resources for digital transformation by focusing on targeted capabilities and enablers and ensure more effective and efficient allocation of resources towards digital transformation.

The paper describes the new method for image compression based on coordinated group signal transformation. The algorithm is a type of difference coding. Coordinated processing significantly simplifies the difference signal conversion scheme using a single group codec for all signals. The described method considers color channels as correlated signals of a multi-channel communication system. The specifics of the processed data required modification of the coordinated group processing algorithm. First, we changed how to calculate the difference signals to prevent data loss. Secondly, the group codec was supplemented with a neural network to improve the quality of reconstructed images. We considered the following types of neural networks: fully connected, recurrent, convolution, and convolution in the Fourier space. Based on the simulation results, it is best to use fully connected neural networks if the goal is to minimize processing delay time. These networks have a response time of 13 ms. On the other hand, if the priority is to improve quality in cases where delays are not critical, then convolution neural networks in the Fourier space should be used. These networks have a response time of 140 ms and are of significant interest.

This paper experimentally reveals some of the resources offered by the evolution of the instantaneous active electric power in describing the state of three-phase AC induction asynchronous electric motors (with squirrel-cage rotor) operating under no-load conditions. A mechanical power is required to rotate the rotor at no-load and this mechanical power is satisfactorily reflected in the constant and variable part of instantaneous active electric power. The variable part of this electrical power should necessarily have a periodic component with the same period as the period of rotation of the rotor. The paper proposes a procedure for extracting this periodic component description (as a pattern, by means of a selective averaging of instantaneous active electrical power) and analysis. The time origin of this pattern is defined by the time of a selected first passage through the origin of an angular marker placed on the rotor, detectable by a proximity sensor (e.g. a laser sensor). The usefulness of the pattern in describing the state of the motor rotor has been demonstrated by several simple experiments which show that a slight change in the no-load running conditions of the motor (e.g. by placing a dynamically unbalanced mass on the rotor) has clear effects in changing the shape of the pattern.

This study explores the public library service evaluation domain through user-generated content on Google Maps, highlighting digital feedback's significant yet underexplored potential in understanding public library patronage across Taiwan's six major cities. Utilizing a mixed-methods research design, this study integrates Google Maps review content analysis with Social Network Analysis to delineate public perceptions and identify areas for service enhancement in public libraries. It innovatively leverages personal experiences extracted from over 60,000 Google Maps reviews to evaluate public library services in cities such as Taipei, New Taipei, Taoyuan, Taichung, Tainan, and Kaohsiung. The research taps into the National Library of Taiwan's National Library Statistics System to provide a robust analysis of library performance and user satisfaction, offering a novel perspective by emphasizing user-centric feedback from Google Maps as a primary data source. This approach provides quantitative data on library usage and geographic distribution and enriches our understanding of the qualitative experiences of library users. In analyzing the keywords from Google Maps reviews of public libraries, we categorize and interpret these under the three core LibQUAL+ dimensions—Affect of Service, Information Control, and Library as Place. The findings expose variances in perceived service quality among the cities, with Kaohsiung and Taichung receiving the highest accolades for service satisfaction. Simultaneously, the study identifies potential areas for improvement, particularly in cities with lower satisfaction ratings like Taipei. This personalized feedback illustrates the intimate relationship between public libraries and their communities, offering invaluable insights for policymakers and library management to enhance service delivery and user experience.

In the field of business environments, the integration of novel artificial intelligence (AI) techniques has been identified in the literature and theoretical discourse as an essential catalyst for enhancing the decision-making processes. In the contemporary landscape of digital transformation, the application of these techniques becomes essential, despite encountering various hurdles that impede their seamless integration into the rational decision-making process within e-government, which constitutes a critical component of digital government initiatives. This study explores whether similar advancements can be observed within the e-government sector and investigates the impact of using machine learning (ML) as a technique of artificial intelligence on rational decision-making (RDM). Employing an empirical research approach, the study utilized a quantitative methodology, relying on an electronically structured questionnaire survey administered to 163 employees in the e-government sector in Jordan through purposive random sampling. Data analysis was conducted using the SPSS v25 program, complemented by a media-tion analysis using AMOS v23 software. The research findings revealed a significant contribution of using machine learning to enhance rational decision-making and trust levels, with trust positively impacting RDM. Trust was identified as a beneficial mediator in the relationship between machine learning and rational decision-making. Despite certain limitations, this study's outcomes offer substantial insights for researchers and government policy-makers alike, contributing to the advancement of sustainable practices in the e-government domain.

In recent years, bioengineered and biotechnological products have gained tremendous attention to identify unique biologic features associated with oncology. To overwhelm the complexity of tumor personalized medicines (PM) or vaccines could be a promising way-out to be considered. Cancer is a heterogeneous disease. This characteristic variability of cancer provides itself to the emergent era of PM i.e., precision oncology. The prime objective of PM is merely to deliver the right treatment to the right patient at the right time. For instance, imatinib was effectively targeted chronic myeloid leukemia. To date, attainment in using PM approaches to cancer treatment have been mixed. The next-generation sequencing (NGS), and the use of several biomarkers (e.g., HER2, BRAF, ALK, etc.), to drive precise cancer treatment through a timely and cost-effective manner. In current review, author summarizes the advancement in the field of PM such as CRISPR-Cas9, NGS, pharmacogenomics, and the benefits of machine learning techniques. The precise communication between healthcare professionals, regulatory bodies, and patients is essential to translate advances in PM into immense benefits for patients with cancer globally.

L-Theanine, a unique non-protein amino acid, is an important bioactive component of green tea. Previous studies have shown that L-theanine has many potent health benefits, such as anti-anxiety effect, regulation of immune response, relaxing neural tension, reducing oxidative damage. However, little is known concerning whether L-theanine can improve the clearance of mitochondrial DNA (mtDNA) damage in organisms. Here, we reported that L-theanine treatment increased ATP production and improved mitochondrial morphology to extend the lifespan of UVC-exposed nematodes. Mechanistic investigations showed that L-theanine treatment enhanced the removal of mtDNA damage and extended lifespan by activating autophagy, mitophagy, mitochondrial dynamics, and mitochondrial unfolded protein response (UPRmt) in UVC-exposed nematodes. In addition, L-theanine treatment also up-regulated the expression of genes related to mitochondrial energy metabolism in UVC-exposed nematodes. Our study provided a theoretical basis for the possibility that tea drinking may prevent mitochondrial related diseases.

While the digital age takes place through digital information networks, the triggering force of the age, digital transformation, has rapidly transformed the current economic and social order. This article study examines how the recent digital transformation process has affected corporate commitment in educational institutions. Educational institutions were analyzed at the sector level in terms of digital transformation and institutional commitment, while educational institutions with digital change were researched at the school level. The findings of the study reveal that administrators and teachers working in educational institutions are role models for their students and their environment, think more about concepts such as efficiency and quality, and their transformational qualities. In addition, if they develop professionally, their institutional commitment increases, otherwise they are not motivated and encouraged towards the goals and objectives of the institution, they are not guided to look at daily events, situations and problems from a wider perspective, and because of the speed of the transformation process, science and technology can also It has been determined that organizational commitment has decreased in today's conditions where it has become widespread.

The mineralogical indices, e.g., Quartz Index (QI), Carbonate Index (CI), Mafic Index (MI) for ASTER multispectral thermal infrared (TIR) data have been applied to various geological materials. The indices are proved to be robust in extracting geological information at the local scale. Considering the relatively narrow spatial coverage of each ASTER scene compared to LANDSAT, many scenes are needed when mosaicking the images to be mapped at regional scales. We develop a system to search ASTER data for a target area given the vast and expanding ASTER data archive. The data is then conditioned, prioritized, and the indices are calculated before finally mosaicking the imagery. The maps of the indices covering the very wide region of the central Tibetan Plateau are produced with this system. The characteristic features of the indices relating to their geology in the study area are analyzed and discussed. Many interesting lithological and structural information that are not currently well understood in the central Tibetan Plateau, the highest and most extensive plateau in the world, with an average elevation of over 4,500 meters above sea level, for example, distributions of the mafic-ultramafic rocks along the suture zones, the quarzitic and marine sedimentology accreted to the Eurasian continent and sulfate layers related to the Tethys and neo-Tethys geological setting can be retrieved from the processed ASTER images.

The described data set contains features from the machine control of a five-axis milling machine. The features were recorded during thirteen series productions. Each series production includes a changeover process in which the machine was set up for the production of a different product. In addition to the timestamps and the twenty recorded features derived from Numerical Control (NC) control variables, the data set also contains labels for the different production phases. For this purpose, up to 23 phases were assigned, which are based on a generalized milling process. The data set consists of thirteen .csv files, each representing a series production. The data set was recorded in a production company in the contract manufacturing sector for components with real series orders in ongoing industrial production.

Digital transformation and green innovation are critical to firms’ competitive advantage and, thus, firms desire to make digital transformation enhance green innovation. However, the relationship between the digital transformation on green innovation is unclear. Drawing on digital empowerment, we delineate types of digital transformation and their impacts on green innovation in manufacturing firms. Our empirical results indicate that production digitalization and service digitalization have a significant promoting effect on both of green technological innovation and green management innovation. However, the effect of production digitalization on green management innovation is stronger than that of service digitalization. Moreover, environmental regulation enhances the positive effect of service digitalization on green technological innovation and green management innovation. While it enhances the effect of production digitalization on green management innovation. Peer firms’ green innovation strengthens the effect of service digitalization on both of green technological innovation and green management innovation.

At present, countries around the world adopt digital strategy, so that enterprises apply digital technology, digital supply chain, blockchain, and a series of digital measures to enhance their competitiveness. By analyzing the relationship between digital transformation of manufacturing export enterprises, and their business performance and export performance, this paper focused on the current situation and existing problems of digital transformation of manufacturing export enterprises in China. Based on the Resource Based View theory and Trade theory, the impact of digital transformation of enterprises performance, proposing hypotheses, and building regression models were explored. With the samples of 1007 listed companies in Shanghai and Shenzhen Stock markets from 2012 to 2019, the regression analysis, intermediate effect test, robustness test, stage lag, and heterogeneity analysis were conducted. The results shew that: (1) Listed manufacturing export enterprises carried out digital transformation, which had a significantly positive effect on enterprise performance; (2) It was found that digital transformation could improve enterprise performance by reducing costs, improving R&D intensity and improving human resources etc. (3) According to the fractal analysis, non-state-owned enterprises had more positive effects on enterprise performance, and digital transformation of manufacturing export enterprises in developed areas had a more significant impact on enterprise performance. Finally, according to the empirical results, relevant suggestions for digital transformation were provided.

The EFQM Model, established in 1991, serves as a framework for organizational excellence, helping achieve sustainable success through an integrated management system. The model's evo-lution is driven by the need to adapt to global trends, threats, and technological innovations such as: digital transformation, employee engagement and well-being, remote work, agile performance management, diversity, equity and inclusion (DEI), sustainability, AI, and big data. The new EFQM Model 2025, presented in Istanbul in June 2024, introduces significant changes, emphasiz-ing sustainability, innovation, and technology. These changes are analyzed based on the logic of three questions: Why, How, and What. Why: There is a necessity for better alignment of organizations with contemporary challenges through flexibility, risk resilience, and innovation capabilities in a dynamic business environ-ment, including alignment with the UN Sustainable Development Goals (SDGs). How: The model introduces an integrated approach to performance management and transfor-mation, incorporating advanced technologies such as AI and data analytics. What: Implementing the EFQM Model 2025 enhances organizational adaptability, innovation, and flexibility, improving financial, operational, and strategic outcomes. Better alignment with sustainability goals leads to long-term value and social responsibility. Improved stakeholder engagement methods enhance understanding and satisfaction. The use of advanced technologies supports innovation and operational efficiency, while better strategic and operational performance and risk management ensures greater stability and the ability to anticipate future challenges.

This study aims to evaluate the utilization and effectiveness of artificial intelligence (AI) applications in managing symptoms of anxiety and depression. The primary objectives are to identify current AI tools, analyze their practicality and efficacy, and assess their potential benefits and risks. A comprehensive literature review was conducted using databases such as ScienceDirect, Google Scholar, PubMed, and ResearchGate, focusing on publications from the last five years. The search utilized keywords including "artificial intelligence," "applications," "mental health," "anxiety," "LLMs" and "depression". Various AI tools, including chatbots, mobile applications, wearables, virtual reality settings, and large language models (LLMs), were examined and categorized based on their functions in mental health care. The findings indicate that AI applications, including LLMs, show significant promise in symptom management, offering accessible and personalized interventions that can complement traditional mental health treatments. Tools such as AI-driven chatbots, mobile apps, and LLMs have demonstrated efficacy in reducing symptoms of anxiety and depression, improving user engagement and mental health outcomes. LLMs, in particular, have shown potential in enhancing therapeutic chatbots, diagnostic tools, and personalized treatment plans by providing immediate support and resources, thus reducing the workload on mental health professionals. However, limitations include concerns over data privacy, the potential for over-reliance on technology, and the need for human oversight to ensure comprehensive care. Ethical considerations, such as data security and the balance between AI and human interaction, were also addressed. The study concludes that while AI, including LLMs, has the potential to significantly aid mental health care, it should be used as a complement to, rather than a replacement for, human therapists. Future research should focus on enhancing data security measures, integrating AI tools with traditional therapeutic methods, and exploring the long-term effects of AI interventions on mental health. Further investigation is also needed to evaluate the effectiveness of AI applications across diverse populations and settings.

Panax notoginseng is a traditional Chinese medicine, and saponins are its main active ingredients. The content of saponins in flower of P. notoginseng is the highest in the whole plant. Minor ginsenosides have stronger pharmacological activity than main ginsenosides, but its content in nature is very low. We can use extracted total saponins from P. notoginseng flower as the substrate to produce minor ginsenosides by biotransformation method. In this study, we used the fungus Talaromyces flavus and its crude enzymes to transform the extracted total saponins of P. notoginseng flower. Through the qualitative and quantitative analysis of the transformation substrates and products, it was found that minor ginsenosides Gyp-XVII, ginsenosides C-Mc1, C-Mx1, F2, Rg3, 20(R)-Rg3, Rk1 and Rg5 were found in the products and the conversion rate of the major saponins in P. notoginseng flower ranged from 44.16 to 55.22%, the yields of the minor ginsenosides ranged from 0.05 to 3.24%. This study provided a good experimental idea, greatly increased the utilization rate of total saponins in P. notoginseng flower, and laid a foundation for promoting the wide application of minor saponins.

Digital transformation, organizational resilience, and agility are now becoming one of the keys to meeting the competitive challenges of modern organizations. It is no surprise that digital transformation and digital technologies have also begun to significantly impact the energy industry, moving towards improving the sector's profitability and efficiency. However, to navigate through the difficult process of digital transformation in today's turbulent environment, organizations, including those in the energy sector, need to build organizational resilience. Nevertheless, the nature of the relationship between digital transformation and organizational resilience has not yet been sufficiently clarified. Focusing on the level of digital transformation, and more precisely within the two dimensions of digital maturity, i.e. digital intensity and transformation management intensity, as well as based on the perspective of dynamic capabilities, this study developed a configurational framework and proposed a theoretical model to study the equifinal paths through which digital transformation and dynamic capabilities influence organizational resilience in energy sector companies. Based on a fuzzy set qualitative comparative analysis (fs/QCA) conducted on selected companies in the energy sector, i.e. Polish CHP plants, the relationship between digital transformation, dynamic capabilities and organizational resilience was investigated. The results show that a high level of organizational resilience can be achieved through two main paths based on the dominance of dynamic capabilities and the dominance of digital maturity. The study found that digital maturity can significantly influence CHP resilience. Moreover, the transformation management intensity is strongly related to high organizational resilience. The paper concludes by describing theoretical and practical implications, as well as research limitations and prospects for future research.

In the next decade, further digitalisation of the entire wind energy project lifecycle is expected to be a major driver for reducing project costs and risks. In this paper, a literature review on the challenges related to implementation of digitalisation in the wind energy industry is first carried out, showing that there is a strong need for new solutions that enable co-innovation within and between organisations. Therefore, a new collaboration method called the WeDoWind Ecosystem is developed and demonstrated. The method is centred around specific "challenges", which are defined by "challenge providers" within a topical "space" and made available to participants via a digital platform. The data required in order to solve a particular "challenge" is provided by the "challenge providers" under the confidentiality conditions they specify. The method is demonstrated via a case study, the EDP Wind Turbine Fault Detection Challenge. Six submitted solutions using diverse approaches are evaluated. Two of the methods perform significantly better than EDP’s existing method in terms of Total Prediction Costs (saving up to €120,000). The WeDoWind Ecosystem is found to be a promising solution for enabling co-innovation in wind energy, providing a number of tangible benefits for both challenge and solution providers.

Good health and well-being is one of the essential SDGs that ensure healthy lives and promote well-being for all ages and further entails providing substantial medical services to the public at low cost and with minimal adverse effects on the environment. Information and communication technologies (ICTs) have taken on an increasingly important function as significant facilitators of healthcare reform, with the goals of enhancing access to health services, the quality of treatment provided, and the overall productivity of the healthcare system. However, the integration of ever-increasing ICT technologies into the healthcare systems, also referred to as digital transformation, is not a straightforward process, but it comes with different types of challenges from integration level to application design level and security level. Although several studies have been proposed to address the integration of ICT technologies into healthcare systems, there is still a need for a comprehensive research study on the integration and design challenges, security and privacy challenges, application areas, and possible positive and negative impacts. Therefore, this paper contributes as the research literature study covering an important SDG, "Good health and well-being," and its digital transformation, along with summarising our research findings in a detailed and taxonomical way. To start with, firstly, we present a detailed comparison of existing studies on healthcare and well-being, mainly focusing on integrating ICT technologies in healthcare in terms of sustainable aspects, security and privacy challenges, design and integration challenges, E-health-related applications, and future directions. We also present an overview and the need for digital transformation in healthcare, discuss its significant components, highlight E-health's importance and benefits, explore its integration and design challenges, and categorise the security and privacy challenges. Next, we present an in-depth discussion on the role of Blockchain technology as today's leading technology in E-health, discussing Blockchain technology and its characteristics, highlighting its benefits, and describing the possible types of Blockchain-based E-health use cases. Furthermore, we discuss the positive and negative impact of ICT integration along with identifying open issues and challenges of integrating ICT technologies into the healthcare systems and discuss future research directions, which provide the strength for researchers to address the issues in future solutions.

This study presents an advanced Simulated Shearer Machine Cutting Experiment System enhanced with digital twin technology. Central to this system is a simulated shearer drum, designed based on similarity theory to mirror the operational dynamics of actual mining cutters accurately. The setup incorporates a modified machining center equipped with sophisticated sensors that monitor various parameters such as cutting states, forces, torque, vibration, temperature and sound. These sensors are crucial for precisely simulating the shearer cutting actions. The integration of digital twin technology is pivotal, featuring a real-time data management layer, a dynamic simulation mechanism model layer, and an application service layer that facilitates virtual experiments and algorithm refinement. This multifaceted approach allows for in-depth analysis of simulated coal cutting, utilizing sensor data to comprehensively evaluate the shearer's performance. The study also includes tests on simulated coal samples. The system effectively conducts experiments and captures cutting condition signals via the sensors. Through time-domain analysis of these signals, gathered while cutting materials of varying strengths, it is determined that the cutting force signal characteristics are particularly distinct. By isolating the cutting force signal as a key feature, the system can effectively distinguish between different cutting modes. This capability provides a robust experimental basis for coal-rock identification research, offering significant insights into the nuances of shearer operation.