This dossier is aimed at individuals involved in the development, implementation, and application of AI-supported systems in the field of education, particularly those who are dealing with metadata in this context. The insights and recommendations described in this dossier are based on interviews with five experts from the USA and UK, research on metadata standards, and our personal experiences in the context of the INVITE competition. This dossier provides an overview of the current development of learning metadata and existing standards. It shows the efforts and obstacles to introducing those metadata standards from an international perspective based on interviews with five experts in the field. Furthermore, this dossier shows the international experiences in selecting and using metadata standards and how these findings can inspire discussions in the German-speaking discourse regarding the use of metadata in education.

Artificial Intelligence (AI) has emerged as a disruptive technology in various areas, and medicine is no exception. In the last decade, there has been rapid progress in the application of AI in healthcare, from diagnosing and prognosticating diseases to improving operational efficiency in hospitals. This literature review explores the advances of artificial intelligence in the field of medicine, discusses its benefits, and discusses the ethical and regulatory challenges that need to be addressed to maximize its potential.

Asia has formally entered the big data era thanks to the ongoing advancement of information technology. the company business management has discovered bright operations management based on technological advances, and the arrival of the big data era has improved work efficiency and brought about a number of economic benefits while also posing a number of challenges, such as the ongoing expansion of business scope. The business's current management model is unable to keep up with the demands of development as its scope continues to grow. The primary development trend for the future is the integration of intelligence technologies with enterprise business management, big data technology application in corporate business management promotes fairness, transparency, and justice across corporate development and consumer activities; it also facilitates the production of goods and the creation of value for products in the direction of socialization. In order to further deepen the understanding of artificial intelligence among relevant personnel, we aim to further elaborate the future growth direction of the application of artificial intelligence in the field of enterprise business management in this paper, which begins with an in-depth awareness of the current situation of the combined use of artificial intelligence technology and big companies’ business management. We then focus on the utilization path of artificial cognitive ability technology in businesses business management.

The utilization of Artificial Intelligence (AI) in biotechnology is widespread, addressing challenges such as elucidating disease pathophysiology and novel therapeutic approaches. AI plays a critical role in tasks such as drug manufacturing, chemical analysis, RNA and DNA sequencing, and enzyme studies by automating processes and expediting data collection and analysis. AI has particularly transformed the analysis of omics data for researchers, providing tools to handle complex and multifaceted datasets effectively. Through AI, integrative analysis of multi-omics data can be conducted in efficient ways. This article discusses the application of multi-omics and AI technologies in studying various diseases related to infection (COVID-19), mutation (cancer), and senescence (alopecia) to understand their pathophysiology. The applications of AI were performed as the aspect of the diagnosis, prognosis, and treatment for each disease. We introduce AI-driven analysis of pathophysiology can overcome traditional limitations in pathology, with diverse features showing potential in defining the causes of diseases, and potentially enhancing primary omics data in the distinguish the subtype of diseases. In this context, integrated approaches using multi-omics data and AI technology could provide fresh biological insights by comprehending the molecular mechanism of diseases and is instrumental in advancing precision medicine. We anticipate that the rapidly advancing AI-based multi-omics data analysis will significantly contribute to promoting healthy longevity.

Recent years have seen dramatic growth in the number of scholarly scientific works dedicated to the topic of Artificial Intelligence (AI), banking, and finance. With the development of scientometric tools, it is now possible to map, visualize, analyze, and assess scientific activities in many fields of engineering and social sciences including Islamic banking and finance. Based on data retrieved from the Scopus database and using a qualitative method, this study investigates, evaluates, and identifies significant development, trends, and players in the application of AI in Islamic banking and finance using the Visualization Of Similarities Method (VOS) between objects in VOSviewer. From 2006 to 2022, 387 documents were retrieved from the Scopus database; Results revealed that the top five most active countries in terms of publications are Malaysia (117 documents), Indonesia (89 documents), the United Kingdom (36 documents), the USA (24 documents), and Saudi Arabia and Bahrain (24 documents each). The International Islamic University Malaysia and Universitas Indonesia came out on top of active institutions while the top funding source came from the Ministry of Higher Education, Malaysia. The comprehensive findings and analysis reported in this study serve as a roadmap for future academics to create theory and practice for applying AI in Islamic banking and finance.

In the past ten years, the generative artificial intelligence trend has evolved from simply enhancing the creation of audiovisuals using generative adversarial networks to becoming an entirely disruptive force, transforming operations in various industries and markets, including education and business. The introduction and subsequent widespread popularity of Open AI’s ChatGPT in 2022 raised concerns about the use of Generative AI in professional spaces and its impact on authenticity, innovation, data privacy, and integrity. This study seeks to assess the current applications of this disruptive technology in corporate America and build on that knowledge to provide insight into the current innovative practices and the factors driving its use. The research will further outline some risks associated with generative AI technology in business functions and industries and how this technology is being misused in industries worldwide. Following these revelations, recommendations will be made on the need for regulations surrounding the use of generative AI and the future of this technology for researchers and corporations alike. This study aims to equip corporate strategists and leaders with the knowledge needed to embrace AI, be aware of its possible challenges, and find the best ways to use Generative AI to gain competitive advantage. In addition, it seeks to further equip technology experts and professionals with key information about Generative AI to help them make informed choices when working. The study also aims at research management experts and personnel as it contains vital information that will set the foundation for future research.

According to the World Health Organization, about 15% of the world’s population has some form of disability. Assistive Technology, in this context, contributes directly to the overcoming of difficulties encountered by people with disabilities in their daily lives, allowing them to receive education and become part of the labor market and society in a worthy manner. Assistive Technology has made great advances in its integration with Artificial Intelligence of Things (AIoT) devices. AIoT processes and analyzes the large amount of data generated by IoT devices and applies Artificial Intelligence models, specifically Machine Learning, to discover patterns for generating insights and assisting in decision making. Based on a systematic literature review, this article aims at identifying the Machine Learning models used in multiple different research about Artificial Intelligence of Things applied to Assistive Technology. The survey of the topics approached in this article also highlights the context of such research, their application, IoT devices used, and gaps and opportunities for further development. Survey results show that 50% of the analyzed research address visual impairment, and for this reason, most of the topics cover issues related to computational vision. Portable devices, wearables, and smartphones constituted the majority of IoT devices. Deep Neural Networks represent 81% of the Machine Learning models applied in the reviewed research.

Over the years, Artificial Intelligence (AI) has seen a steady progress in development and evolution that can aid many sectors. Today, AI plays an essential role in the Fourth Industrial Revolution, and it is making its way into the military sector as well. Many countries are actively looking into AI military technology. Some of these innovations include image recognition, text analysis, Self-driving vehicles (SDV), gaming, robotic process automation (RPA) & Robotic and Autonomous Systems (RAS), and Autonomous Weapons Systems (AWS). In this research, we discuss the advantages of each innovation mentioned and analyze the many different cybersecurity threats that awaken due to military artificial intelligence systems. We then discuss the open research areas to better improve the current state of military artificial intelligence applications in terms of ethics, system security, proper strategy plan, accepted responsibility matrix, and introduction of relevant laws.

There are four categories of medical waste that cannot be mixed as this will cause serious problems such as environmental pollution or infection. In the past, the classification of medical waste often involved a lot of human and material resources to process, with workers at risk of exposure to infectious substances. Therefore, an augmented reality (AR) and artificial intelligence (AI) medical waste classification system and method were developed. This innovative medical waste classification system and method combines AR and AI identification technology to reduce the risk of manual judgment errors by clinical staff when handling medical waste.

Artificial Intelligence (AI) play an important role in many fields, including medical education, practice, and research. Many medical educators started using ChatGPT at the end of 2022 for many purposes. The aim of this article was to explore the potential uses, benefits and risks of using ChatGPT in integrated pharmacotherapy of infectious disease module education. This study was an experimental study with content analysis of the potential applications of the ChatGPT model for integrated pharmacotherapy of infectious diseases module education was performed. The findings of this study shows that there are many potential uses, benefits and risks associated with the use ChatGPT. In conclusion, medical and health sciences educators can use ChatGPT as a guide in many aspects related to the integrated pharmacotherapy of infectious disease curriculum development, syllabus design, lecture notes preparation and exam preparation with caution.

This article is an effort to scrutinize the role of Information Technology development in the chronological transformation of Urban Planning domain using the exploratory research approach. In this research, it is argued that the theoretical and practical understanding of Urban Planning should absorb and integrate the bright outcome of the rise of Information and Technology to foster congruent future urban development. The article addresses the trends of transformation in the urban planning domain through the myopic lens of the expansion of information and communications technology era followed by investigating the key drivers shaping the interaction between modern-day urban planning and information technology considering both the dark and bright sides into account.

The integration of Artificial Intelligence (AI) in dairy farm management through biometric facial recognition for cows is a significant stride in livestock management. This review critically evaluates the evolution, applications, and challenges of AI-driven biometric facial recognition in dairy farming. It emphasizes the role of this technology in enhancing individual monitoring of dairy cows, providing accurate health, behavior, and productivity tracking. Originally derived from human facial recognition systems, this approach utilizes distinctive bovine facial features for essential, non-invasive, real-time monitoring in large-scale operations. The progression of AI from elementary pattern recognition to advanced Convolutional Neural Networks (CNNs) and deep learning models marks a shift toward data-driven farming. This study addresses key challenges such as environmental variability, data collection hurdles, ethical concerns, and technological limitations. It also contrasts various AI models, spotlighting their unique strengths and practical utility in dairy farming scenarios. Despite these challenges, facial recognition technology holds promise for improving farm efficiency, animal welfare, and sustainable practices, highlighting the need for continuous research and development. The review concludes by advocating for future research focused on environmental adaptability, ethical AI application, cross-breed compatibility, and integration with other farming technologies. Ultimately, it underscores AI's transformative potential in modernizing dairy farming towards a more data-oriented, responsible agricultural future.

This paper discusses the application and advantages of artificial intelligence technology in digital media interactive product design. Firstly, the development background of artificial intelligence technology and its promoting effect on design innovation is introduced, and the application of new technologies, such as generative adversarial networks in art creation and design personalization, is definitely analyzed. It then explores in detail the ability of AI to break traditional constraints in interactive design, innovate design, and optimize user experience, especially in digital media. Finally, the case study of Microsoft Cortana shows the method of classifying and processing user queries by machine learning system and its experimental results. The research of this paper provides the theoretical basis and empirical support for the application of artificial intelligence technology in the future interactive design of digital media and has important academic and practical significance.

The objective of this study was to apply simulation and computational intelligence techniques using artificial intelligence and genetic algorithm for economic and environmental optimization of the reverse network (manufacturers, waste managers and recyclers in São Paulo, Brazil) of waste electrical and electronic equipment (WEEE) to promote circular economy. For the economic evaluation, the reduction of: fuel, drivers, insurance, depreciation, maintenance and charges was considered and for the environmental evaluation, the environmental impact was measured in the abiotic, biotic, water, land, air and greenhouse gases compartments. It is concluded that the optimized structure of the WEEE reverse chain for São Paulo, Brazil reduced the number of collections, making the most of the cubage. It also generated economic and environmental gains, contributing to the strategic actions of the circular economy. Thus, the proposed simulation allows replication in organizational practice, mainly to meet the 2030 agenda on reducing the carbon footprint generated in transport in large cities. Thus, this study can guide companies on structuring the reverse WEEE chain in São Paulo, Brazil for economic and environmental optimization, a relevant aspect considering the exponential generation of WEEE, requiring the implementation of the national solid waste policy, and subsequently the signature of the electronics sector agreement in São Paulo.

This research paper examines the transformative impact of Artificial Intelligence (AI) on the global journalism industry. The integration of AI technologies, such as machine learning and natural language processing, has drastically changed journalistic practices and media operations. The study aims to understand how AI affects news production, distribution, and ethical considerations within journalism. By providing historical context and reviewing existing literature, this paper situates its analysis within the broader academic discourse. A mixed-methods approach is used for data collection and analysis, ensuring comprehensive and reliable findings. Results indicate that AI tools enhance journalistic efficiency and capabilities but also introduce challenges related to accuracy, authenticity, and editorial judgment. The implications for employment in journalism are significant, necessitating new skills and roles. The discussion interprets these findings in the context of media evolution and ethical standards. This study concludes by advocating for a balanced approach to AI adoption in journalism, emphasizing the importance of ethical guidelines, continuous journalist training, and ongoing research to navigate this evolving landscape.

Background: Pharmacists need up-to-date knowledge and decision-making support in HIV care. We aim to develop MARVIN-Pharma, an adapted artificial intelligence-based chatbot initially for people with HIV, to assist pharmacists, considering evidence-based needs. Methods: From Dec 2022 to Dec 2023, an online needs-assessment survey evaluated Québec pharmacists' knowledge, attitudes, involvement, and barriers relative to HIV care, alongside perceptions relevant to the usability of MARVIN-Pharma. Recruitment involved convenience and snowball sampling, targeting National HIV and Hepatitis Mentoring Program affiliates. Results: Forty-one pharmacists (28 community, 13 hospital-based) across 15 Québec municipalities participated. Participants perceived their HIV knowledge as moderate (M=3.74/6). They held largely favorable attitudes towards providing HIV care (M=4.02/6). They reported a “little” involvement in the delivery of HIV care services (M=2.08/5), most often ART adherence counseling, refilling, and monitoring. The most common barriers reported to HIV care delivery were a lack of time, staff resources, clinical tools, and HIV information/training, with pharmacists at least somewhat agreeing that they experienced each (M≥4.00/6). On average, MARVIN-Pharma's acceptability and compatibility were in the ‘undecided’ range (M=4.34, M=4.13/7, respectively), while pharmacists agreed to their self-efficacy to use online health services (M=5.6/7). Conclusion: MARVIN-Pharma might help address pharmacists' knowledge gaps and barriers to HIV treatment and care, but pharmacist engagement in the chatbot’s development seems vital for its future uptake and usability.

Recent research emphasises the importance of Artificial Intelligence applications as supporting tools for students in higher education. Simultaneously, an intensive exchange of views has started in the public debate in the international educational community. However, for a more proper use of these applications, it is necessary to investigate the factors that explain their intention and actual use in the future. With the Unified Theory of Acceptance and Use of Technology (UTAUT2) model, this work analyses the factors influencing students' use and intention to use artificial intelligence technology. For this purpose, a sample of 199 Greek students at the School of Humanities and Social Sciences from the University of Patras participated in a survey. The findings highlight that expected performance, habit, and enjoyment of these Artificial Intelligence applications are key determinants influencing teachers' intentions to use them. Moreover, behavioural intention, habit, and facilitating conditions explain the usage of these Artificial Intelligence applications. The study did not reveal any moderating effects. The limitations, practical implications, and proposed directions for future research based on these results are discussed.

Thanks to rapid development of artificial intelligence technology in recent years, the current artificial intelligence technology is contributing to many part of society. Education, environment, medical care, military, tourism, economy, politics, etc. are having a very large impact on society as a whole. For example, in the field of education, there is an artificial intelligence tutoring system that automatically assigns tutors based on student's level. In the field of economics, there are quantitative investment methods that automatically analyze large amounts of data to find investment laws to create investment models or predict changes in financial markets. As such, artificial intelligence technology is being used in various fields. So, it is very important to know exactly what factors have an important influence on each field of artificial intelligence technology and how the relationship between each field is connected. Therefore, it is necessary to analyze artificial intelligence technology in each field. In this paper, we analyze patent documents related to artificial intelligence technology. We propose a method for keyword analysis within factors using artificial intelligence patent data sets for artificial intelligence technology analysis. This is a model that relies on feature engineering based on deep learning model named KeyBERT, and using vector space model. A case study of collecting and analyzing artificial intelligence patent data was conducted to show how the proposed model can be applied to real-world problems.

Background: The research is conducted against the backdrop of the rising incidence of diabetes and the demand for novel approaches to improve patient outcomes. With the goal of revolutionizing conventional methods and addressing the changing healthcare landscape, the study concentrated on the application of AI technology in diabetes treatment. Objective: Examining the various ways AI can be used in diabetes care and assessing how it can enhance automated screenings, tailored treatment recommendations, predictive modeling, and patient self-management were the main goals of the study. Result: The findings demonstrated how well AI works to anticipate a patient's unique reaction to a therapy, assist medical personnel in making decisions, and give patients more control over their own care. Artificial intelligence (AI)-powered solutions showed promise in improving real-time monitoring and encouraging patient adherence, supporting a comprehensive strategy for managing diabetes. Conclusion: The research indicates that artificial intelligence (AI) has great potential to change the way diabetes is treated. However, it also highlights the need for more research, ethical issues, and validation of AI. The results advocate for joint efforts to ensure responsible and patient-centered technology innovation in diabetes care and encourage the integration of AI with new technologies.

Cancer is the second major cause of disease-related dead worldwide, and its accurate early diagnosis and therapeutic intervention are fundamental for saving the patient’s life. Cancer, as a complex and heterogeneous disorder, results from disruption and alteration of a wide variety of biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites that eventually emerge as clinical symptoms. Traditionally, diagnosis is based on clinical examination, blood tests for biomarkers, histopathology of biopsy, and imaging (MRI, CT, PET, US). Additionally, omics biotechnologies help to further characterize the genome, metabolome, microbiome traits of the patient that could have an impact on the prognosis and patient’s response to the therapy. The integration of all these data relies on gathering of several experts and may require considerable time, and, unfortunately, it is not without the risk of error in the interpretation and therefore in the decision. Systems biology algorithms exploit Artificial Intelligence (AI) combined with omics technologies to perform a rapid and accurate analysis and integration of patient’s big data and support the physician in making diagnosis and tailoring the most appropriate therapeutic intervention. However, AI is not free from possible diagnostic and prognostic errors in the interpretation of images or biochemical-clinical data. Here, we first describe the methods used by systems biology for combining AI with omics and then discuss the potential, challenges, limitations, and critical issues in using AI in cancer research.

In the wake of rapid advancements in artificial intelligence (AI) and sensor technologies, a new horizon of possibilities has emerged across diverse sectors. Livestock farming, a domain often sidelined in conventional AI discussions, stands at the cusp of this transformative wave. This paper delves into the profound potential of AI and sensor innovations in reshaping animal welfare in livestock farming, with a pronounced emphasis on a human-centric paradigm. Central to our discourse is the symbiotic interplay between cutting-edge technology and human expertise. While AI and sensor mechanisms offer real-time, comprehensive, and objective insights into animal welfare, it's the farmer's intrinsic knowledge of their livestock and environment that should steer these technological strides. We champion the notion of technology as an enhancer of farmers' innate capabilities, not a substitute. Our manuscript sheds light on: Objective Animal Welfare Indicators: An exhaustive exploration of health, behavioral, and physiological metrics, underscoring AI's prowess in delivering precise, timely, and objective evaluations. Farmer-Centric Approach: A focus on the pivotal role of farmers in the adept adoption and judicious utilization of AI and sensor technologies, coupled with discussions on crafting intuitive, pragmatic, and cost-effective solutions tailored to farmers' distinct needs. Ethical and Social Implications: A discerning scrutiny of the digital metamorphosis in farming, encompassing facets like animal privacy, data safeguarding, responsible AI deployment, and potential technological access disparities. Future Pathways: Advocacy for principled technology design, unambiguous responsible use guidelines, and fair technology access, all echoing the fundamental principles of human-centric computing and analytics. In essence, our paper furnishes pioneering insights at the crossroads of farming, animal welfare, technology, and ethics. It presents a rejuvenated perspective, bridging the chasm between technological advancements and their human beneficiaries, resonating seamlessly with the ethos of the Human-Centric Intelligent Systems journal. This comprehensive analysis thus marks a significant stride in the burgeoning domain of human-centric intelligent systems, especially within the digital livestock farming landscape, fostering a harmonious coexistence of technology, animals, and humans.

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.

Despite the potential benefits of generative Artificial Intelligence (genAI), concerns about its psy-chological impact on medical students, especially with regard to job displacement, are apparent. This pilot study, conducted in Jordan during July–August 2024, aimed to examine the specific fears, anxieties, mistrust, and ethical concerns medical students could harbor towards genAI. Using a cross-sectional survey design, data were collected from 164 medical students studying in Jordan across various academic years, employing a structured self-administered questionnaire with an internally consistent FAME scale—representing Fear, Anxiety, Mistrust, and Ethics and comprising 12 items, with three items for each construct. The results indicated variable levels of anxiety towards genAI among the participating medical students: 34.1% reported no anxiety about genAI role in their future careers (n = 56), while 41.5% were slightly anxious (n = 61), 22.0% somewhat anxious (n = 36), and 2.4% extremely anxious (n = 4). Among the FAME constructs, Mistrust was the most agreed upon (mean: 12.35±2.78), followed by Ethics construct (mean: 10.86±2.90), Fear (mean: 9.49±3.53), and Anxiety (mean: 8.91±3.68). Sex, academic level, and Grade Point Average (GPA) did not significantly affect the students’ perceptions of genAI. However, there was a notable direct association between the students’ general anxiety about genAI and elevated scores in the Fear, Anxiety, and Ethics constructs of the FAME scale. Prior exposure to genAI and its previous use did not significantly modify the scores of the FAME scale. These findings highlighted the critical need for refined educational strategies to address the integration of genAI in medical training. The results demonstrated a pervasive anxiety, fear, mistrust, and ethical concerns among medical students regarding the deployment of genAI in healthcare, indicating the necessity for curriculum modifi-cations that focus specifically on these areas. Interventions should be tailored to increase genAI familiarity and competency, which would alleviate apprehension and equip future physicians to engage with this inevitable technology effectively. The study also highlighted the importance of incorporating ethical discussions into medical courses to address mistrust and concerns about the human-centered aspects of genAI. Conclusively, the study calls for a proactive evolution of medical education to prepare students for AI-driven healthcare practices shortly to ensure that physicians are well-prepared, confident, and ethically informed in their professional interactions with genAI technologies.

Abstract: The COVID-19 pandemic has disrupted global energy markets and caused significant socio-economic impacts worldwide. The pandemic has also impacted the energy sector, with demand for energy reducing due to lockdowns and reduced economic activity. Hence, this paper aims to provide a comprehensive and analytical review of the impact of COVID-19 on the energy sector and discuss the potential role of artificial intelligence (AI) in mitigating some of these effects. This review examines the changes in energy demand patterns resulting from the pandemic and the implications for the energy industry, including the shifts in policymaking, communication, digital technology, energy conversion, environmental, energy market, and power system operation. The analysis of the energy pattern is discussed according to the timeframes which include pre-, mid-, and post-pandemic periods. Furthermore, we explore how AI can be used to improve energy efficiency, optimize energy use, and reduce energy wastages. The potential for AI to contribute to developing more efficient and sustainable energy systems has also been addressed. Lastly, we highlighted AI’s challenges, which play a significant role in the energy sector's response to the COVID-19 pandemic. The recommendations for AI applications in the energy sector for the transition to a more sustainable energy future are outlined. Information corroborated in this review is expected to provide important guidelines for crafting future research areas and directions in preparing the energy sector for any unforeseen circumstances or pandemic-like situations.

Recruitment is a fundamental aspect of Human Resource Management to drive organizational performance. Traditional recruitment processes, with manual stages, are time-consuming and inefficient. Artificial Intelligence (AI), which demonstrates its potential in various sectors such as healthcare, education and notable cases of ChatGPT, is currently reshaping recruitment by automating tasks to improve efficiency. However, in Thailand, where there is a growing demand for talents, the application of AI in recruitment remains relatively limited. This research focuses on human resources (HR) and recruitment professionals in Thailand, aiming to understand their perspectives on the integration of AI in recruitment. It extended the Unified Theory for Acceptance and Use of Technology (UTAUT) model, customized to suit the specific requirements of Thai recruitment practices. The study explores the factors influencing users' intention to adopt AI in recruitment. Survey questionnaire items were created based on prior literatures and refined with insights from HR and recruitment experts to ensure applicability in the context of recruitment in Thailand. A survey involving 364 HR and recruiting professionals in Bangkok metropolitan area supplied comprehensive responses. The research reveals that several factors, including Perceived Value, Perceived Autonomy, Effort Expectancy, and Facilitating Conditions, significantly impact the intention to adopt AI for recruitment. While Social Influence and Trust in AI Technology do not have a direct influence on intention, Social Influence directly affects Perceived Value. Trust in AI Technology positively influences Effort Expectancy. This study provides valuable benefits for HR and recruitment professionals, organizations and AI developers by offering insights into AI adoption and sustainability, enhancing recruitment processes and promoting the effective use of AI tools in this sector.

This brief communication intends to develop critical thinking on the development at different levels that could be evaluated in the well-being of humans from the local development towards a global scale or planetary point of view. Therefore it was presented and discussed how Artificial Intelligence tools could provide important contributions and influence in the design of critical thinking to develop ideas and insights within Research as well as funding for technology transfer. In this context it should be highlighted that the generation of funding and economy for sustainability that it should be based on critical thinking and analysis of what is need it from the close surrounding towards longer distances as well as in the inversed direction. In this regard, the main axes that support it are based on the importance of entailment of individuals in their close surrounding as well as to longer physical distances and cultural differences. An in this context it should be highlighted the existence of large diversity and varied Multicultural differences. These factors could affect the developments of technology, education; level, and quality of life. Thus the Economical and development indexes could be largely varied and different between them. For this reason it should highlighted the local development within shortened times by interacting to receive or provide materials with aggregated value or non-tangible values. Therefore, it could be accelerated the process of improvement in a targeted field looking for average homogeneous development everywhere. And there, it is where the link-up and transfer strategies of technology and education by creation of varied strategies in communication media. As for example the communication plays an important role to transfer knowledge within the different social status and imagining the development of thinking that later it will be the basis for the next generation of technology. By this manner, it could be coupled different levels of educations degrees by a factor in common related with curiosity and open minds to learn, work, and live together in the context of developments of new smart manners of living where economy and new encrypted currency are involucred and provide by different manners sustainability. In these perspectives the development and education at the University and higher degrees taking into account the transmission of important factors such as; i) entailment , ii) development of knowledge, and iii) transference, is fundamental. Finally, it should be highlighted the particular need of good human relationships, emotional intelligence, and diplomacy to afford from the simplest challenges to the higher ones.

In the digital age, social media platforms have become a significant source of both information and misinformation, presenting challenges and opportunities for threat intelligence. Natural Language Processing (NLP) has emerged as a powerful tool for extracting actionable insights from the vast amounts of unstructured text generated on these platforms. This paper explores the application of NLP techniques to enhance social media threat intelligence, focusing on methodologies for detecting and analyzing threats such as disinformation, cyberbullying, and extremist content. We examine various NLP approaches, including sentiment analysis, topic modeling, and entity recognition, and their effectiveness in identifying and mitigating potential risks. The paper also addresses the challenges associated with processing social media data, such as dealing with slang, context, and multilingual content. By leveraging NLP, organizations can improve their ability to monitor and respond to emerging threats in real-time, ultimately enhancing their overall security posture. The findings suggest that while NLP offers significant benefits, it must be complemented by human expertise and ethical considerations to ensure accurate and responsible threat assessment.

In the early 1990s, cyberfeminism emerged as an area of knowledge to discuss the connection between gender and technology. According to UNESCO, women are underrepresented in the field of science, technology, engineering, and mathematics and less than a third of women worldwide work in scientific research and development. However, this number has grown and this reality is constantly changing. In this context, using business intelligence techniques, this study analyzes data from the computer and information and communication technology market to characterize the impact of the performance of women in these areas. It is expected to show that this performance in the highlighted fields is still a challenge in Brazil.

Web-Based GIS is a platform that deals with the representation, management and storage of locational information for real-time online retrieval, viewing and manipulations. Web-based GIS incorporate all the functions of information systems: gathering, storing, retrieving, visualizing and dissemination of spatial information on the Internet. In modern-day GIS serve as vital means to not only geo-referenced locational information that are been retrieved but also the development in web-based technologies as well as there involvements in decision making processes. In recent time the increased used of GIS on the Internet have totally transformed and made three distinct changes across our GIS environment. These are: (i) how are GIS data being accessed (ii) what are modes of its transmission and (iii) what available GIS functionalities are within the environment for analysis. This paper examines the applications and technologies associated with web-based GIS architectures with an emphasis on the internet GIS based structure and case studies of GIS Web-based applications in MapInfo, Microsoft Silverlight Technology and the ArcGIS Server using ArcGIS JavaScript API.

This study is grounded on evidence-based decision-making in the implementation of devolved functions in Kenya. Promulgation of the constitution of Kenya 2010 set stage for devolution of functions, power, resources, and representation closer to the people. The researcher applies principles of technology acceptance modelling to investigate the extrinsic factors, Perceived-Ease-Of-Use, Perceived-Usefulness, attitudes, behavioural-intention and actual use of Research findings, Innovations and Technologies. Furthermore, the researcher applies contingent valuation technique to investigate cost-benefit analysis of using research findings, innovations and technologies. Multi-stage cluster sample of 156 managers from ten counties, randomly distributed in the six devolved functions that were randomly selected, including: agriculture, county health service, early childhood education and village polytechnic, natural resources and environment, county administration and governance, and county public works and services, took part in this study. Building on basic statistical analysis, structural equation modelling was employed to explain the adoption process. Results proved that all TAM model constructs were decisive in the adoption process. Furthermore, managers were willing to pay extra to optimize decision making and for accrued benefits on service delivery. Findings from this study are critical for stimulating the uptake of research findings, innovations, and technologies in making informed decisions for improved service delivery.

Blockchain and Machine Learning gives the best solutions together in performing various tasks in the Smart Health care system. With these two new emerging technologies, that have materialized in the last decade. In this paper, we proposed secure, transparent and intelligent methods in the Internate of medical things industry using Machine learning models and blockchain technology to enhance security level and train our models to improve diagnostic, prevention, treatment of the patient, patient rights, patient autonomy and equality in the health care system.

This paper presents the results of a study aimed at understanding how technology maturity level influences the incidence of university technology transfer to the private sector. The study examined the topic from the perspective of private sector organizations. It used data from a random sample of patent applications filed with the United States Patent and Trademark Office and a theoretically guided sampling of multiple cases of private sector organizations that contemplated obtaining and assimilating technologies created at universities in the United States. The patent application data were analyzed using nonparametric statistical techniques and the case data were analyzed using qualitative comparative analysis (QCA). The findings of the study suggest that the typical maturity level of technologies created at U.S. universities is a TRL-5 or lower on as scale adapted from the NASA technology readiness level (TRL) scale. A technology maturity level of TRL-6 or higher is likely an insufficient but necessary part of at least one unnecessary but sufficient configuration of conditions that tends to result in the occurrence of university technology transfer. However, under certain circumstances, a technology maturity level of at least TRL-6 could be a sufficient but unnecessary condition for the occurrence of university technology transfer. These findings have several important implications. First, they provide support for the notion that university technology transfer is subject to causal complexity. Moreover, it may be possible to increase the incidence of university technology transfer in the United States by implementing public policy and practices that explicitly take technology maturity level into consideration.

Trends focused on the Global Innovation Index (GII) as a measure for progress of nanochemistry. This paper provides projections of recent developments in the word in nanochemistry based on the Global Innovation Index as a predictor for certain Arab countries. The GII is an annual ranking of countries by its ability and performance in innovation and is calculated on a basic average from five and two pillars in two sub-indexes, the Innovation Input Index and the Innovation Output Index. Each pillar represents a trait of creativity and consists of up to five measures, with a weighted average formula for measuring their ranking. In 2008, the GII rose to 36.3 in 2016 from 0.5. The GII is smaller than the GII in Arabic countries worldwide. During the years 2013-2016, the worldwide GII was increasing while for the same period, for Arabic countries, this decline could be explained by economic and industrial wars in the Arab region.

In some parts of Nigeria, many girls do not attend school, and among those sent to school, many still drop out early. This and other socio-cultural factors affect girls psychologically. There is no doubt that girls need consistent love and tutoring to guide them through the turbulent teen years and beyond. They need a mentor who acts as a friend and a role model. The Mobile-based Mentoring Platform seeks to leverage on mobile technology's affordances to focus on the needs of the girl-child, such as improvement in academic achievement, guidance in career choice, development of self-concept, and esteem. The girl-mentees comments revealed that using the platform provided them frequent access to mentors and access to learning opportunities. The challenges they faced include epileptic internet network, intrusions by parents, and others. Therefore, this paper examined the challenges and benefits of mentoring girls via a mentoring platform.

1) Background: Genomic medicine harbors the real potential to improve the health and healthcare jour-ney of patients, care provider experiences, and improve health system efficiency – even reducing health care costs. There is expected to be an exponential growth in medically necessary new genome- based tests and test approaches in coming years. Testing can also create scientific research and commercial opportu-nities beyond healthcare decision-making. The purpose of this research is to generate a better under-standing of Canada’s state of readiness for genomic medicine, and to provide some insights for other healthcare systems; (2) Methods: a mixed-methodsapproach of literature review and key informant in-terviews with a purposive sample of experts was used. Health system readiness was assessed using a pre-viously published set of conditions. (3) Results: Canada has created some of the established conditions but more needs to be done to improve the state of readiness for genome-based medicine. Important gaps are the need for linked information systems and data integration; evaluative processes that are timely, and transparent; navigational tools for care providers; dedicated funding to facilitate rapid onboarding and supports test development and proficiency testing; and broader engagement with a broader set of inno-vation stakeholders. These findings highlight the known role of organizational context, social influence, and other factors that are known to affect the diffusion of innovation within health systems

Over the years there has been an increase in the development of assistive technologies (AT’s). The implementation and scaling of AT’s at care organizations remains challenging. Moreover, to date, it has been underexplored during which stages of dementia these technologies can be best implemented. Previous work has developed an infographic providing guidance on when best to introduce AT’s for people with dementia. Although this infographic is relevant, it was based solely on the experience of the authors during various national and international projects. However, over the years a wide spread of studies has been conducted in the field of dementia on the use and implementation of technology for PwD. Therefore, in this paper we will revise the current infographic based on the insight gained from existing literature and complement these by interviews with expert on the implementation of care technology

Electricity consumption is often the hotspot of life cycle assessment (LCA) of products, industrial activities, or services. The objective of this paper is to provide a consistent, scientific, region-specific electricity-supply-based inventory of electricity generation technology for national and regional power grids. Marginal electricity generation technology is pivotal in assessing impacts related to additional consumption of electricity. China covers a large geographical area with regional supply grids; these are arguably equally or less integrated. Meanwhile, it is also a country with internal imbalances in regional energy supply and demand. Therefore, we suggest an approach to achieve a geographical subdivision of the Chinese electricity grid, corresponding to the interprovincial regional power grids, namely the North, the Northeast, the East, the Central, the Northwest, and the Southwest China Grids, and the China Southern Power Grid. The approach combines information from the Chinese national plans on for capacity changes in both production and distribution grids, and knowledge of resource availability. The results show that nationally, marginal technology is coal-fired electricity generation, which is the same scenario in the North and Northwest China Grid. In the Northeast, East, and Central China Grid, nuclear power gradually replaces coal-fired electricity and becomes the marginal technology. In the Southwest China Grid and the China Southern Power Grid, the marginal electricity is hydropower towards 2030.

Innovation is rapidly growing and affecting various industries, including hydrocarbon processing. This study aims to conduct a comprehensive and organized review of the literature on innovation ecosystems and their performance. It will examine existing definitions of innovation ecosystems and related concepts and identify metrics and indicators for measuring innovation and entrepreneurial ecosystems. The term "innovation ecosystem" has gained considerable attention from scholars and practitioners over the past fifteen years. Despite the proliferation of research in this area, there are concerns about its fragmented knowledge base. While previous reviews have highlighted the theoretical connections between innovation ecosystems and related concepts, there is still a need for a more comprehensive understanding of the current state of innovation ecosystem research. The study used a systematic literature review approach that combines bibliographic coupling and content analysis methods, drawing on over 100 studies to identify five streams of current innovation ecosystem research: technology innovation, platform innovation ecosystems, regional development, innovation ecosystem conceptualization and theorization, and entrepreneurship and innovation. The study's contribution lies in decoding the intellectual structure of current innovation ecosystem research and providing targeted recommendations for future research.

Recently considered modifications to public policy and trends in technology transfer practices have underscored the need for validated instruments to assess the maturity of technologies. This paper details a study that aimed to develop and present a generalized technology readiness level (GTRL) scale that a variety of professionals and researchers could use to assess the maturity of technologies in a range of contexts. Of particular interest is developing and validating an instrument that technology transfer professionals in federal laboratories, universities, and the private sector in the United States of America can use to assess the maturity of technologies to facilitate their technology transfer efforts. The primary goal of the research study was to highlight the need for validated measurement instruments for assessing the maturity of technologies in these contexts, test methods for validating the GTRL scale and other readiness level scales, and fill the knowledge gap regarding methods for properly validating such instruments. This included producing insights to inform future efforts to validate the GTRL scale as well as efforts to develop and validate other instruments to measure technology maturity. The study provides specific recommendations relevant to determining the face validity, content validity, inter-rater reliability, and intra-rater reliability of the GTRL scale and other such instruments for assessing technology maturity.

This paper presents an acoustic emission (AE) detection method for refined oil storage tanks, enabling online detection of corrosion points and microleakage in the tank floor. The method utilizes an explosion-proof acoustic emission instrument to detect the floor of a refined oil storage tank. By calculating the time difference between the defective acoustic signal and the speed of acoustic wave transmission, a mathematical model is constructed to analyze the detected signals. A technical scheme for acoustic emission detection in storage tanks is designed, and the instru-ment is used to carry out acoustic emission testing on the product oil storage tank floor. Based on these test results, location analysis of acoustic emission sources is conducted on the bottom plate of the tank, evaluating its corrosion condition accurately. The consistency between the evaluation and subsequent open tank tests confirms that using acoustic emission technology effectively captures corrosion signals from product oil storage tank bottoms. Consequently, it significantly improves inspection efficiency by avoiding unnecessary tank-opening operations. Combining this method with open tank inspection operations such as the leakage magnetic method, one can fully grasp the status of the refined oil storage tanks.

This paper presents an alternative conceptualization and definition of technology in the context of university technology transfer. The ambiguity regarding the conceptualization of technology is apparent in the technology transfer literature. An expanded conceptualization of technology potentially opens new approaches to researching the topic of technology transfer. It may also cause policymakers to think more comprehensively about what it means to successfully transfer technologies derived from federally funded research to the private sector for use that benefits the public interest. This paper integrates constructs and ideas in the related literature to provide a new perspective of technology that can support future scholarly research and public policy formulation about technology transfer in general, and university technology transfer specifically. Although the paper focuses on university technology transfer to the private sector in the United States, the insights it presents are relevant to technology transfer more broadly and applicable in other geopolitical contexts.

Container crane has been widely used for port operation. However, the structure of port container crane is large, which always lead to crane damage under the vibration and strong wind. Therefore, a method for vibration reduction of container crane structure based on particle damping technology is proposed. In this investigation, the scale 1:80 crane model is built, the equivalent mechanical model is also established to preliminary verify the effect of vibration suppression. Furthermore, the dependence of vibration suppression effect on the key parameters of the damper, i.e., filling material, filling rate, particle size, and installation position of dampers are analyzed through experiments. The results indicate that the vibration peak of the crane structure displayed in Simulink oscilloscope is weakened and lags behind, the installation of the damper brings the effect of vibration suppression for the crane, the vibration suppression effect reaches 25%; the crane mode obtains the best vibration suppression effect under the condition that the material filled in the damper has a large density and elastic modulus, sufficient collision space and high collision frequency, and the dampers are installed far away from the crane center of gravity. The optimum parameters of the damper obtained from experiments, 12 mm lead beads, filling rate of 60%, installed in the distal ends within the strength range of the forearm of the crane. It is thus concluded that the particle damping technology has provided an effective way in wind and vibration reduction for container crane.

One main objective of this study was to increase the utilization of the raw material in the rose (Rosa damascena Mill.) essential oil industry by application of membrane technologies. A new ultrafiltration-based process for green recovery of polyphenols from distilled (de-aromatized) rose petals, the primary by-product in essential oil production, was created. The process includes enzyme-assisted extraction and subsequent membrane separation for partial concentration. Following a green membrane technology strategy, the newly developed process proposes an environmentally-friendly alternative to the conventional processing, comprising organic solvent ex-traction and adsorber resin purification. The results showed that the permeate flux decreased with volume reduction ratio’s rise and increased with the transmembrane pressure and feed flow rate rises. At the beginning of the process the highest flux was with UF1-PAN membrane, but at the end of the process with UF10-PAN membrane. Total polyphenols of the retentates in-creased by 27-39% and 26-67% during ultrafiltration with UF1-PAN and UF10-PAN membrane, respectively, with the highest value obtained for UF10-PAN membrane at VRR 6. The highest concentration factor and rejection of total solids, total polyphenols, redox-active antioxidants and radical scavenging antioxidants were obtained at VRR 6 with UF10-PAN membrane. This membrane and concentration level at transmembrane pressure of 0.4 MPa and feed flow rate of 330 dm3/h are recommended to be used for application of ultrafiltration of rose-petals by-product for potential use in the food industry.

Reference evapotranspiration (ETo) is a key agrometeorological index for rational irrigation management. The standard method for ETo estimation, proposed by FAO, is based on a complicated Penman-Monteith equation, requires great number of meteorological inputs thus making it difficult for practical use by farmers. To the moment, there are many alternative simplified approaches for ETo estimation, most of them are directed to cutting the number of required meteorological inputs for calculation. Among them, special attention should be paid to various temperature-based methods of ETo assessment. One of the temperature-based models for ETo computation was realized in free mobile app ETo Calculator (Ukraine). The app gives Ukrainian farmers an opportunity to assess ETo values on daily or monthly scale using mean air temperature as the only input. The goal of the study was to test the app accuracy comparing to FAO-based calculations in five key regions of Ukraine, each of which representing a particular climatic zone of the country. It was established that the app provides relatively good accuracy of ETo estimation even in raw (not adjusted to windspeed and relative air humidity) run; the results of statistical comparison with the FAO-calculated values are: R2 within 0.82-0.87, RMSE within 0.74-0.81 mm, MAE within 0.60-0.70, MAPE within 18.07-25.50% depending on the region. ETo Calculator (Ukraine) is a good alternative for complicated Penman-Monteith method and could be recommended for Ukrainian farmers to be used for irrigation management.

Conducting related research is crucial since there are still many problems that need to be resolved in the research on soft robots in the areas of material selection, structure design and manufacture, and drive control. Soft manipulators, a subset of soft robots, are now a popular area of study for many researchers. In comparison to typical manipulators, soft manipulators feature a high degree of gripping flexibility and a basic morphological structure. They are composed of flexible materials. It has a wide range of potential applications in healthcare, rehabilitation, bionics, and detection, and it can compensate for the drawbacks of rigid manipulators in some use scenarios. A modular soft-body torsional gripping system is developed after a torsional and gripping actuator is conceived, constructed, and its performance is examined. The torsion actuator and the grasping actuator can be combined in the system in a modular fashion. With the help of RGB-D vision algorithms, this multi-modular setup makes it possible to combine soft actuators with various twisting degrees and achieve exact gripping. Through pneumatic control, the target object is precisely grasped and rotated at various angles, enabling rotation of the target object in three dimensions.

A large amount of raw data collected by satellites is processed by the production chain to obtain a large amount of product data, of which the secure exchange and storage is of interest to researchers in the field of remote sensing information science. Authentic, secure data is a critical foundation for data analysis and decision-making. And traditional centralized cloud computing systems are vulnerable to attacks, and once the central server is successfully attacked, all data will be lost. Distributed Ledger Technology (DLT) is an innovative computer technology that can ensure information security, traceability and tamper-proof, and can be applied to the field of remote sensing. Although there are many advantages to using DLT in remote sensing applications, there are some obstacles and limitations to its application. Remote sensing data has the characteristics of large data volume, spatiotemporal nature, global and so on, and it is difficult to store and interconnect remote sensing data in the blockchain. To address these issues, this paper proposes a trustworthy and decentralized system using blockchain. The novelty of this paper is to propose a multi-level blockchain architecture in which the system collects remote sensing data and stores it in the Interplanetary File System (IPFS) network, after generating the IPFS hash, the network rehashes the value again and uploads it on the Ethereum chain for public query. Distributed data storage improves data security, supports the secure exchange of information, and improves the efficiency of data management.

This study empirically examines the spatial spillover effect of various environmental regulations on green technology innovation using panel data from 284 county-level cities in China between 2007 and 2019. A geographical-economic spatial weight matrix is constructed, and the spatial Durbin model is employed to identify the specific characteristics of this spillover effect. The findings indicate that the spatial spillover effect of green technology innovation primarily occurs through geographical transmission. However, there is no significant spatial autocorrelation when using the economic distance weight matrix. Various types of environmental regulations influence the spatial spillover effect of green technology innovation in distinct ways. Specifically, Market-motivated environmental regulation exhibits a U-shaped relationship with the spatial spillover effect, while Command-controlled environmental regulation demonstrates an inverted U-shaped relationship, suggesting a complementary effect. Additional research shows that the upgrading of industrial structure acts as a mediator between command environmental regulation and the spatial spillover effect of green technology innovation. Government departments should comprehensively coordinate Market-motivated environmental regulation and Command-controlled environmental regulation, accurately assess the intensity of Command-controlled measures, and prevent the migration of green technology innovation elements caused by excessive regulatory measures within enterprises.

Based on big data and artificial intelligence technology, the judicial big data intelligent service platform provides users with efficient and accurate legal consulting services. It has many functions such as intelligent question and answer, case analysis, and law inquiry. It also analyzes the application value of the intelligent legal consulting service platform in the field of judicial big data. By building an intelligent service platform, the rapid processing and analysis of massive judicial data has been realized, and the efficiency and quality of judicial work have been significantly improved. In addition, it emphasizes the importance of data management and quality control in the application of judicial big data, and puts forward specific measures to strengthen data management and quality control. With the continuous advancement of technology and the expansion of application fields, the platform is expected to achieve deep integration with other legal services, expand new application fields, and provide users with more comprehensive and efficient legal services. At the same time, it points out the problems that need to be paid attention to in the future development, such as data security, privacy protection and so on. Through in-depth analysis and research on judicial big data, the platform can provide strong support for judicial decision-making and promote the improvement of judicial justice and efficiency.

To ensure consumers can purchase high-quality rice, accurate identification of rice varieties is particularly important. Methods: This article conducts research based on machine learning algorithms from the perspectives of image, spectrum, and spectrogram fusion. Six types of hyperspectral image data of rice were preprocessed using convolutional smoothing (SG) and multiple scatter correction (MSC). Texture information of the images was extracted using gray-level co-occurrence matrix. Spectra, texture, and spectrogram data were fused into a new matrix. Spectral, texture, and spectrogram fusion data were used as inputs for the model, and support vector machines (SVM), logistic regression (LR), and K-nearest neighbors (KNN) classification models were constructed and compared. Results: From the classification results, the spectrogram fusion classification performance was better than classification models using only spectra or texture. Conclusion: The research results showed that the accuracy of SVM and LR classification models exceeded 90%, and the LR model performed the best, effectively classifying rice varieties.

Consumer IoT (CIoT) manufacturers seek customer feedback to enhance their products and services, creating a smart ecosystem like a smart home. Due to security and privacy concerns, Blockchain-based federated learning (BCFL) ecosystems can let CIoT manufacturers update their Machine Learning (ML) model using end-user data. FL uses privacy-preserving ML techniques to forecast customers' needs and consumption habits, and blockchain replaces the centralised aggregator to safeguard the ecosystem. However, Blockchain technology (BCT) struggles with scalability and quick ledger expansion. In BCFL, local model generation and secure aggregation are other issues. This research contributes a novel architecture emphasising Gateway Peer (GWP) in blockchain network to resolve scalability, ledger optimisation and secure model transmission issues. In the architecture we replace the centralised aggregator by the blockchain network, while GWP restricts the number of local transactions to execute in BCN. Considering the security and privacy of FL processes, we have added differential privacy and advanced normalisation techniques to ML processes. The approaches strengthen end-users' cyber security and encourage the adoption of technological innovation standards by service providers. The proposed approach has been tested extensively using a well-respected Stanford Cars dataset. We experimentally demonstrate that the proposed architecture makes the network scalable and optimises the ledger significantly. In addition, the normalisation technique outperforms batch normalisation when features are under DP protection.

The relentless advancement and integration of mechanization and intelligence within the coal mining industry, combined with the increasing depths of mining operations, necessitate significantly higher safety standards. This necessity arises due to the increasingly complex geological and coal seam conditions encountered as mining operations delve deeper into the earth. Traditionally, 3D seismic exploration technology has been fundamental in ensuring both operational efficiency and safety in the coal mining sector. However, despite its critical role, this technology still has limitations in detecting and characterizing small-scale geological structures which can pose substantial risks if not accurately identified and mitigated. To address these challenges, this paper introduces a sparsity-constrained adaptive hard thresholding algorithm designed to tackle the spectral inversion optimization problem with an norm constraint for the enhanced retrieval of reflection coefficients, thereby establishing a comprehensive workflow for enhancing seismic data resolution through compressed sensing spectral inversion. Real data validation has demonstrated that this method significantly enhances the resolution of seismic data while maintaining the amplitude integrity and fidelity of the original signals. This improvement allows for a more detailed and accurate characterization of small-scale geological features, which are critical in identifying and mitigating potential hazards within coal mining operations, thus offering robust support for the implementation of more effective safety protocols and disaster prevention strategies in coal mines.

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

The purpose of feature selection is to find important features from the original high-dimensional space. As atypical feature selection algorithm, Locally linear embedding(LLE)-based feature selection algorithm, which applies the idea of LLE to the graph-preserving feature selection framework, has been received wide attention. However, LLE-based feature selection framework is sensitive to noise and K-nearest neighbors. To address these problems, an improved LLE-based feature selection algorithm, robust LLE (RLLE) vote, is proposed. In this algorithm, $l_1$ and $l_2$ regularization are introduced into the high-dimensional reconstruction model of LLE. Furthermore, RLLE vote also proposes a criterion to measure the difference between the reconstruction features and the original features, and then the importance features can be selected by this criteria. Extensive experiments are carried out on a benchmark fault data set and the bearing data set collected from our own laboratory, and the experimental results demonstrate that RLLE vote achieves the most significant performance compared existing state-of-art methods.

(1) Background: Most of the existing studies focus on the evaluation of technology finance; the relationship between technology finance and technology innovation. But there are few studies on the development of technology finance and the quality of economic development in our country; (2) Methods: Based on the panel data of 30 provinces in China, this paper constructs an index system to measure the development of technology finance through the improved entropy method, and tests the spatial correlation of the development of technology finance in China by Moran'I index. According to the test results, this paper constructs a spatial econometric model to empirically analyze the promoting effect of scientific, technological and financial development on high-quality economic development, and analyzes its promoting effect in different regions and different time periods; (3) Results: The results show that the quality of China's economic growth is spatially dependent, and the development of science, technology and finance can significantly promote the quality economic development in China. And the promotion coefficient of the central region is the largest, as well as the coefficient of the eastern region is the smallest. The promotion coefficient was small and not significant before 2015, and was significantly positive after 2015; (4) Conclusions: this paper puts forward the corresponding policy recommendations according to the research results.

The main goal of the research is to assess the effectiveness of aerial ground-penetrating radar (GPR) in delineating boundaries between disturbed clayey ground and rock formations along the old Wadi. The study also tackles technical challenges associated with drone-based GPR applications in urban open areas, considering geological heterogeneity, topographic variations, and environmental conditions. The potential of using drone-based GPR equipped with an unshielded dipole antenna as a transformative tool in geospatial analysis was demonstrated. The research encompasses academic insights and field borehole drilling experiments to validate the accuracy and reliability of drone-based GPR data in real-world scenarios. Anticipated contributions include advancements in understanding drone-based GPR technology for mapping disturbed soil boundaries in foundation engineering applications and other relevant areas and recommendations for optimizing its performance in challenging terrains.

Bridging the gap between traditional education and matching the pace of education with the rest of the world, Govt. of India implemented a new education policy this year which was much awaited for a long time. The new education policy is a big step to change India's overall education scenario by changing the examination pattern. However, this new type of decision-making process seeks to ensure that it achieves great benefits covering all uncovered elements while keeping technology as a parameter, so this new policy in the previous education system is also missing some key issues that must be addressed in the next amendments. Making it an example of impact, this paper describes salient issues where new educational policies are silent or those grey areas have been only superficially touched upon.

The growing of Brain-Computer Interface (BCI) applications is closely related to the increasing accessibility of Electroencephalography (EEG) hardware (EEG headsets), which are noninvasive, portable, wireless and often with open software. However, there is a limited number of BCI software platforms tailored to help inexperienced programmers in the development of BCI applications. Only few of them integrate BCI applications with IoT devices and services. To address these challenges, a model for visual node-based programming has been designed utilizing BrainFlow library within the Node-RED platform, which can be applied to more than 20 biosensors. New Nodes for Node-RED Library within OpenBCI Category (openBCI toolkit for Node-RED) have been developed for design of EEG-Based Brain-Machine Interface and Integration in IoT. The proposed toolkit have been implemented and validated through a case study for controlling a robotic arm by OpenBCI headset. The results from the pilot experiment demonstrated that through concentration levels classified by BrainFlow performance metrics, the control of a TinkerKit Braccio robot arm is possible.

Indonesia is among the countries developing the concept of smart city. The national government envisions Indonesia 2030 which shall implement the smart city towards sustainable development. Many regions in Indonesia have already implemented the concept of smart city, but still on the level of smart government and smart technology, lacking on the smart citizen component of a smart city. This paper aims to analyze the citizens behavior of using technology on Sleman smart citizen 2021 through the lapor sleman mobile application. This research uses mixed method of quantitative and qualitative approaches in examining the data. The authors distributed 100 questionares and conducted interviews to the government which is department of communication and information, Sleman and the several community that ever use the lapor sleman application. The result of the study reveals the following: firstly, performance expectancy, social influence, and behavioral intention are three independent variables which affect the users’ behavior. Secondly, the behavioral intention has the biggest significant effect on users’ behavior. Thirdly, the citizen behavior of using technology has affect the making a smart citizen. Therefore, using technology in term of lapor sleman mobile application can make the citizens as active participants in public life, in terms of social cooperation, freedom of expression and flexibility. However, there are still many problems in the use of lapor sleman mobile application such as the limited access to only android smartphones, lack of technical know-how, and people’s reluctance to use the lapor sleman mobile application.

Sensor networks, as a special subtype of wireless networks, consist of sets of wirelessly connected sensor nodes often placed in hard-to-reach environments. Therefore, it is expected that sensor nodes will not be powered from the power grid. Instead, sensor nodes have their own power sources, the replacement of which is often impractical and requires additional costs, so it is necessary to ensure minimum energy consumption. For that reason, the energy efficiency of wireless sensor networks used for monitoring environmental parameters is essential, especially in remote networking scenarios. In this paper, an overview of the latest research progress on wireless sensor networks based on LoRa was provided. Furthermore, the analyses of the energy consumption of sensor nodes used in agriculture to observe environmental parameters were carried out. Optimization methods of energy consumption, in terms of choosing the appropriate data collection processes, as well as the settings of wireless network radio parameters were suggested. In the conducted analyses, special emphasis was placed on choosing the optimal package size. In this paper, it was proven that the adjustment of the transmission speed to the actual size of the packet is important for better energy efficiency of communication.

With the rise of powerful computational technologies, healthcare systems are going through a paradigm shift to the era of healthcare 5.0, also known as" smart healthcare". This new-age healthcare system not only enhances the life of patients but also aims to reduce healthcare costs significantly. People's health data collected through various means, including various Internet of Things (IoT) devices, is too sensitive to be regulated by particular organisations owing to increasing privacy and security threats. The sheer size of data makes it difficult for centralised bodies to regulate, due to which third-party involvement is introduced, which again poses threats to the privacy of the data holder. Motivated by the abovementioned gaps, we propose a decentralised approach using Ethereum blockchain technology and Oasis protocol. The patient-driven solution approach effectively solves the privacy problem by providing complete control of data to the owner. Along with this, byproducts such as fine-grained access control and on-chain data processing capabilities are also obtained.

Crop breeding is an important global strategy to meet sustainable food demand. CRISPR-Cas is a most promising gene-editing technology for rapid and precise generation of novel germplasm, and leads to revolutionary crop breeding innovation. In this review, we summarize recent advance of CRISPR/Cas technology in gene function analyses and generation of new germplasms with increased yield, improved product quality, and enhanced resistance to biotic and abiotic stress. We highlight their applications and breakthroughs in agriculture including crop de novo domestication, decoupling the gene pleiotropy tradeoff, crop hybrid seed conventional production, hybrid rice asexual reproduction, and double haploid breeding. Moreover, the challenges and development of CRISPR/Cas technology in crops are also discussed.

Background: An oscilloscope is a basic tool for measuring electrical quantities, as well as studying all types of waveforms. Generally, measurement of a quantity is regarding peak voltage, frequency, phase difference, pulse width, delay time, etc. However, what about the oscilloscope technology that existed in the early the 21st-century? Purpose: Aim of this study is to identify and analyze the application and use of current oscilloscopes in the early 21st-century. Method: The research methodology used is qualitative research with a systematic literature review by taking journals from 2019-2020 on the IEEE, IOP, Science Direct, ArXiv, and Measurement Science Review journal databases. Results: The results obtained are a batch of 12 articles showing the application and use of oscilloscope technology at the beginning of the 21st century. Conclusion: The conclusions applied to the application of oscilloscope technology in the early 21st-century are centered on the field of education in the measurement of physical quantities and the use of measurement technology which has high efficiency and low cost.

The ignorance of or lack of knowledge about cybersecurity aspects causes a critical problem regarding confidentiality and privacy. This security problem will continue to exist even if the user possesses less expertise in information security. The modern IT technologies are well developed, and almost everyone uses the features of IT technologies and services within the Internet. However, people are being affected due to cybersecurity threats. People can adhere to the recommended cybersecurity guidelines, rules, adopted standards, and cybercrime preventive measures. However, it is not possible to entirely avoid cybercrimes. Cybercrimes often lead to sufficient business losses and spread forbidden themes (hatred, terrorism, child porn, etc.). Therefore, to reduce the risk of cybercrimes, a web-based Blockchain-enabled cybersecurity awareness program (WBCA) process is introduced in this paper. The proposed web-based cybersecurity awareness program trains users to improve their security skills. The proposed program helps with understanding the common behaviors of cybercriminals and improves user knowledge of cybersecurity hygiene, best cybersecurity practices, modern cybersecurity vulnerabilities, and trends. Furthermore, the proposed WBCA uses the Blockchain technology to protect the model from the potential threats. The proposed model is validated and tested using real-world cybersecurity topics with real users and cybersecurity experts. We anticipate that the proposed program can be extended to other domains, such as national or corporate courses, to increase the cybersecurity awareness level of users.

Background: Alzheimer's disease causes great changes, with the prefrontal cortex be-ing frequently damaged zones, affecting physical and cognitive behavioural and compromising autonomy and quality of life. Objective: To evaluate the effectiveness of feedback-based tech-nology on the improvement of physical-cognitive capacities in Alzheimer. Methods: Forty pa-tients with Alzheimer were recruited from the Santa Elena Alzheimer's Association, Seville, of which 15 ended up being pilot participants, divided randomly into controls (8) and experi-mental (7). They were evaluated with: Cognitive Mini-Examination Scale, Oddball Test and At-tention Network, Berg Scale, Tinetti, Timed up and Go and Geriatric Deterioration Scale. The experimental group was treated with feedback-based technology under the supervision of physiotherapists, during 16 thirty-minute sessions, twice a week. The control group performed their usual care. Result: A significant improvement was observed in the experimental group (Cognitive Mini-Examination and Tinetti). In the group of control, a worsening of physical and motor skills was evidenced because of the degenerative disease. Discussion/Conclusion: This treatment could be a strategy to keep the patient in early stages for longer, and improve quali-ty of life delaying dependency. It creates intergenerational bonds and use at home to promote treatment adherence.

Mucosal vaccines offer several advantages over injectable conventional vaccines, such as adaptive immunity induction, secretory IgA production at the entry site of most pathogens, and needleless vaccinations. Despite their potential, only a few mucosal vaccines are currently used. Developing new effective mucosal vaccines strongly relies on identifying innovative anti-gens, efficient adjuvants, and delivery systems. Several approaches based on phages, bacteria, or nanoparticles have been proposed to deliver antigens to mucosal surfaces. Bacterial spores have also been considered antigen vehicles, and various antigens have been successfully exposed on their surface. Due to their peculiar structure, the spores conjugate the advantages of live microorganisms with synthetic nanoparticles. When mucosally administered, spores expressing anti-gens have been shown to induce antigen-specific, protective immune responses. This review accounts for recent progress in the formulation of spore-based mucosal vaccines, describing the spore's structure, specifically the spore surface, and the diverse approaches developed to improve its efficiency as a vehicle for heterologous antigen presentation.

Reliable detection of sunflower (Helianthus annuus) in edible and used cooking oil (UCO) is crucial for the sustainable production of food and biodiesel. In this study, a variety of sunflower oils (crude, cold pressed, extra virgin, refined, and UCO) were examined using different methods of DNA extraction and PCR amplification to develop an efficient technology for the identification of sunflower in oils. DNA extraction kits such as NucleoSpin Food, DNeasy mericon Food, and Olive Oil DNA Isolation as well as modified CTAB method were found to be able to isolate amplifiable genomic DNA from highly processed oils. Novel uniplex, double, and nested PCR systems targeting the sunflower-specific helianthinin gene were developed for efficient identification of sunflower. New sunflower DNA markers were revealed by uniplex PCRs. The combination of modified CTAB and nested PCR was demonstrated as a reliable, rapid, and cost-effective technology for detecting traces of sunflower in 700 μl of highly processed oil, including refined and used cooking oil. The study will contribute to both the food industry and the energy sector as developed methods can be used for oil authenticity testing in food and biodiesel production.

As the natural resources are getting exhausted, the concept of sustainable development of region has received increasing attentions, especially for resource-dependent cities. In this paper an innovative method that emergy analysis and IPAT (Human Impact Population Affluence Technology) model were combined in order to analyze the quantitative relationship of economic growth and energy consumption and further to evaluate its overall sustainability level. Taiyuan, a traditional, resource-dependent city in China, is selected as the case study region. The main results show that total emergy of Taiyuan increased from 9.023× 1023 sej in 2007 to 9.116× 1023sej in 2014, with 38% reduction on non-renewable emergy and 125% growth on imported emergy. Regional emergy money ratio (RMB) was reduced by 48% from 5.31× 1013sej/$ in 2007 to 2.74× 1013sej/$ in 2014, indicating that the increasing speed of consuming resources and energy was faster than the increase of GDP, and that Taiyuan’s money purchasing power declined. The lower emergy sustainability index (ESI) indicates that Taiyuan was explored and produced large quantities of mineral resources, which puts more stress on the environment as a consequence, and that this is not sustainable in the long run. The IPAT analysis demonstrates that Taiyuan sticks to the efforts of energy conservation and environmental protection, in order to promote regional sustainable development, it is necessary to take an integrated effort. Policy insights suggest that resourceful regions should improve include energy and resource efficiency, optimizing energy and resourceful structure.

Musculoskeletal disorders are a common occurrence in Europe, affecting millions of people and costing billions of euros in social security services. Traditional treatments using drugs like non-steroidal anti-inflammatory drugs (NSAIDs) can cause gastric damage, making prolonged use difficult. To address this issue, the Author has developed NegEnt Spray - Muscles and Joints, a topical medical device that uses liposomal cannabidiol and chamomile hydrolate to reduce inflammation and pain in musculoskeletal disorders. The liposomal technology used in NegEnt Spray allows for better bioavailability and a more targeted action, while chamomile hydrolate provides a natural anti-inflammatory and soothing effect. The spray is non-greasy, absorbs quickly, and can be used under any circumstances. Its pocket-sized design makes it easy to carry and use throughout the day. With NegEnt Spray, patients can alleviate discomfort and pain associated with musculoskeletal disorders without worrying about the side effects of traditional treatments.

The application of OpenAI's advanced artificial intelligence language model, ChatGPT, in education has garnered widespread attention from the academic community. The openness and intelligence features of ChatGPT demonstrate great potential and value in supporting students' learning, daily life, and communication. This study, grounded in the theoretical frameworks of Innovation Diffusion Theory (IDT) and the Technology Acceptance Model (TAM), conducted a systematic empirical analysis through a questionnaire survey of 677 university students who had used or were currently using ChatGPT. The results indicate that the six perceived attributes of innovation significantly influence university students' behavioral intention to use ChatGPT, while attitudes towards ChatGPT, perceived usefulness, and perceived ease of use indirectly influence students' behavioral intention to continue using ChatGPT. Based on the findings of this study, we propose an extended Technology Acceptance Model (TAM) that incorporates key elements of Innovation Diffusion Theory (IDT) to optimize the application of advanced technologies, such as ChatGPT, in education, thereby improving students' learning efficiency and behavioral performance. These findings not only provide important insights into the complex relationships between college students’ perceptions of ChatdGPT technology and predictors of behavioral intentions, but can also help relevant education departments, higher education institutions, and university teachers to effectively plan and evaluate the use of ChatGPT as a learning tool.

The underlying assumption of the discourse about technology transfer is that government intervention is legitimate. Little scholarship has examined whether this assumption is valid or not and on what basis. Legitimacy is an important construct in the context of public policy. Government intervention can only be sustained if the public views such action as legitimate. The creation and transfer of technologies to the private sector is an area where there is significant government intervention. This paper reconceptualizes political legitimacy in the context of technology transfer policy. The analysis illuminates several concerns and challenges regarding the traditional approach to understanding whether specific government interventions in technology transfer are legitimate. It subsequently applies social constructionism and the notion of morality tales to describe an alternative conceptualization of political legitimacy that integrates aspects of other frameworks. In doing so, it reimagines political legitimacy as less of an unattainable normative principle of limited practical value to policymakers and more of a descriptively understood social phenomenon that policymakers can apply while formulating not only technology transfer policy, but other kinds of public policy as well. The paper demonstrates that there is a broader basis for claims of political legitimacy for government intervention in technology transfer, there is likely a more expansive range of technology transfer problems with which the government can rightly concern itself as well as possible solutions that policymakers can justifiably consider for addressing those problems, and that the political consequences of potential overreach in technology transfer policy are likely minimal.

We propose a new methodology for long-time biopotential recording based on MEMS multisensor integrated platform featuring a commercial electrostatic charge transfer sensor. That family of sensors was originally intended for presence tracking in automotive, so the existing setup has been engineered for the acquisition of electrocardiogram, electroencephalogram, electrooculogram and electromyography, designing a dedicated front-end and writing a proper firmware for the specific application. Systematic tests on controls and nocturnal acquisitions from patients in domestic environment will be discussed in detail. The excellent results candidate this technology to provide a low power, unexplored solution to biopotentials acquisition. The technology breakthrough is in that it enables adding this type of functionality to existing MEMS boards at near-zero additional power consumption. For these reasons, they open to additional possibilities for wearable sensing and strengthen the role of MEMS technology in medical wearable applications for long time synchronous acquisition of a wide range of signals.

Businesses now formally enter the phase of "think power differentiation and gap" in response to the new economic climate. While AI, big data, empowerment via the internet, and other aspects of the digital economy help to improve technical capabilities and save operating costs, they also push businesses to innovate their strategies at a faster rate. Enterprises are strategically choosing to de-enterprise, moving from focusing on a single business functioning benefit to building an ecosystem of platform services that will give them a competitive edge. Additionally, the organization's structure is evolving continually from a vertical model to a curved working style, in order to achieve this, businesses must recognize the importance of data analysis in day-to-day management and operations, encourage the development of an organizational atmosphere that is driven by data, fully utilize digital technology, modularize areas or production links that perform related tasks, and encourage the reform of organizational structures. By streamlining the output of the product or service to the external community while also enhancing the company's financial performance model, switching from process-to result-oriented evaluation, and putting in place a customized authority and accountability framework. Replace outcome-focused evaluation with process-focused evaluation, and create a customized incentive scheme that balances power, accountability, and rewards.

For the soil in different regions, the nutrient fertility contained in it is different, and the detection and zoning management of soil nutrients before tillage every year can improve grain yield. In this paper, an integrated learning strategy model based on black soil hyperspectral data is designed for rapid classification of organic matter content classification of black soil. Soil hyperspectral image dataset of Xiangyang Experimental Base was collected; by changing the internal structure of the stacking model, an LSVM-stacking model with (MLP, SVC, DTree, XGBl, kNN) five classifiers as the L1 layer was built, and the simulated annealing algorithm was used for hyperparameter optimization. Compared to other stacking models, the LSVM-stacking metrics are significantly improved. The accuracy rate of hyperparameter optimization is improved by 38.6515%, the accuracy rate of the independent test data set is 0.9488, and the comparison of individual learners can improve the recognition classification accuracy of label"1" to 1.0.

Optical wireless video transmission technology combines the advantages of high data rates, enhanced security, large bandwidth capacity, and strong anti-interference capabilities inherent in optical communication, establishing it as a pivotal technology in contemporary data transmission networks. However, video data comprises a large volume of image information, resulting in substantial data flow with significant redundant bits. To address this, we propose an adaptive block sampling compressive sensing algorithm that overcomes the limitations of sampling inflexibility in traditional compressive sensing, which often leads to either redundant or insufficient local sampling. This method significantly reduces the presence of redundant bits in video images. First, the sampling mechanism of the block-based compressive sensing algorithm was optimized. Subsequently, a wireless optical video transmission experimental system was developed using a Field-Programmable Gate Array chip. Finally, experiments were conducted to evaluate the transmission of video optical signals. The results demonstrate that the proposed algorithm improves the Peak Signal-to-Noise Ratio by over 3 dB compared to other algorithms, with an enhancement exceeding 1.5 dB even in field tests, thereby significantly optimizing video transmission quality. This research contributes essential technical insights for the enhancement of wireless optical video transmission performance.

This study provides a thorough examination of the introduction of 5G technology in Bangladesh's agriculture sector, providing insights into the problems and potential implications related to its adoption. Focusing on the contextual landscape of Bangladesh, the study evaluates the effectiveness, efficiency, scalability, and ease of implementation of 5G technology concerning key challenges faced by the agricultural sector. Drawing comparisons with global perspectives, including countries such as China, Taiwan, Colombia, Netherlands, and India, the analysis provides a nuanced understanding of the diverse technological landscapes. The examination encompasses the evolution from 4G to 5G, emphasizing the commitment of major telecommunications providers in Bangladesh to advance technological capabilities. The challenges in rural adoption, limited technology literacy among farmers, and high smartphone costs are discussed, highlighting potential barriers to seamless integration. To address the creation of a more comprehensive technological infrastructure, the study also explores the efforts and regulatory support provided by the government and the Bangladesh Telecommunication Regulatory Commission (BTRC). Furthermore, the comparative analysis sheds light on the unique characteristics and advancements in other countries, contributing to a holistic understanding of 5G's role in transforming global agricultural practices. By exploring the anticipated socio-economic factors influencing the pace and success of 5G adoption in the agricultural sector, this paper emphasizes the need for collaborative efforts, infrastructure development, and addressing accessibility challenges to achieve widespread agricultural technology integration.

Fingerprint recognition systems have achieved widespread integration into various technological devices, including cell phones, computers, door locks, and time attendance machines. Nevertheless, individuals with worn fingerprints encounter challenges when attempting to unlock original fingerprint systems, resulting in disruptions to their daily activities. This study explores two distinct methods for fingerprint backup: traditional fingerprint impression and 3D printing technologies. Unlocking tests were conducted on commonly available optical fingerprint lock-equipped cell phones to assess the efficacy of these methods, particularly in unlocking worn fingerprints. The research findings indicated that traditional fingerprint impression method exhibited high fidelity in reproducing fingerprint patterns, achieving an impressive unlocking success rate of 97.8% for imprinting unworn fingerprints. However, when dealing with worn fingerprints, traditional fingerprint impression technique showed a reduced unlocking success rate, progressively decreasing with increasing degrees of finger wear. In contrast, 3D printed backup fingerprints, with image processing and optimization of ridge height, mitigated the impact of fingerprint wear on unlocking capability, resulting in an unlocking success rate of 84.4% or higher. Thus, the utilization of 3D printing technology proves advantageous for individuals with severely worn or incomplete fingerprints, providing a viable solution for unforeseen circumstances.

Voice search technology has gained popularity in recent years, transforming the way individuals interact with digital devices. This study aims to explore the sustainable use intention of voice search, focusing on the psychological factors that influence new technology adoption. Using the Technology Acceptance Model (TAM) and anthropomorphism theory, it provides insights for developers, marketers, and policymakers interested in voice search technology. Through an online survey targeting experienced voice search users in the U.S., the study examines the impact of TAM factors (perceived usefulness, ease of use, quality satisfaction) and anthropomorphism traits (narcissism, Machiavellianism) on sustainable use intention of voice search. Results from hierarchical regression analysis show that perceived usefulness, ease of use, and quality satisfaction positively affect sustainable intention of voice search. Additionally, narcissism and Machiavellianism predict sustainable intention of voice search. By merging TAM and anthropomorphism theory, this study enhances our understanding of voice search behavior and technology adoption.

Estimates by the World Health Authority suggest that 1 billion people do not have access to the assistive technologies they require. Over the past decade, the design of products that empower people with a disability has shifted from specialised and dedicated products designed only for those with a disability to features and functions integrated into cost-effective consumer technologies for the benefit of all. The opportunity for expansion of the availability of such technologies is at risk of being ignored as a result of models of delivery that are founded in medical devices and which have failed to reflect trends in our understanding of technology and the choices and preferences expressed by persons with a disability. This research undertaken suggests that the opportunities of such expansion offer significant benefits to people with a disability and better both economic and social return on investment for authorities.

As the global human population increases, animal agriculture must adapt to provide more animal products while also addressing concerns about animal welfare, environmental sustainability, and public health. The purpose of this review is to discuss the digitalization of animal farming with Precision Livestock Farming (PLF) technologies, specifically biosensors, big data, and block chain technology. Biosensors are noninvasive or invasive sensors that monitor an animal’s health and behavior in real time, allowing farmers to monitor individual animals and integrate this data for population-level analyses. The data from the sensors is processed using big data-processing techniques such as data modelling. These technologies use algorithms to sort through large, complex data sets to provide farmers with biologically relevant and usable data. Blockchain technology allows for traceability of animal products from farm to table, a key advantage in monitoring disease outbreaks and preventing related economic losses and food-related health pandemics. With these PLF technologies, animal agriculture can become more transparent and regain consumer trust. While the digitalization of animal farming has the potential to address a number of pressing concerns, these technologies are relatively new. The implementation of PLF technologies on farms will require increased collaboration between farmers, animal scientists, and engineers to ensure that technologies can be used in realistic, on-farm conditions. These technologies will call for data models that can sort through large amounts of data while accounting for specific variables and ensuring automation, accessibility, and accuracy of data. Issues with data privacy, security, and integration will need to be addressed before there can be multi-farm databases. Lastly, the usage of blockchain technology in animal agriculture is still in its infancy; blockchain technology has the potential to improve the traceability and transparency of animal products, but more research is needed to realize its full potential. The digitalization of animal farming can supply the necessary tools to provide sustainable animal products on a global scale.

The R&D and propagation of sustainable appropriate technology requires the provision of stable funding. Crowdfunding is a form of funding whereby small sums of investments or contributions are collected from among the general public and used to finance the development of goods or services. This method has been in use widely in arts and culture fields, and presents a useful alternative means for funding appropriate technology projects. The aim of this study is to identify the factors influencing the backers who participate in appropriate technology projects through crowdfunding platforms, analyzing the relationships between these factors, and thereby establishing the usefulness of crowdfunding as a viable new alternative. The analysis results indicate that key factors influencing user intention to crowdfund appropriate technology projects include social influence, effort expectancy, and perceived trust. In contrast to the findings of previous studies, performance expectancy was not found to have a significant effect. This reflects the fact that, compared to the crowdfunding conducted for other purposes, crowdfunding for appropriate technology is closer in nature to donations. These findings indicate that for funding to be successful, aggressive online exposure using the SNS of backers should be pursued from the early stages of funding.

Brewers’ spent grain (BSG) is an important waste produced in beer companies with a high po-tential to be transformed into commercial by-products. The present paper reports a forecasting technology study with the aid of bibliographic review and patent analysis tools aiming to in-vestigate the maturity of certain technologies, considering the use of BSG on a closed-loop bio-refinery and circular bioeconomy concept integrated into a brewery. To evaluate the possibili-ties of production of high-added value products in Brazil, it was presented an overview of BSG’s scientific, technological, and marketing products and applications. The comparison between numbers of articles versus patents shows that the solutions proposed by research articles are not being transformed into maturated viable technologies. The results suggest that there is a gap between the scientific research in the institutions and their applications in industry, which lead to the destination of the BSG for more economically attractive investments when compared to research in Brazil. Ultimately, from the carried-out analysis, it was possible to propose a brew-ing process connected to the biorefinery system, showing the possibility to the newly arising brewery industries.

For equivalent modeling of mixed wind farms (WFs), existing clustering indicators cannot consider the complex coupling characteristics between different types of wind turbines (WTs). This paper proposes a refined equivalent modeling method for mixed WFs based on artificial intelligence technology. Firstly, the electromechanical transient performance of mixed WFs is analyzed. The WT type, wind speed and direction, and voltage dip are considered the main factors affecting the external dynamic response of mixed WFs. Secondly, the equivalent node model is established, including the selection of independent and dependent variables. Then, the multiple artificial neural networks (ANNs) are trained one by one based on sample data, to explore the nonlinear relationship between the dependent variables and the independent variables. Finally, the electromechanical transient simulations of the power systems with a mixed WF is carried out by using the MATLAB platform. Simulation results show that the proposed model can reflect the external characteristics of the mixed WF in different wind scenarios and voltage dips.

To better understand effects of agronomic practices on yield-nutrition relationships in wheat (Triticum aestivum L.) grains for Zn biofortification while improving yields simultaneously, effects of different soil fertilization and different drone-based foliar spraying treatments were investigated. The mixture of Zn or increasing N but simultaneously reducing P in compound fertilizers were all practical to increase grain yields and Zn concentrations in calcareous soils tested. However, the overall effects are limited, with a maximal yield increase by only 7.0% and a maximal increase of the grain Zn density from 19.4 to 27.0 mg/kg, which is far below the target biofortification value of 40-50 mg/kg. Unfortunately, there was a negative side effect, which decreased Fe and Mn concentrations and the Fe bioavailability. Notably, grain yields and Zn concentrations were significantly increased by drone-based foliar Zn sprayings from 7.5 to 8.6~8.8 t/ha by 12.0-17.3% and from 33.5 to 41.9~43.6 mg/kg by 25.1~30.1%, respectively, indicating a double-win in yield and nutrition. A spraying of ZnSO4·7H2O increased grain Zn concentrations and accumulation more so than ZnO, indicating the importance of chemical Zn forms in determining the effectiveness of foliar spraying. Moreover, foliar Zn sprayings simultaneously increased grain concentrations and accumulation of Fe, Mn and Cu, demonstrating multiple benefits. There were positive correlations between Zn and Fe, Mn or Cu, indicating synergistic but not antagonistic interactions. Compared to micronutrients, concentrations of grain macro- and medium nutrients (N, P, K, Ca, Mg) were less affected. Thus, a double-win in grain yields and micronutrient (particulaly for Zn) nutrition could be effectively achieved through appropriate soil fertilization and foliar Zn spraying. These results provide a better understanding of the yield-nutrition relationship among wheat grain yields, Zn and other nutrient elements for a better integrated manipulation to achieve a win-win situation in yield and nutrition.

Industrial green technology innovation has become an important content in achieving high-quality economic growth and comprehensively practicing the new development concept in the new era. This paper measures the efficiency of industrial green technology innovation and regional differences based on Chinese provincial panel data from 2005 to 2018, using a combination of the super-efficient SBM model for considering undesirable outputs and the Dagum Gini coefficient method, and discusses and analyses the factors influencing of the industrial green technology innovation efficiency by constructing a spatial econometric model. The results show that: firstly, the industrial green technology innovation efficiency in China shows a relatively stable development trend, going through three stages: " stationary period", " recession period " and "growth period ". However, the efficiency gap between different regions is obvious, specifically in the eastern > central > western regions of China, and the industrial green technology efficiency innovation in the central and western regions is lower than the national average. Secondly, regional differences in the efficiency of industrial green technology innovation in China are evident but tend to narrow overall, with the main reason for the overall difference being regional differences. In terms of intra-regional variation, variation within the eastern region is relatively stable, variation within the central region is relatively low and shows an inverted 'U' shaped trend, and variation within the western region is high and shows a fluctuating downward trend. Thirdly, the firm size, government support, openness to the outside world, environmental regulations and education levels contribute to the efficiency of industrial green technology innovation. In addition, the industrial structure hinders the efficiency of industrial green technology innovation, and each influencing factor has different degrees of spatial spillover effects.

Artificial intelligence (AI) applications in different fields are developing rapidly, among which AI painting technology, as an emerging technology, has received wide attention from users for its creativity and efficiency. This study aimed to investigate the factors that influence user acceptance of the use of AIBPS by proposing an extended model that combines the Extended Technology Acceptance Model (ETAM) with the AI-based Painting System (AIBPS).A questionnaire was administered to 528 Chinese participants, using validated factor analysis data and Structural Equation Modeling (SEM) was used to test the hypotheses. The findings showed that hedonic motivation (HM) and perceived trust (PE) had a positive effect (+) on users' perceived usefulness (PU) and perceived ease of use (PEOU), while previous experience (PE) and technical features (TF) had no effect (-) on users' perceived usefulness (PU). This study provides an important contribution to the literature on AIBPS and the evaluation of systems of the same type, which helps to promote the sustainable development of AI in different domains and provides a possible space for further extension of TAM, thus helping to improve the user experience of AIBPS. The results of the study provide insights for system developers and enterprises to better motivate users to use AIBPS.

This paper applies a theoretical model to analyze the ways that widespread use of generative AIs (GAIs) in education and, more broadly, in contributing to and reflecting the collective intelligence of our species, can and will change us. The model extends Brian Arthur’s insights into the nature of technologies as the orchestration of phenomena to our use by explaining the nature of humans participation in their enactment, whether as part of the orchestration (hard technique, where our roles must be performed correctly) or as orchestrators of phenomena (soft technique performed creatively or idiosyncratically). Education may be seen as a technological process for developing the soft and hard techniques of humans to participate in the technologies and thus the collective intelligence of our cultures. Unlike all earlier technologies, by embodying that collective intelli-gence themselves, GAIs can closely emulate and implement not only the hard technique but also the soft that, until now, was humanity’s sole domain: the very things that technologies enabled us to do can now be done by the technologies themselves. The consequences for what, how, and even whether we learn are profound. The paper explores some of these consequences and concludes with theoretically informed approaches that may help us to avert some dangers while benefiting from the strengths of generative AIs.

In order to analyze the spatial distribution characteristics of the cultural landscape of the Yangtze River (Nanjing section) and show the role of the Yangtze River culture in promoting regional economic development and cultural radiation, Based on the Knowledge graph technology and 3S technology, this paper conducts geographical positioning and spatial analysis of the Yangtze River cultural landscape in Nanjing, and analyzes the surrounding environment by combining remote sensing images and topographic data. Researches show that: The Yangtze River cultural landscape in Nanjing includes 74 landscape points of seven landscape types, and the distribution of landscape points shows a clustering trend, but the spatial distribution and aggregation degree of different types of cultural landscapes are different. Due to the influence of climate environment and water resources distribution, the surrounding cultural landscape has a low altitude and high vegetation coverage, showing obvious distribution characteristics along the river. This study is helpful to improve the coordination degree between cultural landscape protection and planning management, and can provide useful reference for the protection of cultural landscape of the Yangtze River.

The integration of Artificial Intelligence (AI) in predicting and managing risks for coastal port developments significantly enhances planning and operational efficiency. This paper presents a theoretical framework utilizing AI to predict future scenarios based on geological, hydrodynamic, and oceanographic factors. Focusing on the effective implementation of AI in ports, with a specific emphasis on Bulgarian Black Sea coastal areas such as Nesebur, Sarafovo, Sozopol, St. Anastasia Island, and the region of Karantinata in Varna, the study explores AI applications for site selection, risk assessment, and operational optimization to ensure resilient, efficient, and sustainable port infrastructure.

This paper reviews the social dimensions of the rolling out and utilization of the automobile in the United States over the past one hundred years, starting with the early days steeped largely (but not exclusively) in technological idealism; moving through an age of car-ascendancy with its peak in the 1950s and 1960 (with the birth of a literal “car culture”); and into the present day of automobile dependency in the midst of a profound changes in auto technology that have unclear societal implications. This review generally indicates that the machine, rather than the human operator, was from early on the center of the creation of a car-based system in America which effectively made the human being and society a reactor to technical progress rather than the other way around. The technological and mechanical aspects of automobiles remain impressive. But the history reviewed here shows that while technical advances are good, these should be guided and channeled into the pursuit of and service to more enlightened and humanist applications, and that such guiding did not happen naturally with the automobile.

In the current situation, the world is busy with technological advances, including Indonesia. Since its arrival, many business fields have competed with each other to take part as technology users. One business sector that cannot be separated from technological support is cooperatives. On the other hand, some cooperatives in the developing phase experience technological lag. At the same time, East Kutai Regency, which is the agricultural center in East Kalimantan Province, tends to rely on the cooperative sector to encourage small and medium-scale economies. This research aims to investigate the causality between access to computers (AC), internet networks (IN), digital administration skills (DAS), and financial literacy (FL) on profits (PFT). The objectivity of the study compares agricultural cooperatives that adopt technology with adopt non-technology. Using panel data regression from eighteen sub-districts in East Kutai, it is proven that technology adopting agricultural cooperatives were more prominent than non-technology adopting agricultural cooperatives during 2017–2022. However, there is a harmony in the statistical findings from both observations, where access to computers and financial literacy both have a significant effect on profits. Other analysis results show that internet networks and digital administration skills have an insignificant impact on profits. The study's implications provide valuable output for the future sustainability of agricultural cooperatives. The success of agricultural cooperatives depends greatly on the effectiveness of the application of technology.

Industrial effluents contain contaminants that pose threats to the environment and human health. Bioremediation is an environmentally friendly, sustainable, and cost-effective approach to degrade or transform pollutants using microbial enzymes. Laccase is a versatile enzyme that oxidizes a wide range of substrates, such as dyes, phenolic compounds, pesticides, and polycyclic aromatic hydrocarbons (PAHs), with the consumption of molecular oxygen. Laccase can also remove heavy metals by forming complexes with organic ligands or reducing them to fewer toxic forms. However, the application of laccase in bioremediation is limited by its low production, stability, and specificity. Recombinant technology has been employed to enhance the expression, activity, and stability of laccase in various bacterial hosts. Immobilization techniques have been developed to improve the reusability and stability of laccase in different environmental conditions. This review summarizes the recent developments in the production optimization of recombinant laccase enzymes and their role in the bioremediation of industrial effluents. It also discusses the challenges of laccase-based biocatalytic systems for environmental cleanup.

The development in information technology has played an influential role in transforming the restaurant industry services. Therefore, this research’s main agenda is to investigate factors that motivate employees to adopt and continue using information technology services by integrating two famous information system (IS) theories, namely, task technology fit (TTF) and technology continuance theory (TCT). The extant integrative perspective model details the cause-effect relationship between technology adoption and continuance intention. The positivist paradigm forms the basis of this research design, and the approach followed is quantitative research. Using the stratified random sampling technique, the empirical data was collected from 417 restaurant industry employees in the US (United States) on a five-point Likert scale. The PLS-SEM technique was utilized to analyze data while using Smart PLS 3 because of its suitability and wider application currently in the hospitality sector. Results suggest that the recently developed integrated technology continuance research model has considerable influence on predicting pre- and post-adoption behavior with continuance intention for technology usage within the restaurant industry. All hypotheses were found significant except one for the direct association of hedonic motivation and continuance intention of technology adoption. Moreover, the results revealed that factors like perceived security & information privacy and assisting conditions were the most important factors in determining the usage of information technology with continuance intention. Unlike previous research studies that focus majorly only on issues before adoption of informational technology usage, the current focus on investigating continuance intention toward information technology usage by focusing on factors that can also boost post-adoption behavior and pre-adoption usage information technology.

The commercialization of historic streets constitutes a pivotal aspect of urban cultural heritage, and the comprehension of their visual perception serves as a valuable asset to urban planning and cultural conservation efforts. However, current research concerning the disparities in visual perception among diverse demographics within historic streets, as well as their spatiotemporal dynamics, remains insufficient. This study aims to utilize mobile eye-tracking technology to delve deeply into the visual perception characteristics of diverse demographics within commercialized historic streets, while also analyzing their spatiotemporal dynamics. We explore the disparities in visual perception of historic streets among groups with varying degrees of architectural knowledge and professional literacy. Through meticulous observation using mobile eye-tracking technology, we elucidate the visual perception patterns of different demographic characteristics through statistical analysis and spatiotemporal modeling. The findings reveal significant discrepancies in visual attention and perception among various groups. Participants with a higher understanding of architecture tend to focus more on architectural details and structures within the streetscape compared to those with a lower understanding, who exhibit greater interest in elements such as shops and signage. Moreover, during the process of perceiving the streetscape, participants’ eye movements transition from surrounding landscapes and historical edifices upon initial entry to the district, gradually shifting towards the commercial activities and distinctive architecture within the district. This study delves deeply into the visual perception characteristics and spatiotemporal dynamics of commercialized historic streets, providing invaluable insights for urban planning and cultural preservation endeavors. By revealing the impact of different demographic characteristics on the visual perception of historic streets, it offers new insights for urban planning and cultural heritage conservation.

The worldwide population is undergoing a fundamental change in its age structure, which challenges the health and social services system. The need to migrate towards a more person-centered and coordinated model of care that supports the optimization of abilities and capacities for older people has to be matched. In this sense, eHealth technologies can play a fundamental role. In this paper, through a questionnaire-based data collection on 30 primary (older people) and 32 secondary (informal caregivers) end-users, we share the sustainable way to develop a product optimizing the user experience and ensuring adoption. Both older adults and caregivers consider the use of smartphone and smartwatch to be very important, but also the use of digital devices for healthcare can be helpful. Seniors care about self-monitoring of health parameters using wearable devices, regardless of their health status, and would like to be included in the process of making good health decisions, because they need to feel in control of their healthcare process. Digital solutions in health and care can support the wellbeing of older adults in many areas of their daily lives at home and in their communities, but only if such innovation is designed around the natural voice of the intended target.

The objective of this study was to compare the pathogens and susceptibilities of the current automated rule-based technology (RBT) antibiogram with one manually collected through chart review with additional rules applied. This study was a 2-year, retrospective cohort study and included all bacterial cultures within the first 30 days from patients admitted to a single Burn Center. The current RBT antibiogram served as the control and new antibiogram versions were created using additional rules and compared to the control. Six-hundred fifty-seven patients were admitted (61% excluded for lack of cultures). Fifty-nine percent had at least one hospital acquired risk factor with over one-third having recent illicit drug use and one-third having a recent hospitalization. Of the 410 cultures included, 57% were Gram-negative and half were from wound infections. Sensitivities were significantly different when comparing manual and the RBT version after including factors, such as days since admission, presence of hospital acquired risk factors, or previous antibiotic courses. Recommended empiric Gram-negative antibiotics changed from double coverage to a single β-lactam with > 90% susceptibility. The susceptibilities between first and subsequent courses were dramatically different. Before developing an antibiogram or interpreting the output, it is important to consider which automated criteria are utilized, especially for units with extended lengths of stay.

This paper outlines the evolution of a learning technology interface from a Beta version to launch with educators and its alignment to pedagogical theory, designed to maximise collaboration between teacher and student. It explores how the “co-creation” of curricula via textual sources that underpin teaching and learning can be enhanced through interactive e-books and outlines the challenges involved in this praxis. With a number of stakeholders involved in the live use of the platform (including teachers, students, HE educators, course instructors, curriculum designers and software developers), iterative feedback loops informed real-time design improvements to both the interface and the-ory. This collaboration utilised a design-based research (DBR) approach (Wang and Hannafin, 2005), which is outlined here as explanation of how the tool developed and how iterative feedback helped to reconsider the challenges educators face in implementing pedagogical innovation across differing contexts. We propose how speculative design (Lukens and DiSalvo, 2012) and Activity Theory (Engeström, 1987) workshops can methodologically enhance the planning and evaluation phases inherent to DBR approaches, which involve students to prepare for educators’ innovation. Finally, we conclude with how educators can utilise these design principles using stratified approaches to help prepare organisational approaches for innovation.

Water scarcity presents one of the greatest challenges of our time. Especially in naturally water scarce regions, the need of additional water resources is rising, requiring innovative and site-adapted technologies. In this review, selected non-conventional water technologies for sustainable water management are strategically evaluated. Focused on a matrix-based assessment incorporating key indicators, the study discusses the availability, applicability, environmental impact, scalability, and economic viability of the selected technologies. Based on a comprehensive literature review, the methodology involves a systematic comparison of technologies for desalination, water reuse, groundwater utilization, agricultural reuse, and unconventional approaches like cloud seeding, dew water, and fog water harvesting. The indicators covering the respective categories are presented in the results. On this basis, the different technologies are analyzed by matrix-based evaluation, highlighting various strengths and weaknesses and providing insights into technology selection based on regional needs. The discussion interprets the findings, deriving implications for dry environments, acknowledging limitations and suggesting pathways for future research. Through a rigorous analytical framework, this study contributes to the discourse on sustainable water solutions, offering valuable insights for policymakers, researchers and industries during a decision-making progress.

Cooking is a very decentralized and private way of energy consumption in human activities. The existing investigation and statistical analysis can’t effectively calculate the proportion of population relying on clean cooking fuel and utilization technology in the region. Therefore, based on the big data of the earth, this paper adopts the combination of spatial analysis and statistical analysis to determine the survey sample area, and according to the economic conditions, topographic characteristics, national policies for new energy construction and living habits of the provinces and cities under investigation, the questionnaire survey is conducted for the research area to calculate the proportion of the regional population relying on clean cooking fuels and utilization technologies. Taking the south of the Yangtze River in China as an example, the paper effectively calculates that 88.25% of the population depends on clean cooking fuel and technology in this region, of which 89.81% are in urban areas and 79.87% are in rural areas. Analysis of the survey data shows that the proportion of the population using clean cooking fuels and technologies is related to factors such as economic development, income and resource endowment. There is a large urban–rural gap in terms of energy consumption tendency and structure in the south of the Yangtze River in China and cooking energy consumption in rural households also varies from region to region. The technical ideas and conclusions of the paper have high reference application value, which can help promote the upgrading of clean energy utilization and provide data basis for relevant decision and policy making.

In the literature, the question of the amount of data necessary and sufficient to validate different models of the occurrence of risk of adverse events for patients or the classification of the presence or absence of pathological features has been repeatedly raised. In the presented study, we propose a new approach to determine the necessary and enough studies for validation of medical software based on artificial intelligence technology, whose main task is to classify medical X-rays according to the presence of normality and pathology. It is shown that for several studies in a dataset, when AUC ROC has maximum heterogeneity, it varies depending on the balance of "norm"/"pathology" classes. Thus, for a balance of "normal"/"abnormality", where 90% is "normal" and 10% is "abnormality", maximum heterogeneity is achieved for 190 studies, for a balance of 80% ("normal")/20% ("abnormality") for 80 studies, for a balance of 70% ("norm")/30% ("abnormality") - 120 trials, for a balance of 60% ("norm")/40% ("abnormality") the maximum heterogeneity is reached at 110 trials, and for a balance of 50% ("norm")/50% ("abnormality") - at 70 trials. The obtained data are in good agreement with the previous results. They allow us to determine a sufficient (necessary) number of studies in the dataset to perform an unbiased assessment of AUC ROC.

This article examines empirically the research on digital strategy, addressing its temporal evolution, sources of scientific production, countries and organizations generating knowledge, and the topics investigated. It highlights key authors and journals from 1991 to 2023, contributing to Sustainable Development Goal (SDG) 9. Methodologically, traditional bibliometric laws and computational tools (VOSviewer software) were applied for data processing. The results show an exponential increase in publications from 2005 to 2022 with a critical mass in digital strategy studies, concentrated in 11 journals and 83 authors with 2 or more publications. The main organizations leading scientific production in this field are in the USA. Finally, the primary Web of Science categories for the leading journals are Business, Management, and Psychology. The research examines the use and adoption of digital strategies in marketing, the impact of digital transformation on business models, and the challenges it poses for human resources. In addition, the challenges arising from the impact of COVID-19, the rise of artificial intelligence, and the integration of digital strategies in public administration should continue to be addressed. Finally, industrial digital transformation and the challenges of Big Data in higher education have less connection with other key concepts analyzed.