Intelligent Integration Research Articles

Organoid intelligence for developmental neurotoxicity testing.

The increasing prevalence of neurodevelopmental disorders has highlighted the need for improved testing methods to determine developmental neurotoxicity (DNT) hazard for thousands of chemicals. This paper proposes the integration of organoid intelligence (OI); leveraging brain organoids to study neuroplasticity in vitro, into the DNT testing paradigm. OI brings a new approach to measure the impacts of xenobiotics on plasticity mechanisms - a critical biological process that is not adequately covered in current DNT in vitro assays. Finally, the integration of artificial intelligence (AI) techniques will further facilitate the analysis of complex brain organoid data to study these plasticity mechanisms.

University Students’ Attitudes and Perceptions towards AI Tools: Implications for Sustainable Educational Practices

The integration of artificial intelligence (AI) tools in educational settings offers significant opportunities to promote sustainability by transforming learning experiences. This study analyses the usage, attitudes, and perceptions of AI tools among university students in Slovenia providing a comprehensive analysis that informs both academic practices and policy-making with emphasis on sustainability. We used a structured questionnaire with a sample of 422 participants reflecting a diverse demographic profile across various fields of study. The questionnaire was designed to measure the frequency of AI tool usage, the purposes for which these tools are employed, and students’ attitudes and perceptions towards AI’s potential benefits and drawbacks in education. Statistical analyses, including Analysis of Variance (ANOVA), were utilized to test hypotheses concerning differences in AI tool usage based on the level and field of study. Findings reveal that students recognize the efficiency of AI, but express concerns about its impact on learning quality and academic integrity, emphasizing the need for a balanced and responsible integration of AI in education to achieve sustainable outcomes. Results indicated that a majority of students are engaging with AI tools, with varied frequencies of use largely dependent on their field of study and academic level. The findings suggest that while AI tools are becoming an integral part of the educational landscape in Slovenia, there is a critical need to address the educational, ethical, and psychological impacts of these technologies. The results highlight the necessity for further research into the educational implications of AI, suggesting a balanced and sustainable approach to integrating these technologies into higher education curricula. Such an approach ensures that the adoption of AI not only enhances learning outcomes but also aligns with the principles of sustainability, promoting long-term benefits for both education and society.

Elevating STEM Learning: Unleashing the Power of AI in Open Distance eLearning

The integration of Artificial Intelligence (AI) into Open Distance eLearning (ODeL) represents a significant evolution in STEM education, offering transformative benefits in teaching, learning and administrative processes. This conceptual paper explores how AI-driven platforms are revolutionising ODeL by providing personalised learning experiences, real-time feedback, adaptive assessments and immersive simulations through technologies like Augmented Reality (AR) and Virtual Reality (VR). These advancements not only enhance student engagement and understanding but also support the development of critical thinking and problem-solving skills. The paper highlights successful case studies from institutions like Carnegie Mellon University and Stanford Online High School, showcasing practical implementations of AI in STEM education. However, the integration of AI also brings forth challenges, particularly concerning data privacy, security and the need for equitable access to technology. As AI continues to advance, it promises to play an important role in lifelong learning, offering modular and just-in-time educational opportunities. The paper concludes with a call for robust ethical frameworks to guide the responsible use of AI in education, ensuring that the benefits are maximized while potential risks are mitigated.

Artificial Intelligence Integration and Social Innovation: Interdisciplinary Research Trends Aligned with the Sustainable Development Goals

This study investigates the integration of Artificial Intelligence (AI), Machine Learning, Natural Language Processing (NLP), and Prompt Engineering into the social sciences and their impact on collaborative networks, thematic developments, and research trends aligned with the Sustainable Development Goals (SDGs). Utilizing bibliometric analysis and topic modeling, the research analyzes a dataset of 389 publications from the Web of Science (WoS) database, spanning the last decade. The findings highlight significant growth in interdisciplinary research at the intersection of these technologies and social sciences, with notable contributions in management, business, and environmental studies. Key themes identified include AI-driven innovation in product development, energy sector advancements, and the role of AI in educational and healthcare settings. The study underscores the transformative potential of AI in driving sustainable development, while also emphasizing the importance of addressing ethical considerations and ensuring responsible implementation. This research contributes to a deeper understanding of how AI and related technologies are reshaping the social sciences and their role in achieving global sustainability goals.

Revolutionizing Transdermal Patches: Navigating Clinical Challenges and Technological Progress

ransdermal patches, conceived to extend drug release, enhance bioavailability, and foster patient adherence, witnessed their inaugural approval by the USFDA in the 1980s. These patches, varying in size, constitute medicinal formulations with one or more active components that permeate the bloodstream through the skin. This review undertakes a comprehensive examination of recent strides in transdermal patch technology, encompassing critical facets such as their merits and demerits, advancements in microneedle technology, the evolution of transdermal patch generation, the integration of Artificial Intelligence, and the role of 3D printing technology. Additionally, the focus is placed on USFDA-approved patches. Various literature databases viz. Science Direct, PubMed, and Web of Sciences were explored for this research. The review furnished insights into the application of 3D printing technology in the fabrication of transdermal patches and disseminated information on USFDA-approved patches. The exploration delved into diverse strategies aimed at augmenting the efficiency of drug delivery and promoting patient compliance. Major transdermal products being marketed with details of their active substance have been discussed. Various applications of artificial intelligence in drug delivery have been summarized. It may be summarized that transdermal patch technology is not a thing of the past but a technology to stay and meet the demands of drug delivery in the present and future.

AI anxiety and fear: A look at perspectives of information science students and professionals towards artificial intelligence

The rapid integration of artificial intelligence (AI) within society and the emergence of the fourth industrial revolution (4IR), has ignited a spectrum of emotions in society, ranging from enthusiasm to anxiety. This study investigates the depths of AI anxiety and fear among a population of information science students and professionals. Utilising a survey of over 200 current students and professionals, this study explores the connections between age, gender identity, ethnicity, geographic location, educational attainment and residence, and the levels of anxiety and fear associated with AI and the 4IR. The findings reveal nuanced relationships, with age, ethnicity, academic achievement and regional context serving as critical differentiators in 4IR and AI anxiety within this population. Students and professionals alike may benefit from seeking further education about this emerging technology.

Artificial Intelligence: Contributions to Efficiency and Sustainability in the Court of Justice of The State of Goiás

Objective: To present analyses related to the integration between Artificial Intelligence (AI) and Sustainable Regional Development (SRD) regarding the expansion of access to justice, based on the experience of the Court of Justice of the State of Goiás (TJGO). Theoretical Framework: Advanced technologies in the Judicial System and their contribution to global sustainability, as well as implications for the formulation of public policies for the responsible integration of AI in judicial processes. Methodology: Qualitative and quantitative analysis of data acquired from research, bibliographical and documentary from Reports and Resolutions of the Court of Justice of Goiás – TJGO and National Council of Justice – CNJ, and literature related to AI and Sustainable Regional Development – SRD, about the relationship between sustainability, SRD and AI; the implementation of AI in Brazilian courts, with emphasis on the guidelines of the CNJ (TJGO); the evolution and impact of AI regulations in the Brazilian Judiciary (TJGO); and the effective use of AI in the context of TJGO and other Brazilian courts. Results and Discussion: As a result of this research, we have identified an improvement in procedural efficiency in the judicial system (in access to justice), based on the implementation of AI, especially for vulnerable groups. Implications of the research: The functionality and dynamism of the social and environmental spheres require studies and analyses that assist in the integration for development based on the balanced alignment between both. In this sense, it is important to direct efforts to identify how and to what extent technological advances can be useful for social development, in this case, based on the practices developed by TJGO. Originality/Value: This study presents recent information and analyses on strategies and practices of the Brazilian Judiciary for the integration of Artificial Intelligence and Sustainable Regional Development regarding the expansion of access to justice, based on the experience of the Court of Justice of the State of Goiás (TJGO).

Next-Gen Rehab: VR & AI Revolutionize Physiotherapy

The integration of Virtual Reality (VR) and Artificial Intelligence (AI) in healthcare can significantly improve patient outcomes, particularly in physiotherapy. Combining VR and AI enhances accuracy and personalization in physiotherapy, ushering in a new era of patient centered rehabilitation. VR and AR use input devices to gather data on body position, movement, and the environment, and output devices to deliver sensory feedback, including visuals, vibrations, and sounds.1

A Comprehensive Review of Electromyography in Rehabilitation: Detecting Interrupted Wrist and Hand Movements with a Robotic Arm Approach

Electromyography (EMG) is a diagnostic technique that measures the electrical activity generated by skeletal muscles. Utilizing electrodes placed either on the skin (surface EMG) or inserted directly into the muscle(intramuscular EMG), it detects electrical signals produced during muscle contractions. EMG is widely employed in clinical and research settings to assess muscle function, diagnose neuromuscular disorders,and guide rehabilitation therapy. Over the years, EMG has evolved from a basic measurement tool into an essential technology within clinical and research environments, propelled by advances in recording techniques and digital innovations. The integration of wearable technology and artificial intelligence (AI) has significantly expanded its applications, particularly in rehabilitation and sports science. By capturing muscle electrical activity through surface or intramuscular electrodes, EMG benefits from enhanced signal processing that improves accuracy and data analysis. Despite challenges such as signal interference and the complexities of movement patterns—especially in wrist and hand rehabilitation—EMG combined with robotic systems offers real-time feedback for precise and personalized therapy. However, obstacles like cost, complexity, and variability among patients still remain. Future advancements aim to make EMG more accessible and to integrate AI for tailored rehabilitation strategies, alongside improvements in sensors and wireless communication to enhance reliability and performance. This review explores various facets of EMG,from its fundamental principles to its application in detecting disrupted wrist and hand movements through robotic approaches. It provides a comprehensive analysis of EMG’s historical and technological evolution, recent innovations like AI and wearable devices, and its extensive applications in rehabilitation and sports science. Detailed case studies illustrate its effectiveness in areas such as stroke recovery and spinal cord injury rehabilitation. Additionally, the review addresses challenges like technical limitations and patient variability while emphasizing the integration of EMG with robotic systems for personalized therapy. It also discusses the significance of real-time feedback, future enhancements in AI and sensor technology, and the pressing need for more affordable, user-friendly solutions to improve therapeutic outcomes.

ISETECH-AI-Powered Dental Consultation Chatbot: Enhancing Public Healthcare Accessibility and Awareness

The evolving landscape of healthcare has witnessed remarkable transformations owing to the integration of Artificial Intelligence (AI) and chatbot technology. This paper presents the development of an AI-powered dental consultation chatbot aimed at revolutionizing dental healthcare accessibility and awareness, particularly in the context of Malaysia. The chatbot serves as a valuable resource for dental self-diagnosis, information dissemination, and remote consultations, reducing the need for in-person visits to dental clinics, especially considering the COVID-19 pandemic. The adoption of chatbots in healthcare has gained momentum, driven by their ability to engage in natural language conversations, disseminate reliable information, and provide personalized assistance. Chatbots have emerged as essential tools in public health management, offering services ranging from disease understanding and information provision to counselling and facilitating doctor-patient communication. In the specific domain of dental healthcare, chatbots have the potential to alleviate challenges associated with iatrophobia (fear of disclosing dental problems) and the need for professional dental consultations. The objective of this work is to develop a user-friendly, AI-powered dental consultation chatbot accessible online. This chatbot allows users to engage in real-life conversations with the machine, conducting short self-diagnosis of their oral health conditions. Users can seek information about their dental issues, understand symptoms, and gain insights into their dental health before or after visiting a dentist. Furthermore, the chatbot system presented in this study holds the potential to ease the workload of dental practitioners by offering an alternative method of consultation. Modern digital dental technologies are increasingly focused on diagnosis and classification tasks, but few have explored the realm of dental consultation chatbots for public use. This project bridges this gap and extends the utility of AI-powered chatbots to the field of dental healthcare. This AI-powered dental consultation chatbot not only addresses the evolving healthcare needs of the post-pandemic era but also contributes to advancing dental healthcare accessibility and awareness among the population.

The Future of Healthcare Data Governance AI-Enabled ETL Testing Frameworks on Data Warehousing Testing automation using ML, AI and NLP

The healthcare sector enhances the integration of artificial intelligence, natural language processing and machine learning in data governance for developing automation procedures. Data management offered AI-powered ETL testing with the adoption of an automation framework for resolving data governance issues . This insight promotes operational efficiency with assurance of data quality that empowers data security by ML, AI and NLP frameworks. The framework allows healthcare institutions to arrange unstructured data in effective insights into patient care and treatment procedures. Advanced analytics leads to the development of predictions on patient outcomes in treatment procedures. The automated process of ETL improves efficient information collection by NPL with the creation of predictive models in monitoring data management by ML

Research progress on knowledge discovery in traditional Chinese medicine medical records based on artificial intelligence

Traditional Chinese Medicine (TCM) medical records represent the clinical practice records of physicians, encapsulaing a wealth of TCM clinical diagnosis and treatment experiences as well as academic thoughts of numerous TCM practitioners. Such various information provide certain basic conditions for the integration of artificial intelligence (AI) technology with TCM. Through analysing successful clinical records, AI technology can extract practical and understandable knowledge. Therefore, this paper summarizes and analyzes the application of information technology in the field of knowledge discovery research from Chinese medical records. It focuses on three key aspects: the resources of TCM Medical Records, the objects of TCM records mining, and the application of AI technology. The aim is to provide beneficial references for maximizing the value of TCM medical records.

Unveiling Barriers and Challenges of AI Technology Integration in Education: Assessing Teachers’ Perceptions, Readiness and Anticipated Resistance

This study seeks to fill a gap in the literature concerning the extrinsic barriers that affect the integration of artificial intelligence (AI) technologies, such as ChatGPT, within the educational sphere. While previous studies have predominantly focused on intrinsic factors like teachers’ attitudes and beliefs, as well as the general benefits and drawbacks of AI in education, there has been limited investigation into how external obstacles, such as large class sizes, insufficient resources, slow internet connections, and outdated technology, affect this integration. The importance of this study lies in its assessment of whether these extrinsic barriers contribute to teachers' resistance to embracing AI technology. This study employs a quantitative method to explore this issue, using a questionnaire administered to thirteen (n=13) EFL instructors from the Department of English at Ibn Khaldoun University of Tiaret, Algeria. The results derived from this study reveal that while participants recognised AI's potential to improve student learning and expressed confidence in its integration, they also identified extrinsic barriers such as technical complexity, inadequate training, limited resources, and large class sizes as critical factors contributing to their resistance to incorporating AI tools into their teaching practices. This research emphasises the necessity of addressing these external barriers to better align technological innovations with teachers' needs and readiness, thereby enhancing the effective integration of AI in education.

To What Extent Can Telehealth/Telemedicine Replace In-Person Doctor Visits In the USA?

The twenty-first century has seen a wave of revolutionary technical advancement that has drastically altered the landscape of many fields. Medicine and healthcare have been affected by these advancements, especially in the areas of artificial intelligence, 3D printing, CRISPR gene editing, and virtual reality. One of the main noticeable advancements however is the rapid rise of telehealth, which has altered the patient-doctor relationship, and is one of the most notable developments in recent decades, notably in the United States. While telehealth doesn’t have a very long history, it rose to popularity during the COVID-19 epidemic, delivering important benefits like as cost-effectiveness, decreased wait times, greater accessibility, and improved mental health assistance. Nonetheless, it can be observed that Telehealth presents some issues, including the possibility of misdiagnosis, difficulties in adapting to technology for older people, and worries about data privacy. Future advancements in telehealth may include selective applications, public education, enhanced security measures, and regulations for artificial intelligence integration. Furthermore, the political atmosphere in the United States may impact and change the rate at which telehealth develops, since it appears to have gained traction in Democratic-leaning areas but not so much in Republican-leaning states. This study will look into the possible benefits, problems, and future advancements of telehealth in the United States, as well as whether it can totally replace in-person doctor visits and to what extent.

Correlation-aware Cross-modal Attention Network for Fashion Compatibility Modeling in UGC Systems

Empowered by the continuous integration of social multimedia and artificial intelligence, the application scenarios of information retrieval (IR) progressively tend to be diversified and personalized. Currently, User-Generated Content (UGC) systems have great potential to handle the interactions between large-scale users and massive media contents. As an emerging multimedia IR, Fashion Compatibility Modeling (FCM) aims to predict the matching degree of each given outfit and provide complementary item recommendation for user queries. Although existing studies attempt to explore the FCM task from a multimodal perspective with promising progress, they still fail to fully leverage the interactions between multimodal information or ignore the item-item contextual connectivities of intra-outfit. In this paper, a novel fashion compatibility modeling scheme is proposed based on Correlation-aware Cross-modal Attention Network. To better tackle these issues, our work mainly focuses on enhancing comprehensive multimodal representations of fashion items by integrating the cross-modal collaborative contents and uncovering the contextual correlations. Since the multimodal information of fashion items can deliver various semantic clues from multiple aspects, a modality-driven collaborative learning module is presented to explicitly model the interactions of modal consistency and complementarity via a co-attention mechanism. Considering the rich connections among numerous items in each outfit as contextual cues, a correlation-aware information aggregation module is further designed to adaptively capture significant intra-correlations of item-item for characterizing the content-aware outfit representations. Experiments conducted on two real-world fashion datasets demonstrate the superiority of our approach over state-of-the-art methods.

The application of multimodal AI large model in the green supply chain of energy industry

With the accelerated advancements in artificial intelligence and the increasing emphasis on sustainable supply chain management, the integration of multimodal artificial intelligence (AI) into green supply chains has emerged as a critical research frontier. This study delves into the synergistic potential and challenges of combining multimodal AI, which leverages diverse data types such as text, images, and numerical data, to enhance decision-making processes in green supply chains. Through the meticulous design of a data strategy and model framework, this research establishes a sophisticated and efficient data processing and model training pipeline. The experimental results reveal that the comprehensive analysis and fusion of multimodal data significantly improve the prediction accuracy of key supply chain metrics, with observed increases in accuracy and recall rates by 12.4% and 9.8%, respectively. Additionally, the model's limitations are critically assessed, and targeted improvement strategies are proposed. The practical implications of this study are profound, offering actionable insights for the application of multimodal AI in real-world energy sector scenarios. The findings underscore the potential of this technology to optimize operations, reduce environmental impact, and drive sustainable growth in the energy industry.

Electrocatalytic Nitrate Reduction for Brackish Groundwater Treatment: From Engineering Aspects to Implementation

In recent years, nitrate has emerged as a significant groundwater pollutant due to its potential ecotoxicity. In particular, nitrate contamination of brackish groundwater poses a serious threat to both ecosystems and human health and remains difficult to treat. A promising, sustainable, and environmentally friendly solution when biological treatments are not applicable is the conversion of nitrate to harmless nitrogen (N2) or ammonia (NH3) as a nutrient by electrocatalytic nitrate reduction (eNO3R) using solar photovoltaic energy. This review provides a comprehensive overview of the current advances in eNO3R for the production of nitrogen and ammonia. The discussion begins with fundamental concepts, including a detailed examination of the mechanisms and pathways involved, supported by Density Functional Theory (DFT) to elucidate specific aspects of ammonium and nitrogen formation during the process. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) offers promising advancements in enhancing the predictive power of DFT, accelerating the discovery and optimization of novel catalysts. In this review, we also explore various electrode preparation methods and emphasize the importance of in situ characterization techniques to investigate surface phenomena during the reaction process. The review highlights numerous examples of copper-based catalysts and analyses their feasibility and effectiveness in ammonia production. It also explores strategies for the conversion of nitrate to N2, focusing on nanoscale zerovalent iron as a selective material and the subsequent oxidation of the produced ammonia. Finally, this review addresses the implementation of the eNO3R process for the treatment of brackish groundwater, discussing various challenges and providing reasonable opinions on how to overcome these obstacles. By synthesizing current research and practical examples, this review highlights the potential of eNO3R as a viable solution to mitigate nitrate pollution and improve water quality.

An empirical study of AI techniques in mobile applications

The integration of artificial intelligence (AI) into mobile applications has significantly transformed various domains, enhancing user experiences and providing personalized services through advanced machine learning (ML) and deep learning (DL) technologies. AI-driven mobile apps typically refer to applications that leverage ML/DL technologies to perform key tasks such as image recognition and natural language processing. Despite existing research exploring how mobile apps exploit AI techniques, they have the following main limitations: (1) Most existing studies focus on DL-based apps, with limited research on ML-based apps. (2) Existing research typically focuses on investigating the apps and the technologies utilized in the apps, lacking user-level analysis. (3) The number of apps studied is limited, with only 1,000 to 2,000 ML/DL apps identified after filtering. To fill the gap, in this paper, we conducted the most extensive empirical study on AI applications, exploring on-device ML apps, on-device DL apps, and AI service-supported (cloud-based) apps. Our study encompasses 56,682 real-world AI applications, focusing on three crucial perspectives: (1) Application analysis, where we analyze the popularity of AI apps and investigate the update states of AI apps; (2) Framework and model analysis, where we analyze AI framework usage and AI model protection; (3) User analysis, where we examine user privacy protection and user review attitudes. Our study has strong implications for AI app developers, users, and AI R&D. On one hand, our findings highlight the growing trend of AI integration in mobile applications, demonstrating the widespread adoption of various AI frameworks and models. On the other hand, our findings emphasize the need for robust model protection to enhance app security. Additionally, our study highlights the importance of user privacy and presents user attitudes towards the AI technologies utilized in current AI apps. We provide our AI app dataset (currently the most extensive AI app dataset) as an open-source resource for future research on AI technologies utilized in mobile applications.

Revolutionizing Education: Harnessing AI for Personalized Learning Pathways and Student Success

The integration of Artificial Intelligence (AI) into education is rapidly transforming traditional learning environments by enabling personalized learning experiences. This paper explores the role of AI in developing adaptive learning systems that tailor educational content and pathways to individual students' needs. By leveraging machine learning algorithms and learning analytics, AI-driven platforms dynamically adjust instructional materials based on students’ performance, cognitive abilities, and engagement levels. The study examines the potential of AI to enhance student outcomes by creating more interactive and engaging educational experiences, thus fostering better retention and comprehension. Key challenges, such as data privacy, algorithmic bias, and equitable access, are also addressed to ensure the effective deployment of AI in diverse learning environments. Through case studies and real-world examples, this research highlights how AI is reshaping education by making it more flexible, student-centered, and accessible.

Exploiting AI Capabilities: An in-Depth Analysis of Artificial Intelligence Integration in Cybersecurity for Threat Detection and Response

Exploiting AI Capabilities: An in-Depth Analysis of Artificial Intelligence Integration in Cybersecurity for Threat Detection and Response