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Medical imaging plays a pivotal role in diagnostic medicine with technologies like Magnetic Resonance Imagining (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and ultrasound scans being widely used to assist radiologists and medical experts in reaching concrete diagnosis. Given the recent massive uplift in the storage and processing capabilities of computers, and the publicly available big data, Artificial Intelligence (AI) has also started contributing to improving diagnostic radiology. Edge computing devices and handheld gadgets can serve as useful tools to process medical data in remote areas with limited network and computational resources. In this research, the capabilities of multiple platforms are evaluated for the real-time deployment of diagnostic tools. MRI classification and segmentation applications developed in previous studies are used for testing the performance using different hardware and software configurations. Cost–benefit analysis is carried out using a workstation with a NVIDIA Graphics Processing Unit (GPU), Jetson Xavier NX, Raspberry Pi 4B, and Android phone, using MATLAB, Python, and Android Studio. The mean computational times for the classification app on the PC, Jetson Xavier NX, and Raspberry Pi are 1.2074, 3.7627, and 3.4747 s, respectively. On the low-cost Android phone, this time is observed to be 0.1068 s using the Dynamic Range Quantized TFLite version of the baseline model, with slight degradation in accuracy. For the segmentation app, the times are 1.8241, 5.2641, 6.2162, and 3.2023 s, respectively, when using JPEG inputs. The Jetson Xavier NX and Android phone stand out as the best platforms due to their compact size, fast inference times, and affordability. [ABSTRACT FROM AUTHOR]

Applying scientific theology to possible existence of astrobiology, aliens, and Extra-Terrestrial Intelligence (ETI) invites constructive analysis along at least four lines of inquiry. First are potential parallels between advanced Artificial Intelligence (AI) and biological ETI. Second is the extent to which angels offer precedents for thinking about astrobiology given that the Bible, for example, portrays angels as intelligent beings who interact with the natural, physical world. Third are cosmological and eschatological implications about the ends of time and space. Finally, are questions about whether God, as Christians affirm about Jesus, ought to incarnate as—or into—every intelligent life form. [ABSTRACT FROM AUTHOR]

In recent years, advancements in medical treatment and imaging technologies have revolutionized the assessment of tumor response. However, the Response Evaluation Criteria in Solid Tumors (RECIST) has long been established as the gold standard for evaluating tumor treatment. As treatment modalities evolve, the need for continuous refinement and adaptation of RECIST becomes increasingly apparent. This review explores the historical evolution, current applications, limitations, and future directions of RECIST. It discusses the challenges of distinguishing true progression from pseudo-progression in ICIs (immune checkpoint inhibitors), the integration of advanced imaging tools, and the necessity for RECIST criteria tailored to specific therapies like neoadjuvant treatments. The review highlights the ongoing efforts to enhance RECIST's accuracy and reliability in clinical decision-making and the potential for developing new standards to better evaluate treatment efficacy in the rapidly evolving landscape of oncology. [ABSTRACT FROM AUTHOR]

Purpose: This study delves into the broader implications of artificial intelligence (AI) text generation technologies, including large language models (LLMs) and chatbots, on the scientific literature of otolaryngology. By observing trends in AI-generated text within published otolaryngology studies, this investigation aims to contextualize the impact of AI-driven tools that are reshaping scientific writing and communication. Methods: Text from 143 original articles published in JAMA Otolaryngology – Head and Neck Surgery was collected, representing periods before and after ChatGPT's release in November 2022. The text from each article's abstract, introduction, methods, results, and discussion were entered into ZeroGPT.com to estimate the percentage of AI-generated content. Statistical analyses, including T-Tests and Fligner-Killeen's tests, were conducted using R. Results: A significant increase was observed in the mean percentage of AI-generated text post-ChatGPT release, especially in the abstract (from 34.36 to 46.53%, p = 0.004), introduction (from 32.43 to 45.08%, p = 0.010), and discussion sections (from 15.73 to 25.03%, p = 0.015). Publications of authors from non-English speaking countries demonstrated a higher percentage of AI-generated text. Conclusion: This study found that the advent of ChatGPT has significantly impacted writing practices among researchers publishing in JAMA Otolaryngology – Head and Neck Surgery, raising concerns over the accuracy of AI-created content and potential misinformation risks. This manuscript highlights the evolving dynamics between AI technologies, scientific communication, and publication integrity, emphasizing the urgent need for continued research in this dynamic field. The findings also suggest an increasing reliance on AI tools like ChatGPT, raising questions about their broader implications for scientific publishing. [ABSTRACT FROM AUTHOR]

Medical anomaly identification using machine learning is a significant subject that has received a lot of attention. Artificial neural networks' successor, deep learning, is a well-developed technology with strong computational capabilities. Its popularity has increased in recent years due to the availability of rapid data storage and hardware parallelism. Numerous, sizeable medical imaging datasets have recently been made available to the public, which has sparked interest in the field and increased the number of research studies and publications. The main goal of this study is to give a complete theoretical examination of prominent deep learning algorithms for detecting medical anomalies. The study further presents the architecture of current methodologies, compare and contrasts training algorithms, and gives a robust assessment of current methodologies. A thorough analysis of the state-of-the-art is provided, covering the benefits and limitations associated with using open-source data, and the specifications for clinically relevant systems. This study further identifies the gaps in the body of existing knowledge and suggests future research directions. [ABSTRACT FROM AUTHOR]

Recently, the increasing rapid number of cars on the roads and the great demand for traffic prediction, because of the increase in traffic congestion, has posed a great challenge to governments' economic development and social stability of most countries in the world. While many challenges are facing governments to solve traffic congestion and reduce car accidents and pollution, the objective of this study is to apply a dynamic approach for the Jordanian community to lessen traffic congestion on the roads in Amman by utilising social media, artificial intelligence efficiency methods, and decision support to lessen traffic issues in the capital city of Jordan. It also aims to communicate directly with social media users to cut down on the time and effort needed to make traffic predictions, which will help to relieve the congestion of areas and roads in Amman. To obtain the research aims, we handled a TRF2021JOR dataset related to traffic records in Amman city, collected from Amman Municipality for several years. Then, we applied the Netnography model, which was built using the data science concepts, to create an efficient model in Amman city for traffic prediction and time series based on selective features of previous congestion in city roads. The experiment results showed the accuracy preference for the proposed method in Amman city. Furthermore, the experiment tested the accuracy of results based on the machine learning methods using the KNIME Analytics Platform tool, one of the Artificial Intelligence (AI) methods used; the experiment results showed that the classifier SVM gives a low accuracy reached (89.9%). However, the accuracy using the decision tree classifier reached (90.59%), while the classifier random forest gives a high accuracy reached (93.16%). [ABSTRACT FROM AUTHOR]

Background: The integration of generative artificial intelligence (AI) tools like OpenAI's ChatGPT into nursing education marks a transformative advance in personalized learning and interactive engagement. Problem: Variability in faculty's experience with AI outputs highlights the need for well-crafted prompts that align with educational objectives, maximize learning outcomes, and ensure contextual relevance. Effective prompting is a key to eliciting accurate, relevant responses from AI, fostering a dynamic learning environment that bolsters student comprehension of complex topics. Approach: This article examines the critical role of prompt engineering in optimizing AI-generated content's effectiveness within academic settings. With a detailed guide and strategies specifically designed for nursing education, the article prepares faculty to proficiently use generative AI. Conclusions: By mastering prompt engineering, educators can leverage AI tools as powerful aids, potentially significantly enhancing teaching effectiveness, work efficiency, and student learning outcomes. [ABSTRACT FROM AUTHOR]

While the prominence of neuroscience and neurotechnologies has generated intensive debates about the normative analysis of the ethical-legal challenges in public opinion and academia, these debates are relatively scarce among law and religion experts. To overcome the shortfall, this contribution describes the impact of the main neuroscientific applications on the right of freedom of thought, conscience, and religion. Considering how neurosciences techniques can alter the mind, decode thoughts, and enhance cognitive functions, particularly individual thought and conscience, I suggest revising the traditional dichotomy of forum internum and forum externum of the right to freedom of thought, conscience, and religion and rethinking the protection of the forum internum , which is at risk in the Age of Neuroscience. [ABSTRACT FROM AUTHOR]

This systematic review presents a pioneering analysis of advanced technology applications in the luxury industry, proposing a structured framework for effective integration across various supply chain and consumer journey stages. The study ensured a refined and comprehensive literature review by employing unique keywords derived from a preliminary exploration of technologies in retail and marketing. Our framework categorizes technologies according to their supply chain roles and highlights their impact on consumer perceptions, preferences, and purchasing decisions. The findings reveal a growing trend of luxury brands adopting advanced technologies to enhance customer experiences, boost operational efficiency, foster sustainable practices, and reinforce market positioning. This research contributes to sustainable supply chain management and ethical consumerism in the luxury sector by aligning technological innovation with sustainability and core luxury values. It provides actionable insights for industry practitioners and identifies future research directions, marking this study as a first‐mover in advancing academic understanding and industry practice in luxury technology integration. [ABSTRACT FROM AUTHOR]

The field of digital forensics relies on expertise from multiple domains, including computer science, criminology, and law. It also relies on different toolsets and an analyst's expertise to parse enormous amounts of user-generated data to find clues that help crack a case. This process of investigative analysis is often done manually. Artificial Intelligence (AI) can provide practical solutions to efficiently mine enormous amounts of data to find useful patterns that can be leveraged to investigate crimes. Natural Language Processing (NLP) is a subdomain of research under AI that deals with problems involving unstructured data, specifically language. The domain of NLP includes several tools to parse text, including topic modeling, pairwise correlation, word vector cosine distance measurement, and sentiment analysis. In this research, we propose a digital forensic investigative technique that uses an ensemble of NLP tools to identify a person of interest list based on a corpus of text. Our proposed method serves as a type of human feature reduction, where a total pool of suspects is filtered down to a short list of candidates who possess a higher correlation with the crime being investigated. [ABSTRACT FROM AUTHOR]

The rapid evolution of artificial intelligence (AI) and especially machine learning (ML) has significantly transformed the technological landscape, influencing diverse sectors and making notable inroads into the realm of finance. This paper delves into the challenges posed by ML models, known for their black-box nature, non-deterministic behaviour, reliance on big data and inherent complexity, often lacking clear specifications. These characteristics present novel testing challenges compared to traditional software. In addressing these challenges, the authors introduce a conceptual framework tailored for testing ML-based systems, with a specific focus on financial applications. Theoretical concepts are exemplified through a real-world case study of the implementation of an ML-based bond trading system within a banking context. As the AI technologies become increasingly integrated into critical financial systems, a deeper understanding of their testing strategies will be essential for mitigating risks and harnessing their full potential. The heightened significance of in-depth testing knowledge, propelled by AI-driven progress, holds relevance for both testers and managers in navigating the complexities of the technological changes. [ABSTRACT FROM AUTHOR]

Background: Changes in the speech and language of patients with Alzheimer's disease (AD) have been reported. Using machine learning to characterize these irregularities may contribute to the early, non-invasive diagnosis of AD. Methods: We conducted cognitive function assessments, including the Mini-Mental State Examination, with 83 patients with AD and 75 healthy elderly participants, and recorded pre- and post-assessment conversations to evaluate participants' speech. We analyzed the characteristics of the spectrum, intensity, fundamental frequency, and minute temporal variation (∆) of the intensity and fundamental frequency of the speech and compared them between patients with AD and healthy participants. Additionally, we evaluated the performance of the speech features that differed between the two groups as single explanatory variables. Results: We found significant differences in almost all elements of the speech spectrum between the two groups. Regarding the intensity, we found significant differences in all the factors except for the standard deviation between the two groups. In the performance evaluation, the areas under the curve revealed by logistic regression analysis were higher for the center of gravity (0.908 ± 0.036), mean skewness (0.904 ± 0.023), kurtosis (0.932 ± 0.023), and standard deviation (0.977 ± 0.012) of the spectra. Conclusions: This study used machine learning to reveal speech features of patients diagnosed with AD in comparison with healthy elderly people. Significant differences were found between the two groups in all components of the spectrum, paving the way for early non-invasive diagnosis of AD in the future. [ABSTRACT FROM AUTHOR]

Universal neonatal hepatitis B virus (HBV) vaccination and the advent of direct‐acting antivirals (DAA) against hepatitis C virus (HCV) have reshaped the epidemiology of chronic liver diseases. However, some aspects of the management of chronic liver diseases remain unresolved. Nucleotide analogs can achieve sustained HBV DNA suppression but rarely lead to a functional cure. Despite the high efficacy of DAAs, successful antiviral therapy does not eliminate the risk of hepatocellular carcinoma (HCC), highlighted the need for cost‐effective identification of high‐risk populations for HCC surveillance and tailored HCC treatment strategies for these populations. The accessibility of high‐throughput genomic data has accelerated the development of precision medicine, and the emergence of artificial intelligence (AI) has led to a new era of precision medicine. AI can learn from complex, non‐linear data and identify hidden patterns within real‐world datasets. The combination of AI and multi‐omics approaches can facilitate disease diagnosis, biomarker discovery, and the prediction of treatment efficacy and prognosis. AI algorithms have been implemented in various aspects, including non‐invasive tests, predictive models, image diagnosis, and the interpretation of histopathology findings. AI can support clinicians in decision‐making, alleviate clinical burdens, and curtail healthcare expenses. In this review, we introduce the fundamental concepts of machine learning and review the role of AI in the management of chronic liver diseases. [ABSTRACT FROM AUTHOR]

The global electricity demand from electric vehicles (EVs) increased by 3631% over the last decade, from 2600 gigawatt hours (GWh) in 2013 to 97,000 GWh in 2023. The global electricity demand from EVs will rise to 710,000 GWh by 2030. These EVs will depend on smart grids (SGs) for their charging requirements. Like EVs, SGs are a booming market. In 2021, SG technologies were valued at USD 43.1 billion and are projected to reach USD 103.4 billion by 2026. As EVs become more prevalent, they introduce additional complexity to the SG landscape, with EVs not only consuming energy, but also potentially supplying it back to the grid through vehicle-to-grid (V2G) technologies. The entry of numerous independent sellers and buyers, including EV owners, into the market will lead to intense competition, resulting in rapid fluctuations in electricity prices and constant energy transactions to maximize profit for both buyers and sellers. Blockchain technology will play a crucial role in securing data publishing and transactions in this evolving scenario, ensuring transparent and efficient interactions between EVs and the grid. This survey paper explores key research challenges from an engineering design perspective of SG operation, such as the potential for voltage instability due to the integration of numerous EVs and distributed microgrids with fluctuating generation capacities and load demands. This paper also delves into the need for a synergistic balance to optimize the energy supply and demand equation. Additionally, it discusses policies and incentives that may be enforced by national electricity carriers to maintain grid reliability and manage the influx of EVs. Furthermore, this paper addresses emerging issues of SG technology providing primary charging infrastructure for EVs, such as incentivizing green energy, the technical difficulties in integrating diverse hetero-microgrids based on HVAC and HVDC technologies, challenges related to the speed of energy transaction processing during fluctuating prices, and vulnerabilities concerning cyber-attacks on blockchain-based SG architectures. Finally, future trends are discussed, including the impact of increased EV penetration on SGs, advancements in V2G technologies, load-shaping techniques, dynamic pricing mechanisms, and AI-based stability enhancement measures in the context of widespread SG adoption. [ABSTRACT FROM AUTHOR]

Fuzzy systems are powerful modeling systems for uncertainty applications. In contrast to traditional crisp systems, fuzzy systems offer the opportunity to extend the binary decision to continuous space, which could offer benefits for various application areas such as intrusion detection systems (IDSs), because of their ability to measure the degree of attacks instead of making a binary decision. Furthermore, fuzzy systems offer a suitable environment that is able to deal with uncertainty. However, fuzzy systems face a critical challenge represented by the sparse fuzzy rules. Typical fuzzy systems demand complete fuzzy rules in order to offer the required results. Additionally, generating complete fuzzy rules can be difficult due to many factors, such as a lack of knowledge base or limited data availability, such as in IDS applications. Fuzzy rule interpolation (FRI) was introduced to overcome this limitation by generating the required interpolation results in cases with sparse fuzzy rules. This work introduces a threefold approach designed to address the cases of missing fuzzy rules, which uses a few fuzzy rules to handle the limitations of missing fuzzy rules. This is achieved by finding the interpolation condition of neighboring fuzzy rules. This procedure was accomplished based on the concept of factors (which determine the degree to which each neighboring fuzzy rule contributes to the interpolated results, in cases of missing fuzzy rules). The evaluation procedure for the threefold approach was conducted using the following two steps: firstly, using the FRI benchmark numerical metrics, the results demonstrated the ability of the threefold approach to generate the required results for the various benchmark scenarios. Secondly, using a real-life dataset (phishing attacks dataset), the results demonstrated the effectiveness of the suggested approach to handle cases of missing fuzzy rules in the area of phishing attacks. Consequently, the suggested threefold approach offers an opportunity to reduce the number of fuzzy rules effectively and generate the required results using only a few fuzzy rules. [ABSTRACT FROM AUTHOR]

This study presents a state-of-the-art risk evaluation approach, designed for spatiotemporal multivariate environmental dynamic wind-wave systems, being either measured or Monte Carlo (MC) numerically simulated over a representative time-lapse. The main objective of this study has been to benchmark/validate the effectiveness and accuracy of the Gaidai multivariate risk evaluation methodology, using the application to in situ raw windspeeds, along with correlated wave-heights measurements, delivered by North Pacific area of the National Oceanic and Atmospheric Administration (NOAA) ocean buoys. The current study outlines a novel risk evaluation methodology, suitable for environmental dynamic systems, that are either MC numerically modeled, or directly physically measured. The intercorrelations between the wind-wave environmental system's critical/key dimensions and components, along with the high dimensionality of complex environmental systems, are not easily addressed by contemporary classic reliability methods. The objective of this study is to apply a novel reliability/risk evaluation methodology to a combined windspeed and correlated wave-height raw data set, recorded by the NOAA buoys within the North Pacific area, to demonstrate the efficiency of the proposed methodology. By reliability/risk assessment in the current study authors primarily mean probability forecast of certain multivariate hazard event. It is well known that when combined, windspeeds along with correlated wave heights form nonlinear dynamic environmental systems, that are complex, multidimensional, nonstationary, and yet intercorrelated. Global warming is only one of several significant factors that have ongoing impact on ocean windspeeds along with correlated wave heights, and environmental system risk evaluation is essential for marine, naval, and offshore structures, operating within specific in situ offshore areas of interest subject to realistic in situ ocean/sea weather conditions. The main goal of this study had been to benchmark and validate novel multivariate risk analysis methodology, making it possible to extract essential information directly from in situ raw environmental measurements. The methodology presented in this study opens the possibility of efficiently yet accurately assessing global failure/damage and hazard risks for multivariate nonstationary nonlinear environmental sea/ocean wind-wave systems. [ABSTRACT FROM AUTHOR]

Nanobodies, derived from camelids and sharks, offer compact, single‐variable heavy‐chain antibodies with diverse biomedical potential. This review explores their generation methods, including display techniques on phages, yeast, or bacteria, and computational methodologies. Integrating experimental and computational approaches enhances understanding of nanobody structure and function. Future trends involve leveraging next‐generation sequencing, machine learning, and artificial intelligence for efficient candidate selection and predictive modeling. The convergence of traditional and computational methods promises revolutionary advancements in precision biomedical applications such as targeted drug delivery and diagnostics. Embracing these technologies accelerates nanobody development, driving transformative breakthroughs in biomedicine and paving the way for precision medicine and biomedical innovation. [ABSTRACT FROM AUTHOR]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This study compares the Fourth Industrial Revolution (4IR) preparedness of two advanced academic libraries each in Ghana and South Africa. Design/methodology/approach: The study utilized a conceptual framework that emanated from the Socio-Technical Theory (STT) and Global Competitiveness Index (GCI) models. Employing convergent parallel mixed methods, including questionnaires and interviews, 167 respondents, comprising librarians, IT staff and directors, shared insights on challenges faced by academic libraries in adapting to 4IR. Findings: Findings highlight a deficiency in Lib 4.0 skills crucial for survival in the 4IR, with South Africa showing better readiness. Both countries lack Lib 4.0 policies and face budget constraints, inadequate ICT infrastructure and limited support. The findings underscore the impact of relevant training on staff embracing Lib 4.0 technologies. Originality/value: The study then proposes a comprehensive model, highlighting the significance of librarians adopting a growth mindset and priori V Btizing continuous learning, relearning, reskilling and upskilling to effectively navigate the complexities posed by the 4IR. [ABSTRACT FROM AUTHOR]

Copyright of Transfer: Zeitschrift für Kommunikation & Markenmanagement is the property of Deutsche Werbewissenschaftliche Gesellschaft and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transfer: Zeitschrift für Kommunikation & Markenmanagement is the property of Deutsche Werbewissenschaftliche Gesellschaft and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The understanding of the physics course (PC) at the university level faces many challenges, highlighting the gap in mathematical knowledge and conceptual phenomenological analysis that affect the assimilation of more advanced physical concepts. The synergistic integration of artificial intelligence (AI) and gamification into the teaching and learning process transforms this process into an engaging and collaborative experience, improving interaction and personalization of learning and incorporating game elements to increase student participation and motivation. The objective of this research was to measure the influence of the integration of ChatGPT, AI, and gamification on the academic results and motivation of students in the physics 1 course. A total of 188 students participated in this study: 98 students in the experimental group and 90 students in the control group. The grades obtained in the standardized evaluations were compared in both the partial exam and the final exam of the physics 1 course, and a Likert-type questionnaire was used for motivation. The results show a better academic performance (AP) of the students in the experimental group than the control group, which is statistically evidenced (p < 0.01). It also shows that students who developed the course using ChatGPT AI and gamification developed a more positive attitude towards interest, usefulness, self-efficacy, active participation, and personal satisfaction in the physics 1 course. [ABSTRACT FROM AUTHOR]

Drones can be used to create wireless communication networks in swarms using Artificial intelligence (AI). Their mobility and line-of-sight capability have made them key solutions for civil and military applications. AI is also developing rapidly nowadays and is being successfully applied due to the huge amount of data available. This has led to the integration of AI into networks and its application to solve problems associated with drone swarms. Since AI systems have to process huge amounts of information in real time, this leads to increased data packet loss and possible loss of communication with the control center. This article is devoted to the calculation of packet losses and the impact of traffic parameters on the data exchange in swarms. Original swarm models were created with the help of MATLAB and NetCracker packages. Dependences of data packet losses on the transaction size are calculated for different drone number in a swarm using NetCracker software. Data traffic with different parameters and statistical distribution laws was considered. The effect of different distances to drones on the base station workload has been simulated. Data transmission in a swarm was studied using MATLAB software depending on the signal-to-noise ratio, nonlinearity levels of base station amplifier, signal modulation types, base station antenna diameters, and signal phase offsets. The data obtained allows foresee the operation of drone communication channels in swarms. [ABSTRACT FROM AUTHOR]

In close‐range remote sensing data collected in a forest, occlusion often causes incomplete or sparse point cloud representations of individual trees, impeding accurate 3D reconstruction of tree architecture and estimation of tree height and volume. Recent developments in deep learning (DL) for 3D data have produced approaches for point cloud completion, which could potentially be applied to trees.We explored the potential of a DL approach to fill gaps in dense point clouds representing the main structures of deciduous trees by applying an existing transformer‐based completion model (PoinTr). Complete point clouds are required as training data, but even dense terrestrial laser scanning (TLS) data sets contain gaps caused by occlusion, making it nearly impossible to acquire such data. We therefore investigated the ability of point cloud completion models trained on a range of synthetic data sets to handle occlusion patterns in real‐world point clouds.Despite the limited data set, we successfully fine‐tuned a general pre‐trained completion model to fill gaps within 1 m3 segments of tree point clouds. Fine‐tuning on synthetic tree data improved the model's ability to complete tree objects compared with training on diverse artificial objects. However, the quality of the predictions was influenced by the level of sophistication of the synthetic data. Our results demonstrate that incorporating even limited real‐world TLS data during training can considerably improve completion results but may introduce additional noise in the predictions.3D point cloud completion with DL has the potential to improve and fill gaps in point clouds of individual trees, facilitating further steps in the processing and analysis of 3D forest data. [ABSTRACT FROM AUTHOR]

Infectious diseases caused by pathogenic microorganisms pose a serious threat to human health. Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious diseases remain a significant public health concern. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance requires concerted interdisciplinary efforts. With the development of computer technology and the continuous exploration of artificial intelligence(AI)applications in the biomedical field, the automatic morphological recognition and image processing of microbial images under microscopes have advanced rapidly. The research team of Institute of Microbiology, Chinese Academy of Sciences has developed a single cell microbial identification technology combining Raman spectroscopy and artificial intelligence. Through laser Raman acquisition system and convolutional neural network analysis, the average accuracy rate of 95.64% has been achieved, and the identification can be completed in only 5  min. These technologies have shown substantial advantages in the visible morphological detection of pathogenic microorganisms, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this review, we discuss the application of AI-based machine learning in image analysis, genome sequencing data analysis, and natural language processing (NLP) for pathogen identification, highlighting the significant role of artificial intelligence in pathogen diagnosis. AI can improve the accuracy and efficiency of diagnosis, promote early detection and personalized treatment, and enhance public health safety. [ABSTRACT FROM AUTHOR]

The rules of the game in the food and beverage industry have changed with the introduction of artificial intelligence (AI) and machine learning. While workers in this sector face new challenges due to automation, AI is opening up new opportunities to enhance operations, ensure product quality, and meet the evolving needs of consumers. This article explores how AI is impacting the workforce in the food and beverage industry, reviews proposed solutions to address these challenges, and highlights successful case studies of companies in this field. [ABSTRACT FROM AUTHOR]

Many newsrooms around the world are increasingly turning to artificial intelligence (AI) algorithms to generate journalistic content. Often, these machine-generated texts are distributed without being clearly identified as synthetic or hybrid. Since the launch of ChatGPT in late 2022, the tool’s extraordinary ability to mimic human language has been widely celebrated. Given that subjectivity is an integral part of human language, this study examines how different texts generated using AI tools are imbued with subjective features in order to anthropomorphize their linguistic content. Our aim is to gain insight into the ways in which these texts express subjectivity in order to appear anthropomorphic, as well as the limits of this expression and its implications for communication. To this end, a corpus of AIgenerated journalistic texts published in various media, as well as texts created using AI tools such as ChatGPT and Gemini, is analysed to assess these tools’ capabilities. Ten criteria are used to characterize the expression of subjectivity in journalistic discourse on the surface of texts and in terms of situational appropriateness. The results show that AI tools can incorporate subjective markers on the text surface, but have important limitations when it comes to situational appropriateness, making it difficult to imitate certain features of journalistic writing. The paper also discusses the implications of asymmetrical audience interaction with machines that simulate human characteristics, and the varying degrees of opacity and transparency with which AI is used in newsrooms. [ABSTRACT FROM AUTHOR]

During the last few years, the popularity of chatbots has risen and grown exponentially with the increase in demand for smartphones and messaging applications. Chatbots can be utilized in education by providing information about educational content, communication, and assistance, enhancing classroom participation, and facilitating collaborative learning. Despite the high use of chatbots in learning, little research has attempted to understand students' perceptions of that environment and support them with quantitative data. This mixed exploratory study examines undergraduate students' perceptions of the chatbot-based learning environment by analyzing qualitative and quantitative data in the Internet Research and E-Commerce Unit in the Computer Skills course. Quantitative evidence was collected to support qualitative results. Data was identified and sorted into categories organized as sub-titles and the effect of this environment, which led to five main topics: flexible environment, enhanced learning, well-being, effective communication, and motivation. The results showed the acceptability of chatbots among students and a positive trend toward their use in the learning process and gives the learner the ability to control the selection of the appropriate place and time to learn, the repetition of exercises, and access to content quickly and in multiple ways that suit his/ her preferences. [ABSTRACT FROM AUTHOR]

AbstractBackgroundMethodsResultsConclusionArtificial intelligence (AI) has significantly revolutionized the diagnosis and treatment of various medical and surgical conditions, including gastroesophageal reflux disease (GORD). AI has the potential to enhance diagnostic and treatment capabilities, contributing to overall advancements in healthcare. The current study aimed to investigate the medical students’ views regarding the use of AI in GORD management.An anonymous, self-administered questionnaire was distributed among undergraduate medical students of various grades within different national medical institutions. The questionnaire comprised three sections, addressing sociodemographic data, knowledge, and attitudes. Knowledge and attitudes were assessed through 5- and 7-item questionnaires, respectively. The knowledge scores constituted a scale of 0–5. This reflected varying levels of understanding. Categories include poor knowledge (two or less), moderate knowledge (three), and good knowledge (4 and 5). Attitudes were classified as negative, neutral, or positive based on 50% and 75% cutoff points, with scores below 50% indicating negative attitudes, 50–75% considered neutral, and scores above 75% reflecting positive attitudes.A total of 506 medical students participated, including 273 females and 233 males, with a ratio of 1.2–1. The majority fell within the age range of 20–26 years. Additionally, 388 participants (76.7%) reported grade point averages (GPAs) higher than 4. Regarding knowledge, 9% demonstrated a poor score of knowledge, while 33% had a moderate knowledge score. However, 65% of the participating students held a neutral attitude toward the role of AI in GORD management, with 6.9% expressing a negative stance on the matter.Although AI is highly involved in GORD management, the study revealed that medical students and interns possess a limited perception of this emerging technology. This may highlight the necessity for active involvement in AI education within the medical curricula. [ABSTRACT FROM AUTHOR]

Nuclear Medicine is witnessing a revolution across a large spectrum of patient care applications, hardware, software and novel radiopharmaceuticals. We propose to offer a framework of the nuclear medicine practice of the future that incorporates multiple novelties and coined as the NEW (nu) Clear medicine. All these new developments offer a significant clarity and real clinical impact, and we need a concerted effort from all stakeholders in the field for bedside implementation and success. [ABSTRACT FROM AUTHOR]

Vitiligo is a skin disorder that is associated with a decreased risk of skin cancer, but it can lead to increased susceptibility to sunburn, psychological distress, and disruptions in daily life, consists of two primary subtypes: segmental and nonsegmental vitiligo, each with distinct underlying mechanisms. However, the reliable identification of diagnostic markers and the ability to differentiate between these subtypes have remained elusive challenges. This study aims to pioneer predictive algorithms for vitiligo diagnosis, harnessing the capabilities of AI (Artificial Intelligence) to amalgamate multisource data and uncover essential features for distinguishing vitiligo subtypes.An ensemble algorithm was thoughtfully developed for vitiligo diagnosis, utilizing a spectrum of machine learning techniques to evaluate the likelihood of vitiligo, whether segmental or nonsegmental. Diverse machine learning methodologies were applied to distinguish between healthy individuals and vitiligo patients, as well as to differentiate segmental from nonsegmental vitiligo. The ensemble algorithm achieved a remarkable AUC (Area Under the Curve) of 0.99 and an accuracy of 0.98 for diagnosing vitiligo. Furthermore, in predicting the development of segmental or nonsegmental vitiligo, the model exhibited an AUC of 0.79 and an accuracy of 0.73. Key parameters for vitiligo identification encompassed factors such as age, FBC (full blood count)-neutrophils, FBC-lymphocytes, LKF(liver and kidney function)-direct bilirubin, LKF-total bilirubin, and LKF-total protein levels. In contrast, vital indicators for monitoring the progression of segmental and nonsegmental vitiligo included FBC-B lymphocyte count, FBC-NK (Natural Killer) cell count, and LKF-alkaline phosphatase levels. This retrospective study underscores the potential of AI-driven analysis in identifying significant risk factors for vitiligo and predicting its subtypes at an early stage. These findings offer great promise for the development of effective diagnostic tools and the implementation of personalized treatment approaches in managing this challenging skin disorder. [ABSTRACT FROM AUTHOR]

Nanoparticles impose multidimensional effects on living cells that significantly vary among different studies. Machine learning (ML) methods are recommended to elucidate more consistence and predictable relations among the affected parameters. In this study, nine ML algorithms [Support-Vector Regression (SVR), Linear, Bagging, Stochastic Gradient Descent (SGD), Gaussian Process, Random Sample Consensus (RANSAC), Partial Least Squares (PLS), Kernel Ridge, and Random Forest] were applied to evaluate their efficiency in predicting the effects of zinc oxide nanoparticles (ZnO NPs: 0.5, 1, 5, 25, and 125 µM) and microparticles (ZnO MPs: 1, 5, 25, and 125 µM) on Carum copticum. The plant root/shoot biomass; number of leaves, branches, umbellates, and flowers; protein content; reducing sugars; phenolic compounds; chlorophylls (a, b, Total); carotenoids; anthocyanins; H2O2; proline; malondialdehyde (MDA); tissue zinc content; superoxide dismutase (SOD) activity; and media ΔpH were measured and considered input variables. All levels of ZnO MPs treatments increased growth parameters compared to the control (ZnSO4). The highest shoot/root fresh and dry mass were recorded at 5 µM ZnO MPs compared with the control. The root fresh/dry mass under ZnO NPs treatments was more sensitive than shoot parameters. The number of flowers increased by 134 and 79% in MPs and NPs treatments compared to the control, respectively. ZnO NPs reduced protein content by up to 81% in 125 µM NPs compared to ZnSO4. Reducing sugar content increased to 25, 40 and 36% in 5, 25, 125 µM MPs and 67, 68, 26, 26 and 21% in 0.5, 1, 5, 25 and 125 µM NPs treatments, respectively. The pH alteration was more significant under NPs and affected zinc uptake. All levels of ZnO NPs treatments increased growth parameters compared to the control. All ML algorithms showed varied efficiencies in predicting the nonlinear relationships among parameters, with higher efficiency in predicting the behavior of root and shoot dry mass, root fresh weight and number of flowers according to R2 index. The model obtained from SVR with the radial basis function (RBF) kernel was selected as a comprehensive model for predicting and determining the efficacy of the results. [ABSTRACT FROM AUTHOR]

Background: Though artificial intelligence holds great value for healthcare, it may also amplify health inequalities through risks of bias. In this paper, we explore bias risks in targeted medicines manufacturing. Targeted medicines manufacturing refers to the act of making medicines targeted to individual patients or to subpopulations of patients within a general group, which can be achieved, for example, by means of cell and gene therapies. These manufacturing processes are increasingly reliant on digitalised systems which can be controlled by artificial intelligence algorithms. Whether and how bias might turn up in the process, however, is uncertain due to the novelty of the development. Methods: Examining stakeholder views across bioethics, precision medicine, and artificial intelligence, we document a range of opinions from eleven semi-structured interviews about the possibility of bias in AI-driven targeted therapies manufacturing. Result: Findings show that bias can emerge in upstream (research and development) and downstream (medicine production) processes when manufacturing targeted medicines. However, interviewees emphasized that downstream processes, particularly those not relying on patient or population data, may have lower bias risks. The study also identified a spectrum of bias meanings ranging from negative and ambivalent to positive and productive. Notably, some participants highlighted the potential for certain biases to have productive moral value in correcting health inequalities. This idea of "corrective bias" problematizes the conventional understanding of bias as primarily a negative concept defined by systematic error or unfair outcomes and suggests potential value in capitalizing on biases to help address health inequalities. Our analysis also indicates, however, that the concept of "corrective bias" requires further critical reflection before they can be used to this end. [ABSTRACT FROM AUTHOR]

The early prediction of ocular disease is certainly an obligatory concern in the domain of ophthalmic medicine. Although modern scientific discoveries have shown the potential to treat eye diseases by using artificial intelligence (AI) and machine learning, explainable AI remains a crucial challenge confronting this area of research. Although some traditional methods put in significant effort, they cannot accurately predict the proper ocular diseases. However, incorporating AI into diagnosing eye diseases in healthcare complicates the situation as the decision-making process of AI demonstrates complexity, which is a significant concern, especially in major sectors like ocular disease prediction. The lack of transparency in the AI models may hinder the confidence and trust of the doctors and the patients, as well as their perception of the AI and its abilities. Accordingly, explainable AI is significant in ensuring trust in the technology, enhancing clinical decision-making ability, and deploying ocular disease detection. This research proposed an efficient transfer learning model for eye disease prediction to transform smart vision potential in the healthcare sector and meet conventional approaches' challenges while integrating explainable artificial intelligence (XAI). The integration of XAI in the proposed model ensures the transparency of the decision-making process through the comprehensive provision of rationale. This proposed model provides promising results with 95.74% accuracy and explains the transformative potential of XAI in advancing ocular healthcare. This significant milestone underscores the effectiveness of the proposed model in accurately determining various types of ocular disease. It is clearly shown that the proposed model is performing better than the previously published methods. [ABSTRACT FROM AUTHOR]

Introduction The widespread uptake of the Internet of Things (IoT) has brought forth numerous security concerns as IoT environments become more heterogeneous, larger, and complex.Because of the evolving threats and large-scale management needed for IoT, traditional security models are often ill-equipped to keep up. In this context, researchers have started exploring the integration of Artificial Intelligence (AI) and Software-Defined Networking as an alternative solution for adapting and scaling IoT security management. In this article, we take a look at how both technologies are used together and contribute to increasing security for IoT. It starts by describing the major challenges and issues that are seen in securing IoT systems, like device heterogeneity, mass scale updating software security patches etc., visibility and control. It next explores the benefits of AI and SDN separately, describing how security rule settings can be automated using an AI-based method to enhance threat detection (through better performance prediction) and provide intelligent resource orchestration with respect to IoT networks as well as centralized programming by employing a single control plane that seamlessly scales for data processing. The article also went on to explain the promise of AI-SDN hybrid solutions in which data from both technologies is correlated creating a comprehensive IoT security environment that can power automated decision making and quick application of security policies. Apart from that, it documents some relevant case studies and also provides several practical insights into enforcing AI/SDN-driven IoT security solutions with the help of existing infrastructure in tandem with discussing future trends hitting cybersecurity perimeter. The presented work reveals that integration of AI and SDN has the vast capability to combat security issues in IoT environment. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and artificial intelligence (AI) have emerged as pivotal tools for enhancing network security in the 21st century, offering innovative approaches that surpass traditional methods. This paper conducts a comprehensive literature review to explore how AI and ML contribute to advancing network security. The findings underscore the critical role of these technologies in detecting and mitigating cyber threats, automating threat responses, and predicting future vulnerabilities. The research highlights how AI and ML can significantly enhance the accuracy and efficiency of security processes, thereby bolstering an organization's overall cybersecurity posture. Additionally, the study emphasizes the importance of seamlessly integrating AI and ML with existing security architectures to maximize their benefits. Despite the significant potential of these technologies, the study identifies several gaps in the literature, such as the need for more empirical research on the effectiveness of AI and ML-based security solutions and the practical challenges of implementing these technologies in real-world scenarios. Finally, this paper discusses the limitations of the current research and suggests future research directions to further explore the convergence of network security, AI, and ML. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is now used in many sectors but its transformative impact on cybersecurity is unmatched. Cybersecurity is seen to rely heavily on artificial intelligence (AI), which has brought about automation of responses, detection of network threats and security consciousness. This paper examines various modern techniques involving deep learning, machine learning and behavior analysis among others that are used in dealing with the increasing number and complexity of cyber threats. The research also looks at how automated cybersecurity response and decision-making capacities have been transformed through AI. It also investigates into security intelligence modeling as a means of offering actionable insights towards strengthening organizational resilience. Additionally, the goal of this paper is to provide a nuanced understanding of how AI is reshaping cybersecurity and to outline future pathways for research and development in this dynamic field. [ABSTRACT FROM AUTHOR]

Patients and healthcare practitioners have commended the systems' effectiveness, ease, and user-friendliness in several situations. Utilizing cutting-edge ideas and methods from the multidisciplinary domains of electricity, computing, medical engineering, and medicine, mobile healthcare (m-health) technology advances these professions' contributions to healthcare systems. The monitoring and delivery of healthcare interventions are becoming increasingly dependent on mobile phones. Because of their sophisticated processing functions, improved preferences, and wide range of capabilities, they are frequently referred to as pocket computers. Their advanced sensors and intricate software programmers increase the viability and innovation of m-health solutions. To design the m-health application, the design science research methodology (DSRM) framework was used. Additionally, the architecture for connecting the hospital information system and mobile device network together was established. Additionally, a few exemplary intelligent healthcare applications are examined to demonstrate how data analytics and mobile computing can be used to improve the quality of healthcare services. [ABSTRACT FROM AUTHOR]

In many educational institutions, the adoption of mobile learning continues to be a growing topic. As has been considered recently, wireless technologies are currently employed by mobile technology to spread and exchange data via thinking, communicating, exchanging, and understanding. As a consequence, merging mobile technologies into teaching and learning can enhance the ambiance in higher education. Thus, the purpose of this investigation is to implement mobile learning to examine students' applications in the framework of educational technology. The use of mobile technologies in STEM education is always efficient and engaging for the students. According to its potential to redefine traditional classroom learning paradigms, the inclusion of cellular phones into STEM (science, technology, engineering, and mathematics) education has drawn significant interest. Three artificial intelligence education (AIEd) paradigm structures are utilized to narrow our exploration of how AI is influencing the STEM sectors. An established cross-disciplinary topic of research dealing with leveraging arti- ficial intelligence (AI) approaches to improve training is defined as AIEd. There seems to be an increasing desire to harness AIEd's promise to tackle academic barriers in STEM fields. The implications of mobile phones on the educational outcomes of students in STEM education settings are explored in this study. By performing a deep review of existing scholarship and empirical investigation, we look for the impact of mobile devices, functions, and platforms on pupil engagement, understanding, and performance in multifaceted STEM fields. A learning approach entitled STEM Project-Based Learning merges project-based curriculum design with the STEM approach to education. As a whole, pupils' science and technology literacy were improved by the STEM mobile learning package on the ecosystem. Certain learning packages deserve to be studied isolated, while others might be given outright during offline or personal conversations. [ABSTRACT FROM AUTHOR]

This study addresses the United Nations 2030 Agenda Sustainable Development Goals 4, 8, 10, and 12 by developing a resource-efficient tool that promotes equitable quality education and lifelong learning opportunities, supports decent work and economic growth, reduces inequalities, and ensures sustainable consumption and production patterns. This study contributes to sustainable education by providing a tool that is designed to be easy to use, easy to modify, and resource-efficient, making it accessible to institutions with limited technological resources. The tool uses artificial intelligence and a long short-term memory (LSTM) neural network to provide personalized teaching, adapting to the unique learning styles of its users. A custom survey adapted from the Felder–Silverman model was used to track weekly learning style transitions among 72 engineering students at the Faculty of Engineering at the University of Los Llanos. These data were used to build the LSTM model to predict learning style transitions over a 16-week semester. Two interfaces were created: one for instructors, integrating the LSTM model, and one for students, incorporating a custom survey. An OpenAI API-powered chat was also built into both interfaces to provide study advice to students according to their styles and enable professors to personalize their teaching methodologies in engineering education. [ABSTRACT FROM AUTHOR]

This study aimed to make and introduce a curriculum in Taiwan for fifth-grade students, merging Scratch animation with the Sustainable Development Goals (SDGs). The curriculum combined the Scratch Reflective AI digital learning platform with conventional teaching methods to assess its effect on students' empathy, self-efficacy, and scriptwriting and animation skills. A total of 133 fifth-grade students from elementary school were involved in the study. The experimental group (67 students) received the SDGs-themed Scratch animation course with the AI-Enhanced Comics Reflection Diaries, while the control group (66 students) received the traditional SDGs-themed Scratch animation course. The findings revealed a significant improvement in students' self-efficacy levels in the Scratch animation course within the SDGs theme when using the AI digital learning platform with Comic Reflection. However, there was no significant variation in empathy levels among the students. The Comic Reflection AI digital learning platform significantly enhanced students' scriptwriting and animation skills, boosting creativity and self-efficacy in their abilities within the SDGs-themed Scratch animation course. Classroom observations revealed students' enthusiasm for the platform, emphasizing its potential to foster creativity and learning engagement. The study offers insights into the effectiveness of integrating digital learning platforms like Comic Reflection can be beneficial for enhancing students' self-efficacy and scriptwriting skills, highlighting the importance of adequate time for cognitive processing. The study's findings provide valuable guidance for educators and researchers aiming to enhance students' empathy with regard to global sustainability development goals. [ABSTRACT FROM AUTHOR]

This research is motivated by the need for higher education institutions to enhance economic efficiency in the learning process through innovative technology. ChatGPT, as an artificial intelligence (AI) model, is expected to support more effective and efficient learning processes significantly. This study aims to evaluate ChatGPT integration's impact on economic efficiency at Budi Utomo National Institute. The research uses a quantitative method with a survey approach involving 150 respondents consisting of lecturers and students. Data collection was conducted through questionnaires, and the results were analyzed using simple linear regression techniques. The findings show that the integration of ChatGPT significantly improves operational cost efficiency in education, reduces administrative burdens, and accelerates access to learning resources. The discussion focuses on AI's long-term potential in enhancing teaching quality and the challenges of implementing such technology in higher education institutions. This study concludes that ChatGPT can be an effective tool for achieving economic efficiency in higher education, though proper strategies are needed to optimize its use sustainably. [ABSTRACT FROM AUTHOR]

Emergency departments, crucial in managing patient emergencies, are often challenged by overcrowding and diagnostic errors during triage—the process that assesses the urgency of patients' conditions. Traditional triage systems, heavily dependent on human judgement, are prone to errors like under‐triage, where severe conditions are missed, delaying treatment, and over‐triage, where less severe conditions are overly prioritized, causing unnecessary resource use and morbidity. This thesis presents a novel multi‐model machine‐learning framework designed to enhance triage accuracy by evaluating multiple medical conditions concurrently, including chronic illnesses like heart disease. The framework employs various machine‐learning techniques—such as logistic regression, support vector machines, random forests, deep neural networks, and decision trees—to analyze different medical conditions in two comprehensive phases. In the first phase, patients' conditions are categorized, and a multi‐tiered analysis using multiple classifiers refines the assessment by considering probabilistic outcomes from each classifier. The second phase synthesizes these insights into a unified and precise triage decision, distinguishing between patients who require critical care and those needing less urgent hospitalization. This integration of diverse machine learning models allows for a fused and precise triage decision, overcoming traditional triage systems' limitations that usually focus on single conditions and rely on isolated model predictions. A hybrid feature selection method is also utilized to identify critical predictors, enhancing the decision‐making process. The framework is validated through a specially curated dataset that simulates multiple triage scenarios, evaluated by medical experts for efficacy. Comparative analysis with traditional triage methods demonstrates significant improvements in decision accuracy, as evidenced by higher area under curve (AUC) values—0.95 for critical care and 0.90 for hospitalization. The implementation of this data‐driven approach substantially reduces human error, boosts operational efficiency, and aids medical staff in making rapid, informed decisions. This research represents a significant advancement in emergency medical care, illustrating the benefits of integrating sophisticated machine learning techniques to improve patient outcomes and resource management. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence-driven Clinical Decision Support Systems (AI-CDSS) are being increasingly introduced into various domains of health care for diagnostic, prognostic, therapeutic and other purposes. A significant part of the discourse on ethically appropriate conditions relate to the levels of understanding and explicability needed for ensuring responsible clinical decision-making when using AI-CDSS. Empirical evidence on stakeholders' viewpoints on these issues is scarce so far. The present study complements the empirical-ethical body of research by, on the one hand, investigating the requirements for understanding and explicability in depth with regard to the rationale behind them. On the other hand, it surveys medical students at the end of their studies as stakeholders, of whom little data is available so far, but for whom AI-CDSS will be an important part of their medical practice. Methods: Fifteen semi-structured qualitative interviews (each lasting an average of 56 min) were conducted with German medical students to investigate their perspectives and attitudes on the use of AI-CDSS. The problem-centred interviews draw on two hypothetical case vignettes of AI-CDSS employed in nephrology and surgery. Interviewees' perceptions and convictions of their own clinical role and responsibilities in dealing with AI-CDSS were elicited as well as viewpoints on explicability as well as the necessary level of understanding and competencies needed on the clinicians' side. The qualitative data were analysed according to key principles of qualitative content analysis (Kuckartz). Results: In response to the central question about the necessary understanding of AI-CDSS tools and the emergence of their outputs as well as the reasons for the requirements placed on them, two types of argumentation could be differentiated inductively from the interviewees' statements: the first type, the clinician as a systemic trustee (or "the one relying"), highlights that there needs to be empirical evidence and adequate approval processes that guarantee minimised harm and a clinical benefit from the employment of an AI-CDSS. Based on proof of these requirements, the use of an AI-CDSS would be appropriate, as according to "the one relying", clinicians should choose those measures that statistically cause the least harm. The second type, the clinician as an individual expert (or "the one controlling"), sets higher prerequisites that go beyond ensuring empirical evidence and adequate approval processes. These higher prerequisites relate to the clinician's necessary level of competence and understanding of how a specific AI-CDSS works and how to use it properly in order to evaluate its outputs and to mitigate potential risks for the individual patient. Both types are unified in their high esteem of evidence-based clinical practice and the need to communicate with the patient on the use of medical AI. However, the interviewees' different conceptions of the clinician's role and responsibilities cause them to have different requirements regarding the clinician's understanding and explicability of an AI-CDSS beyond the proof of benefit. Conclusions: The study results highlight two different types among (future) clinicians regarding their view of the necessary levels of understanding and competence. These findings should inform the debate on appropriate training programmes and professional standards (e.g. clinical practice guidelines) that enable the safe and effective clinical employment of AI-CDSS in various clinical fields. While current approaches search for appropriate minimum requirements of the necessary understanding and competence, the differences between (future) clinicians in terms of their information and understanding needs described here can lead to more differentiated approaches to solutions. [ABSTRACT FROM AUTHOR]

Background: This study presents a Robot‐Inspired Computer‐Assisted Adaptive Autism Therapy (RoboCA3T) focusing on improving joint attention and imitation skills of children with autism spectrum disorder (ASD). By harnessing the inherent affinity of children with ASD for robots and technology, RoboCA3T offers a therapeutic environment designed to maximise engagement and facilitate effective skill development. It harnesses the advantages of Robot‐Assisted Therapies (RATs) by employing robot avatars and integrating them with Computer‐Assisted Therapies (CATs) within a web‐based solution. The integration of automatic gaze and pose detection algorithms within RoboCA3T addresses the challenge posed by potential human error and observation bias in assessing the child's progress, thereby ensuring accurate results. This research responds to the need for more effective, technology driven therapies for autism, filling gaps in existing methods. Objectives: The primary goal of this research is to create a robot inspired computer assisted adaptive autism therapy that maximises engagement and enhances joint attention and imitation skills. Methods: The study involves 11 ASD children with 30 sessions (divided into two halves) per module over eight months, comprising 660 experimental trials, 110 familiarizations, and 110 follow‐up sessions. The joint attention module evaluates the subject's gaze pattern using WebGazer for gaze detection in response to four least‐to‐most robot‐generated cues. The imitation module utilises robot‐generated pose for comparing subjects' imitated actions using Tensorflow Lite for pose estimation. Results and Conclusions: The effectiveness of therapy was substantiated by comparing Childhood Autism Rating Scale (CARS) scores before and after intervention. Significant improvements were noted between the first and second therapy halves, validated by Wilcoxon signed‐rank tests (p < 0.01) and spearman's correlation analysis, reinforcing the observed improvements in joint attention and imitation skills. Lay Description: What is currently Known about this Topic?: Autism Spectrum Disorder presents challenges in social skills and behaviour, particularly in joint attention and imitation skills. Existing therapies including Applied Behaviour Analysis and Social Skills Training have shown improvement but have limitations, such as high cost and limited access. What does this paper add?: Theoretically built on the principles of Applied Behaviour Analysis, this study introduces a novel Robot‐Inspired Computer‐Assisted Adaptive Autism Therapy (RoboCA3T).It leverages the natural affinity of children with autism spectrum disorder (ASD) towards technology and robots, to offer a unique web‐based therapy solution facilitating comprehensive joint attention and imitation skills development.Childhood Autism Rating Scale (CARS) scores before and after intervention, along with statistical tests, strongly support significant enhancements in both joint attention and imitation skills, validating the observed improvements. Implications for practice/or policy: Our study introduces an innovative therapy method, emphasising technology's role and advocating for inclusive, predictable, and personalised therapy approach for better engagement and skill development for ASD children.It emphasises the importance of automated algorithms in reducing assessment bias and human error. [ABSTRACT FROM AUTHOR]

The utilization of pharmaceuticals in medical and veterinary treatment has not only improved human and animal health but has also boosted food-production and economic welfare. However, the release of pharmaceuticals in the environment through various pathways, such as manufacturing, human excretion, and substandard disposal, can have detrimental effects on ecosystems and various biological entities associated with these systems. High levels of pharmaceutical residues have been detected further downstream of manufacturing facilities, and untreated veterinary medication leftovers can end up in waterbodies. Methods utilizing artificial intelligence (AI) and machine learning (ML) have been employed to establish connections between chemical structure and biological activity, referred to as quantitative structure--activity relationships (QSARs) for the compounds. QSAR models use chemical structures to predict hazardous activity when experimental data is lacking, thereby helping prioritize chemicals for testing and compilation. Combinatorial chemistry, by enabling high-throughput compound synthesis, accelerates the generation of targeted molecules for testing across various fields. Green chemistry helps in creating, designing, and implementing chemical products and procedures with the aim of minimizing or eradicating the generation and subsequent utilization of harmful substances. In addition, pharmaceutical sensor technologies (PST) are critical tools in modern medicine, enabling precise detection and monitoring of various biochemical and physiological markers and parameters. The synergy between AI, ML, QSAR modeling, and the implementation of combinatorial and green chemistry methodologies is pivotal in driving the development of innovative products and PST in pharmaceutics. This interdisciplinary approach is crucial for creating solutions with reduced toxicity in pharmaceutical processes, thereby ensuring enhanced public safety and promoting the sustainability of environmental resources. By integrating these advanced methodologies, the pharmaceutical industry can achieve greater detection accuracy, efficiency in production of eco-friendly products, ultimately leading to safer pharmaceutics and a healthier planet. [ABSTRACT FROM AUTHOR]

This paper examines the transformative impact of artificial intelligence (AI) on pharmacogenomics, signaling a paradigm shift in personalized medicine. With a focus on enhancing drug response prediction and treatment optimization, AI, particularly machine learning and deep learning algorithms, navigates the complexity of genomic data. By elucidating intricate relationships between genetic factors and drug responses, AI augments the identification of genetic markers and contributes to the development of comprehensive models. The review emphasizes AI's role in guiding treatment decisions, minimizing adverse reactions, and optimizing drug dosages in clinical settings. Ethical considerations, challenges, and future directions are also discussed. This work underscores the synergy of AI and pharmacogenomics, offering a more effective and patient-centric approach to drug therapy, marking a significant advancement in the field of personalized medicine. [ABSTRACT FROM AUTHOR]

This paper explores the implications of marrying Internet of Things (IoT) with artificial intelligence (AI) in the realms of automation and security and what it entails. It presents an innovative application description arising from this technology convergence and its future-generation applications. It is found that AI brings strong cyber security, predictive maintenance and decision-making capabilities to the table due to its advanced algorithms and machine learning (ML) models. IoT has the potential to allow businesses to extract and generate big data, automate processes and monitor in real-time thousands and even millions of devices at once. It is believed that smart city, standalone systems and user experience are quite consumer-friendly. Importantly, the study also assesses facets such as data protection and system interoperability. The results include achieving significant 95.8% accuracy in anomaly detection and 93.4% accuracy in predictive maintenance, showcasing the efficacy of AI models in enhancing system reliability and operational efficiency. Vigorous encryption protocol performance ensures data security, thus addressing issues of privacy. The findings underscore the transformative potential of AI-IoT integration in next-generation automation and security applications. [ABSTRACT FROM AUTHOR]

Shear logs, also known as shear velocity logs, are used for various types of seismic analysis, such as determining the relationship between amplitude variation with offset (AVO) and interpreting multiple types of seismic data. This log is an important tool for analyzing the properties of rocks and interpreting seismic data to identify potential areas of oil and gas reserves. However, these logs are often not collected due to cost constraints or poor borehole conditions possibly leading to poor data quality, though there are various approaches in practice for estimating shear wave velocity. In this study, a detailed review of the recent advances in the various techniques used to measure shear wave (S-wave) velocity is carried out. These techniques include direct and indirect measurement, determination of empirical relationships between S-wave velocity and other parameters, machine learning, and rock physics models. Therefore, this study creates a collection of employed techniques, enhancing the existing knowledge of this significant topic and offering a progressive approach for practical implementation in the field. [ABSTRACT FROM AUTHOR]

Physical activity can be monitored via small low-power sensor nodes (SNs) that are widely dispersed over the earth. For WSN sensor nodes, GPS is one of the most commonly utilised localization algorithms. Military, industrial, and more recently, consumer and civil uses of GPS are all examples of its vast range of applications. Wi-Fi enabled smart sensors are the product of a combination of WSNs and embedded intelligent sensor structures. Building smart sensor systems relies heavily on AI methods. An innovative Hybrid DA-FA and several meta-heuristics are compared in this research paper as initial contribution. A single anchor node meta-heuristic algorithm is suggested to determine the location of a node using a range-based approach. In contrast to the randomly moving target nodes, the anchor node is fixed in the middle of the region. Line-of-Sight difficulties can now be alleviated to a greater extent thanks to the introduction of virtual anchor nodes. They have shown a significant improvement in localization accuracy and rapid convergence in mobility-based scenarios with a reduced number of anchor nodes. A comparison of the accuracy, localization error, and other metrics of both methods is included in the new approach. We have evaluated the DA-FA techniques performance for maximum error which is reduced to 21.53% in comparison of existing approach. However, the minimum error is reduced to 3.91%. [ABSTRACT FROM AUTHOR]