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In artificial intelligence (AI), optimization techniques are used to solve several problems in different fields. One of these areas is software reliability verification, which is an important part of software products, as it helps determine how reliable the software is to complete its functions. This is done by estimating the parameters of software reliability growth models (SRGMs). SRGMs predict the expected number of failures after completion, while also serving as an indicator of software readiness for delivery. Therefore, this study aims to optimize the estimation of these parameters based on the available failure data using one of the stochastic optimization algorithms, the simulated annealing algorithm (SA) due to its power and effectiveness. Three SRGMs' models are studied: delayed S-shaped, Musa-Okumoto logarithmic and Power models, to examine the feasibility of the proposed algorithm using five different data sets. The results were compared and analyzed with several algorithms: Particle swarm optimization (PSO), cuckoo search (CS), modify whale optimization algorithm (MWOA), S-shaped model with logistic TEF and social spider algorithm (SSA). A comparison was also made with recent SRGMs that do not rely on AI techniques. The results showed that the proposed algorithm based on SA outperformed all other methods in finding the optimal parameters. [ABSTRACT FROM AUTHOR]

The article examines the impact of AI tools in extracurricular online dance classes on student learning outcomes. The approach to interactive dance learning within Choreography (which is the mandatory discipline) using innovative M-learning platforms and apps such as Moodle and STEEZY has been introduced. An educational experiment was conducted among 40 students of the Institute of Music and Choreography at Ningxia Pedagogical University. The dance accomplishments in the control and experimental groups were assessed using the Choreographic Creativity Rating Scale in three areas: physical skills, presentation, and creativity. The mean levels of dancers' choreographic skills, as assessed by experts and audience at the end of the educational experiment, were determined. Students' projects were presented in such directions as: Hip-hop, Open Style, K-pop, House, Breaking, Popping, Whacking, Krump, Jazz Funk. The assessment of 4 levels of dance choreography (level 1—below expectations, level 2—meets some expectations, level 3—fully meets expectations, and level 4—exceeds expectations) in the areas of physical skills, presentation, and creativity of dancers' skills made it possible to compare learning outcomes in the control group and the experimental group. The expert assessment of students' achievements suggested that additional online extracurricular activities contribute to better dance skills, effective development of dancers' physical (+1.6), presentation (+1.16) and creative (+1.01) skills. This article is intended for dance instructors developing effective courses using relevant digital tools. [ABSTRACT FROM AUTHOR]

Smooth interaction with a disaster‐affected community can create and strengthen its social capital, leading to greater effectiveness in the provision of successful post‐disaster recovery aid. To understand the relationship between the types of interaction, the strength of social capital generated, and the provision of successful post‐disaster recovery aid, intricate ethnographic qualitative research is required, but it is likely to remain illustrative because it is based, at least to some degree, on the researcher's intuition. This paper thus offers an innovative research method employing a quantitative artificial intelligence (AI)‐based language model, which allows researchers to re‐examine data, thereby validating the findings of the qualitative research, and to glean additional insights that might otherwise have been missed. This paper argues that well‐connected personnel and religiously‐based communal activities help to enhance social capital by bonding within a community and linking to outside agencies and that mixed methods, based on the AI‐based language model, effectively strengthen text‐based qualitative research. [ABSTRACT FROM AUTHOR]

In this article, the authors describe CATER (Computer‐Assisted Texts for Early Reading), an innovation that uses artificial intelligence (AI) to create texts for beginning reading instruction tailored to the interests, strengths, and learning needs of individual readers. The authors present an overview of CATER and share their findings from their work with elementary‐age children as part of a five‐week summer graduate course. The use of CATER allowed the authors to create texts that were engaging and that provided an appropriate balance of challenge and accessibility for the children with whom they worked. The authors share two examples that highlight different ways CATER can be implemented and provide recommendations for readers who are interested in trying it out in their classrooms. [ABSTRACT FROM AUTHOR]

AI chatbots such as ChatGPT help people produce texts. According to media reporting, these texts are also used for educational purposes. Thus, AI influences people's knowledge and perception of current issues. This paper examines the narrative of ChatGPT's stories on climate change. Our explorative analysis reveals that ChatGPT's stories on climate change show a relatively uniform structure and similar content. Generally, the narrative is in line with scientific knowledge on climate change; the stories convey no significant misinformation. However, specific topics in current debates on global warming are conspicuously missing. According to the ChatGPT narrative, humans as a species are responsible for climate change and specific economic activities or actors associated with carbon emissions play no role. Analogously, the social structuration of vulnerability to climate impacts and issues of climate justice are hardly addressed. ChatGPT's narrative consists of de-politicized stories that are highly optimistic about technological progress. [ABSTRACT FROM AUTHOR]

In the past, decisions on wastewater treatment methods have predominantly rested on expert opinions, utilizing the Delphi method. Yet, with an anticipated increase in diversification and customization, especially in the "small-batch and diverse" market over the next decade, addressing the formulation and execution of wastewater treatment for these non-traditional production processes will present substantial challenges. Relying solely on Delphi experts' decision-making within a short and time-constrained production planning window is expected to prove inadequate. Predominantly relies on the authors' over 15 years of industry experience in wastewater treatment, this perspective paper proposes an inventive solution that integrates Membrane Bioreactors (MBRs) with Artificial Intelligence (AI) applications. This approach signifies a more advanced method for industrial wastewater treatment compared to conventional methods, with the intention of garnering increased interest for future research endeavors. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is increasingly aiding human decision makers in construction bidding processes by analyzing competitors' bidding patterns. However, concerns are emerging about the potential for AI-driven algorithmic collusion, which might inflate prices and disrupt fair competition in various sectors. Given the unique dynamics of the construction sector and its growing reliance on AI, understanding the impact of these algorithms on the bidding landscape is essential, both academically and practically. Thus, this study investigates the impact of AI in the construction bidding market on bid pricing patterns to predict how the landscape of the market might change as AI starts to play a more prominent role. We subjected AI bidding agents to repeated competitions with each other in computer-simulated construction bidding marketplaces. We focused on the markup decisions made by the AI bidders. Our findings indicate that AI bidders tend to develop cooperative strategies over time, leading to higher bids overall compared to lower, competitive bids. This collusive behavior was facilitated by frequent interactions (previous bidding competitions over time) between AI bidders. This collusive behavior was enabled by algorithms that aimed to maximize the profit and was hindered by algorithms that aimed to maximize the number of project wins. These findings highlight potential fairness and competitiveness issues in construction bidding with dominant AI bidders. [ABSTRACT FROM AUTHOR]

The increasing use of intelligent devices connected to the internet has contributed to the introduction of a new paradigm: the Internet of Things (IoT). The IoT is a set of devices connected via the internet that cooperate to achieve a specific goal. Smart cities, smart airports, smart transportation, smart homes, and many applications in the medical and educational fields all use the IoT. However, one major challenge is detecting malicious intrusions on IoT networks. Intrusion Detection Systems (IDSs) should detect these types of intrusions. This work proposes an effective model for detecting malicious IoT activities using machine learning techniques. The ToN-IoT dataset, which consists of seven connected devices (subdatasets), is used to construct an IoT network. The proposed model is a multilevel classification model. The first level distinguishes between attack and normal network activities. The second level is to classify the types of detected attacks. The experimental results prove the effectiveness of the proposed model in terms of time and classification performance metrics. The proposed model and seven baseline techniques in the literature are compared. The proposed model outperformed the baseline techniques in all subdatasets except for the Garage Door dataset. [ABSTRACT FROM AUTHOR]

AbstractObjectivesMaterials and methodsResultsConclusionsRecently, artificial intelligence (AI) has been applied to clinical diagnosis. Although AI has already been developed for gastrointestinal (GI) tract endoscopy, few studies have applied AI to endoscopic ultrasound (EUS) images. In this study, we used a computer-assisted diagnosis (CAD) system with deep learning analysis of EUS images (EUS-CAD) and assessed its ability to differentiate GI stromal tumors (GISTs) from other mesenchymal tumors and their risk classification performance.A total of 101 pathologically confirmed cases of subepithelial lesions (SELs) arising from the muscularis propria layer, including 69 GISTs, 17 leiomyomas and 15 schwannomas, were examined. A total of 3283 EUS images were used for training and five-fold-cross-validation, and 827 images were independently tested for diagnosing GISTs. For the risk classification of 69 GISTs, including very-low-, low-, intermediate- and high-risk GISTs, 2,784 EUS images were used for training and three-fold-cross-validation.For the differential diagnostic performance of GIST among all SELs, the accuracy, sensitivity, specificity and area under the receiver operating characteristic (ROC) curve were 80.4%, 82.9%, 75.3% and 0.865, respectively, whereas those for intermediate- and high-risk GISTs were 71.8%, 70.2%, 72.0% and 0.771, respectively.The EUS-CAD system showed a good diagnostic yield in differentiating GISTs from other mesenchymal tumors and successfully demonstrated the GIST risk classification feasibility. This system can determine whether treatment is necessary based on EUS imaging alone without the need for additional invasive examinations. [ABSTRACT FROM AUTHOR]

Background: This study evaluates the diagnostic accuracy of an AI-assisted tool in assessing the proximity of the mandibular canal (MC) to the root apices (RAs) of mandibular teeth using computed tomography (CT). Methods: This study involved 57 patients aged 18–30 whose CT scans were analyzed by both AI and human experts. The primary aim was to measure the closest distance between the MC and RAs and to assess the AI tool's diagnostic performance. The results indicated significant variability in RA-MC distances, with third molars showing the smallest mean distances and first molars the greatest. Diagnostic accuracy metrics for the AI tool were assessed at three thresholds (0 mm, 0.5 mm, and 1 mm). Results: The AI demonstrated high specificity but generally low diagnostic accuracy, with the highest metrics at the 0.5 mm threshold with 40.91% sensitivity and 97.06% specificity. Conclusions: This study underscores the limited potential of tested AI programs in reducing iatrogenic damage to the inferior alveolar nerve (IAN) during dental procedures. Significant differences in RA-MC distances between evaluated teeth were found. [ABSTRACT FROM AUTHOR]

Objectives: In vitro fertilization (IVF) has the potential to give babies to millions more people globally, yet it continues to be underutilized. We established a globally applicable and locally adaptable IVF prognostics report and framework to support patient–provider counseling and enable validated, data-driven treatment decisions. This study investigates the IVF utilization rates associated with the usage of machine learning, center-specific (MLCS) prognostic reports (the Univfy® report) in provider-patient pre-treatment and IVF counseling. Methods: We used a retrospective cohort comprising 24,238 patients with new patient visits (NPV) from 2016 to 2022 across seven fertility centers in 17 locations in seven US states and Ontario, Canada. We tested the association of Univfy report usage and first intra-uterine insemination (IUI) and/or first IVF usage (a.k.a. conversion) within 180 days, 360 days, and "Ever" of NPV as primary outcomes. Results: Univfy report usage was associated with higher direct IVF conversion (without prior IUI), with odds ratios (OR) 3.13 (95% CI 2.83, 3.46), 2.89 (95% CI 2.63, 3.17), and 2.04 (95% CI 1.90, 2.20) and total IVF conversion (with or without prior IUI), OR 3.41 (95% CI 3.09, 3.75), 3.81 (95% CI 3.49, 4.16), and 2.78 (95% CI 2.59, 2.98) in 180-day, 360-day, and Ever analyses, respectively; p < 0.05. Among patients with Univfy report usage, after accounting for center as a factor, older age was a small yet independent predictor of IVF conversion. Conclusions: Usage of a patient-centric, MLCS-based prognostics report was associated with increased IVF conversion among new fertility patients. Further research to study factors influencing treatment decision making and real-world optimization of patient-centric workflows utilizing the MLCS reports is warranted. [ABSTRACT FROM AUTHOR]

Positron emission tomography (PET) plays a crucial role in breast cancer management. This review addresses the role of PET imaging in breast cancer care. We focus primarily on the utility of 18F-fluorodeoxyglucose (FDG) PET in staging, recurrence detection, and treatment response evaluation. Furthermore, we delve into the growing interest in precision therapy and the development of novel radiopharmaceuticals targeting tumor biology. This includes discussing the potential of PET/MRI and artificial intelligence in breast cancer imaging, offering insights into improved diagnostic accuracy and personalized treatment approaches. [ABSTRACT FROM AUTHOR]

Background/Objectives: The purpose of this preliminary study was to evaluate the diagnostic performance of an AI-driven platform, Diagnocat (Diagnocat Ltd., San Francisco, CA, USA), for assessing endodontic treatment outcomes using panoramic radiographs (PANs). Materials and Methods: The study included 55 PAN images of 55 patients (15 males and 40 females, aged 12–70) who underwent imaging at a private dental center. All images were acquired using a Hyperion X9 PRO digital cephalometer and were evaluated using Diagnocat, a cloud-based AI platform. The AI system assessed the following endodontic treatment features: filling probability, obturation adequacy, density, overfilling, voids in filling, and short filling. Two human observers independently evaluated the images, and their consensus served as the reference standard. The diagnostic accuracy metrics were calculated. Results: The AI system demonstrated high accuracy (90.72%) and a strong F1 score (95.12%) in detecting the probability of endodontic filling. However, the system showed variable performance in other categories, with lower accuracy metrics and unacceptable F1 scores for short filling and voids in filling assessments (8.33% and 14.29%, respectively). The accuracy for detecting adequate obturation and density was 55.81% and 62.79%, respectively. Conclusions: The AI-based system showed very high accuracy in identifying endodontically treated teeth but exhibited variable diagnostic accuracy for other qualitative features of endodontic treatment. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) broadly influences different aspects of human life, especially human communication. One of the main concerns of the broad use of AI in daily interactions among different people could be whether it helps them interact easily or complicates their interactions. To answer the mentioned question, this study assessed the impacts of AI on intercultural communication among postgraduate students in a multicultural university environment. A newly developed survey instrument was used to conduct this study. The participants of this study were 115 postgraduate students from nine different countries. The descriptive statistics, reliability analysis, and Bivariate correlation tests of the 29th version of IBM-SPSS software were used to analyze the quantitative data, and inductive coding and conceptual content analysis were used to code and analyze the qualitative data. Based on descriptive results, the vast majority (93%) of the participants already used and experienced AI in their daily lives, and the majority of them believed that AI and AI technologies connect different cultures, reduce language and cultural barriers, and help people of different cultures to interact and be connected. Based on the results from the correlation test, there were strong positive correlations between AI attitudes and AI benefits, and also between AI regulation and AI benefits. [ABSTRACT FROM AUTHOR]

AbstractIntroductionMethodsResultsDiscussionConclusionsEfficient and complete medical charting is essential for patient care and research purposes. In this study, we sought to determine if Chat Generative Pre-Trained Transformer could generate cogent, suitable charts from recorded, real-world poison center calls and abstract and tabulate data.De-identified transcripts of real-world hospital-initiated poison center consults were summarized by Chat Generative Pre-Trained Transformer 4.0. Additionally, Chat Generative Pre-Trained Transformer organized tables for data points, including vital signs, test results, therapies, and recommendations. Seven trained reviewers, including certified specialists in poison information and board-certified medical toxicologists, graded summaries using a 1 to 5 scale to determine appropriateness for entry into the medical record. Intra-rater reliability was calculated. Tabulated data was quantitatively evaluated for accuracy. Finally, reviewers selected preferred documentation: original or Chat Generative Pre-Trained Transformer organized.Eighty percent of summaries had a median score high enough to be deemed appropriate for entry into the medical record. In three duplicate cases, reviewers did change scores, leading to moderate intra-rater reliability (kappa = 0.6). Among all cases, 91 percent of data points were correctly abstracted into table format.By utilizing a large language model with a unified prompt, charts can be generated directly from conversations in seconds without the need for additional training. Charts generated by Chat Generative Pre-Trained Transformer were preferred over extant charts, even when they were deemed unacceptable for entry into the medical record prior to the correction of errors. However, there were several limitations to our study, including poor intra-rater-reliability and a limited number of cases examined.In this study, we demonstrate that large language models can generate coherent summaries of real-world poison center calls that are often acceptable for entry to the medical record as is. When errors were present, these were often fixed with the addition or deletion of a word or phrase, presenting an enormous opportunity for efficiency gains. Our future work will focus on implementing this process in a prospective fashion. [ABSTRACT FROM AUTHOR]

Myocardial strain is a dimensionless parameter reflecting the degree of deformation of the whole or local myocardium under stress, which can quantitatively detect myocardial injury and guide the early diagnosis, intervention and prognostic assessment of cardiac diseases. Cardiac ultrasound, cardiac CT and cardiac magnetic resonance can all be used for strain imaging and analysis, with two-dimensional speckle-tracking echocardiography being the most widely used means of myocardial strain detection today. However, due to inter-observer variations in manual analysis of myocardial strain and differences in the imaging systems and analysis software, the consistency and reproducibility of measured strain values among vendors are poor, limiting the clinical application of myocardial strain. Artificial intelligence (AI) can overcome the defects of strain measurement to a certain extent through automatic strain calculation and image quality assessment, which has a broad developmental prospect. This review focuses on the progress of AI-assisted measurement of myocardial strain in ultrasound, magnetic resonance, and other imaging modalities, as well as its application to disease diagnosis and patient prognosis assessment. This will improve the efficiency and consistency of strain measurement and promote the routine application of myocardial strain to clinical practice, which will play an incremental role in assessing myocardial injury and cardiac function. However, most of the current studies involve small sample sizes and lack external validation, and the reliability of their results needs to be further verified. [ABSTRACT FROM AUTHOR]

This paper examines the human relationship to technology, and AI in particular, including the proposition that algorithms are the new unconscious. Key is the question of how much human ability will be duplicated and transcended by general machine intelligence. More and more people are seeking connection via social media and interaction with artificial beings. The paper examines what it means to be human and which of these traits are already or will be replicated by AI. Therapy bots already exist. It is easier to envision AI therapy guided by CBT manuals than psychoanalytic techniques. Yet, a demonstration of how AI can already perform dream analysis reaching beyond a dream's manifest content is presented. The reader is left to consider whether these findings demand a new role for psychoanalysis in supporting, sustaining, and reframing our humanity as we create technology that transcends our abilities. [ABSTRACT FROM AUTHOR]

This comprehensive review assessed the integration of robotics and artificial intelligence (AI) in dentistry, offering a transparent overview of developments across dental fields. Covering articles in prosthodontics, orthodontics, implantology, surgery, and radiology, the review included 39 articles on robotics and 16 on AI. Screening adhered to PRISMA guidelines, with searches conducted on Medline, Google Scholar, and IEEE. Incorporating the search strategy, the review used keywords related to dentistry, robotics, and AI. For robotics, 296 articles were screened, resulting in 39 qualifying for qualitative synthesis. A separate AI search on PubMed identified 142 studies within the last decade, with 16 studies selected for a detailed full-text analysis, offering a consolidated overview of the current state-of-the-art knowledge in the AI domain. Geographic distribution highlighted East Asia as a major research contributor. The findings indicate an increasing trend in dentistry robotics since 2000 and, particularly since 2016, in AI dentistry. The majority of the literature fell under the category of basic research. The technology readiness level did not cross "three" (proof of concept) in 41% of all articles. Therefore, the overall literature quality remains low, particularly regarding clinical validation. [ABSTRACT FROM AUTHOR]

Digital transformation and "artificial intelligence (AI)"—which can more adequately be called "data-based systems (DS)"—comprise ethical opportunities and risks. Therefore, it is necessary to identify precisely ethical opportunities and risks in order to be able to benefit sustainably from the opportunities and to master the risks. The UN General Assembly has recently adopted a resolution aiming for 'safe, secure and trustworthy artificial intelligence systems'. It is now urgent to implement and build on the UN General Assembly Resolution. Allowing humans and the planet to flourish sustainably in peace and guaranteeing globally that human dignity is respected not only offline but also online, in the digital sphere, and in the domain of DS, requires two policy measures: (1) human rights-based data-based systems (HRBDS): HRBDS means that human rights serve as the basis of digital transformation and DS. (2) International Data-Based Systems Agency (IDA): IDA should be established at the UN as a platform for cooperation in the field of digital transformation and DS, fostering human rights, security, and peaceful uses of DS, as well as a global supervisory institution and regulatory authority in digital transformation and DS. The establishment of IDA is realistic because humanity has already shown in its past that we are able to not always "blindly" pursue the technically possible but also to limit ourselves to what is technically feasible when humanity and the planet are at stake. For instance, humans researched the field of nuclear technology, developed the atomic bomb, and detonated it several times. Nonetheless, the same humans limited research and development in the field of nuclear technology to prevent even worse consequences by establishing the International Atomic Energy Agency (IAEA) at the UN. [ABSTRACT FROM AUTHOR]

This study delves into the dual nature of artificial intelligence (AI), illuminating its transformative potential that has the power to revolutionize various aspects of our lives. We delve into critical issues such as AI hallucinations, misinformation, and unpredictable behavior, particularly in large language models (LLMs) and AI-powered chatbots. These technologies, while capable of manipulating human decisions and exploiting cognitive vulnerabilities, also hold the key to unlocking unprecedented opportunities for innovation and progress. Our research underscores the need for robust, ethical AI development and deployment frameworks, advocating a balance between technological advancement and societal values. We emphasize the importance of collaboration among researchers, developers, policymakers, and end users to steer AI development toward maximizing benefits while minimizing potential harms. This study highlights the critical role of responsible AI practices, including regular training, engagement, and the sharing of experiences among AI users, to mitigate risks and develop the best practices. We call for updated legal and regulatory frameworks to keep pace with AI advancements and ensure their alignment with ethical principles and societal values. By fostering open dialog, sharing knowledge, and prioritizing ethical considerations, we can harness AI's transformative potential to drive human advancement while managing its inherent risks and challenges. [ABSTRACT FROM AUTHOR]

When agricultural experts explore the use of artificial intelligence technology to identify and detect crop diseases, they mainly focus on the research of a stable environment, but ignore the problem of noise in the process of image acquisition in real situations. To solve this problem, we propose an innovative solution called the Critical Information Mining Network (CIMNet). Compared with traditional models, CIMNet has higher recognition accuracy and wider application scenarios. The network has a good effect on crop disease recognition under noisy environments, and can effectively deal with the interference of noise to the recognition effect in actual farmland scenes. Consider that the shape of the leaves can be symmetrical or asymmetrical.First, we introduce the Non-Local Attention Module (Non-Local), which uses a unique self-attention mechanism to fully capture the context information of the image. The module overcomes the limitation of traditional convolutional neural networks that only rely on local features and ignore global features. Global features are particularly important when the image is disturbed by noise. Non-Local improves a more comprehensive visual understanding of crop disease recognition. Secondly, we have innovatively designed a Multi-scale Critical Information Fusion Module (MSCM). The module uses the Key Information Extraction Module (KIB) to dig into the shallow key features in the network deeply. The shallow key features strengthen the feature perception of the model to the noise image through texture and contour information, and then the shallow key features and deep features are fused to enrich the original deep feature information of the network. Finally, we conducted experiments on two public datasets, and the results showed that the accuracy of our model in crop disease identification under a noisy environment was significantly improved. At the same time, our model also showed excellent performance under stable conditions. The results of this study provide favorable support for the improvement of crop production efficiency. [ABSTRACT FROM AUTHOR]

The misdiagnosis of headache disorders is a serious issue, and AI-based headache model diagnoses with external validation are scarce. We previously developed an artificial intelligence (AI)-based headache diagnosis model using a database of 4000 patients' questionnaires in a headache-specializing clinic and herein performed external validation prospectively. The validation cohort of 59 headache patients was prospectively collected from August 2023 to February 2024 at our or collaborating multicenter institutions. The ground truth was specialists' diagnoses based on the initial questionnaire and at least a one-month headache diary after the initial consultation. The diagnostic performance of the AI model was evaluated. The mean age was 42.55 ± 12.74 years, and 51/59 (86.67%) of the patients were female. No missing values were reported. Of the 59 patients, 56 (89.83%) had migraines or medication-overuse headaches, and 3 (5.08%) had tension-type headaches. No one had trigeminal autonomic cephalalgias or other headaches. The models' overall accuracy and kappa for the ground truth were 94.92% and 0.65 (95%CI 0.21–1.00), respectively. The sensitivity, specificity, precision, and F values for migraines were 98.21%, 66.67%, 98.21%, and 98.21%, respectively. There was disagreement between the AI diagnosis and the ground truth by headache specialists in two patients. This is the first external validation of the AI headache diagnosis model. Further data collection and external validation are required to strengthen and improve its performance in real-world settings. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility of developing artificial intelligence (AI) systems capable of exhibiting limited cognitive processes analogous to aspects of human religious belief. The literature review pertains to the most essential cognitive mechanisms of belief and the most relevant models for AI with belief. Accordingly, and as a result of the theoretical review, drawing inspiration from belief cognition to endow AI with enhanced cognitive capacities, the core objective is to try to build a theoretical model that simulates cognitive processes of belief, equipping AI agents with abilities to recognize subtle divine synchronistic patterns and form provisional convictions computationally modeled on belief cognitive mechanisms. The hypothesis is that this could hopefully unlock a higher level of cognitive function and could enhance capacities for nuanced, context-sensitive reasoning and prediction for these AI models. The method is a novel "Trans-Belief" theoretical model that will be considered, integrating fuzzy and doxastic logic models to trace synchronistic divine patterns, in the results section. Finally, in the discussion, additional moral aspects and the nature of the data set of the model will be examined, and directions for future research will be proposed. While not implying AI can or should fully replicate complex human spirituality, tentative artificial belief could impart beneficial qualities like contextual awareness. However, developing belief-inspired algorithms requires grappling with profound philosophical questions regarding singularity and implementing strong ethical safeguards on any AI-granted agency over human affairs. This represents an early exploration of belief's implications for machine learning, necessitating future research and discussion. [ABSTRACT FROM AUTHOR]

This study explores the progress of chatbot technology, focusing on the aspect of error correction to enhance these smart conversational tools. Chatbots, powered by artificial intelligence (AI), are increasingly prevalent across industries such as customer service, healthcare, e-commerce, and education. Despite their use and increasing complexity, chatbots are prone to errors like misunderstandings, inappropriate responses, and factual inaccuracies. These issues can have an impact on user satisfaction and trust. This research provides an overview of chatbots, conducts an analysis of errors they encounter, and examines different approaches to rectifying these errors. These approaches include using data-driven feedback loops, involving humans in the learning process, and adjusting through learning methods like reinforcement learning, supervised learning, unsupervised learning, semi-supervised learning, and meta-learning. Through real life examples and case studies in different fields, we explore how these strategies are implemented. Looking ahead, we explore the different challenges faced by AI-powered chatbots, including ethical considerations and biases during implementation. Furthermore, we explore the transformative potential of new technological advancements, such as explainable AI models, autonomous content generation algorithms (e.g., generative adversarial networks), and quantum computing to enhance chatbot training. Our research provides information for developers and researchers looking to improve chatbot capabilities, which can be applied in service and support industries to effectively address user requirements. [ABSTRACT FROM AUTHOR]

Practical wisdom enables moral decision making and action by aligning one's apprehension of proximate goods with a distal, socially embedded interpretation of a more ultimate Good. A focus on purpose within the overall process mutually informs human moral psychology and moral AI development in their examinations of practical wisdom. AI practical wisdom could ground an AI system's apprehension of reality in a sociotechnical moral process committed to orienting AI development and action in light of a pluralistic, diverse interpretation of that Good. Defining AI purpose in terms of the human struggle to determine the moral "ought" (versus the way reality is) can overcome practical and normative limitations of principle-based approaches to AI ethics without requiring idiosyncratic moral reasoning or a premature commitment to a single ethical theory, which could hinder navigating the moral implications of future unknowns. Dividing practical wisdom into proximate, mediating, and distal processes enables each division to incorporate the moral dimensions of the situation, individual AI system, and sociotechnical community, respectively. Drawing upon externalist (4E) and ecological cognitive psychology theories to extend Xavier Zubiri's sentient intelligence, proximate apprehension of what an object affords the perceiver includes affordances for practical and moral purposes. Those apprehensions are structured by moral and other social-cognitive schemas that mediate moral constructs, the apprehended situation, and individual commitments and other motivating identifications in a process Ignacio Ellacuria calls historical reality. Distally, Josiah Royce's community of interpretation characterizes one way of organizing a sociotechnical process to synthesize diverse interpretations of the Good, orient AI moral practice, and align AI development with human and AI moral purpose. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is not only disrupting industries and businesses, particularly the ones have fallen behind the adoption, but also significantly impacting public life as well. This calls for government authorities pay attention to public opinions and sentiments towards AI. Nonetheless, there is limited knowledge on what the drivers behind the public perception of AI are. Bridging this gap is the rationale of this paper. As the methodological approach, the study conducts an online public perception survey with the residents of Sydney, Melbourne, and Brisbane, and explores the collected survey data through statistical analysis. The analysis reveals that: (a) the public is concerned of AI invading their privacy, but not much concerned of AI becoming more intelligent than humans; (b) the public trusts AI in their lifestyle, but the trust is lower for companies and government deploying AI; (c) the public appreciates the benefits of AI in urban services and disaster management; (d) depending on the local context, public perceptions vary; and (e) the drivers behind the public perception include gender, age, AI knowledge, and AI experience. The findings inform authorities in developing policies to minimise public concerns and maximise AI awareness. [ABSTRACT FROM AUTHOR]

Previous research in security studies contends that information warfare (IW) is becoming a critical element in states' overall security strategies. Additionally, many researchers posit that artificial intelligence (AI) is quickly emerging as an important component of digital communications and states' military applications worldwide. However, less is known regarding how states are incorporating AI in their information warfare and influence operations (IWIO). Thus, given the growing importance of AI and IW in global security, this paper examines how the United States, China, and Russia are incorporating AI in their IWIO strategies and tactics. We find that the US, China, and Russia are utilizing AI in their IWIO approaches in significant ways depending on each state's overall IW strategy, with important implications for international security. [ABSTRACT FROM AUTHOR]

Background: Large language models (LLMs) represent a recent advancement in artificial intelligence with medical applications across various healthcare domains. The objective of this review is to highlight how LLMs can be utilized by clinicians and surgeons in their everyday practice. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Six databases were searched to identify relevant articles. Eligibility criteria emphasized articles focused primarily on clinical and surgical applications of LLMs. Results: The literature search yielded 333 results, with 34 meeting eligibility criteria. All articles were from 2023. There were 14 original research articles, four letters, one interview, and 15 review articles. These articles covered a wide variety of medical specialties, including various surgical subspecialties. Conclusions: LLMs have the potential to enhance healthcare delivery. In clinical settings, LLMs can assist in diagnosis, treatment guidance, patient triage, physician knowledge augmentation, and administrative tasks. In surgical settings, LLMs can assist surgeons with documentation, surgical planning, and intraoperative guidance. However, addressing their limitations and concerns, particularly those related to accuracy and biases, is crucial. LLMs should be viewed as tools to complement, not replace, the expertise of healthcare professionals. [ABSTRACT FROM AUTHOR]

• Research on possible scenarios of implementing AI in nanosatellite constellations. • Challenges, applications and possible strategies in Low Earth Orbits, Transfer Region and Lunar Orbits is discussed. • Several examples and major necessities have been included. • Figures portray severe issues to applications and strategic ways to adopt the AI in nanosat constraints is shown in detail. • The future prospects and ways to step-wise organize and implement AI is discussed. The small satellites have been changing the paradigm of space technology and implementation techniques ever since the NewSpace era has begun a stiff competition and dynamic atmosphere for innovation and recreation. Nanosatellites (ranging from 1 to 10 Kg) have been of significant importance since they have been launched and experimented in the past few decades. The constellations of these satellites will be a test of technology and management in due course of time. The complexity of operations and skilled functioning of the satellites in LEO and beyond is becoming crucial for the space industry and scientific communities. This paper will demonstrate the challenges, applications, and strategies to implement Artificial Intelligence (AI) optimally to make these constellations efficient and productive under the given scenarios and constraints. A thorough investigation through literature study has been initially done to observe and monitor the recent developments and envision the futuristic implementation of cognitive computing in CisLunar space. Hence, a detailed description of strategizing this artificial intelligence implementation in three classified regions as Low Earth Orbits (LEO), Transfer Region (TR), and Lunar Orbits (LO) to emphasize their crucial role in operating nanosatellites is significantly presented. [ABSTRACT FROM AUTHOR]

Urban tree classification enables informed decision-making processes in urban planning and management. This paper introduces a novel data reformation method, pseudo tree crown (PTC), which enhances the feature difference in the input layer and results in the improvement of the accuracy and efficiency of urban tree classification by utilizing artificial intelligence (AI) techniques. The study involved a comparative analysis of the performance of various machine learning (ML) classifiers. The results revealed a significant enhancement in classification accuracy, with an improvement exceeding 10% observed when high spatial resolution imagery captured by an unmanned aerial vehicle (UAV) was utilized. Furthermore, the study found an impressive average classification accuracy of 93% achieved by a classifier built on the PyTorch framework, with ResNet50 leveraged as its convolutional neural network layer. These findings underscore the potential of AI-driven approaches in advancing urban tree classification methodologies for enhanced urban planning and management practices. [ABSTRACT FROM AUTHOR]

This Commentary critiques an extremely relational view of robot moral status, drawing out its practical implications for ethics and law. It also suggests next steps for AI ethics if extremely relational reasoning is compelling. Section I introduces the topic, distinguishing an ‘extremely relational’ view from more moderate relational views. Section II illustrates extremely relational views using the example of embodiment. Section III explores practical implications of extremely relational views for ethics and law. Section IV offers possible responses to extreme relationism. Section V concludes by suggesting next steps for AI ethics. [ABSTRACT FROM AUTHOR]

This Commentary offers a critique of an eco-relational approach in robot ethics, highlighting the importance of articulating an ecologically-sensitive ethical orientation that incorporates the entire more-than-human world, including technological entities like forms of artificial intelligence. While the eco-relational approach enhances our understanding of the complex way in which morally significant properties operate on a phenomenological level, it is not without its flaws. In particular, this perspective focuses on ethical concepts when it needs to be rooted in ethical systems, misrepresents the extant literature on relational approaches to moral status, and is insufficiently ecological in a way that would bring machines into the moral universe by way of their location in and importance to the larger environment. At a time when the legal status of robots is being decided before moral questions are settled, it remains important to get the contours of relevant debates right so that the robot ethics discourse brings us closer to developing practical solutions for ethical dilemmas emerging in our increasingly technological age. [ABSTRACT FROM AUTHOR]

This Commentary critiques Paul Showler’s combination view of robot moral status, which combines sophisticated cognitive capacities like consciousness with highly valued machine-human relationships. Showler holds that a combined approach carries the advantage of more fully accounting for ordinary folk psychology views about of what it means to have moral standing and be a person. This commentary paper is largely sympathetic to Showler, but argues for a stronger view: being a person is a cluster concept that can include a combination of different possible sufficient conditions, none of which is necessary. What must be constrained, then, is the strong claim that a quality is necessary for personhood. Since most approaches that appeal to sophisticated cognitive capacities make the strong claim, they require constraint. Since most approaches that appeal to highly valued relationships, do not make the stronger claim, they generally do not require constraint. [ABSTRACT FROM AUTHOR]

The emergence of increasingly capable artificial intelligence (AI) systems has raised concerns about the potential extreme risks associated with them. The issue has drawn substantial attention in academic literature and compelled legislators of regulatory frameworks like the European Union AI Act (AIA) to readapt them to the new paradigm. This paper examines whether the European Parliament's draft of the AIA constitutes an appropriate approach to address the risks derived from frontier models. In particular, we discuss whether the AIA reflects the policy needs diagnosed by recent literature and determine if the requirements falling on providers of foundation models are appropriate, sufficient, and durable. We find that the provisions are generally adequate, but insufficiently defined in some areas and lacking in others. Finally, the AIA is characterized as an evolving framework whose durability will depend on the institutions' ability to adapt to future progress. [ABSTRACT FROM AUTHOR]

This article explores the impact of artificial intelligence (AI) on text interpretation and the role of authors. It discusses the debates surrounding whether AI language generators can truly understand language or if they simply mimic it. While early models required skillful prompting, newer models have made the process easier. The article examines the capabilities and limitations of large language models (LLMs) like GPT models, highlighting their ability to prioritize meaning over grammar and syntax and their potential to create metaphors and similes. It also acknowledges the ethical concerns and dangers associated with AI development but emphasizes the exciting possibilities it presents. [Extracted from the article]

The development of next-generation sequencing (NGS) has enabled the discovery of cancer-specific driver gene alternations, making precision medicine possible. However, accurate genetic testing requires a sufficient amount of tumor cells in the specimen. The evaluation of tumor content ratio (TCR) from hematoxylin and eosin (H&E)-stained images has been found to vary between pathologists, making it an important challenge to obtain an accurate TCR. In this study, three pathologists exhaustively labeled all cells in 41 regions from 41 lung cancer cases as either tumor, non-tumor or indistinguishable, thus establishing a "gold standard" TCR. We then compared the accuracy of the TCR estimated by 13 pathologists based on visual assessment and the TCR calculated by an AI model that we have developed. It is a compact and fast model that follows a fully convolutional neural network architecture and produces cell detection maps which can be efficiently post-processed to obtain tumor and non-tumor cell counts from which TCR is calculated. Its raw cell detection accuracy is 92% while its classification accuracy is 84%. The results show that the error between the gold standard TCR and the AI calculation was significantly smaller than that between the gold standard TCR and the pathologist's visual assessment ( p < 0.05 ). Additionally, the robustness of AI models across institutions is a key issue and we demonstrate that the variation in AI was smaller than that in the average of pathologists when evaluated by institution. These findings suggest that the accuracy of tumor cellularity assessments in clinical workflows is significantly improved by the introduction of robust AI models, leading to more efficient genetic testing and ultimately to better patient outcomes. [ABSTRACT FROM AUTHOR]

Connected Autonomous vehicles (CAVs) are expected to improve the safety and efficiency of traffic by automating driving tasks. Amongst those, lane changing is particularly challenging, as it requires the vehicle to be aware of its highly-dynamic surrounding environment, make decisions, and enact them within very short time windows. As CAVs need to optimise their actions based on a large set of data collected from the environment, Reinforcement Learning (RL) has been widely used to develop CAV motion controllers. These controllers learn to make efficient and safe lane changing decisions using on-board sensors and inter-vehicle communication. This paper, first presents four overlapping fields that are key to the future of safe self-driving cars: CAVs, motion control, RL, and safe control. It then defines the requirements for a safe CAV controller. These are used firstly to compare applications of Multi-Agent Reinforcement Learning (MARL) to CAV lane change controllers. The requirements are then used to evaluate state-of-the-art safety methods used for RL-based motion controllers. The final section summarises research gaps and possible opportunities for the future development of safe MARL-based CAV motion controllers. In particular, it highlights the requirement to design MARL controllers with continuous control for lane changing. Moreover, as RL algorithms by themselves do not guarantee the level of safety required for such safety-critical applications, it offers insights and challenges to integrate safe RL methods with MARL-based CAV motion controllers. [ABSTRACT FROM AUTHOR]

Aquaculture plays a crucial role in meeting the growing global demand for seafood, but it faces challenges in terms of fish growth and health monitoring. The advancement of artificial intelligence (AI) techniques offers promising solutions for optimizing fish farming practices and ensuring sustainable aquaculture. This abstract provides an overview of the role of AI in fish growth and health status monitoring, emphasizing its significance in promoting a sustainable aquaculture industry. AI technologies, such as machine learning and computer vision, have shown immense potential in analyzing large volumes of data collected from fish farms. By leveraging AI algorithms, fish farmers can gain valuable insights into fish growth patterns, feeding behavior, and environmental factors affecting fish health. These algorithms can detect and predict anomalies, diseases, and stress indicators, enabling proactive interventions to mitigate health issues and reduce losses. One of the key applications of AI in aquaculture is the development of smart monitoring systems. These systems employ various sensors, cameras, and data analytics tools to continuously collect real-time data on water quality, temperature, oxygen levels, and fish behavior. AI algorithms analyze this data to identify deviations from optimal conditions and provide timely alerts to farmers, allowing them to take appropriate actions such as adjusting feeding schedules, modifying water parameters, or administering treatments as needed. Furthermore, AI-based models can assist in optimizing feed management and reducing wastage. By analyzing historical data on fish growth and feed consumption, machine learning algorithms can determine the most efficient feed formulation and feeding regimes, leading to improved growth rates and minimized environmental impact. Another significant aspect of AI in fish farming is disease detection and prevention. Through image analysis and pattern recognition, AI algorithms can identify early signs of diseases, parasites, or abnormalities in fish appearance and behavior. This enables prompt disease diagnosis and targeted treatment, reducing the need for excessive use of antibiotics and chemicals while improving fish welfare. In summary, the integration of AI techniques in fish growth and health status monitoring holds great promise for the sustainability of aquaculture. By leveraging AI's capabilities in data analysis, pattern recognition, and predictive modeling, fish farmers can optimize their practices, enhance productivity, reduce environmental impact, and ensure the welfare of farmed fish. However, continued research, data sharing, and collaboration between scientists, industry stakeholders, and policymakers are essential for harnessing the full potential of AI in achieving a sustainable aquaculture industry. [ABSTRACT FROM AUTHOR]

The modern language generation model ChatGPT, created by Open Artificial Intelligence (AI), is recognised for its capacity to comprehend context and produce pertinent content. This model is built on the transformer architecture, which enables it to process massive volumes of data and produce text that is both cohesive and illuminating. Service is a crucial component everywhere as it provides the basis for establishing client rapport and offering aid and support. In healthcare, the application of ChatGPT for patient service support has been one of the most significant advances in recent years. ChatGPT can help overcome language obstacles and improve patient satisfaction by facilitating communication with healthcare personnel and understanding of care. It can assist in enhancing the entire patient experience by offering personalised information and support to patients and making it more straightforward for them to communicate with healthcare professionals. Its goal can be to expedite and streamline service by promptly and accurately responding to customers. Businesses of all sizes increasingly use ChatGPT since it allows them to provide 24/7 customer support without requiring human contact. This paper briefly discusses ChatGPT and the need for better services. Various perspectives on improving customer and patient services through ChatGPT are discussed. The article also discussed the major key enablers of ChatGPT for refining customer and patient assistance. Further, the paper identifies and discusses the critical application areas of ChatGPT for customer and patient service. With its ability to handle several requests simultaneously, respond quickly and accurately to client questions, and gain knowledge from every interaction, ChatGPT is revolutionising customer and patient service. Its accessibility and compatibility with various communication channels make it a desirable solution for businesses looking to improve support. As technology advances, ChatGPT is positioned to become an essential tool for businesses wishing to provide speedy and customised service. Although ChatGPT may give convincing solutions, the chance of providing accurate and updated information poses a problem for its usage in service jobs that need accurate and up-to-date information. In future, various services will become better and more efficient due to ChatGPT and AI. [ABSTRACT FROM AUTHOR]

Pain is a significant health issue, and pain assessment is essential for proper diagnosis, follow-up, and effective management of pain. The conventional methods of pain assessment often suffer from subjectivity and variability. The main issue is to understand better how people experience pain. In recent years, artificial intelligence (AI) has been playing a growing role in improving clinical diagnosis and decision-making. The application of AI offers promising opportunities to improve the accuracy and efficiency of pain assessment. This review article provides an overview of the current state of AI in pain assessment and explores its potential for improving accuracy, efficiency, and personalized care. By examining the existing literature, research gaps, and future directions, this article aims to guide further advancements in the field of pain management. An online database search was conducted via multiple websites to identify the relevant articles. The inclusion criteria were English articles published between January 2014 and January 2024). Articles that were available as full text clinical trials, observational studies, review articles, systemic reviews, and meta-analyses were included in this review. The exclusion criteria were articles that were not in the English language, not available as free full text, those involving pediatric patients, case reports, and editorials. A total of (47) articles were included in this review. In conclusion, the application of AI in pain management could present promising solutions for pain assessment. AI can potentially increase the accuracy, precision, and efficiency of objective pain assessment. [ABSTRACT FROM AUTHOR]

Background: Introducing new technologies in education sparks debates, disrupting traditional practices, and requiring teacher adaptation. ChatGPT is an example. Research explores its benefits and concerns in education, with recommendations for classroom use. Nevertheless, limited evidence supports ChatGPT as a tool for supporting English as a Foreign Language (EFL) students' research competency. Moreover, while the literature primarily focuses on the perspectives of scholars and teachers, it is essential to incorporate students' viewpoints in order to maximize the potential of this technology. Objectives: The present study investigated EFL undergraduates' perceptions and practices of ChatGPT as a tool for promoting their research competency. Additionally, the study examined the potential influence of metacognitive awareness (MA) levels in this context. Methods: The study utilized a mixed‐method research design, collecting quantitative and qualitative data from 27 EFL undergraduates (12 with low MA and 15 with high MA) over one semester. Data sources included a self‐report questionnaire, self‐study log, and semi‐structured interviews. Results and conclusions: Quantitative analysis showed significant differences between low and high MA groups in their perceptions of ChatGPT (U = 37.500, Z = 2.570). Regression analysis revealed a positive correlation between MA scores and students' perceptions and practices of ChatGPT. Qualitative analysis revealed diverse perceptions and practices of ChatGPT between the high MA and low MA groups, showcasing behaviours ranging from replication to using ChatGPT as a thought‐stimulating and supportive tool, accessing supplementary information, and seeking guidance. Key factors for effective ChatGPT use included prompt formulation, systematic thinking, self‐regulation strategies, and trust in AI. Lay Description: What is currently known about this topic: Research competency and metacognitive awareness are important for EFL undergraduates.Technology has been explored for promoting research competency.ChatGPT is used as a learning tool in EFL instruction contexts. What does this paper add: It provides a deeper understanding of the practical application of AI tools like ChatGPT for developing research competency among EFL students.It enhances understanding of metacognition's role in successful learning in digital environments by exploring MA's impact on using ChatGPT for research purposes. Implications for practice/policy: Provide explicit instructions and guidelines for EFL students to effectively use ChatGPT for research competency, emphasizing clear prompt design and fostering responsible use through critical thinking and awareness of biases and limitations.Enhance EFL students' metacognitive awareness through scaffolding, explicit instruction, and activities.Implement assessments and monitoring mechanisms to track metacognitive development and guide instructional interventions.Offer professional development opportunities for EFL teachers to enhance their understanding and skills in integrating AI tools into research instruction and other EFL aspects. [ABSTRACT FROM AUTHOR]

Early detection of oral cancer (OC) improves survival prospects. Artificial intelligence (AI) is gaining popularity in diagnostic medicine. Oral cancer is a primary global health concern, accounting for 177,384 deaths in 2018; most cases occur in low‐ and middle‐income countries. Automated disease identification in the oral cavity may be facilitated by the ability to identify both possibly and definite malignant lesions. This study aimed to examine the evidence currently available on the effectiveness of AI in diagnosing OC. They highlighted the ability of AI to analyse and identify the early stages of OC. Furthermore, radial basis function networks (RBFN) were employed to develop automated systems to generate intricate patterns for this challenging operation. The stochastic gradient descent algorithm (SGDA) selected the model parameters that best matched the predicted and observed results. It can be used. The initial data was collected for this study to evaluate. Two deep learning‐based computer vision algorithms have been developed to recognize and categorize oral lesions, which is necessary for the early detection of oral cancer. Several examples of HoG include the Canny edge detector, SIFT (scale invariant and feature transform), and SIFT (scale invariant and feature transform). In computer vision and image processing, it is used to find objects. We investigated the potential uses of deep learning‐based computer vision techniques in oral cancer and the viability of an automated system for OC recognition based on photographic images. That made calculations to determine the accuracy, sensitivity, specificity, and receiver operating characteristic curve areas across all validation datasets, including internal, external, and clinical validation (AUC). The RBFN‐SDC model outperformed all others. For 1000 data points, the accuracy of the RBFN‐SDC model is 99.99%, while the accuracy of the R‐CNN, CNN, DCNN, and SVM models is 91.54%, 90.14%, 93.89%, and 94.87%, respectively. [ABSTRACT FROM AUTHOR]

The healthcare industry went through reformation by integrating the Internet of Medical Things (IoMT) to enable data harnessing by transmission mediums from different devices, about patients to healthcare staff devices, for further analysis through cloud-based servers for proper diagnosis of patients, yielding efficient and accurate results. However, IoMT technology is accompanied by a set of drawbacks in terms of security risks and vulnerabilities, such as violating and exposing patients' sensitive and confidential data. Further, the network traffic data is prone to interception attacks caused by a wireless type of communication and alteration of data, which could cause unwanted outcomes. The advocated scheme provides insight into a robust Intrusion Detection System (IDS) for IoMT networks. It leverages a honeypot to divert attackers away from critical systems, reducing the attack surface. Additionally, the IDS employs an ensemble method combining Logistic Regression and K-Nearest Neighbor algorithms. This approach harnesses the strengths of both algorithms to improve attack detection accuracy and robustness. This work analyzes the impact, performance, accuracy, and precision outcomes of the used model on two IoMT-related datasets which contain multiple attack types such as Man-In-The-Middle (MITM), Data Injection, and Distributed Denial of Services (DDOS). The yielded results showed that the proposed ensemble method was effective in detecting intrusion attempts and classifying them as attacks or normal network traffic, with a high accuracy of 92.5% for the first dataset and 99.54% for the second dataset and a precision of 96.74% for the first dataset and 99.228% for the second dataset. [ABSTRACT FROM AUTHOR]

Micro‐Doppler effect is a vital feature of a target that reflects its oscillatory motions apart from bulk motion and provides an important evidence for target recognition with radars. However, establishing the micro‐Doppler database poses a great challenge, since plenty of experiments are required to get the micro‐Doppler signatures of different targets for the purpose of analyses and interpretations with radars, which are dramatically limited by high cost and time‐consuming. Aiming to overcome these limits, a low‐cost and powerful simulation platform of the micro‐Doppler effects is proposed based on time‐domain digital coding metasurface (TDCM). Owing to the outstanding capabilities of TDCM in generating and manipulating nonlinear harmonics during wave‐matter interactions, it enables to supply rich and high‐precision electromagnetic signals with multiple micro‐Doppler frequencies to describe the micro‐motions of different objects, which are especially favored for the training of artificial intelligence algorithms in automatic target recognition and benefit a host of applications like imaging and biosensing. [ABSTRACT FROM AUTHOR]

AbstractAs the potential for human-AI design collaboration increases, understanding the role of artificial intelligence (AI) in the design process becomes more important. How does AI currently support the design process and how could it do so in the future? To answer this question, we categorized existing AI design support systems (DSS) according to the Double-Diamond design process model, and found that they are mostly used in the later stages of the design process, focusing on generating design solutions. In contrast, very few systems focus on the early stages of the process, which include discovering and defining design problems. To explore this finding’s alignment with designers’ expectations in real-world design, we present a case study involving emerging AI technologies such as ChatGPT and robots. This study proposes that AI agents can potentially assist designers by providing inspirations, defining design problems with constraints, offering grounded metaphors, and exploring design materials. [ABSTRACT FROM AUTHOR]

The convergence of hydrogen energy with artificial intelligence (AI) in smart infrastructure has significant potential to revolutionise the worldwide energy sector. This article thoroughly examines the progress, difficulties, and potential breakthroughs in the integration of AI technology with smart grids to enhance the use of hydrogen energy. The study focuses on utilising AI technologies such as deep learning and machine learning to optimise the processes of generating, distributing, and utilising energy. The discoveries stemming from this investigation facilitate prognostic maintenance, instantaneous decision-making, and effective demand-side management, augmenting the durability and eco-friendliness of energy systems. Nevertheless, this auspicious panorama is surrounded by significant obstacles. Significant issues develop regarding data privacy and security when sensitive information is sent over AI-powered grid systems. Interoperability difficulties necessitate standardising the communication protocols to enable smooth data flow. Additionally, further research is essential to tackle the technological limitations of AI in grid management. This article presents a forward-thinking viewpoint on the incorporation of AI-enhanced smart grid technologies, with a focus on future expectations. Autonomous energy management systems offer improved flexibility, proactive maintenance, and flexible energy distribution. Simultaneously, the combination of Edge AI and decentralisation facilitates the establishment of energy generating and storage facilities at local levels. This helps to make immediate decisions, minimises delays, and improves the durability of the power grid. The report emphasises the pivotal importance of governmental and regulatory factors in directing these advancements. The foundation for a safe and flexible power system is established by data privacy legislation, grid modernisation initiatives, and incentives for hydrogen energy. The essay promotes energy market reforms, technological neutrality, and measures that improve grid resilience as a means of achieving environmental sustainability. The integration of hydrogen energy into smart infrastructure is facilitated by AI, and strategic planning and collaborative design are crucial for achieving a resilient, sustainable, and efficient energy future. This article provides a strategic plan for efficiently handling complexities, leveraging advantageous situations, and collaboratively building an energy industry that is adaptable, productive, and environmentally aware. • Explore AI's role in optimising energy processes for enhanced grid resilience. • Address privacy, security, interoperability, and technical constraints as significant hurdles. • Envision autonomous energy management, edge AI, and decentralisation for adaptive, resilient grids. • Emphasise regulatory frameworks for data privacy, grid modernisation, and renewable energy adoption. [ABSTRACT FROM AUTHOR]

Background: OpenAI's ChatGPT (San Francisco, CA, USA) and Google's Gemini (Mountain View, CA, USA) are two large language models that show promise in improving and expediting medical decision making in hand surgery. Evaluating the applications of these models within the field of hand surgery is warranted. This study aims to evaluate ChatGPT-4 and Gemini in classifying hand injuries and recommending treatment. Methods: Gemini and ChatGPT were given 68 fictionalized clinical vignettes of hand injuries twice. The models were asked to use a specific classification system and recommend surgical or nonsurgical treatment. Classifications were scored based on correctness. Results were analyzed using descriptive statistics, a paired two-tailed t-test, and sensitivity testing. Results: Gemini, correctly classifying 70.6% hand injuries, demonstrated superior classification ability over ChatGPT (mean score 1.46 vs. 0.87, p-value < 0.001). For management, ChatGPT demonstrated higher sensitivity in recommending surgical intervention compared to Gemini (98.0% vs. 88.8%), but lower specificity (68.4% vs. 94.7%). When compared to ChatGPT, Gemini demonstrated greater response replicability. Conclusions: Large language models like ChatGPT and Gemini show promise in assisting medical decision making, particularly in hand surgery, with Gemini generally outperforming ChatGPT. These findings emphasize the importance of considering the strengths and limitations of different models when integrating them into clinical practice. [ABSTRACT FROM AUTHOR]

Background Objectives Methods Results and Conclusions 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.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.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.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. [ABSTRACT FROM AUTHOR]

The integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques into computational science has ushered in a new era of innovation and efficiency in various fields, with particular significance in computational fluid dynamics (CFD). Several methods based on AI and Machine Learning (ML) have been standardized in many fields of computational science, including computational fluid dynamics (CFD). This study aims to couple CFD with artificial neural networks (ANNs) to predict the fluid forces that arise when a flowing fluid interacts with obstacles installed in the flow domain. The momentum equation elucidating the flow has been simulated by adopting the finite element method (FEM) for a range of rheological and kinematic conditions. Hydrodynamic forces, including pressure drop between the back and front of the obstacle, surface drag, and lift variations, are measured on the outer surface of the cylinder via CFD simulations. This data has subsequently been fed into a Feed-Forward Back (FFB) propagation neural network for the prediction of such forces with completely unknown data. For all cases, higher predictivity is achieved for the drag coefficient (CD) and lift coefficient (CL) since the mean square error (MSE) is within ± 2% and the coefficient of determination (R) is approximately 99% for all the cases. The influence of pertinent parameters like the power law index (n) and Reynolds number (Re) on velocity, pressure, and drag and lift coefficients is also presented for limited cases. Moreover, a significant reduction in computing time has been noticed while applying hybrid CFD-ANN approach as compared with CFD simulations only. [ABSTRACT FROM AUTHOR]

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