LLMs are a compressed repository of a vast corpus of valuable information on which they are trained. Therefore, this work hypothesizes that LLMs such as Llama, Orca, Falcon, and Mistral can be used for misinformation detection by making them cross-check new information with the repository on which they are trained. Accordingly, this paper describes the findings from the investigation of the abilities of LLMs in detecting misinformation on multiple datasets. The results are interpreted using explainable AI techniques such as LIME, SHAP, and Integrated Gradients. The LLMs themselves are also asked to explain their classification. These complementary approaches aid in better understanding the inner workings of misinformation detection using LLMs and lead to conclusions about their effectiveness at the task.

This comprehensive article explores the intricate responsibilities of executives leading AI projects within the framework of Federal Directives, specifically Executive Order 14110 and Executive Order 14091. The executive role is dissected, covering aspects such as defining leadership in the AI context, historical perspectives, current trends, and challenges in AI project leadership. The article delves into the significance of Federal Directives, particularly Executive Order 14110, which emphasizes safe, secure, and trustworthy AI development, and Executive Order 14091, focusing on racial equity and support for underserved communities. Key provisions of these orders are outlined, including safe AI development, secure AI implementation, trustworthy AI use, and commitments to racial equity. The article also highlights steps that should guide documenting case studies that align with successful AI projects, showcasing compliance with Executive Orders and positive impacts on communities and organizations. The future outlook focuses on anticipating regulatory changes, adapting to evolving ethical standards, and promoting a culture of continuous improvement within AI projects. The conclusion summarizes executive responsibilities, emphasizing the call to action for ethical and inclusive AI leadership, contributing to the positive evolution of AI technologies for societal well-being.

The concept and policies of multiple early cancer detection (MCED) have gained significant attention from governments worldwide in recent years. In the era of burgeoning artificial intelligence (AI) technology, the integration of MCED with AI has become a prevailing trend, giving rise to a plethora of MCED AI products. However, due to the heterogeneity of both the detection targets and the AI technologies, the overall diversity of MCED AI products remains considerable. The types of detection targets encompass protein biomarkers, cell-free DNA, or combinations of these biomarkers. In the development of AI models, different model training approaches are employed, including datasets of case-control researches or real-world cancer screening datasets. Various validation techniques, such as cross-validation, location-wise validation, and time-wise validation are used. All of the factors show significant impacts on the predictive efficacy of MCED AIs. After the completion of AI model development, deploying the MCED AIs in clinical practice presents numerous challenges, including presenting the predictive reports, identifying the potential locations and types of tumor, and addressing cancer-related information such as clinical follow-up and treatment. This study reviews several mature MCED AI products currently available in the market, detecting their composing factors from serum biomarkers detection, MCED AIs training/validation, and the clinical application. The review illuminates the challenges encountered by existing MCED AI products across these stages, offering insights into the continued development and obstacles within the field of MCED AI.

Scaffoldings play a critical role as temporary structures in supporting construction processes. Accidents at construction sites frequently stem from issues related to scaffoldings, including insufficient support caused by deviations from the construction design, insecure rod connections, or absence of cross bracing, which result in uneven loading and potential collapse, leading to casualties. In this study, we introduce a deep-learning-based, augmented reality (AR)-enabled system called scaffolding assembly deficiency detection system (SADDS) to assist field inspectors in identifying deficiencies in scaffolding assemblies. Inspectors have the flexibility to utilize SADDS through various devices, such as video cameras, mobile phones, or AR goggles (i.e., Microsoft HoloLens 2), for automated detection of deficiencies in scaffolding assemblies. The training test yielded satisfactory results, with a mean average precision of 0.89 and individual precision values of 0.96, 0.82, 0.90, and 0.89 for “qualified” frames and frames with the “missing cross-tie rod,” “missing lower-tie rod,” and “missing footboard” deficiencies, respectively. Field tests conducted at two construction sites demonstrated improved system performance compared to that in the training test. However, these field tests also revealed certain limitations of the SADDS system.

Post-menopausal women are at risk of osteoporotic vertebral fractures (OVFs), which if undetected and untreated, can lead to significant pain and morbidity. However, OVFs are often not reported by radiologists on routine chest radiographs. Artificial Intelligence (AI) is increasingly used in medical applications with improvements in detection and diagnosis. This study aims to investigate the clinical value of a newly developed AI tool, Ofeye 1.0 for automated detection of OVFs on lateral chest radiographs in post-menopausal women (>60 years) who were referred to undergo chest x-rays for other reasons. A total of 510 de-identified lateral chest radiographs from three clinical sites were retrieved and analysed by Ofeye1.0 tool. These images were then reviewed by a consultant radiologist to decide whether there is any fracture present or not with findings served as the reference standard for determining the diagnostic performance of the AI tool. The percentage of radiologist reports which included the OVF was analysed by comparing diagnostic reports with AI findings, while the accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive values (NPV) of the AI tool were calculated. Out of all the original radiologist reports, missed OVFs were found in 14% images but were detected by the AI tool. The AI tool demonstrated a relatively high specificity 92.8% (95% CI: 89.6, 95.2%), moderate accuracy 80.3% (95% CI: 76,3, 80.4%), PPV 73.7% (95% CI: 65.2, 80.8%) and NPV 81.5% (95%CI: 79, 83.8%), but low sensitivity 49% (95%CI: 40.7, 57.3%). The high false negative results were mainly due to mild OVFs with <20% vertebral height loss. The new AI software tool has high specificity with a low false positive rate of 5.1%, showing that it can be used as a complimentary tool to the routine diagnostic reports for reduction of missed OVF in elderly women. The low sensitivity with high false negative rates indicate the necessity of radiologist’s in identifying and reporting early OVFs.

In the modern era of genomic research, the scientific community is witnessing an explosive growth in the volume of published findings. While this abundance of data offers invaluable insights, it also places a pressing responsibility on genetic professionals and researchers to stay informed about the latest findings and their clinical significance. Genomic variant interpretation is currently facing a challenge in identifying the most up-to-date and relevant scientific papers, while also extracting meaningful information to accelerate the process from clinical assessment to reporting. Computer-aided literature search and summarization can play a pivotal role in this context. By synthesizing complex genomic findings into concise, interpretable summaries, this approach facilitates the translation of extensive genomic datasets into clinically relevant insights. To bridge this gap, we present VarChat (varchat.engenome.com), an innovative tool based on generative AI, developed to find and summarize the fragmented scientific literature associated with genomic variants into brief yet informative texts.VarChat provides users with a concise description of specific genetic variants, detailing their impact on related proteins and possible effects on human health. Additionally, VarChat offers direct links to related scientific trustable sources, and encourages deeper research. Availability: VarChat is freely available at varchat.engenome.com

Generative AI has gained enormous interest nowadays due to new applications like chatGPT, DALL E, Stable Difussion and Deep Fake. Particularly DALL E, Stable Difussion and others (Adobe Firefly, ImagineArt...) are able to create images from a text prompt and are also able to recreate real photographs. Due to this fact, intense research has arisen to create new image forensics applications able to distinguish between real captured images and videos and artificial ones. Detecting forgeries made with Deep Fake is one of the most researched issues. This paper is about another kind of forgery detection. The purpose of this research aims to detect photo realistic AI created images versus real photos coming from a physical camera. For this purpose, techniques that perform a pixel level feature extraction are used. First one is Photo Response Non-Uniformity (PRNU). PRNU is a special noise due to imperfections on the camera sensor that is used for source camera identification. The underlying idea is that AI images will have a different PRNU pattern. Second one is Error level analysis (ELA). This is other type of feature extraction traditionally used for detecting image editions. In fact, ELA is being used nowadays by photographers to detect manually AI created images. Both kinds of features are used to train Convolutional Neural Networks to differentiate between AI images and real photographs. Good results are obtained achieving accuracy rates over 95%. Both extraction methods are carefully assessed by computing precision/recall and F1-score measurements.

A digital twin is a virtual representation that serves as the real-time digital counterpart of a physical object or system. Recent advancements have extended the concept of digital twins to humans, incorporating complex biological data such as DNA (Predictiv Care, Inc.) and immune system profiles. These sophisticated models go beyond mere pictorial representations, offering a more holistic digital reflection. However, a significant gap remains. The current human digital twin models are not capable of learning one’s preferences. In this pilot study, we introduce Diginality, a chatbot powered by HyperCLOVA X, a Large Language Model (LLM) developed by NAVER, Inc. Diginality, learns one’s preferences with custom training from data collected by interview-style questions on the user’s topic of interest. Our findings demonstrate that Diginality successfully answers one’s preferences, thereby adding a new dimension to the concept of human digital twins. This work represents a pioneering step towards creating a more comprehensive and psychologically nuanced human digital twin.

In an ever-evolving landscape of higher education, this study delves into the transformative potential of merging deep learning principles and meaningful pedagogy in language acquisition, aided by artificial intelligence (AI) technologies. Drawing from a qualitative research design, it navigates the post-pandemic educational landscape, examining shifts in language education, student perspectives, and adaptations. Through comprehensive data collection methods, including literature review, interviews, and classroom observations, the study presents insights from 30 participants from SMC. The qualitative findings reveal emerging themes related to deep learning, meaningful learning, AI integration, and language acquisition, underscoring their profound impact on teaching methods and student engagement. This research contributes to the field by shedding light on innovative approaches that elevate language acquisition in an AI-evolving educational environment. The study concludes with implications for school administrators, teachers, and students, offering recommendations for future research directions, ultimately striving to enhance language education in the modern era.

The Internet of Things (IoT) is still a relatively new field of research, and its potential for use in the healthcare and medical sectors is enormous. In the last five years, IoT has been a go-to option for various applications such as using sensors for different features, machine-to-machine communication, etc., but precisely, in the medical sector, it is still lagging far behind compared to other sectors. Hence, this study emphasizes the IoT applications in medical fields, Medical IoT sensors and devices, IoT platforms for data visualization, and artificial intelligence in medical applica-tions. A systematic review considering PRISMA guidelines on research articles as well as the websites on IoMT sensors and devices have been carried out. A total of 986 articles after the year 2001 have been initially selected, and by applying the inclusion-exclusion criterion, a total of 597 articles have been identified. From these, 23 studies have been finally selected for this systematic review. This review analyzes different sensor monitoring circuits in detail, considering an Inten-sive Care Unit (ICU) scenario, device applications, and the data management system, including IoT platforms for the patients. Lastly, detailed discussion and challenges have been outlined, and possible prospects have been presented.

In the age of artificial intelligence (AI) and automation, understanding the factors that contribute to successful upskilling and reskilling is crucial for workforce adaptability. Research on AI and automation has shown that they affect the workforce, but there is still a gap in understanding what factors contribute to successful upskilling and reskilling initiatives, especially from an organizational perspective. This study employs fuzzy-set qualitative comparative analysis (fsQCA) to analyze these factors and identify effective combinations for promoting upskilling and reskilling. By examining complex causal relationships and incorporating case studies, this article aims to enhance the understanding of upskilling initiatives in the AI and automation era. The findings provide insights into key factors driving successful upskilling and reskilling and offer implications for organizations seeking to improve their initiatives, ultimately promoting workforce adaptability and competitiveness.

Acute ischemic stroke (AIS) is the loss of neurological function due to a sudden reduction in cerebral blood flow and is a leading cause of disability and death worldwide. In recent years, the field of radiological imaging has experienced an explosion that may reach unprecedented heights with the advent of artificial intelligence. One of the latest innovations in artificial intelligence is radiomics, which is based on the fact that a large amount of quantitative data can be extracted from radiological images, from which patterns can be identified and associated with specific pathologies. Since its inception, radiomics has been particularly associated with the field of oncology, with promising results in a wide range of clinical situations. The performance of radiomics in non-tumour pathologies has been increasingly explored in recent years and the results continue to be promising. The aim of this review is to explore the potential applications of radiomics in AIS patients and to theorise how radiomics may change the paradigm for these patients in the coming years.

In the wake of globalization and the revolutionary impact of Artificial Intelligence (AI), literature and language education play a pivotal role in preparing the next generation to navigate an ever-changing world. This reflective concept paper explores the profound insights shared by educators, focusing on literature and language education amidst globalization and AI. Drawing on interviews and observations, teachers' reflections reveal the enduring value of literature in shaping young minds, fostering critical thinking, and promoting empathy. Additionally, they emphasize the importance of embracing multilingualism and cultural diversity to equip students for an interconnected world. In the face of challenges, educators showcase their resilience and adaptability, harnessing technology, particularly AI-driven tools, to foster future-ready learners. Identifying gaps and implications in literature and language education, this paper sets the stage for collective efforts among stakeholders to shape the future of education. Ultimately, teachers hold the key to nurturing a generation capable of embracing the complexities of the AI-driven global landscape with confidence and compassion, empowering students as transformative agents in a rapidly changing world.

Deploying UAVs as aerial base stations is an exceptional approach to reinforce terrestrial infrastructure owing to their remarkable flexibility and superior agility. However, it is essential to design their flight trajectory effectively to make the most of UAV-assisted wireless communications. This paper presents a novel method for improving wireless connectivity between UAVs and terrestrial users through effective path planning. This is achieved by developing a goal-directed trajectory planning method using active inference. First, we create a global dictionary using TSPWP instances executed on various training examples. This dictionary contains letters representing available hotspots, tokens representing local paths, and words depicting complete trajectories and hotspot order. By using this world model, the UAV can understand the TSPWP’s decision-making grammar and how to use the available letters to form tokens and words at various levels of abstraction and time scales. With this knowledge, the UAV can assess encountered situations and deduce optimal routes based on the belief encoded in the world model. Our proposed method outperforms traditional Q-learning by providing fast, stable, and reliable solutions with good generalization ability.

The deployment of Artificial Intelligence (AI) systems in decentralized environments is on the rise, yet representation of shared conceptualization in such scenarios remains a challenging issue. The absence of a shared understanding can lead to suboptimal performance of AI systems and hinders the ability to comprehend the knowledge and beliefs of agents in the domain. This paper proposes a formal model for modeling conceptualization in AI systems that integrates ontology, epistemology, and epistemic logic. The model aims to address the gap in representing shared conceptualization in decentralized environments and enhance the performance of AI systems operating in such environments. The proposed model is a hybrid structure that blends extensional and intensional structures and leverages logic-based languages for modeling purposes. A case study in the healthcare sector is presented to illustrate the application of the proposed model. The study contributes to the existing literature by providing a formal model for representing shared conceptualization in decentralized environments, which can be utilized to optimize the performance of AI systems in these environments.

Jaundice is a common condition for newborns, and its complications can be severe and cause permanent damage to the patient’s brain if no action is taken at its early stages. Current methods for jaundice detection are invasive, which include collecting blood samples from the patient, which can be painful and stressful and may cause some complications. Alternatively, a non-invasive approach can be used to diagnose jaundice through image-processing and artificial intelligence (AI) techniques, requiring a database of infant images to achieve a high-accuracy diagnosis. This data article provides a collection of newborn images, called NJN, with various birthweight and skin tones, with ages ranging from 2 to 8 days, and an excel sheet file in CSV format for the values of RGB and YCrCb channels and the status for each raw which is freely accessible at (https://sites.google.com/view/neonataljaundice). It also provides Python code for data testing using different AI techniques. Thus, this article offers a unique resource for all AI researchers to train their AI system and develop algorithms to help neonatal intensive care unit (NICU) healthcare specialists monitor neonates and provide fast, real-time, non-invasive, and accurate jaundice diagnosis.

Tropical cyclones devastate large areas, take numerous lives and damage extensive property in Bangladesh. Research on landfalling tropical cyclones affecting Bangladesh has primarily focused on events occurring since AD1960 with limited work examining earlier historical records. We rectify this gap by developing a new tornado catalogue that include present and past records of tornados across Bangladesh maximizing use of available sources. Within this new tornado database, 119 records were captured starting from 1838 till 2020 causing 8,735 deaths and 97,868 injuries leaving more than 1,02,776 people affected in total. Moreover, using this new tornado data, we developed an end-to-end system that allows a user to explore and analyze the full range of tornado data on multiple scenarios. The user of this new system can select a date range or search a particular location, and then, all the tornado information along with Artificial Intelligence (AI) based insights within that selected scope would be dynamically presented in a range of devices including iOS, Android, and Windows. Using a set of interactive maps, charts, graphs, and visualizations the user would have a comprehensive understanding of the historical records of Tornados, Cyclones and associated landfalls with detailed data distributions and statistics.

This paper aims to raise awareness on certain interoperability issues as we intend to shape industry 5.0 in order to enable a human-centric resilient society. We advocate that the need of sharing small and specific data will become more intensive as AI-based solutions will become more pervasive. Consequently, dataspaces should be carefully designed to address this need. We advance the conversation by presenting a case study from HR demonstrating how to predict the possibility of an employee experiencing attrition. Our experimental results show that we need more than 500 samples for developing a machine learning model to be sufficiently capable to generalize the problem. Consequently, our experimental results show the feasibility of the idea. However, in small and medium sized companies this approach cannot be implemented due to the limited number of samples. At the same time, we advocate that this obstacle may be overcome if multiple companies will join a shared dataspace, thus raising interoperability issues

While immutability is Blockchain's* much celebrated covenant, change is the rule of life. The paradox has seriously limited real-world deployability of Smart Contracts (SC), faltering its mainstream adoption and sustainability. Once implemented, SC remains unstoppable even if its execution makes losses, as evident in the recently exploded $50+B DeFi industry. How do we reconcile the two and make DeFI/Blockchain profitable and sustainable? A DISC (Dynamic Immutable Smart Contract) hypothesis was proposed to resolve the paradox. Using an existing decentralized IoT device framework we test the DISC hypothesis, by designing/implementing a DISC protocol that delivers an algorithmically controlled dynamic off-chain data feed into a self-executing SC. The experiment successfully introduced limited dynamism into SC without compromising its immutability or undermining user control over their SC terms. If consistently reproduced in diverse settings, the DISC protocol could earn an important milestone in the evolution of Blockchain's decentralized economy of the future.

Advances in machine learning (ML) and artificial intelligence (AI) are transforming the way we treat patients in ways not even imagined a few years ago. Cancer research is at the forefront of this movement. Infertility, though not a life-threatening condition, affects around 15% of couples trying for a pregnancy. Increasing availability of large datasets from various sources creates an opportunity to introduce ML and AI into infertility prevention and treatment. At present in the field of assisted reproduction, very little is done in order to prevent infertility from arising, with the main focus put on treatment when often advanced maternal age and low ovarian reserve make it very difficult to conceive. A shift from this disease-centric model to a health centric model in infertility is already taking place with more emphasis on the patient as an active participator in the process. Poor quality and incomplete data as well as biological variability remain the main limitations in the widespread and reliable implementation of AI in the field of reproductive medicine. That said, one of the areas where this technology managed to find a foothold is identification of developmentally competent embryos. More work is required however to learn about ways to improve natural conception, the detection and diagnosis of infertility, and improve assisted reproduction treatments (ART) and ultimately, develop clinically useful algorithms able to adjust treatment regimens in order to assure a successful outcome of either fertility preservation or infertility treatment. Progress in genomics, digital technologies and advances in integrative biology has had a tremendousimpact on research and clinical medicine. With the rise of ‘big data’, artificial intelligence, and the advances in molecular profiling, there is an enormous potential to transform not only scientific research progress, but also clinical decision making towards predictive, preventive, and personalized medicine. In the field of reproductive health, there is now an exciting opportunity to leverage these technologies and develop more sophisticated approaches to diagnose and treat infertility disorders. In this review, we present a comprehensive analysis and interpretation of different innovation forces that are driving the emergence of a system approach to the infertility sector. Here we discuss recent influential work and explore the limitations of the use of Machine Learning models in this rapidly developing area.

We suggest a deep learning based sensor signal processing method to remove chemical, kinetic and electrical artifacts from ion selective electrodes’ measured values. An ISE is used to investigate the concentration of a specific ion from aqueous solution, by measuring the Nernst potential along the glass membrane. However, application of ISE on a mixture of multiple ion has some problem. First problem is a chemical artifact which is called ion interference effect. Electrically charged particles interact with each other and flows through the glass membrane of different ISEs. Second problem is the kinetic artifact caused by the movement of the liquid. Water molecules collide with the glass membrane causing abnormal peak values of voltage. The last artifact is the interference of ISEs. When multiple ISEs are dipped into same solution, one electrode’s signal emission interference voltage measurement of other electrodes. Therefore, an ISE is recommended to be applied on single-ion solution, without any other sensors applied at the same time. Deep learning approach can remove both 3 artifacts at the same time. The proposed method used 5 layers of artificial neural networks to regress correct signal to remove complex artifacts with one-shot calculation. Its MAPE was less than 1.8% and R² of regression was 0.997. A randomly chosen value of AI-processed data has MAPE less than 5% (p-value 0.016).

IoT is a fast growing technology that has Promising potential for shaping our future. In this fast growing world of IoT, IoT systems are released without proper testing which effect its quality and does not guarantee user satisfaction. Different testing methodologies are carried out to ensure Quality assurance of IoT before releasing it to the market. In this paper we have reviewed different testing techniques using AI and different tools to ensure Quality of IoT. In this paper we have also reviewed different IoT challenges related to its quality.

In recent years, the economics of emotions (EoE) field, which aims to create models of the human mind, has grown quickly. EoE models work well with simulation theory (ST), which is one of the main theories of mind. EoE models show how people's behavior and emotions change based on their knowledge and perception of others. It is hoped that by developing this model, it will be possible to quantitatively analyze not only the mental states of real others, but also the subtleties of the minds of characters in novels in the future. Until now, the decision-making or the factual-reasoning mechanisms have been a black box in ST. This study analyzes how EoE models add to the understanding of ST.

The value of artificial intelligence and machine learning applications for use in heritage research is increasingly appreciated [1]. In specific areas, notably remote sensing, data sets have increased in extent and resolution to the point that manual interpretation is problematic and availability of skilled interpreters to undertake such work is limited. Interpretation of geophysical data sets associated with prehistoric submerged landscapes is particularly challenging. Following the last glacial, sea levels rose 120 metres globally, and vast, habitable landscapes were lost to the sea. These landscapes were inaccessible until extensive remote sensing data sets were provided by the offshore energy sector. In this paper, we provide results of a research programme centred on AI applications using data from the southern North Sea. Here, an area of c.188,000 km2 of habitable terrestrial land was inundated between c. 20,000 BP and 7,0000 BP, along with the cultural heritage it contained [2]. As part of this project, machine learning tools were applied to detect and interpret features of archaeological significance from shallow seismic data. The output provides a proof-of-concept model demonstrating verifiable results and the potential for further, more complex leveraging of AI interpretation for the study of submarine palaeolandscapes. Image: Adobe Firefly Generative AI using keywords - marsh landscape deep underwater, auroch skull half-buried in peat

This study investigated the capability of vocal-identity-cloning Artificial Intelligence (AI) to encode human-specific confident, doubtful, and neutral-intending emotive states. Linear mixed-effects models and machine learning classification with eXtreme Gradient Boosting were employed to examine the underlying acoustic signatures from 2,700 audio clips, comprising of sentences spoken by human speakers and two sets of equivalences (AI-Geography/AI-Trivia, based on the trained text) generated by voice-cloning models designed to clone human speakers’ identities. Compared with neutral-intending voice, human speakers lengthened their vocal tract, raised the fundamental frequency, and increased Chroma constant-Q transform when they intended to be confident; An opposite pattern was shown when they intended to be doubtful. The two sets of AI sounds displayed a similar pattern to human speech, suggesting a shared mechanism for encoding vocal expression across sources. The 1,000 times training-testing classification models reported an in-group advantage for AI sources. The algorithms, trained on AI-Geography/AI-Trivia, resulted in higher accuracies when tested within these AI sources than when tested on human audio. All between-source classifications reported above-chance-level (1/3) accuracies. These findings highlighted that voice-cloning AI, the widely used conversational agent, can learn and generate human-specific vocally-expressed confidence.

Autoantibodies (Abs) to thyroid antigens are associated with autoimmune thyroid disease (AITD), although sometimes they have no clear clinical significance. The aim of this study was to evaluate the avidity of thyroid Abs in the sera of AITD patients and non-AITD Abs carriers. A multiplex microarray-based assay was developed to detect the avidity of thyroid disease-associated Abs, including Abs to thyroperoxidase (TPO), thyroglobulin (Tg), pendrin, thyroid hormones T3 and T4, pyruvate kinase, and carbonic anhydrase 2. The method is based on indirect multiplex assay with an additional step involving the chaotrope destruction of the autoantibody-antigen complexes. The avidity index (AI) is then calculated. The method was tested on clinical cohort (n =117) and then confirmed by studies with another independent cohort (n =123). For the confirmation study, the median AI for AbTg was 29.9% among healthy Abs carriers, 52.6% for AITD patients, and 92.7% for non-AITD type 1 diabetes (T1D) patients. Median AI for AbTPO were 39.9% in healthy carriers, 73.4% in AITD patients, 83.2% in T1D patients, and 98.5% in AITD patients with thyroid neoplasm. The presence of high-avidity AbTg in T1D patients was also confirmed by a modified ELISA. Identification of high-avidity autoantibodies to thyroid proteins in non-thyroid T1D patients requires further studies.

This study delves into the dynamic intersection of artificial intelligence (AI) and English Language Teaching (ELT) within the post-pandemic educational landscape. Grounded in Constructivism, Connectivism, and Technological Pedagogical Content Knowledge (TPACK), this research explores how AI integration impacts language instruction efficiency, benefits and challenges perceived by St. Michael's College students, differences in learning outcomes, and its potential to address the digital divide. Employing a qualitative approach, the study employs a rigorous method that includes intensive in-depth reviews of literature, maximum purposeful sampling, informal interviews, observation, and thematic analysis. Through these methods, data was collected from 30 participants, leading to the identification of five key themes. Findings suggest that AI enhances instructional efficiency, offers personalized learning experiences, and bridges gaps in remote settings. However, challenges and the importance of pedagogical balance emerged. The study underscores the need for adaptive pedagogical strategies to fully harness AI's potential. Future directions and limitations are discussed, emphasizing the study's implications for shaping the future of ELT.

Amidst the dynamic landscape of education, this paper embarks on a transformative journey that explores the integration of Artificial Intelligence (AI) in literature and language education. Through a qualitative exploratory approach, purposeful and maximum variation sampling method, the researcher delves into the experiences of educators and learners, unraveling the multifaceted dimensions of AI-driven pedagogy in SMC. Drawing upon a theoretical framework encompassing Constructivism, Connectivism, and Socio-Cultural Theory, the study examines AI's role in reshaping pedagogical landscapes, fostering critical thinking, and expanding access to education. The results resonate with participants' narratives, unveiling AI's potential in fostering personalized learning pathways, enhancing collaborative engagement, and bridging digital divides. Ethical considerations in AI education are explored, leading to the proposition of responsible guidelines. These findings underline the transformative power of AI in education, aligned with the institution's commitment to quality education and innovation. Overall, the study calls for ongoing exploration, emphasizing the qualitative research method's depth and richness, as well as the theoretical framework's guidance in realizing the study's objectives. As AI continues to shape education's frontiers, this research underscores the significance of adaptability, ethical awareness, and holistic engagement for school administrators, educators, learners, and researchers alike.

Malaria remains a primary public health challenge in Africa, and there is growing interest in leveraging artificial intelligence (AI) to raise malaria interventions. This research examines the possible influence, challenges, and recommendations for implementing AI-based personalized malaria interventions in Africa. AI offers several opportunities in malaria management, including early detection and prediction of outbreaks, improved diagnosis, personalized interventions, optimal treatment recommendations, surveillance and response, resource optimization, and research innovation. However, the implementation of AI in malaria interventions faces various challenges. These include data availability and quality, infrastructure and resource constraints, contextual relevance and generalizability, ethical and privacy considerations, integration into healthcare workflows, and the need to build trust and acceptance among stakeholders. To address these challenges, it is recommended to strengthen data infrastructure, build local capacity in AI technologies, contextualize AI models to local settings, address ethical considerations, establish monitoring and evaluation frameworks, promote collaboration and knowledge sharing, and secure sustainable funding and long-term commitment. By considering these recommendations, stakeholders can work towards implementing AI-based personalized malaria interventions in Africa that can contribute to improving malaria control outcomes, reducing the weight of the disease, and advancing public health in the region.

The fusion of machine learning, biomedical, and bioengineering research offers novel ways to understand, diagnose, and treat various health conditions. However, the complexities of biomedical and bioengineering data, coupled with the intricate process of developing and deploying machine learning solutions, often pose significant challenges to researchers in these fields. We introduce a new Automatic Semantic Machine Learning Microservice Framework (AIMS) that addresses these challenges by automating key aspects of the machine learning pipeline, from data preprocessing to model evaluation. Our proposed framework emphasizes model interpretability, the integration of domain knowledge, and the efficient handling of biomedical data. Furthermore, it incorporates self-supervised knowledge learning strategies, enabling the automatic generation, optimization, and continuous adaptation of machine learning models in response to new tasks and data. We illustrate the potential of our framework through three case studies in biomedical research, showcasing its capacity to streamline research processes, improve scientific exploration quality, and provide a foundation for significant breakthroughs.

Deep Learning is developing interesting tools which are of great interest for inverse imaging applications. In this work, we consider a medical imaging reconstruction task from subsampled measurements, which is an active research field where Convolutional Neural Networks have already revealed their great potential. However, the commonly used architectures are very deep and, hence, prone to overfitting and unfeasible for clinical usages. Inspired by the ideas of the green-AI literature, we here propose a shallow neural network to perform an efficient Learned Post-Processing on images roughly reconstructed by the filtered backprojection algorithm. The results obtained on images from the training set and on unseen images, using both the non-expensive network and the widely used very deep ResUNet show that the proposed network computes images of comparable or higher quality in about one fourth of time.

Big Data classification has recently received a great deal of attention due to the main properties of Big Data, which are volume, variety, and velocity. The furthest-pair-based binary search tree (FPBST) shows a great potential for Big Data classification. This work attempts to improve the performance the FPBST in terms of computation time, space consumed and accuracy. The major enhancement of the FPBST includes converting the resultant BST to a decision tree, in order to remove the need for the slow K-nearest neighbors (KNN), and to obtain a smaller tree, which is useful for memory usage, speeding both training and testing phases and increasing the classification accuracy. The proposed decision trees are based on calculating the probabilities of each class at each node using various methods; these probabilities are then used by the testing phase to classify an unseen example. The experimental results on some (small, intermediate and big) machine learning datasets show the efficiency of the proposed methods, in terms of space, speed and accuracy compared to the FPBST, which shows great potential for further enhancements of the proposed methods to be used in practice.

AI is often looked at as a logical rational way to develop a games agent that methodically looks at options and delivers rational solutions. This paper is based on developing an AI agent that plays a game with a similar emotive content like a human. The purpose of the study was to see if the incorporation of this emotive content would influence the outcomes within the game Love Letter. In order to do this an AI agent with an emotive layer was developed to paly the game over a million times. A lower win/loss ratio demonstrates that to some extent this methodology was vindicated and a 100 per cent win for the AI agent did not happen. Machine learning techniques were modelled purposely so as to match extreme models of behavioural change. The results demonstrated a win/loss ration of 0.67 for the AI agent and in many ways reflectd the frustration that a normal player would exhibit during game play. As was hypothesised the final agent investment value was, on average, lower after matchplay than its initial value.

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

This paper explores the transformative impact of computational pharmaceutics, integrating AI, multi-scale modeling, quantum computing, AI-driven drug discovery, and blockchain frameworks. Molecular dynamics simulations provide insights into atomic behaviors crucial for drug design. Quantum computing reduces computational costs for simulating protein-drug interactions. AI and Graph Neural Networks accelerate drug discovery and personalized medicine. Blockchain frameworks facilitate secure pharmacogenetic data sharing. These advancements revolutionize drug development, offering unprecedented efficiency, innovation, and personalized treatments.

This paper presents a theoretical framework aimed at improving IoT network cybersecurity by AI-driven anomaly detection in the context of rapid urbanization and the development of smart cities. The framework draws upon the Complex Adaptive Systems Theory, Theory of Planned Behavior, the Technology Acceptance Model, and the Socio-Technical Systems Theory, and intri-cately examines how AI might be integrated into the intricate ecosystem of smart cities to detect and mitigate cyber threats. It emphasizes AI-driven anomaly detection methods as pivotal in-struments that impact cybersecurity enhancement in smart city infrastructures. The proposed framework also highlights the crucial role that human-related factors, including user behavior and adoption, play in determining how effective AI applications are in enhancing cybersecurity in smart cities. It also recognizes the important moderating roles played by outside factors like legislative frameworks, environmental contexts, and technological breakthroughs in the relationship between AI-enabled anomaly detection and enhanced cybersecurity. The goal of this research is to shed light on the interactions between technology and socio-technical dynamics by offering a thorough theoretical understanding of AI's potential in smart city cybersecurity. It highlights the significance of taking into account a variety of contextual aspects when employing AI to strengthen the digital security of urban areas, and provides a strategic direction for future research and practical implementation.

The adoption of algorithms based on Artificial Intelligence (AI) has been rapidly increasing during the last years. However, some aspects of AI techniques are under heavy scrutiny. For instance, in many use cases, it is not clear whether the decisions of an algorithm are well-informed and conforming to human understanding. Having ways to address these concerns is crucial in many domains, especially whenever humans and intelligent (physical or virtual) agents must cooperate in a shared environment. In this paper, we introduce an application of an explainability method based on the creation of a Policy Graph (PG) based on discrete predicates that represent and explain a trained agent’s behaviour in a multi-agent cooperative environment. We show that from these policy graphs, policies for surrogate interpretable agents can be automatically generated. These policies can be used to measure the reliability of the explanations enabled by the PGs, through a fair behavioural comparison between the original opaque agent and the surrogate one. The contributions of this paper represent the first application of policy graphs in the context of explaining agent behaviour in collaborative multi-agent scenarios and presents experimental results that sets this kind of scenario apart from previous application in single-agent scenarios: when requiring collaborative behaviour, predicates that allow representing observations about the other agents are crucial to replicate the opaque agent’s behaviour and increase the reliability of explanations.

Due to rising cyber threats, IoT devices’ security vulnerabilities are expanding. However, these devices cannot run complicated security algorithms locally due to hardware restrictions. Data must be transferred to cloud nodes for processing, giving attackers an entry point. This research investigates distributed computing on the edge, using AI-enabled IoT devices and container orchestration tools to process data in real time at the network edge. The purpose is to identify and mitigate DDoS assaults while minimizing CPU usage to improve security. It compares typical IoT devices with and without AI-enabled chips, container orchestration, and and assesses their performance in running machine learning models with different cluster settings. The proposed architecture aims to empower IoT devices to process data locally, minimizing the reliance on cloud transmission and bolstering security in IoT environments. The results correlate with the update in the architecture. With the addition of AI-enabled IoT device and container orchestration, there is a difference of 60% between the new architecture and traditional architecture where only Raspberry Pi were being used.

The transportation sector stands out as California's primary contributor to greenhouse gas emissions, necessitating urgent action from government agencies to promote the adoption of low-carbon transportation (LCT) within the heavy-duty vehicles (HDV) and off-road equipment (ORE) sectors. This study conducts content analysis to delve into the impact of behavioral factors, specifically awareness and impression, on LCT adoption within these sectors. The analysis results are honed using a generative artificial intelligence (AI) tool. Positive associations emerge between the potential for LCT adoption and awareness, as well as factors linked to LCT (government incentives, environmental considerations). Notably, the study reveals that financial barriers and incentives hold greater significance for small-fleet organizations compared to their large-fleet counterparts. These findings offer actionable insights for government agencies to formulate targeted strategies, including mandates and incentives, tailored to the unique needs of organizations within the HDV and ORE sectors.

This paper argues that popular misrepresentation of the nature of AI has important consequences concerning how it should be regulated. Viewing AI as something that exists in itself, rather than as a set of cognitive technologies whose characteristics – physical, cognitive, and systemic – are quite different from ours (and at times from each other) leads to inefficient approaches to regulation. It limits our ability to anticipate the consequences of its foreseeable developments and social diffusion. It undermines attempts to protect ourselves from the social and political dangers it presents. After a short introduction, section 2 retraces rapidly the history of the idea that intelligence is essentially a quality, one that AI shares with human intelligence and that this resemblance trumps the differences that exist between artificial and natural cognitive systems. Section 3 reviews two approaches to the dangers of AI that reflect the illusion that AI exists in itself. Section 4 turns to the proper understanding of regulations and what is their main goal and purpose. Section 5 analyses three central characteristics of artificial cognitive systems which section six compares with corresponding characteristics of natural cognitive systems. Finally, section 7 draws some conclusions regarding how we should regulate artificial intelligence.

Modern language models are designed to generate text so realistic that it could easily be mistaken for human-authored content. Additionally, they can engage in conversations with humans in a manner that appears coherent and reasonable. The epitome of this technological advancement is ChatGPT, a model based on OpenAI's Generative Pretrained Transformer (GPT) language model. With its ability to produce high-quality content in mere seconds, ChatGPT surpasses other chatbots in terms of capabilities, drawing significant interest and excitement from both the business community and academic researchers. This study offers a comprehensive review of current research on ChatGPT, delving into its technological foundation, supportive mechanisms, and findings regarding its benefits across various fields and application areas. We evaluate and discuss the strengths and weaknesses of ChatGPT based on this review and suggest potential avenues for future research.

Blood cell analysis is a crucial diagnostic process in medical practice. In particular, detecting white blood cells (WBCs) is essential for diagnosing of many diseases. The manual screening of blood films is a time-consuming and subjective process, which can lead to inconsistencies and errors. Therefore, automated detection of blood cells can improve the accuracy and efficiency of the screening process. In this study, an explainable Vision Transformer (ViT) model was proposed for the automatic detection of WBCs from blood films. The proposed model utilizes the self-attention mechanism to extract relevant features from the input images and leverages transfer learning by incorporating pre-trained model weights to improve its performance. The proposed model achieved a classification accuracy of 99.40% for five distinct types of WBCs and exhibited potential in reducing the time required for manual screening of blood films by pathologists. Upon examination of the misclassified test samples, it was observed that incorrect predictions were correlated with the presence or absence of granules in the cell samples. To validate this observation, the dataset was divided into two classes, namely Granulocytes and Agranulocytes, and a secondary training process was conducted. The resulting ViT model trained for binary classification achieved an accuracy of 99.70%, recall of 99.54%, precision of 99.32%, and F-1 score of 99.43% during the test phase. To ensure the reliability of the ViT model's multi-class classification of WBCs, the pixel areas that the model focuses on in its predictions are visualized through the Score-CAM algorithm.

For highly time-constrained very short-term investors, reading and extracting valuable information from financial news poses significant challenges. The wide range of topics covered in these news articles further compounds the difficulties for investors. The diverse content adds complexity and uncertainty to the text, making it arduous for very short-term investors to swiftly and accurately extract valuable insights. Variations in authors, media sources, and cultural backgrounds also introduce additional complexities. Hence, performing a bull-bear semantic analysis of financial news using text mining technologies can alleviate the volume, time, and energy pressures on very short-term investors while enhancing the efficiency and accuracy of their investment decisions. This study proposes labeling bull-bear words from a futures corpus detection method that extracts valuable information from financial news, allowing investors to understand market trends quickly. Generative AI models are trained to provide real-time bull-bear advice, aiding investors in adapting to market changes and devising effective trading strategies. Experimental results show the effectiveness of various models, with Random Forest and SVMs achieving an impressive 80% accuracy rate. MLP and Deep learning models also perform well. By leveraging these models, the study reduces the time spent reading financial articles, enabling faster decision-making and increasing the likelihood of investment success. Future research can explore the application of this method in other domains and enhance model design for improved predictive capabilities and practicality.

Large Language Models (LLMs) have revolutionized the field of Artificial Intelligence with their ability to understand and generate natural language discourse. This has led to the use of LLMs in various creative fields such as music, art, and storytelling, which has sparked a debate on the impact of LLMs on human creativity. This article explores the cyclic effect of LLMs on human creativity, where LLMs can generate an ever-evolving cycle of creative output from both humans and machines. The article also highlights the importance of responsible and ethical use of LLMs, including privacy and copyright issues with the training data used by LLMs. The article emphasizes the need for transparency, data security measures, and AI governance policies to protect user data and ensure the safe use of LLMs. It also calls for the development of educational materials and resources to increase public understanding of LLMs and their ethical implications. The article concludes by highlighting the potential of LLMs as powerful tools for communication and education, but also emphasizes the need for ethical considerations to ensure the responsible and beneficial use of LLMs in society.

Over recent years, the importance of patent literature has become more recognized in the academic setting. In the context of artificial intelligence, deep learning, and data sciences, patents are relevant to not only industry but also academe and other communities. In this article, we focus on deep tomographic imaging and perform a preliminary landscape analysis of the related patent literature. Our search tool is PatSeer . Our patent bibliometric data is summarized in various figures and tables. In particular, we qualitatively analyze key deep tomographic patent literature.

In response to the need to address the safety challenges in the use of artificial intelligence (AI), this research aimed to develop a framework for a safety controlling system (SCS) to address the AI black-box mystery in the healthcare industry. The main objective was to propose safety guidelines for implementing AI black-box models to reduce the risk of potential healthcare-related incidents and accidents. The system was developed by adopting the multi-attribute value model approach (MAVT), which comprises four symmetrical parts: extracting attributes, generating weights for the attributes, developing a rating scale, and finalizing the system. On the basis of the MAVT approach, three layers of attributes were created. The first level contained 6 key dimensions, the second level included 14 attributes, and the third level comprised 78 attributes. The key first level dimensions of the SCS included safety policies, incentives for clinicians, clinician and patient training, communication and interaction, planning of actions, and control of such actions. The proposed system may provide a basis for detecting AI utilization risks, preventing incidents from occurring, and developing emergency plans for AI-related risks. This approach could also guide and control the implementation of AI systems in the healthcare industry.

Pipeline integrity is an important area of concern for the oil and gas, refining, chemical, hydrogen, carbon sequestration, and electric-power industries, due to the safety risks associated with pipeline failures. Regular monitoring, inspection, and maintenance of these facilities is therefore required for safe operation. Large standoff magnetometry (LSM) is a non-intrusive, passive magnetometer-based mea- surement technology that has shown promise in detecting defects (anomalies) in regions of elevated mechanical stresses. However, analyzing the noisy multi-sensor LSM data to clearly identify regions of anomalies is a significant challenge. This is mainly due to the high frequency of the data collection, mis-alignment between consecutive inspections and sensors, as well as the number of sensor measurements recorded. In this paper we present LSM defect identification approach based on ma- chine learning (ML). We show that this ML approach is able to successfully detect anomalous readings using a series of methods with increasing model complexity and capacity. The methods start from unsupervised learning with "point" methods and eventually increase complexity to supervised learning with sequence methods and multi-output predictions. We observe data leakage issues for some methods with randomized train/test splitting and resolve them by specific non-randomized splitting of training and validation data. We also achieve a 200x acceleration of support-vector classifier (SVC) method by porting computations from CPU to GPU leveraging the cuML RAPIDS AI library. For sequence methods, we develop a customized Convolutional Neural Network (CNN) architecture based on 1D convolutional filters to identify and characterize multiple properties of these defects. In the end, we report the scalability of the best-performing methods and compare them, for viability in field trials.

This work addresses the problem of the application of artificial intelligence to the creation and maintenance of medical documentation and the use of big data in medicine to support efficient patient diagnosis and treatment. This study covers the latest advances in AI and big data, based on literature reviews and interviews with leading experts in these fields. The following conclusions were obtained: (1) Based on the needs of patients and providers of medical services, and given the latest technological advances, all medical documentation should be digital and the processes of its creation, access, sharing, and consistency checking should by supported by suitably designed AI systems. (2) The knowledge contained in medical documentation constitutes a resource of strategic importance for humanity, with almost unlimited potential. (3) All medical documentation should be anonymised and should be made widely available, just like data and research results in the field of experimental physics. (4) This will accelerate development of new treatments, best practice and help to identify new medical emergencies, such as Covid-19. In practice today, unfortunately, the design of medical record systems is fragmented between institutions and countries, often focusing discussions on narrow technical details, and forcing clinicians to waste time on filling up multiple pages of illness history. This leads to many inefficiencies and lost opportunities and necessitates a fundamentally new approach.

Causality is the most important topic in the history of Western Science, and since the beginning of the statistical paradigm, it meaning has been reconceptualized many times. Causality entered into the realm of multi-causal and statistical scenarios some centuries ago. Despite of widespread critics, today Deep Learning and Machine Learning advances are not weakening causality but are creating a new way of finding indirect factors correlations. This process makes possible us to talk about approximate causality, as well as about a situated causality.

Artificial intelligence (AI) is increasingly being used to improve the quality control of point-of-care diagnostics. This is caused by a number of factors, including the following: 1. AI can accelerate and improve testing accuracy. In comparison to humans, AI technology can review data more quickly and precisely, reducing errors and improving overall quality assurance. 2. When it comes to improving POC test findings on healthcare issues such as infectious diseases or medical crises such as heart attacks, AI can be used for sophisticated predictive analytics and modeling that aid in better decision-making. 3. Artificial intelligence facilitates process automation, increasing productivity and lowering labor costs in labor-intensive tasks such as testing and analyzing samples collected at point-of-care facilities. 4. The use of AI enables organizations implementing these solutions to gain insights from large volumes of raw diagnostic data generated faster and more accurately, allowing them to build solid frameworks around preventive care initiatives and significantly influence public health outcomes.5. Artificial intelligence (AI) has been demonstrated to be a useful tool for real-time monitoring systems that identify any problems with test results early so that they can be corrected before affected patients receive inaccurate diagnoses or treatment plans based on false information provided by diagnostic tests performed at points of care such as clinics or hospitals.Using these technologies would allow healthcare organizations to spend less on labor while still receiving exact diagnoses and rapid treatment delivery at a fraction of the cost that manual approaches required earlier.

The recent remarkable progress in Artificial Intelligence (AI) has renewed the discussion on the possibility of human-level AI. Despite the difficulty of the problem and the presence of many large gaps and challenges, most people working in the field think that human-level AI is achievable, but they disagree on the date. In this paper, I briefly discuss the possibility of human-level AI from the perspective of two traditions in the Islamic world, namely Kalam and Sufism, and despite their different mode of investigation, they converge to the same conclusion on this topic.

Current methods for assessing individual well-being in team collaboration at the workplace rely often on manually collected surveys. This limits continuous real-world data collection and proactive measures to improve team member workplace satisfaction. We propose a method to automatically derive social signals related to individual well-being in team collaboration from raw audio and video data collected in teamwork contexts. The goal is to develop computational methods and measurements to facilitate the mirroring of individuals’ well-being to themselves. We are focusing on how speech behavior is perceived by team members to improve their well-being. Our main contribution is the assembly of an integrated toolchain to perform multi-modal extraction of robust speech features in noisy field settings and to find which features are predictors of self-reported satisfaction scores. We apply the toolchain to a case study where we collected videos of 22 teams with 56 participants collaborating over a four-day period in a team project. Our audiovisual speaker diarization extracts individual speech features in a noisy environment. As the dependent variable team members filled out a daily PERMA (positive emotion, engagement, relationships, meaning, and accomplishment) survey. These well-being scores have been predicted with speech features extracted from the videos using machine learning. The results suggest that the proposed toolchain is able to automatically predict individual well-being in teams, leading to better teamwork and happier team members.

Introduction: This empirical study investigates the impact of two distinct mathematical problem-solving methods – the Algebraic Formula Method and the Newton Sum Method – on enhancing ChatGPT's competence in effectively solving quadratic root functions. The integration of Artificial Intelligence (AI) into mathematical problem-solving has paved the way for innovative approaches. In this study, we delve into the Algebraic Formula Method and the Newton Sum Method, essential techniques for solving quadratic root functions. We aim to showcase the profound influence of these methods on ChatGPT's capacity to excel in solving quadratic equations. Evidence Through concrete evidence, we demonstrate ChatGPT's adept utilization of the Newton Sum Method for quadratic root function calculations. While ChatGPT can compute quadratic root functions of the form α^15 + β^15 using this method, its proficiency in using algebraic formula methods typically extends only up to α^4 + β^4. This marked discrepancy underscores the pivotal role that different methods play in amplifying the AI system's mathematical capabilities Result The results of this study provide concrete evidence of ChatGPT's superior utilization of the Newton Sum Method for calculating quadratic root functions. The model adeptly computes expressions of the form α^15 + β^15 using this method, while its proficiency using algebraic formula methods is generally limited to α^4 + β^4. This striking discrepancy underscores the transformative impact that different methods can have on elevating the AI system's mathematical prowess. Conclusion :Pushing Boundaries: Pioneering Novel Maths Approaches for Overcoming Limitations in AIThis study serves as an illuminating testament to the significance of pioneering innovative methodologies, rules, theorems, or formulas to surmount the current limitations in AI systems like ChatGPT. These innovative pursuits hold the key to unlocking the untapped potential that lies within, propelling AI systems to greater heights of proficiency. In essence, they offer a strategic pathway towards expanding the capabilities of AI and pushing the boundaries of what can be achieved. Discussion The outcomes derived from this study underscore the significant influence wielded by the method selection in augmenting the mathematical competencies of ChatGPT. Particularly noteworthy is the application of the Newton Sum Method, which surfaces as a compelling exemplar. This method serves as a pivotal conduit through which the model surpasses its prior constraints, allowing it to venture into the realm of calculations entailing higher exponents. Implications and Future Research: These findings not only contribute to AI's mathematical competencies but also emphasize the need for pioneering new methods, rules, theorems, or formulas to further enhance AI systems like ChatGPT. Future research could explore the development of novel mathematical techniques tailored to AI systems, thus expanding their capabilities across diverse problem-solving domains.

The falling cost of cameras, the advancement of AI based computer vision algorithms, and powerful hardware accelerators for deep learning have enabled wide-spread deployment of surveillance cameras with the ability to automatically analyze streaming video feeds to detect events of interest. While streaming video analytics is currently largely done in the cloud, edge computing has emerged as a pivotal component due to its advantages of low latency, reduced bandwidth, and enhanced privacy. However, a distinct gap persists between the state-of-the-art computer vision algorithms, and successful practical implementation of edge-based streaming video analytics systems. This paper presents a comprehensive review of more than 30 research papers published over the last 6 years on edge video analytics systems. The papers are analyzed across 17 distinct dimensions. Unlike prior reviews, we examine each system holistically, identifying their strengths and weaknesses in diverse implementations. Our findings suggest that certain critical topics necessary for the practical realization of edge video analytics systems are not sufficiently addressed in current research. Based on these observations, we propose research trajectories across short, medium, and long term horizons. Additionally, we explore trending topics in other computing areas that can significantly impact the field of edge video analytics.

The use of visual signals in horticulture has attracted significant attention and encompassed a wide range of data types such as 2D images, videos, hyperspectral images, and 3D point clouds. These visual signals have proven to be valuable in developing cutting-edge computer vision systems for various applications in horticulture, enabling plant growth monitoring, pest and disease detection, quality and yield estimation, and automated harvesting. However, unlike other sectors, developing deep learning computer vision systems for horticulture encounters unique challenges due to the limited availability of high-quality training and evaluation datasets necessary for deep learning models. This paper investigates the current status of vision systems and available data in order to identify the high-quality data requirements specific to horticultural applications. We analyse the impact of the quality of visual signals on the information content and features that can be extracted from these signals. To address the identified data quality requirements, we explore the usage of a deep learning-based super-resolution model for generative quality enhancement of visual signals. Furthermore, we discuss how these can be applied to meet the growing requirements around data quality for learning-based vision systems. We also present a detailed analysis of the competitive quality generated by the proposed solution compared to cost-intensive hardware-based alternatives. This work aims to guide the development of efficient computer vision models in horticulture by overcoming existing data challenges and paving a pathway forward for contemporary data acquisition.

The use of artificial intelligence (AI) applications in education has the potential to revolutionize the learning experience for international students, who face unique challenges when studying in a foreign country. This paper explores various examples of AI applications in education and their potential impact on international students. AI applications such as personalized learning experiences, adaptive testing, predictive analytics, and chatbots for learning and research are examined for their potential to improve learning efficiency and provide customized education support. It also explores the significant risks and limitations associated with AI technologies, such as privacy, cultural differences, language proficiency, and ethical implications. To maximize the potential benefits of AI applications in higher education, it is crucial to implement appropriate safeguards and regulations. This paper provides a starting point for research on the potential impact of artificial intelligence on international students’ educational experiences and how AI may be integrated into educational administration and learning processes.

The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 215 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 14 April 2020, a cumulative total of 1,853,265 (118,854) infected (dead) COVID-19 cases were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify their applications in fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.

Objectives: The study aimed to develop a deep learning-based edge AI model deployed on electrocardiograph (ECG) devices for real-time detection of atrial fibrillation (AF)-risk during sinus rhythm (SR) using standard 10-second 12-lead electrocardiograms (ECGs). Methods: A novel approach was used to convert standard 12-lead ECGs into binary images for model input, and a lightweight convolutional neural network (CNN)-based model was trained using data collected by the Japan Agency for Medical and Research Development (AMED) between 2019 and 2022. Patients over 40 years old with digital, SR ECGs were retrospectively enrolled and divided into AF and non-AF groups. Data labeling was supervised by cardiologists. The dataset was randomly allocated into training, validation, and internal testing datasets. External testing was conducted on data collected from other hospitals.　Results: The best-trained model achieved an AUC of 0.82 and 0.80, sensitivity of 79.5% and 72.3%, specificity of 77.8% and 77.7%, precision of 78.2% and 76.4%, and overall accuracy of 78.6% and 75.0% in the internal and external testing datasets, respectively. The deployed model and app package utilized 2.5MB and 40MB of the available ROM and RAM capacity on the edge ECG device, correspondingly. Processing time for AF-risk detection was approximately 2 seconds.　Conclusion: The model maintains comparable performance and improves its suitability for deployment on resource-constrained ECG devices, thereby expanding its potential impact to a wide range of healthcare settings. Its successful deployment enables real-time AF-risk detection during SR, allowing for timely intervention to prevent AF-related serious consequences like stroke and premature death.

In the livestock industry, maintaining the health of animals is very important, and the use of technology has greatly helped this. One of the existing challenges has been that most of the available sensors and electronic processing devices are designed to fit the human body, and the use of these devices for livestock faces limitations due to the difference in the structure of the body and skin of animals and humans. Reliable solutions tailored for animal husbandry can be achieved by modifying the physical and software structure of existing devices. In this article, we have presented the current situation, by reviewing the research done and the existing technology in the field of animal husbandry. The aim is to obtain comprehensive parameters for monitoring livestock health by leveraging available information and exploring new methods based on the Internet of Things (IOT), Artificial Neural Networks (ANN), and Artificial Intelligence (AI). We take a proactive approach by utilizing available technologies based on sound-based diagnostic techniques. Initially, we propose utilizing piezoelectricity and amplifying received sound using a diaphragm attached to a collar. Subsequently, we discuss the results of relevant tests that can be conducted using the phonocardiography system. For the next phase of research and designing a more advanced livestock health monitoring system, we suggest employing an ultrasonic system to measure heart rate and blood pressure for the next phase of research and designing a more advanced livestock health monitoring system. This system, coupled with a piezoelectric system to make electric energy from the pulse of the veins, could be subcutaneously implanted for continuous monitoring

This study addressed the need for efficient data collection in micro-mobility using low-end hardware, focusing on developing a system capable of handling large real-time data streams. We proposed a novel data collection system that leverages incremental learning techniques, enabling AI-based services for micro-mobility passengers through smartphones. Our approach utilizes 3-axis acceleration sensors integrated into micro-mobility devices, collecting motion data in real-time. This data is processed through an incremental learning pre-processing server, employing the Broad Learning System algorithm, to ensure robust performance in noisy, real-world environments. The use of incremental learning is pivotal, allowing our system to adapt continuously to new data, enhancing the accuracy and relevance of the AI services provided to passengers. This research not only advances the technical capabilities in micro-mobility data handling but also has the potential to enhance the overall passenger experience through improved AI-based services.

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

Atrial fibrillation (AF) is the most common cardiac arrhythmia encountered in clinical practice, affecting millions of individuals worldwide. The management of AF presents significant challenges due to its complex nature and diverse patient profiles. In recent years, the integration of artificial intelligence (AI) and machine learning technologies has emerged as a promising approach to enhance various aspects of AF management. This comprehensive review aims to explore the evolving role of AI in AF management, with a focus on its potential applications in diagnosis, risk prediction, treatment selection, and patient monitoring. Real academic references were employed to provide evidence-based insights into the impact of AI on AF management.

The aim of this paper is to study the main areas in which Artificial Intelligence (AI) will impact the field of project management that relates to cost, risk and scheduling. The research model was based on a previous study of the ten project management knowledge areas presented in the PMI’s PMBOK 6th edition where project schedule-, cost- and risk management knowledge areas were identified as being the ones most likely to be affected by the development of AI. A group of experts that participated in the study agreed that AI will affect the project management profession in the future. Different elements of the three knowledge areas were considered to be affected more by AI than others. The schedule baseline is the element believed to be affected the most out of the project schedule management elements. For project cost management, the estimation of resource cost is believed to be affected the most. In the case of project risk management, the application of AI will have the strongest impact on the probability and impact format.

This article explores the possible ramifications of incorporating ideas from AEC Industry 6.0 into the design and construction of intelligent, environmentally friendly, and long-lasting structures. This statement highlights the need to shift away from the current methods seen in the AEC Industry 5.0 to effectively respond to the increasing requirement for creative and environmentally sustainable infrastructures. The preceding methodology, AEC Industry 6.0, has substantially improved the efficacy and sustainability of construction by using new technology, digitalization, and environmentally conscious methods. Extensive discussions have occurred on the various benefits of AEC Industry 5.0. These merits include heightened stakeholder involvement, increased efficiency via automation and robots, decision-making processes centered around data, and the optimization of resources. Nevertheless, this study also examines the obstacles linked to the complete adoption of AEC Industry 6.0. These challenges require proficient professionals who can effectively utilize contemporary technologies, manage diverse stakeholder technological competencies, establish interoperable systems and standards, and reinforce cybersecurity measures. This study examines the viability of implementing concepts from Industry 6.0 to create intelligent, sustainable, and environmentally friendly structures. The objective is to showcase the potentially revolutionary impact that these principles may have on the construction industry. This study explores the possible applications of AEC Industry 6.0 within the Architecture, Engineering, and Construction (AEC) sector, explicitly highlighting the anticipated industry-wide consequences of the forthcoming paradigm shift. This paper comprehensively examines big data analytics, the Internet of Things (IoT), and collaborative robots as they pertain to the AEC Industry 6.0. This research investigates the interplay between organizational dynamics, human actors, and robotic systems to provide light on the opportunities and obstacles associated with integrating AEC Industry 6.0 concepts into the construction of buildings.

ChatGPT, a freely-accessible AI language model designed to generate human-like text responses to users, has been utilized in several areas, such as healthcare industry, to facilitate interactive dissemination of information. Academic advising has been an important factor in promoting success among university students, particularly those from disadvantaged backgrounds. Unfortunately, however, student advising has been marred with problems, with the availability and accessibility of advisors being among the hurdles. The current study explores how ChatGPT might serve to make academic advising more accessible, efficient, and even effective. Researchers compiled a list of questions frequently asked by current and prospective students in a teacher education Bachelor’s degree program in the United States. Then, the questions were typed into the free version of ChatGPT and the answers generated were evaluated for their content and delivery. ChatGPT generated surprisingly high-quality answers, written in an authoritative yet supportive tone, and it was particularly adept at addressing general career-related questions comprehensively, such as career outlook. We argue that ChatGPT may complement, but not replace, human academic advisors and it may very well promote educational equity through empowering students from a wide range of backgrounds with the means to access effective advising.

Oral diseases remain a major health problem worldwide, with the World Health Organisation reporting that oral health neglect affects almost half of the world’s population. Quorum sensing molecules (QSMs) influence oral biofilms in various ways with knowledge of direct oral QSM-host interaction being limited, and such studies could provide more insight into the cross-kingdom communication occurring during oral disease development. This review aims to explore the literature on oral QSM-host interaction and to highlight areas of advancement in this field. QSM CSP-1 produced by Gram-positive oral bacteria was found to interact with T2R receptors, activating NF-kB signalling and leading to remodeling of the cytoskeleton. AI-2 in the oral cavity was found in various studies to elicit an inflammatory response in specific oral cells. Overall AHL detection methods remain an area for development, through which greater understanding of oral QSMs’ influence on host cells can be achieved. In conclusion, from the current literature it can be inferred that that QSMs are utilised by host cells to detect bacterial presence and in the majority of cases elicit an immune response towards the environmental QSMs. This may provide a base to target QSMs as novel treatment of oral diseases.

Object recognition is an essential element of machine intelligence tasks. However, one model cannot practically be trained to identify all the possible objects it encounters. An ensemble of models may be needed to cater to a broader range of objects. Building a mathematical understanding of the relationship between various objects that share comparable outlined features is envisaged as an effective method of improving the model ensemble through a pre-processing stage, where these objects' features are grouped under a broader classification umbrella. This paper proposes a mechanism to train an ensemble of recognition models coupled with a model selection scheme to scale-up object recognition in a multi-model system. An algorithmic relationship between the learnt parameters of a trained classification model and the features of input images is presented in the paper for the system to learn the model selection scheme. The multiple models are built with a CNN structure, whereas the image features are extracted using a CNN/VGG16 architecture. Based on the models' excitation weights, a neural network model selection algorithm, which links a new object with the models and decides how close the features of the object are to the trained models for selecting a particular model for object recognition is developed and tested on a five-model neural network platform. The experiment results show the proposed model selection scheme is highly effective and accurate in selecting an appropriate model for a network of multiple models.

As recent history has shown, changing climate not only threatens to increase the spread of known disease, but also the emergence of new and dangerous phenotypes. This occurred most recently with West Nile virus: a virus previously known for mild febrile illness rapidly emerged to become a major cause of mortality and long-term disability throughout the world. As we move forward, into increasingly uncertain times, public health research must begin to incorporate a broader understanding of the determinants of disease emergence – what, how, why, and when. The increasing mainstream availability of high-quality open data and high-powered analytical methods presents promising new opportunities. Up to now, quantitative models of disease outbreak risk have been largely based on just a few key drivers, namely climate and large-scale climatic effects. Such limited assessments, however, often overlook key interacting processes and downstream determinants more likely to drive local manifestation of disease. Such pivotal determinants may include local host abundance, human behavioral variability, and population susceptibility dynamics. The results of such analyses can therefore be misleading in cases where necessary downstream requirements are not fulfilled. It is therefore important to develop models that include climate and higher-level climatic effects alongside the downstream non-climatic factors that ultimately determine individual disease manifestation. Today, few models attempt to comprehensively address such dynamics: up until very recently, the technology simply hasn’t been available. Herein, we present an updated overview of current perspectives on the varying drivers and levels of interactions that drive disease spread. We review the predominant analytical paradigms, discuss their strengths and weaknesses, and highlight promising new analytical solutions. Our focus is on the prediction of arboviruses, particularly West Nile virus, as these diseases represent the pinnacle of epidemiological complexity – solution to which would serve as an effective “gatekeeper”. We present the current state-of-the-art with respect to known drivers of arbovirus outbreak risk and severity, differentially highlighting the impact of climate and non-climatic drivers. The reality of multiple classes of drivers interacting at different geospatial and temporal scales requires advanced new methodologies. We therefore close out by presenting and discussing some promising new applications of AI. Given the reality of accelerating disease risks due to climate change, public health and other related fields must begin the process of updating their research programs to incorporate these much needed, new capabilities.

We propose a novel transfer learning method for speech emotion recognition allowing us to obtain promising results when only few training data is available. With as low as 125 examples per emotion class, we were able to reach a higher accuracy than a strong baseline trained on 8 times more data. Our method leverages knowledge contained in pre-trained speech representations extracted from models trained on a more general self-supervised task which doesn’t require human annotations, such as the wav2vec model. We provide detailed insights on the benefits of our approach by varying the training data size, which can help labeling teams to work more efficiently. We compare performance with other popular methods on the IEMOCAP dataset, a well-benchmarked dataset among the Speech Emotion Recognition (SER) research community. Furthermore, we demonstrate that results can be greatly improved by combining acoustic and linguistic knowledge from transfer learning. We align acoustic pre-trained representations with semantic representations from the BERT model through an attention-based recurrent neural network. Performance improves significantly when combining both modalities and scales with the amount of data. When trained on the full IEMOCAP dataset, we reach a new state-of-the-art of 73.9% unweighted accuracy (UA).

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

Glioblastoma (GBM) is the most aggressive brain cancer. To model GBM in research, orthotopic brain tumor models, including syngeneic models like GL261 and genetically engineered mouse models like TRP, are used. In longitudinal studies, tumor growth and treatment response are typ-ically tracked with in vivo imaging, including bioluminescence imaging (BLI), which is quick, cost-effective, and easily quantifiable. However, BLI requires luciferase-tagged cells, and recent studies indicate that the luciferase gene can elicit an immune response, leading to tumor rejection and experimental variation. We sought to optimize the engraftment of two luciferase-expressing GBM models, GL261 Red-FLuc and TRP-mCherry-FLuc, showing differences in tumor take, with GL261 Red-FLuc cells requiring immunocompromised mice for 100% engraftment. Immuno-histochemistry and MRI revealed distinct tumor characteristics: GL261 Red-FLuc tumors were well-demarcated with densely packed cells, high mitotic activity, and vascularization. In con-trast, TRP-mCherry-FLuc tumors were large, invasive, and necrotic, with perivascular invasion. Quantifying tumor volume using the HALO® AI analysis platform yielded results comparable to manual measurements, providing a standardized and efficient approach for reliable high-throughput analysis of luciferase-expressing tumors. Our study highlights the importance of considering tumor engraftment when using luciferase-expressing GBM models, providing in-sights for preclinical research design.

Accurate power curve modelling is essential to continuously evaluate the performance of a wind turbine (WT). In this work, we characterize the wind power curves using SCADA data acquired at a frequency of 5 minutes in a wind farm (WF) consisting of 5 WTs. Regarding the non-parametric methods, we select artificial neural networks (ANNs) to make curve estimations. Given that, we have the curves provided by the manufacturer of the WTs given by some very precisely measured pair of wind speed and power points. We can evaluate the difference between the manufacturer characterization and the ones estimated with the data provided by the SCADA system. Before the estimation, we propose a method of filtering the anomalies based on the characteristics provided by the manufacturer. We use three-quarters of the available data for curve estimation and one-quarter for the test. One WT suffered a break in the test part, so we can check how the test estimates reflect this problem in its wind-power curve compared to the estimations obtained in the WTs that worked adequately.

The utilization of smart IoT devices, commonly referred to as digital twins, is aimed at the digitalization of human knowledge within aquaculture processes. This involves the incorporation of cutting-edge technologies, including information-based management with big data and modeling, to automate machinery and gain comprehensive insights into the aquaculture environment and fish farm conditions. The ultimate objective is to empower farmers to make informed decisions, furnishing them with objective data to enhance their capacity in monitoring and controlling the various factors impacting fish production. As a result, farming decisions can be fine-tuned to enhance fish health and optimize farm output. In the context of large and modern aquaculture farms, technological innovation becomes imperative to automate processes, minimize labor requirements, and streamline fish feeding operations. Remarkably, the literature currently offers limited discussions on the digital transformation of aquaculture through the application of digital twin methodologies. A prior study underscores the critical influence of factors such as market prices and fish survival rates on the profitability of offshore caging culture. In this study, we embark on an analysis of the prerequisites for establishing a digital twin infrastructure tailored to intelligent fish feeding management. This infrastructure is designed to facilitate the integration of technology and data-driven decision-making, ultimately enhancing the efficiency of fish feeding processes. The proposed architecture for the fish feeding digital twin encompasses various digital twin components, encompassing water quality forecasting, fish population assessment, fish metrics estimation, fish feed prediction, and evaluation of fish feeding intensity. Furthermore, we optimize the daily fish feeding process through reinforcement learning algorithms. Finally, we implement a cloud-based AIoT system that provides the runtime environment for executing digital twins and controlling our intelligent fish feeding machinery. Experimental findings underscore the efficacy of the proposed digital twin system in significantly improving traditional fish feeding processes, notably in terms of reducing food costs and labor requirements.

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

The fourth industrial revolution has resulted in the digitalization of projects and operations across all sectors. Accordingly, efforts are being made to utilize advanced technologies to improve the energy efficiency of the building sector. This study has reviewed the current application and limitations of cutting-edge technologies in this regard. An overview of the use of the Internet of Things (IoT), artificial intelligence (AI), digital twin (DT), and building information modeling (BIM) for energy efficiency of buildings has been provided. It has been found that the use of Industry 4.0 technologies, during the construction and operational phase of buildings, has a great potential to reduce energy consumption and emissions of the sector. This study may help stakeholders of the built environment to understand the role of industry 4.0 tools for energy efficiency of the sector.

The use of artificial intelligence in natural language processing (NLP) has significantly contributed to the advancement of natural language applications such as sentimental analysis, topic modeling, text classification, chatbots, and spam filtering. With a large amount of text generated each day from different sources such as webpages, blogs, emails, social media, and articles, one of the most common tasks in natural language processing is the classification of a text corpus. This is important in many institutions for planning, decision-making, and archives of their projects. Many algorithms exist to automate text classification operations but the most intriguing of them is that which also learns these operations automatically. In this study, we present a new model to infer and learn from data using probabilistic logic and apply it to text classification. This model, called GenCo, is a multi-input single-output (MISO) learning model that uses a collaboration of partial classifications to generate the desired output. It provides a heterogeneity measure to explain its classification results and enables the reduction of the curse of dimensionality in text classification. The classification results are compared with those of conventional text classification models, and it shows that our proposed model has a higher classification performance than conventional models.

The continuously developing project aims to build an informatics system enabling analysis of spatial and temporal parameters of movement activities occurring in the sport of speed climbing. The monitoring system (climbing information speed system – CISS) is to be used for conducting comprehensive scientific research in the field of speed climbing. The system enables the evaluation of the training process of climbers at various levels of competition. The study analysis was based on video. The video recording with a camera positioned at a short distance (10 m) from the wall. The marker was positioned closest to the centre of mass (gravity) BMC. Results: development of a system for data collection and analysis of the climbing run based on video recording (application of the Kanade-Lucas-Tomasi (KLT) algorithm). Our results showed that used devices can measure a wide range of specific internal and external variables during speed climbing. Some of the analyzed parameters were significantly correlated with speed climbing time. These results could be a theoretical basis for future research and for training program’s preparation.

The evolution towards Industry 4.0 is driving the need for innovative solutions in the area of network management, considering the complex, dynamic and heterogeneous nature of ICT supply chains. To this end, Intent-Based networking (IBN) which is already proven to evolve how network management is driven today, can be implemented as a solution to facilitate the management of large ICT supply chains. In this paper, we first present a comparison of the main architectural components of typical IBN systems and, then, we study the key engineering requirements when integrating IBN with ICT supply chain network systems while considering AI methods. We also propose a general architecture design that enables intent translation of ICT supply chain specifications into lower level policies, to finally show an example of how the access control is performed in a modelled ICT supply chain system.

The understanding of water quality and its underlying processes is important for the protection of aquatic environments enabling the rare opportunity of access to a domain expert. Hence, an explainable AI (XAI) framework is proposed that is applicable to multivariate time series resulting in explanations that are interpretable by a domain expert. The XAI combines in three steps a data-driven choice of a distance measure with explainable cluster analysis through supervised decision trees. The multivariate time series consists of water quality measurements, including nitrate, electrical conductivity, and twelve other environmental parameters. The relationships between water quality and the environmental parameters are investigated by identifying similar days within a cluster and dissimilar days between clusters. The XAI does not depend on prior knowledge about data structure, and its explanations are tendentially contrastive. The relationships in the data can be visualized by a topographic map representing high-dimensional structures. Two comparable decision-based XAIs were unable to provide meaningful and relevant explanations from the multivariate time series data. Open-source code in R for the three steps of the XAI framework is provided.

As time progresses and technology improves, biological data sets are continuously increasing in size. New methods and new implementations of existing methods are needed to keep pace with this increase. In this paper, we present a high performance computing(HPC)-capable implementation of Iterative Random Forest (iRF). This new implementation enables the explainable-AI eQTL analysis of SNP sets with over a million SNPs. Using this implementation we also present a new method, iRF Leave One Out Prediction (iRF-LOOP), for the creation of Predictive Expression Networks on the order of 40,000 genes or more. We compare the new implementation of iRF with the previous R version and analyze its time to completion on two of the world's fastest supercomputers Summit and Titan. We also show iRF-LOOP's ability to capture biologically significant results when creating Predictive Expression Networks. This new implementation of iRF will enable the analysis of biological data sets at scales that were previously not possible.

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.

An exposition of entropy applications to highlight some potential entropic applications to artificial intelligence and knowledge transfer. Entropy is a basic idea in artificial intelligence that is applied to many different tasks, such as reinforcement learning, data compression, and decision-making. By quantifying uncertainty and information content, it helps artificial intelligence models provide predictions and decisions that are well-informed. Therefore, this paper shines on spotlighting the significance of entropy to attract the attention of artificial intelligence research community to entropy as a powerful tool to advance artificial intelligence.The significance of knowledge transfer (KT), particularly intergenerational knowledge transfer (IGT), in knowledge management is also covered in this paper. To quantify the complexity of knowledge distribution inside an organisation and assess the efficacy of knowledge transfer (KT) initiatives, the notion of knowledge entropy (KE) is presented. Additionally, the KT model—which is based on the conceptions of tacitness and information content—is introduced. It combines personalisation and codification methodologies. Future research directions are offered alongside a few difficult open problems.

Drones are being used more and more frequently and in various ways to attack people and critical infrastructures, which increases their level of danger and makes early detection and risk assessment even more difficult. There is no ultimate drone sensor. Rather, drones can only be effectively detected, if suitable sensor technologies are integrated within a system. Among the best suited sensorics for this purpose are radar and optical sensors, because the advantage of the radar, given by the long detection range, can be well combined with the additional visual-based information gained from a camera in the mid- and short-range, where usually the radar fails detecting micro drones. In this paper, a modular drone detection and assistance system MODEAS is presented, which can be used to automatically detect and track drones within a range up to some kilometers on the sky, but also in complex urban environments, where buildings and vegetation offer a perfect background for camouflage. The system provides an early detection of flying objects as potential drones, when those are not at all or only as small dots visible for the human eye. Main strengths of the system are the robust AI detection to differentiate such an object against clutter (birds, clouds) and its further classification by model and payload recognition, the latter aspect being considered as an indication of dangerousness. Moreover, once a drone is detected, the system follows it automatically, precisely and smoothly with a high-resolution pan-tilt-camera, measuring the distance to the sensor station with a laser rangefinder, compute the geocoordinates and visualize them live on a map. To improve the precision of the detection, a multisensorial fusion of all sensor detections is performed. Furthermore, alarms may be generated when a drone enters a restricted area.

This work studies the discontinuities features of sedimentary flysch materials in a 100 km2 area belonging to the Basque Arc. Such materials are common in this Spanish Alpine region located in the north of the Iberian Peninsula. A total of 33 outcrops are investigated by an intensive geotechnical investigation including geomechanical stations, boreholes and mechanical laboratory tests. Two flysch units are characterized: the Upper Aguinaga Formation or siliciclastic flysch, and the Lower Itziar Formation or calcareous flysch. Differences between both flysch formation are found. Joints in the siliciclastic flysch formation present an undulated roughness, with a spacing narrower and a persistence lower than in the calcareous flysch formation, which exhibits higher friction angles, although roughness is essentially planar. In addition, the potential of using Artificial Intelligence (AI) techniques, particularly Artificial Neural Networks and Support Vector Machine, to estimate the Geological Strength Index (GSI) from the Rock Quality Design (RQD) and some discontinuities features (spacing, persistence, aperture and roughness) is investigated. AI techniques are found to be satisfactory, being the Support Vector Machine with a linear kernel the technique which achieves the best performance.

Pharmaceutical aerosols play a key role in the treatment of lung disorders, but also systemic diseases, due to their ability to target specific areas of the respiratory system (RS). This article focuses on identifying and clarifying the influence of various factors involved in the generation of aerosol micro- and nanoparticles on their regional distribution and deposition in the RS. Attention is given to the importance of process parameters during aerosolization of liquids or powders and the role of aerosol flow dynamics in RS. The interaction of deposited particles with the fluid environment of the lung is also pointed out as an important step in the mass transfer of the drug to the RS surface. The analysis presented highlights the technical aspects of preparing the precursors to ensure that the properties of the aerosol are suitable for a given therapeutic target. Analyzing existing technical limitations, several potential strategies for enhancing the efficiency of targeted therapeutic delivery to or through the respiratory system are identified and presented, including the use of smart inhaling devices incorporating AI algorithms.

Use of fingerprints found at a crime scene is a common practice for identifying suspects in criminal investigations. Over the past two decades, attempts have been made to obtain additional information from fingerprints, beyond locating suspects as part of an investigation. This includes gender, age and nationality. Researchers demonstrated 75%-90% accuracy in gender classification based on fingerprint images. Nonetheless, despite promising results, these studies have several significant shortcomings with respect to their practical feasibility. First, they ignore the low quality and quantity of fingerprints collected from the crime scene since typically the scene of a crime has only one fingerprint collected, and the fingerprint might be partially or of poor quality as well. Second, as most results are based on a single database, public or private, it is difficult to generalize the most suitable method. Third, studies miss the untapped potential of Data-Centric AI (DCAI) approaches for improving results. The aim of this study was to compare, for the first time, the gender classification from a fingerprint using several datasets and with varying fingerprint image quality. The results from four databases are compared, three public and one internal private database. In addition, we utilize the latest Data-Centric AI (DCAI) approaches for improving classification results. The results demonstrate that a conservative Convolutional Neural Networks (CNN) such as VGG is sufficient for this task. Classification accuracy ranges from 80% to 95% depending on the quality of the fingerprint, with DCAI approaches adding 1%-4% improvement. For partially or low-quality fingerprint images, the periphery of a fingerprint is the most significant area for determining gender. The source code is also provided here for practical application.

Background: ChatGPT is becoming a new reality. Where do we go from here? Objective: to show how we can distinguish ChatGPT-generated publications from counterparts produced by scientist. Methods:By means of a new algorithm, called xFakeBibs, we show the significant difference between ChatGPT-generated fake publications and real publications. Specifically, we triggered ChatGPT to generate 100 publications that were related to Alzheimer’s disease and comorbidity. Using TF-IDF, using the real publications, we constructed a training network of bigrams comprised of 100 publications. Using 10-folds of 100 publications each, we also 10 calibrating networks to derive lower/upper bounds for classifying articles as real or fake. The final step was to test xFakeBibs against each of the ChatGPT-generated articles and predict its class. The algorithm successfully assigned the POSITIVE label for real ones and NEGATIVE for fake ones. Results: When comparing the bigrams of the training set against all the other 10 calibrating folds, we found that the similarities fluctuated between (19%-21%). On the other hand, the mere bigram similarity from the ChatGPT was only (8%). Additionally, when testing how the various bigrams generated from the calibrating 10-folds against ChatGPT we found that all 10 calibrating folds contributed (51%-70%) of new bigrams, while ChatGPT contributed only 23%, which is less than 50% of any of the other 10 calibrating folds. The final classification results using the xFakeBibs set a lower/upper bound of (21.96-24.93) number of new edges to the training mode without contributing new nodes. Using this calibration range, the algorithm predicted 98 of the 100 publications as fake, while 2 articles failed the test and were classified as real publications. Conclusions: This work provided clear evidence of how to distinguish, in bulk ChatGPT-generated fake publications from real publications. Also, we also introduced an algorithmic approach that detected fake articles with a high degree of accuracy. However, it remains challenging to detect all fake records. ChatGPT may seem to be a useful tool, but it certainly presents a threat to our authentic knowledge and real science. This work is indeed a step in the right direction to counter fake science and misinformation.

One of the most promising advancements in healthcare is the application of digital twin technology. Digital twins are virtual replicas of real-world human patients and can be used for monitoring, and diagnosis, and as a tool to design treatment strategies tailored to individual patients. Furthermore, digital twins could also be helpful in finding novel treatment targets and predicting the effects of drugs and other chemical substances in development. This review article delves into the various data sources and methodologies that contribute to the construction of digital twins. Each data source, including blood glucose levels, heart MRI and CT scans, cardiac electrophysiology, written reports, and multi-omics data, comes with different challenges regarding standardization, integration, and interpretation. We showcase how various datasets and methods are used to overcome these obstacles and generate a digital twin. While digital twin technology has seen significant progress, there are still hurdles in the way to achieving a fully comprehensive patient digital twin. Developments in non-invasive and high-throughput data collection, as well as advancements in modeling and computational power will be crucial to improve digital twin systems. We discuss a few critical developments in light of the current state of digital twin technology. Despite challenges, digital twin research holds great promise for personalized patient care and has the potential to shape the future of healthcare innovation.

In the realm of modern drug discovery, the integration of chemoinformatics and quantitative structure-activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structural-activity relationships (SARs), play a pivotal role in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. By embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.

In this study, we propose a radiomics-clinical probability weighted model for the prediction of prognosis for NSCLC. The model combines radiomics features extracted from RT planning images with clinical factors such as age, gender, histology, and tumor stage. CT images with radiotherapy structures of 422 NSCLC patients were retrieved from The Cancer Imaging Archive (TCIA). Radiomic features were extracted from gross tumor volume (GTV). Five machine learning algorithms, namely decision trees (DT), random forests (RF), extreme boost (EB), support vector machine (SVM) and generalized linear model (GLM), were optimized by a voted ensemble machine learning (VEML) model. A probabilistic weighted approach is used to incorporate the uncertainty associated with both radiomic and clinical features and to generate a probabilistic risk score for each patient. The performance of the model is evaluated using a receiver operating characteristic (ROC). Radiomic model, clinical factors model and combined radiomic-clinical probability weighted model demonstrated good performance in predicting NSCLC survival with AUC of 0.941, 0.856 and 0.949 respectively. The combined radiomics-clinical probability weighted enhanced model achieved significantly better performance than radiomic model in 1-year survival prediction (chi-square test, p<0.05). The proposed model has the potential to improve NSCLC prognosis and facilitate personalized treatment decisions.

Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks and deep learning. AI can be applied in many different areas, such as econometrics, biometry, e-commerce and the automotive industry. In recent years, AI has found its way into healthcare as well, helping doctors to make better decisions (‘clinical decision support’), localizing tumors in magnetic resonance images, reading and analyzing reports written by radiologists and pathologists, and much more. However, AI has one big risk: it can be perceived as a ‘black box’, limiting trust in its reliability, which is a very big issue in an area in which a decision can mean life or death. As a result, the term Explainable Artificial Intelligence (XAI) has been gaining momentum. XAI tries to ensure that AI algorithms (and the resulting decisions) can be understood by humans. In this narrative review, we will have a look at the current status of XAI in healthcare, describe several issues around XAI, and discuss whether it can really help healthcare to advance, for example by increasing understanding and trust. Finally, alternatives to increase trust in AI are discussed, as well as future research possibilities in the area of XAI.

This report is considered different aspects of the concept of the networked distributed engine control system (DECS) of future air vehicles. These aspects include the following: the structure of multiple networks similar to NATO Generic Vehicle Architecture (NGVA), the role of Artificial Intelligence (AI) in DECS, and the use Augmented Reality (AR) as Human-Machine Interface between AI and pilots. Deployment of AI solutions for monitoring equipment in on-board infrastructure can be provided on physical or virtual servers and in the clouds. In this case, it is possible to use various methods of alerting the pilot and ground personnel on the basis of AR. The use of AI allows covering an unlimited set of scenarios, to provide an assessment of the likelihood of equipment failure, classification alarm is normal, and recognition of the development of defects. To collect Big Data from sensors and the pre-processing of this data before a machine learning (ML) procedure it is proposed to form data sets with the help of the face-splitting matrix product. To decrease the time of reaction of Neural Networks it has been suggested the implementation of advanced tensor-matrix theory on the basis of penetrating face product of matrices. Other important results of the report are a possible version of the AR data format for DECS and a proposal about the use of non-orthogonal frequency discrete multiplexing (N-OFDM) signals to data transfer via fibre optics.

The emergence of Artificial Intelligence (AI) driven Keyword Spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge of end-to-end energy efficiency, memory footprint and system complexity of current Neural Network (NN) powered AI-KWS pipelines has remained ever present. This paper evaluates KWS utilizing a learning automata powered machine learning algorithm called the Tsetlin Machine (TM). Through significant reduction in parameter requirements and choosing logic over arithmetic based processing, the TM offers new opportunities for low-power KWS while maintaining high learning efficacy. In this paper we explore a TM based keyword spotting (KWS) pipeline to demonstrate low complexity with faster rate of convergence compared to NNs. Further, we investigate the scalability with increasing keywords and explore the potential for enabling low-power on-chip KWS.

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems to timely handle public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. On the one hand, blockchain can combat pandemics by enabling early detection of outbreaks, protecting user privacy, and ensuring reliable medical supply chain during the outbreak tracking. On the other hand, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Motivated by these, in this paper we present an extensive survey on the use of blockchain and AI for combating coronavirus (COVID-19) epidemics based on the rapidly emerging literature. First, we introduce a new conceptual architecture which integrates blockchain and AI specific for COVID-19 fighting. Particularly, we highlight the key solutions that blockchain and AI can provide to combat the COVID-19 outbreak. Then, we survey the latest research efforts on the use of blockchain and AI for COVID-19 fighting in a wide range of applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

AGI could arise within the next decades, promising a decisive strategic advantage. This paper discusses risks, associated with the development of AGI: destabilizing effects on strategic balance, underestimating risks in the interest of victory in the race, egoistically exploiting the huge benefits by a tiny minority. Further: Developed AGI could be beyond human control. Human goals could not be implemented and an intelligence explosion to superintelligence could take place leading to a total loss of control and power. If competition for AGI is non-transparent, secret, uncontrolled and not regulated, it’s possible that risks could not be managed and would lead to catastrophic consequences. The danger corresponds to that of nuclear weapons. It is crucial that the key actors of a possible AI Race agree at an early stage on the prevention and transparent regulation of a possible AI Race - similar to measures to secure strategic stability, on arms control measures, disarmament, and prevention of the proliferation of nuclear weapons. The realization that an uncontrolled AI race can lead to the extinction of humanity - this time even independent of human will – requires analogous measures to contain, prevent, regulate and secure an AI race within the framework of AGI development.

Background: This study addresses the burgeoning impact of Large Language Models (LLMs) such as ChatGPT on the integrity of online examinations and seeks to demonstrate latent and advanced LLM reasoning capabilities. The research devises an iterative self-reflective strategy for invoking critical thinking and higher-order reasoning in LLMs based on multimodal exam questions using visuals and text. Methods: The proposed strategy was demonstrated and evaluated on real exam questions by subject experts and the performance of ChatGPT (GPT-4) with vision was estimated on an additional dataset of 600 text descriptions of multimodal exam questions, focusing on its response to complex, visual, and textual prompts. Results: The results indicate that latent multi-hop reasoning capabilities can be invoked using the proposed self-reflective strategy, which can effectively steer ChatGPT towards correct answers by integrating critical thinking from each modality into the final response. Meanwhile, ChatGPT demonstrated considerable proficiency in being able to answer multimodal exam questions across 12 subjects. Conclusions: This work challenges findings from recent studies which claim that existing LLMs are unable to reason on complex multimodal tasks, and therefore this study underscores the need for enhanced online exam security measures like proctoring software, that can mitigate the potential misuse of AI technologies in educational settings.

The study is dedicated to analyzing the complex interaction between Artificial Intelligence (AI) and the Internet of Things (IoT) with institutional structures in the context of sustainable development. This work examines the impact of these technologies on economic and social development, identifying their potential advantages and risks. Methodologically, the research relies on information gathering and case study analysis, assessing the impact of technology in various domains, from smart cities to the manufacturing sector. The findings emphasize the necessity of integrating technological innovations into broader social and environmental initiatives, highlighting the importance of institutional transformations for achieving environmental sustainability.

Urban tree classification is pivotal in urban planning and management, facilitating informed decision-making processes. In this study, we introduce a novel data presentation method termed Pseudo Tree Crown, designed to enhance the accuracy and efficiency of urban tree classification. Leveraging the latest advancements in artificial intelligence (AI), we employ a state-of-the-art classification scheme, PyTorch, to maximize the accuracy of tree classification. Our results demonstrate a robust classification accuracy of over 95% from high spatial resolution imagery from Unmanned Aerial Vehicle (UAV), underscoring our proposed approach’s effectiveness. Moreover, the adaptability of our method renders it applicable to various study areas, highlighting its versatility and potential for widespread implementation in urban planning and management initiatives.

AbstractThe emergence of AI-powered deepfakes, manipulatively realistic synthetic media, raises unprecedented challenges for human information processing and decision-making. This paper explores the cognitive and emotional consequences of exposure to deepfakes, delving into how these digitally fabricated experiences can reshape perception, trust, and social interactions. By reviewing existing research and outlining potential methodological approaches, this paper aims to establish a framework for investigating the multifaceted impact of deepfakes on the human brain.BackgroundThe digital age has ushered in an era of unprecedented information abundance and accessibility. Yet, this abundance breeds vulnerability, particularly with the emergence of AI-powered deepfakes. These hyper-realistic synthetic media creations present a potent threat to the fundamental trust and integrity of information sources. Deepfakes have infiltrated various domains, from entertainment and social media to politics and journalism, blurring the lines between truth and fabrication. Consequently, understanding the cognitive and emotional consequences of encountering deepfakes becomes crucial for navigating this increasingly deceptive landscape.ObjectivesThis research aims to: Investigate how exposure to deepfakes impacts various cognitive functions, including perception, attention, memory, and decision-making. Elucidate the emotional responses elicited by deepfakes and their underlying neural mechanisms. Evaluate the role of individual differences, such as susceptibility to deception and emotional sensitivity, in mediating the impact of deepfakes. Develop a comprehensive framework for understanding the multifaceted influence of deepfakes on the human brain. Results (anticipated)This research is expected to yield insights into: The specific cognitive and emotional processes altered by deepfakes, providing evidence for potential mechanisms of deception and manipulation. The neural circuitry involved in detecting and responding to deepfakes, leading to the development of more effective detection methods. The influence of individual differences (e.g., age, cognitive style) on susceptibility to deepfakes, informing targeted interventions and educational efforts.ConclusionBy deciphering the impact of deepfakes on the human brain, this research will offer valuable tools for mitigating the harmful consequences of misinformation and promoting responsible use of this powerful technology. Ultimately, understanding how deepfakes manipulate our cognitive and emotional landscape is not merely a scientific pursuit, but a critical step towards ensuring a future where truth prevails in the digital world.

This paper presents the second stage of a tandem fixed wing unmanned aerial vehicle (UAV) aerodynamic development. In the initial stage, the UAV was optimized analyzing its characteristics only in symmetrical flight conditions. Posted requirements were that both wings should produce relevant positive lift, the initial stall must occur on the front wing first, the center of pressure should be close to the center of gravity, while longitudinal static stability should be in the optimum range. Computational fluid dynamics (CFD) analyses were performed, where applied calculation model was derived from the authors’ previous successful projects. The eight version TW V8 has satisfied all longitudinal requirements. Lateral-directional CFD analyses of V8 showed that the ratio of the lateral and directional stability at the nominal cruising regime was optimal, but both lateral and directional static stabilities were too high. On further development versions the lower vertical tail was eliminated, negative dihedral was implemented on the front wing, and four inverted blended winglets were added. Version TW V14 has largely improved lateral and directional stability characteristics, while their optimum ratio at cruising regime was preserved. Longitudinal characteristics were also well preserved. Maximum lift coefficient and lift-to-drag ratio were increased, compared to the V8.