This paper considers a specific fragment of Linear Temporal Logic for Finite traces, DECLAREd, which, to the best of our knowledge, we prove for the first time to be a {polytime} fragment of LTLf. We derive this in terms of the following ancillary results: we propose a set of novel LTLf equivalence rules that, when applied to \LTLf specifications, lead to an equivalent specification which can be computed faster by any existing verified temporal artificial intelligence task. We also introduce the concept of temporal non-simultaneity, prescribing that two activities shall never satisfy the same atom, and temporal short-circuit, that occurs when a specification interpreted in LTL would accept an infinitely long trace while, on LTLf, it can be rewritten so to postulate the absence of certain activity labels. We test these considerations over formal synthesis (Lydia), SAT-Solvers (AALTAF) and formal verification (KnoBAB) tools, where formal verification can be also run on top of a relational database and can be therefore expressed in terms of relational query answering. We show that all these benefit from the aforementioned assumptions, as running their tasks over a rewritten equivalent specification will improve their running times.

Artificial intelligence (AI) has witnessed an exponential increase in its use in various applications. Recently, the academic community started to research and inject new AI-based approaches to provide solutions to traditional software engineering problems. However, a comprehensive and holistic understanding of the current status is missing. To close the above gap, synthetic knowledge synthesis was used to induce a research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and five themes, namely natural language processing in software engineering, use of artificial intelligence in the management of software development life cycle, use of machine learning in fault/defect prediction and effort estimation, employment of deep learning in intelligent software engineering and code management, and mining software repositories to improve software quality. The most productive country was China (n=2042), followed by the United States (n=1193), India (n=934), Germany (n=445), and Canada (n=381). A high percentage (n=47.4%) of papers were funded, showing a strong interest in this research topic. The convergence of AI and software engineering can significantly reduce needed resources, improve quality, increase user experience, and improve the well-being of software developers.

The Distributed eLearning Center (DeLC) is a portal, providing extensive support in the day-to-day work when it comes to e-learning content – it helps students and teachers organize their learning materials, fill the gaps in knowledge (for students) and educational approaches (for teachers), organize and conduct exams, and overall, with providing proactive and personalized e-learning environment. The scope of DeLC as a project involves many extensions, covering the aspects of learning, teaching, exams, and collecting statistical information. Such extension is an agent-oriented environment, which enriches the functionalities with intelligent components, that are reactive and proactive, referred to as agents or assistants. This paper is focused on presenting the latest step in the evolution of FraudDetector software agent, which started with base functionality for fraud detection, and now it aims at usage of AI to accomplish its tasks, taking advantage not only from its knowledgebase, but from a much larger one, used by ChatGPT – through integration with it, which is the main contribution of this research, although the real results from production environment are still pending. In this process, agent’s architecture should stay open for collaboration with another external AI provider if necessary, trying to decouple the components, responsible for integration. As of now, the experiments show that involving ChatGPT in FraudDetector’s functionality enriches it and the agent’s precision could be improved this way.

The present minireview assessed the benefits of using artificial intelligence applications in healthcare at predicting the targets of bioactive small molecules in human. Method. The tool is a tuned algorithm, with novel data in web interface. Results. We used tables to order the information of Swiss Target Prediction that allow predictions on combination of chemotherapies and probable side effects. This tool is useful to understand the molecular mechanisms underlying a given phenotype or bioactivity, to rationalize possible favorable or unfavorable side effects and to predict off-targets of known molecules as well as to clear the way for drug repurposing. Predictions are done using a ligand-based approach to compare the similarity between a query molecule and the known ligands of a large collection of protein targets. Conclusion. This study allowed us to formulate recommendations for the use of some of the chemotherapeutic agent groups in cancer patients at a low cost. It is very important and beneficial for patients that take medicine for a longer period and on whose lives depend on such treatment.

Imaging flow cytometry (ImFC) represents a significant technological advancement in the field of cytometry, effectively merging the high-throughput capabilities of flow analysis with the image-based features of microscopy. In this comprehensive review article, we take a historical perspective and delve into the evolution of this methodology, presenting its past, exploring current state-of-the-art and forecasting potential future advancements. The inception of ImFC arose from the innovative combination of a flow cytometer's hydraulics with the technology of an advanced camera. This synergistic coupling facilitated the morphological analysis of cell populations at a high-throughput scale, effectively evolving the landscape of cytometry. However, the application of ImFC faced several challenges. Among the most prominent ones, is the development and implementation of software systems that can reliably and efficiently handle the robust data acquisition and analysis inherent in ImFC. The scale and complexity of the data generated by ImFC necessitates the creation of novel analytical tools that can effectively manage and interpret this data, thus allowing us to unlock the full potential of ImFC. Interestingly, artificial intelligence (AI) algorithms have begun to be implemented on ImFC, offering a promise for enhancing analytical capabilities. The adaptability and learning capacity of AI may prove to be essential in knowledge mining from high-dimensional data produced by ImFC, potentially enabling more accurate analyses. Looking forward, we project that ImFC may become an indispensable tool, not only in research laboratories but also in clinical settings. Given the unique combination of high-throughput cytometry and detailed imaging offered by ImFC, we foresee a critical role for this technology in the next generation of scientific research and diagnostics. As such, we encourage both current and future scientists to consider the integration of ImFC as an addition to their research toolkit and clinical diagnostic routine.

Artificial intelligence (AI) is revolutionizing approaches in plant disease management and phy-topathological research. This review analyzes current applications and future directions of AI in addressing evolving agricultural challenges. Plant diseases annually cause 10-16% yield losses in major crops, prompting urgent innovations. Artificial intelligence (AI) shows aptitude for auto-mated disease detection and diagnosis utilizing image recognition techniques, with reported accuracies exceeding 95% and surpassing human visual assessment. Forecasting models inte-grating weather, soil, and crop data enable preemptive interventions by predicting spa-tial-temporal outbreak risks weeks in advance at 81-95% precision, minimizing pesticide usage. Precision agriculture powered by AI optimizes data-driven, tailored crop protection strategies boosting resilience. Real-time monitoring leveraging AI discerns pre-symptomatic anomalies from plant and environmental data for early alerts. These applications highlight AI's proficiency in il-luminating opaque disease patterns within increasingly complex agricultural data. Machine learning techniques overcome human cognitive constraints by discovering multivariate correla-tions unnoticed before. AI is poised to transform in-field decision making around disease pre-vention and precision management. Overall, AI constitutes a strategic innovation pathway to strengthen ecological plant health management amidst climate change, globalization, and agri-cultural intensification pressures. With prudent and ethical implementation, AI-enabled tools promise to enable next-generation phytopathology, enhancing crop resilience worldwide.Artificial Intelligence, Phytopathology, Emerging Disease, Climate Change, Control diseases.

Recent technological advancements such as IoT and Big Data have granted industries extensive access to data, opening up new opportunities for integrating Artificial Intelligence (AI) across various applications to enhance production processes. We can cite two critical areas where AI can play a key role in industry such as product quality control and predictive maintenance. This paper presents a survey of AI applications in the domain of Industry 4.0, with a specific focus on product quality control and predictive maintenance. Experiments were conducted using two datasets, incorporating different machine learning and deep learning models from the literature. Furthermore, this paper provides an overview of the AI solution development approach for product quality control and predictive maintenance. This approach includes several key steps, such as data collection, data analysis, model development, model explanation, and model deployment.

The integration of artificial intelligence (AI) into the European Foundation for Quality Management (EFQM) business excellence model is a promising approach to improve the efficiency and effectiveness of excellence in organizations. This research paper’s integrated framework follows the ISO/IEC 23053 standard in addressing some of the concerns related to time and cost associated with the EFQM model, achieving higher EFQM scores, hence operational excellence. A case study involving a UAE government organization serves as a sample to train the AI framework. Historical EFQM results from different years are used as training data. The AI framework utilizes the unsupervised machine learning technique known as k-means clustering (with k=2). This technique follows the ISO/IEC 23053 standard to predict EFQM output total scores based on criteria and sub-criteria inputs. The research paper's main output is a novel AI framework that can predict EFQM scores for organizations at an early stage. If the predicted EFQM score is not high enough, then the AI framework provides feedback to decision makers regarding the criteria that need reconsideration. Continuous use of this integrated framework helps organizations attain operational excellence. This framework is considered valuable for decision makers as it provides early predictions of EFQM total scores and identifies areas that require improvement before officially applying for the EFQM excellence award. This approach can be considered as an innovative contribution and enhancement to knowledge body and organizational practices.

Historically the use of artificial intelligence (AI) in paediatrics dates back to 1984 with the intro-duction of a computer-assisted medical decision-making system called SHELP. Since then, research on the use of AI in pediatrics has become much more popular, and the number of reviewed publications largely increased. Consequently, a need for a holistic research landscape enabling researchers and other interested parties to gain insights into, deepen their knowledge about or inform them about the use of AI in pediatrics arised. To fill this gap a novel methodology named synthetic knowledge synthesis was applied. Using SKS we identified most prolific countries, in-stitutions, source titles, funding agencies, research themes, research gaps and hot spots.

AI applications are transforming the technology and digital sector, seamlessly integrating into the daily interactions of individuals with the digital world. Key entry points to the internet, such as search engines, are now complemented by recommendation systems that curate content on social networks, streaming platforms, and online marketplaces. Additionally, virtual assistants have become ubiquitous across various platforms. While these groundbreaking changes are gradually extending to several industries, there remains significant untapped potential for the application of artificial intelligence in diverse domains. The renewable energy sector, in particular, continually reinvents itself through research and innovation to meet the escalating demand. Positioned at the forefront of technological evolution, this industry is well-placed to effectively adopt and tailor the innovations of artificial intelligence to its specific needs. Presently, both emerging and established tools leverage elements of AI to enhance processes and introduce functionalities that were previously unattainable. If the current trajectory persists, AI-enhanced tools have the potential to assume a role as crucial to the future of engineering as computer-aided design (CAD) software is in contemporary times.

Globally, the use of whole-cell matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) in clinical microbiology laboratories has increased considerably. With the rapid growth of artificial intelligence technology, there is an increasing number of areas devoted to solving problems by implementing machine learning-based methods. Applying the use of artificial intelligence to the analysis of the massive amount of information obtained in the mass spectrum derived from MALDI-TOF MS should be a breakthrough in clinical microbiology. Specifically, it would facilitate a more rapid and accurate strain typing or antibiotic susceptibility test (AST) based on whole-cell MALDI-TOF MS spectra, and hence, it is a promising solution. Even though the reproducibility of peaks could result in some critical problems, computational methods have been developed to address this issue. Therefore, clinical microbiologists are able to perform strain typing or an AST as smoothly and easily as identifying species by using MALDI-TOF MS and artificial intelligence.

Artificial intelligence (AI) tools, such as OpenAI's Chat Generative Pre-trained Transformer (ChatGPT), have great potential in various fields, such as healthcare, education, and industry. This review highlights the applications and challenges of the ChatGPT versions 3.5 and 4.0 in radiology. ChatGPT can process medical texts, analyze medical images, and generate reports and summaries for different audiences. ChatGPT can also answer patients' questions and translate complex medical information into a plain language. ChatGPT has shown impressive results in medical tests and quizzes, indicating its potential to enhance diagnostic accuracy and medical education. These models show promise for reducing diagnosis times and healthcare costs. However, ChatGPT also faces challenges and limitations, such as a lack of visual content, ethical and legal issues, data quality, and error detection. This review highlights the benefits and future directions of ChatGPT and artificial intelligence (AI) in radiology.

Global degradation of coral reefs is reflected in the destruction of shelters in various environments and threatens the stability of marine ecosystems. Artificial shelters offer an alternative, but their design is challenging due to limited knowledge regarding the shelter characteristics preferences of desired inhabitants. Investigating these preferences is resource-intensive, particularly regarding small shelters that mimic natural reef condition. Further, fish abundance may be too low for statistical analysis in small shelters. We propose a method to characterize the species-specific shelter preferences using low-volume data. During a study conducted from January 2021 to April 2022, round clay artificial shelters (RAS) were deployed on an abandoned oil pier to examine a coral reef fish community. We recorded 92 species from 30 families; grouped them to systematically (families) and functionally (dietary group) classes. Grouping enabled examining each group’s preference while crossing these group preferences revealed species- specific preferences, that matched field observations. This approach proved effective in profiling the shelter preferences of 17 species while having limited resources. These profiles may latter allow the establishment of ecological-oriented artificial reefs.

As artificial intelligence continues to advance, researchers are increasingly using machine learning algorithms to study the factors that make people more susceptible to phishing scams. Most studies in this area have taken one of two approaches: either they explore statistical associations between various factors and susceptibility, or they use complex models such as deep neural networks to predict phishing behavior. However, these approaches have limitations in terms of providing practical insights for individuals to avoid future phishing attacks and delivering personalized explanations regarding their susceptibility to phishing. In this paper, we propose a machine learning approach that leverages explainable artificial intelligence techniques to examine the influence of human and demographic factors on susceptibility to phishing attacks. Our analysis reveals that psychological factors such as impulsivity and conscientiousness, as well as appropriate online security habits, significantly affect an individual's susceptibility to phishing attacks. Furthermore, our individualized case-by-case approach offers personalized recommendations on mitigating the risk of falling prey to phishing exploits, considering the specific circumstances of each individual.

In today's world, wherein more than 80% of world trade is carried out by maritime routes, the safety and security of the seas where this trade takes place is of vast importance for nations and the in-ternational community. For this reason, ensuring the sustainable safety and security of the seas has become an integral part of the mission of all maritime-related entities. The utilization of big data extracted from the seas and maritime activities into meaningful information with artificial intelli-gence applications and developing applications that can be used in maritime surveillance will be of great importance for augmenting maritime safety and security. In this article, data sources which can be used by a maritime surveillance system based on big data and artificial intelligence tech-nologies and which can be established around sensitive sea areas and critical coastal facilities, are identified and a model proposal using this maritime big data is put forward.

This article focuses on artificial intelligence in educational technology, starting with an introduction to educational technology, an interdisciplinary field of study that covers the design, development, utilization, and evaluation of technology and digital tools in educational Settings. A detailed description of its definition in an academic context - a multidisciplinary field of computer science and cognitive science that deals with the development of computational systems that exhibit intelligent behaviour, describing its areas of coverage and scope of application. It then introduces the benefits of AI in education technology, specifically addressing personalized learning, adaptive learning systems, automated scoring and feedback, virtual tutors and chatbots, data analytics, as well as content recommendations and natural language processing, accessibility and inclusion. Then it introduces the main concepts of AI personalized learning: AI personalized learning uses the power of artificial intelligence to meet the unique needs and preferences of individual learners, as well as its key principles and main characteristics. Adaptive learning systems harness the power of artificial intelligence and data analytics to tailor the learning experience to each student's needs and abilities, as well as its core principles and strengths. It also introduces the main operating principles of automated grading and feedback: it uses artificial intelligence and computer algorithms to evaluate and evaluate student assignments, tests and exams without the direct involvement of human graders, and its associated benefits. Secondly, the main concepts of virtual tutor are introduced: Virtual tutor is a computer-based system that uses artificial intelligence and machine learning algorithms to provide students with personalized and interactive educational support and its key characteristics and main advantages, and then the nature and benefits of chatbots and help in the field of education. The final conclusion summarizes the benefits and future challenges of integrating AI into educational technology.

With the emergence of artificial intelligence (AI), many aspects of our lives, from how we work to how we interact with each other and the world around us is showing dramatic changes. Education is one of the key areas that may be impacted by the rise of AI. Although a large number of studies have been conducted in recent years to shed light on how AI may influence various dimensions of education, a very little work has been carried out to consolidate and synthesis empirical studies on the application of AI in higher education. The present study reviewed empirical studies published between 2013 and 2022 to 1) examine the characteristics of published research in the field, and 2) to present thorough insights on the promises and challenges of this dramatic technology in higher and professional education. This review included 44 empirical studies published as peer-reviewed journal articles. The results indicated that there is rapid increase in the publications focusing on AI in higher education in last a few years. However, a big proportion of these publications are technically theoretical and conceptual proposals for AI intervention. The areas of AI applications in higher education that are supported by evidence based research are presented. Imperative implications are also highlighted for future research and implementation of AI based interventions in higher education.

Water resource pollution, particularly in river channels, presents a grave environmental challenge that necessitates a comprehensive and systematic approach encompassing assessment, forecasting, and effective management. This article provides a comprehensive exploration of the methodology and modeling tools employed to scrutinize the process of river channel pollution due to silting, rooted in the fundamental principles of hydrodynamics and pollutant transport dynamics. The study's methodology seamlessly integrates numerical simulations with state-of-the-art neural network techniques, with a specific focus on the Physics-informed neural networks (PINN) method. This innovative approach represents a groundbreaking fusion of artificial neural networks (ANN) and physical equations, offering a more efficient and precise means of modeling a wide array of complex processes and phenomena. The proposed mathematical model, grounded in the Euler equation, has been meticulously implemented using the Ansys Fluent software package, ensuring accuracy and reliability in the computations. In a pivotal phase of the research, a thorough comparative analysis was conducted between the results derived from the PINN method and those obtained through conventional numerical approaches using the Ansys Fluent software package. The outcomes of this analysis revealed the superior performance of the PINN method, characterized by the generation of smoother pressure fluctuation profiles and significantly reduced computation time, underscoring its potential as a transformative modeling tool. The calculated data originating from this study assumes paramount significance in the ongoing battle against river sedimentation. Beyond this immediate application, these findings also serve as a valuable resource for creating predictive materials pertaining to river channel silting, thereby empowering decision-makers and environmental stakeholders with essential information. The utilization of modeling techniques to address pollution concerns in river channels holds the potential to revolutionize risk management and safeguard the integrity of our vital water resources. However, it is imperative to underscore that the effectiveness of such models hinges on ongoing monitoring and frequent data updates, ensuring that they remain aligned with real-world conditions. This research not only contributes to the enhanced understanding and proactive management of river channel pollution due to silting but also underscores the pivotal role of advanced modeling methodologies in the preservation of our invaluable water resources for present and future generations.

Although learning analytics (LA) holds great potential to improve teaching and learning, LA research and practice are currently riddled with limitations that impact every stage of the LA life cycle. The present paper offers an overview of these challenges before proposing strategies to overcome them and exploring how the recent innovations brought forth by language models can improve LA research and practice. In particular, we encourage the empowerment of teachers during LA development, as this would strengthen the theoretical foundation of LA solutions and increase their interpretability and usability. Furthermore, we provide examples of how process data can be used to understand learning processes and generate more interpretable LA insights. Furthermore, we explore how LLMs could come into play in LA to generate interpretable insights, timely and actionable feedback, increase personalization, and support teachers’ tasks more broadly.

Big data technologies are disruptive technologies that affect every business, including those in the construction industry. The Thai government has also been affected, and attempted to use machine learning techniques with the analytics of big data technologies to predict which construction projects have a winning price over the project budget. However, this technology was never developed, and the government did not implement it because they had data obtained via a traditional data collection process. In this study, traditional data were processed to predict behavior in Thai government construction projects using a machine learning model. The data were collected from the government procurement system in 2019. There were seven input data, including project owner department, type of construction project, bidding method, project duration, project level, winning price over estimated price, and winning price over budget. A range of classification techniques, including an artificial neural network (ANN), a decision tree (DC), and K-nearest neighbor (KNN), were used in this study (ANN). According to the results, after hyperparameter tuning, ANN had the greatest prediction accuracy with 78.9 percent. This study confirms that data from the Thai government procurement system can be investigated using machine learning techniques from big data technologies.

The unique characteristics of frescoes on overseas Chinese buildings can attest to the integration and historical background of Chinese and Western cultures. Reasonable analysis and preservation of overseas Chinese frescoes can provide sustainable development for culture and history. This research adopts the image analysis technology based on artificial intelligence, and proposes a ResNet-34 model and method integrating transfer learning. This deep learning model can identify and classify the source of the frescoes of the emigrants, and can effectively deal with the problems such as the small number of fresco images on the emigrants' buildings, poor quality, difficulty in feature extraction, and similar pattern text and style. The experimental results show that the training process of the model proposed in this article is stable. On the constructed Jiangmen and Haikou fresco JHD datasets, the final accuracy is 98.41%, and the recall rate is 98.53%. The above evaluation indicators are superior to classic models such as AlexNet, GoogLeNet, and VGGNet. It can be seen that the model in this article has strong generalization ability and is not prone to overfitting. It can effectively identify and classify the cultural connotations and regions of frescoes.

This paper explores the integration of multiple intelligences and artificial intelligence (AI) in language learning, focusing on its potential to enhance personalization and engagement. Drawing from existing research and studies conducted in various contexts, including the Philippines, this study aims to contribute to the understanding of the benefits, challenges, and effectiveness of this integration. The paper begins with an introduction that highlights the background and significance of integrating multiple intelligences and AI in language learning, identifying research gaps, objectives, research questions, and the theoretical framework. A literature review provides an overview of multiple intelligences theory by Howard Gardner, the role of AI in language learning, and identifies gaps in the existing literature. The methodology section outlines the research design and approach, participant selection, data collection methods, validity and reliability measures, and ethical considerations. Findings and results are presented through the analysis of qualitative data, exploring emergent themes and patterns. The discussion section critically examines the identified research gaps, discusses the validity and reliability of the study, addresses the scope and limitations, and explores the implications of the findings for theory, practice, and future research. The conclusion summarizes the key findings and contributions of the study, reflects on the achievement of research objectives, offers recommendations for further research, and provides final remarks tying together the main points of the study. This paper contributes to the existing body of knowledge by providing insights into the integration of multiple intelligences and AI in language learning and its impact on personalization, engagement, and language learning outcomes.

Background: The purpose of this study is to carry out bioinformatic analysis of lncRNA data obtained as a result of genomic analysis of kidney tissue samples taken from rats with nephrotoxicity induced by methotrexate (MTX) and from rats without pathology and modeling with tree-based machine learning method. Another aim of the study is to identify potential biomarkers for the diagnosis of nephrotoxicity and to provide a better understanding of the nephrotoxicity formation process by providing the interpretability of the model with explainable artificial intelligence methods as a result of the modeling. Methods: To identify potential indicators of drug-induced nephrotoxicity, 20 female Wistar Albino rats were separated into two groups: nephrotoxicity and control. Kidney tissue samples were collected from the rats, and genomic, histological, and immunohistochemical analyses were performed. The data set obtained as a result of genomic analysis was modeled with Random Forest (RF), one of the tree-based methods. Modeling results were evaluated with sensitivity (Se), specificity (Sp), balanced accuracy (B-Acc), negative predictive value (Npv), accuracy (Acc), positive predictive value (Ppv), and F1-score performance metrics. The Local Interpretable Model-Agnostic Annotations (LIME) method was used to determine the lncRNAs that could be biomarkers for nephrotoxicity by providing the interpretability of the RF model. Results: The outcomes of the histological and immunohistochemical analyses done in the study supported the conclusion that MTX use caused kidney injury. According to the results of the bioinformatics analysis, 52 lncRNAs showed different expression in the groups. As a result of modeling with RF for lncRNAs selected with Boruta variable selection, the B-Acc, Acc, Sp, Se, Npv, Ppv, and F1-score were 88.9%, 90%, 90.9%, 88.9%, 90.9%, 88.9% and 88.9%. respectively. lncRNAs with id rnaXR_591534.3 rnaXR_005503408.1, rnaXR_005495645.1, rnaXR_001839007.2, rnaXR_005492056.1 and rna_XR_005492522.1 the lncRNAs with the highest variable importance values produced from RF modeling can be used as nephroxicity biomarker candidates. Also, according to the LIME results, the high level of lncRNAs with id rnaXR_591534.3 and rnaXR_005503408.1 especially increased the possibility of nephrotoxicity. Conclusions: With the possible biomarkers obtained as a result of the analyses made within the scope of this study, it can be ensured that the procedures for the diagnosis of drug-induced nephrotoxicity can be carried out easily, quickly and effectively.

This article should examine ideas of how a chatbot can support decision making processes by col-lecting general opinion about the certain topics. The ChatGPT is tested in this article in respect of natural gas security of supply of the Republic of Serbia in the context of the current political situation in the world. The author first describes its own stance on the topic and then the ChatGPT is asked for its opinion (which most probably collects and compiles phrases from English speaking sources to build a text in a smart way). The topic is ort only about security of gas supply of Serbia but also political and highly sensitive, so it is examined if the ChatGPT can be useful tool to reach a balance between less on more favorable options in the process of policy making.

Artificial intelligence (AI) technologies, particularly the subfield of machine learning (ML), has been expected to bring significant benefits to all sectors of business and public services. The manufacturing industry is considered one of the domains most likely to benefit from AI tech-nologies. During the recent years, there has been a growing research and development effort on machine learning–based solutions for manufacturing industries, as shown by the growing number of research publications. However, the business, environmental or societal benefits of applying ML in manufacturing industry have not been reviewed and evaluated. This paper re-views the reported benefits of ML in the manufacturing industry by analysis of scientific publi-cations. The focus of the review is on research papers that were published from 2017 to 2021. The review pays particular attention to the industry sector, manufacturing operations, machine learning methods used, maturity of results and the benefits pursued and achieved. The benefits are divided into business benefits, environmental sustainability, and societal sustainability. The analysis shows that the maturity of ML-based solutions is still low, despite the intensive research. Consequently, future research projects should place more emphasis on the maturity of the solu-tions, which would lead to their broader adoption into operational use, thus benefiting industry and society at large. This calls for closer collaboration between the research community and in-dustry.

This paper presents a combined optical imaging/artificial intelligence (OI/AI) technique for real-time analysis of tissue morphology at the tip of the biopsy needle, prior collecting a biopsy specimen. This is an important clinical problem as up to 40% of the collected biopsy cores provide low diagnostic value due to high adipose or necrotic content. Micron-scale resolution optical coherence tomography (OCT) images can be collected with a minimally invasive needle probe and automatically analyzed by a computer neural network (CNN)-based AI software. The results can be conveyed to the clinician in real-time and used to select the biopsy location more adequately. This technology was evaluated on a rabbit model of cancer. OCT images were collected with a hand-held custom-made OCT probe. Annotated OCT images were used as ground truth for AI algorithm training. The overall performance of the AI model was very close to that of the humans performing the same classification tasks. Specifically, tissue segmentation was excellent (~99% accuracy) and provided segmentation that closely mimicked ground truth provided by the human annotations, while over 83% correlation accuracy was obtained for tumor and non-tumor classification.

CNN-based off-the-shelf features have shown themselves as a good baseline for trademark retrieval. However, in recent years, the computer vision area was transitioning from CNNs to a new architecture – Vision Transformer. In this paper, we investigate the performance of off-the-shelf features extracted with vision transformers and explore the effects of pre, post-processing, and pre-training on big datasets. We propose a method of joint usage of global and local features, which leverages the best aspects of both approaches. Experimental results on METU Trademark Dataset show that off-the-shelf features extracted with ViT-based models outperform off-the-shelf features from CNN-based models. The proposed method achieves the mAP value of 31.23, surpassing previous state-of-the-art results. We assume that the proposed approach for the trademark similarity evaluation will allow one to improve the protection of such data with the help of artificial intelligence methods. Moreover, this approach will allow one to identify cases of unfair use of such data and form an evidence base for litigation.

The aim of this publication is to analyze the topic of high-frequency jet ventilation (HFJV), namely the catheter HFJV (C-HFJV) from a mathematical-physical as well as a clinical point of view. There are known issues with applying anesthesia and artificial lung ventilation (ALV) during surgical procedures in the upper airways, e.g. during bronchoscopy or tracheostomy. The principles, advantages, and disadvantages of HFJV are discussed in context with basic physical principles to clarify the proper use of this method. The basic technical principles of catheter construction, as well as its functional properties from a biophysical point of view, are introduced. Also, the placement of the catheter in the airways, the set-up of the HFJV ventilator, and the indications as well as the risks and contraindications of the use of C-HFJV are analyzed. This leads to the explanation of potentially optimal techniques for C-HFJV applications. In this article, we present the positive effects of C-HFJV even with complications such as bacterial or viral pneumonia, including COVID-19. In conclusion, we offer recommendations for clinical practice obtained from a literature review as well as from our own rich clinical experience.

Data-driven models based on artificial intelligence are efficiently used to solve complex problems. The quality of groundwater is of utmost importance, as it directly impacts human health and the environment. In major parts of the world groundwater is the main source of drinking water, it is essential to periodically monitor water quality. Conventional water quality monitoring techniques involve periodical collection of water samples and analysis in the laboratory. This process is expensive, time consuming and involves lot of manual labor. The aim of our study is to build an ant colony optimized neural network for predicting groundwater quality parameters. We have proposed artificial neural network comprising of six hidden layers. The approach was validated using our groundwater quality dataset of a hard rock region in the northern part of Karnataka, India. Groundwater samples were collected by us periodically from March 2014 to October 2020 from 50 wells in this region. These samples where analysed for measuring the pH, Electrical Conductivity, Na+, Ca+, Na+, K+, Mg2+, F-, Cl- and U+. The temporal dataset was split for training, testing and validation of our model. Metrics such as R2 (Coefficient of Determination), RMSE (Root Mean Squared Error), NSE (Nash–Sutcliffe efficiencies) and MAE (Mean Absolute Error) were used to evaluate the prediction error and model performance. These performance evaluation metrics indicated the efficiency of our model in predicting the temporal variation in groundwater quality parameters. The method proposed by us can be used for prediction and the temporal frequency of sample collection can be reduced to save time and cost. The results also confirm that the combination of artificial neural network with ACO is a promising tool to optimize weights while training the network, and hence will help in reasonable prediction of groundwater quality parameters.

In this work, a general framework for the analysis of governance of artificial intelligence is presented. A dashboard is presented for this analysis from the perspective of political theory. The dashboard allows eventual comparisons between democratic and non democratic regimes, useful for countries in the global south and western countries. The dashboard allows us to assess the key features that determine the governance model for artificial intelligence at the national level, for local governments and for other participant actors. The dashboard allows us to appraise aims of AI strategy and aims left aside in alternative strategies. The work shows the theoretical framework could be of use to advance case studies globally, and comparative endeavors. Applying the theoretical dashboard, the case study on China has been undertaken. These are the main points the study tackles in the final discussion, conclusion and further recommendations: 1) the complex reality of AI command and control 2) uncertainties about future society and the polity against AI development and 3) particular cultural values enshrined in countries' AI development.

Biofilm is a complex community of microorganisms that are attached to surfaces and encased in a self-produced extracellular matrix. Machine learning (ML) techniques have been applied to various aspects of biofilm research, such as predicting biofilm formation, identifying key genes, and designing new therapeutic strategies. In this study, we conducted a bibliometric analysis of machine learning in biofilm research to provide a comprehensive overview of the current state of the field. We searched the Web of Science database for articles published included "machine learning biofilm". A total of 126 articles were identified and analysed. Our results showed that the number of publications on machine learning in biofilm has been increasing rapidly over the past decade, indicating a growing interest in the application of ML techniques to biofilm research. The analysis also revealed that the most common research topics in this area were related to biofilm formation, prediction, and control. Furthermore, the most frequently used ML techniques in biofilm research were artificial neural networks and support vector machines. Overall, our study provides valuable insights into the current trends and future directions of machine learning in biofilm research. It also highlights the importance of interdisciplinary collaboration between biofilm researchers and ML experts to drive innovation in this field.

The current epoch, known as the Anthropocene, is characterized by significant human impact on the global ecological system, including altered species survival and distribution patterns due to global warming resulting from industrialization. Accurately quantifying and assessing the extent of human impact on the environment is of utmost importance. To this end, four primary methods have been developed to measure human impact: (1) ecological footprint, (2) Human appropriation of net primary production (HANPP), (3) Planetary boundary, and (4) Living Planet Index. In this paper, we aim to provide a comprehensive understanding of these methods, including their strengths and weaknesses, to evaluate their efficacy in accurately assessing the impact of human activities on the global ecosystem. Furthermore, we explore the potential of utilizing novel machine learning modelling for future predictions in this area. By analyzing and comparing these methods, we can gain insight into the ways in which human activities impact the environment and use this knowledge to inform future policies and practices to mitigate negative effects.

Cancer is a major concern for people, and accurately predicting the probability of cancer incidence and mortality is an important research topic. With the development of big data and artificial intelligence technology, a new machine learning model has emerged. Using 72,591 pieces of data, including age, case count, population size, race, gender, site of onset, and year of discovery, we built a machine learning model. Through calculations, we found that the decision tree, random forest, logistic regression, support vector machine, and neural network achieved testing accuracies of 62.11%, 61.68%, 54.53%, 55.72%, and 63.10%, respectively. With the help of this model, scientists and policymakers can accurately predict future cancer incidence and mortality rates through databases, allowing them to make relevant policies (such as timely allocating doctors and medical resources) to better serve the people.

Backgrounds: The field of Artificial Intelligence (AI) has seen a major shift in recent years due to the development of new Machine Learning (ML) models such as Generative Pre-trained Transformer (GPT) and AI which are progressively becoming part our everyday lives. GPT has achieved previously unheard-of levels of accuracy in most computerized language processing tasks and their chat-based variations. Aim: The aim of this study was to investigate the problem-solving abilities of ChatGPT using two sets of verbal insight problems, with a known performance level established by a sample of human participants. Material and Methods: A total of 30 problems labelled as “practice problems” and “transfer problems”, as listed by Ansburg and Dominowski (2000), were administered to ChatGPT. The answers provided by ChatGPT received a score of "0" for each problem answered incorrectly and a score of "1" for each correct response, as per the correct solution specified in Ansburg and Dominowski's (2000) study. The highest score that could be attributed to both the practice and transfer problems was 15 out of 15. In order to compare ChatGPT performance to the performance to that of human subjects, the solution rate for each problem (based on a sample of 20 subjects) was used, as indicated by Ansburg and Dominowski (2000). Results: The study highlighted that ChatGPT can be trained in out-of-the-box thinking and demonstrated potential in solving verbal insight problems. The global performance of ChatGPT equalled the most probable outcome for the human sample in both practice problems and transfer problems as well as upon their combination. Additionally, ChatGPT answer combinations were among the 5% of most probable outcomes for the human sample both when considering practice problems and pooled problem sets. These findings demonstrate that ChatGPT performance on both set of problems was in line with the mean rate of success of human subjects, indicating that it performed reasonably well. Conclusions: The use of transformer architecture and self-attention in ChatGPT may have helped to prioritize inputs while predicting, contributing to its potential in verbal insight problem-solving. ChatGPT has shown potential in solving insight problems, thus highlighting the importance of incorporating AI in psychological research. However, it is acknowledged that open challenges still exist. Indeed, further research is needed to fully understand the capabilities and limitations of AI in insight problem-solving.

The work proposes a new approach for evolutionary global optimization which, in simple terms, may be described as a fusion of quantum and simulated annealing. This becomes possible by using basic concepts from Homotopy Theory, conjugated to the already existing Fuzzy Adaptive Simulated Annealing paradigm and other components. In this fashion, it occurs a temporal and nonlinear superposition of the two types of annealing, provoking interesting effects in runtime, including the so-called quantum tunneling, when there is a sudden transition between attraction basins of different minima without "climbing" potential barriers. In addition, the proposal includes a generalization of the "deformation" of Hamiltonians used in quantum annealing by suggesting the use of general homotopies between the surface corresponding to the cost function under processing and another specific landscape, being this possible because of the ability of Fuzzy ASA of handling time-varying objective functions. The proposed paradigm shows that it is possible and beneficial to superpose the two types of annealing, displaying amazing numerical results and suggesting a kind of interlacing or synergic behavior - perhaps we could call it an "annealing entanglement". Finally, given the fast advances in commercial quantum annealing processors, it seems sensible to expect that an implementation of the proposed approach using such devices may occur very soon. In order to demonstrate the efficacy of the proposed algorithm, some significant and detailed examples are included in the text, so as to illustrate and clarify the presented ideas.

The evolving global SARS-CoV-2 pandemic emphasizes how unprepared we are for the emergence of the next lethal viral pathogen. A list of potential candidates was created by the World Health Organization which named Nipah virus infection as the highly lethal prototypic member of that list. Building on our earlier viral pandemic preparedness research into SARS-CoV-2 we have created computer simulations of Nipah virus infection complicated by encephalitis, the most common cause of Nipah-associated mortality. In the current experiments, we first created updated simulations of wild-type whole-brain organoids (aiWBO). Upon validation, the aiWBO were infected with the simulated Nipah virus genome. The Nipah encephalitis simulations (aiWBO-NiV) were then used to find optimal single, double, and triple protein combinations for candidates as potential targets for vaccine development. Our data suggest that the use of multi-viral proteins/epitopes chimera is the most promising approach to Nipah vaccine development and that employing artificial intelligence to guide the identification of promising vaccine candidates is an efficient and cost-effective strategy for future viral pandemic preparedness.

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

Imitation Learning is a discipline of Machine Learning primarily concerned with replicating observed behavior of agents known to perform well on a given task, collected in demonstration data sets. In this paper, we set out to introduce a pipeline for collecting demonstrations and training models that can produce motion plans for industrial robots. Object throwing is defined as the motivating use case. Multiple input data modalities are surveyed, and motion capture is selected as the most practicable. Two model architectures operating autoregressively are examined -- feedforward and recurrent neural networks. Trained models execute throws on a real robot successfully, and a battery of quantitative evaluation metrics is proposed, including extrapolated throw accuracy estimates. Recurrent neural networks outperform feedforward ones in most respects, with the best models having an assessed mean throw error on the order of 0.1...0.2 m at distances of 1.5...2.0 m. The data collection, pre-processing, and model training aspects of our proposed approach show promise, but further work is required in developing Cartesian motion planning tools before it is suitable for application in production.

(1) Background: Ventilator-associated pneumonia (VAP) causes high mortality among patients with respiratory disease and imposes major burdens on healthcare infrastructure. Models that use electronic health record data to predict the onset of VAP may spur earlier treatment and improve patient outcomes. We developed and studied the performance of interpretable machine learning (ML) models that predict the onset of VAP from electronic health records (EHRs); (2) Methods: We trained Logistic Regression (LR), full feature Explainable Boosting Machine (fEBM), and eXtreme Gradient Boosting (XGBoost) ML models on data from the MIMIC- III (v1.3) database. Model performance was measured by area under the receiver operating characteristic curves (AUCs). We trained a minimal-feature EBM model (mEBM) with features derived from white blood cell (WBC) counts, duration of ventilation, and Glasgow Coma Scale (GCS). Finally, model robustness was evaluated on randomly sparsified EHR datasets; (3) Results: The fEBM model outperformed the XGBoost and LR models at 24 hours post-intubation. The mEBM model maintained an AUC of 0.893. The fEBM model performance remained robust on sparsified datasets; (4) Conclusions: Our novel interpretable ML algorithm reliably predicts the onset of VAP in intubated patients. Integration of this EBM-based model into clinical practice may enable clinicians to better anticipate and prevent VAP.

The problem of water drinking supply is very important in the world, especially for developing countries, in particular Algeria. In this work we propose to study a distillation system based on solar energy process using an artificial intelligence approach in order to enhance the performance and the daily production. For this purpose, a conventional solar still and capillary film solar still was used. The operating parameters of the two distilleries are analyzed and the neural network approach was used to predict the performance through the amount of distillate, solar radiation and ambient temperature. The sensitivity between the operating parameters of the solar still for two case have been studied through the artificial neuron network model. The obtained results are promising, analyzed and discussed.

The choice of holiday destinations is highly depended on climate considerations. Nowadays, since the effects of climate crisis are being increasingly felt, the need of accurate weather and climate services for hotels is crucial. Such a service could be beneficial for both the future planning of tourists’ activities and destinations and for hotel managers as it could help in decision making about the planning and expansion of the touristic season, due to a prediction of higher temperatures for a longer time span, thus causing increased revenue for companies in the local touristic sector. The aim of this work is to calculate predictions on climatic variables using statistical techniques as well as Artificial Intelligence (AI) for a specific area of interest utilising data from in situ meteorological station, and produce valuable and reliable localised predictions with the most cost-effective method possible. This investigation will answer the question of the most suitable prediction method for time series data from a single meteorological station that is deployed in a specific location. As a result, an accurate representation of the microclimate in a specific are is achieved. To achieve this high accuracy in situ measurements and prediction techniques are used. As prediction techniques, Seasonal Auto Regressive Integrated Moving Average (SARIMA), AI techniques like the Long-Short-Term-Memory (LSTM) Neural Network and hybrid combinations of the two are used. Variables of interest are divided in the easier to predict temperature and humidity that are more periodic and less chaotic, and the wind speed as an example of a more stochastic variable with no known seasonality and patterns. Our results show that the examined Hybrid methodology performs the best at temperature and wind speed forecasts, closely followed by the SARIMA whereas LSTM perform better overall at the humidity forecast, even after the correction of the Hybrid to the SARIMA model.

Generative adversarial networks (GAN) opened new possibilities for image processing and analysis. Inpainting, dataset augmentation using artificial samples or increasing spatial resolution of aerial imagery are only a few notable examples of utilizing GANs in remote sensing. This is due to a unique construction and training process expressed as a duel between GAN components. The main objective of the research is to apply GAN to generate an artificial Normalized Difference Vegetation Index (NDVI) using panchromatic images. The NDVI ground-truth labels were prepared by combining RGB and NIR orthophoto. The dataset was then utilized as input for a conditional generative adversarial network (cGAN) to perform an image-to-image translation. The main goal of the neural network was to generate an artificial NDVI image for each processed 256px × 256px patch using only information available in the panchromatic input. The network achieved 0.7569 ± 0.1083 Structural Similarity Index Measure (SSIM), 26.6459 ± 3.6577 Peak Signal-to-Noise Ratio (PNSR) and 0.0504 ± 0.0193 Root-Mean-Square Error (RSME) on the test set. The perceptual evaluation was performed to verify the usability of the method when working with a real-life scenario. The research confirms that the structure and texture of the panchromatic aerial remote sensing image contains sufficient information for NDVI estimation for various objects of urban space. Even though these results can be used to highlight areas rich in vegetation and distinguish them from urban background, there is still room for improvement in terms of accuracy of estimated values. The purpose of the research is to explore the possibility of utilizing GAN to enhance panchromatic images (PAN) with information related to vegetation. This opens interesting possibilities in terms of historical remote sensing imagery processing and analysis. The panchromatic orthoimagery dataset was derived from RGB orthoimagery.

The aim of this tutorial is to help students grasp the theory and applicability of support vector machines (SVMs). The contribution is an intuitive style tutorial that helped students gain insights into SVM from a unique perspective. An internet search will reveal many videos and articles on SVM, but many of them give simplified explanations that leave gaps in the derivations that beginning students cannot fill. Most free tutorials lack guidance on practical applications and considerations. The software wrappers in popular programming libraries such as Python and R hide many of the operational complexities. Free software tools often use default parameters that ignore domain knowledge or leave knowledge gaps about the important effects of SVM hyperparameters, resulting in misuse and subpar outcomes. The author uses this tutorial as a course reference for students studying artificial intelligence and machine learning. The tutorial derives the classic SVM classifier from first principles and then derives the practical form that a computer uses to train a classification model. An intuitive explanation about confusion matrices, F1 score, and the AUC metric extend insights into the inherent tradeoff between sensitivity and specificity. A discussion about cross-validation provides a basic understanding of hyperparameter tuning to maximize generalization by balancing underfitting and overfitting. Even seasoned self-learners with advanced statistical backgrounds have gained insights from this tutorial style of intuitive explanations, with all related considerations for tuning and performance evaluations in one place.

Identifying and anticipating potential failures in the cloud is an effective method for increasing cloud reliability and proactive failure management. Many studies have been conducted to predict potential failure, but none have combined SMART (Self-Monitoring, Analysis, and Reporting Technology) hard drive metrics with other system metrics such as CPU utilisation. Therefore, we propose a combined metrics approach for failure prediction based on Artificial Intelligence to improve reliability. We tested over 100 cloud servers’ data and four AI algorithms: Random Forest, Gradient Boosting, Long-Short-Term Memory, and Gated Recurrent Unit. Our experimental result shows the benefits of combining metrics, outperforming state-of-the-art.

Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of AD biomarkers and differential diagnosis of AD in the prescreen during eligibility assessment and the likelihood stratification of patients who will progress to AD dementia and fast cognitive decline group from the slow decline group in randomization. Finally, this review provides challenges, developments and the future outlook on the integration of AI into AD clinical trials.

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision-making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of data sets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

COVID-19 poses a significant burden to healthcare systems. Healthcare organisations with a better health innovation infrastructure have faced a reduced burden and achieved success in curbing COVID-19. In Saudi Arabia, digital technologies have played a vital role in fighting SARS-CoV-2 transmission. In this paper, we aimed to summarise the experience of optimising digital health technologies in Saudi Arabia as well as discuss capabilities and opportunities during and beyond the COVID-19 pandemic. A literature review was conducted up to September 2021 to retrieve peer-reviewed articles that critique the use of digital health technology platforms (DHTPs) in Saudi Arabia during the COVID-19 outbreak. A small but significant body of literature examined the digital response to the COVID-19 pandemic in Saudi Arabia. Health officials succeeded in optimising and maintaining a strategy to mitigate the spread of the virus via different digital technologies, such as mobile health applications, artificial intelligence, and machine learning. The quick digital response in Saudi Arabia was facilitated by governmental support and considering users and technology determinants. Future research must concentrate on establishing and updating the guidelines for using DHTPs.

Personality is the most critical feature that tells us about an individual. It is the collection of the individual’s thoughts, opinions, emotions and more. Personality detection is an emerging field in research and Deep Learning models have only recently started being developed. There is a need for a larger dataset that is unbiased as the current dataset that is used is in the form of questionnaires that the individuals themselves answer, hence increasing the chance of unconscious bias. We have used the famous stream-of-consciousness essays collated by James Pennbaker and Laura King. We have used the Big Five Model often known as the five-factor model or OCEAN model. Document-level feature extraction has been performed using Google’s word2vec embeddings and Mairesse features. The processed data has been fed into a deep convolutional network and a binary classifier has been used to classify the presence or absence of the personality trait. Hold- out method has been used to evaluate the model, and the F1 score has been used as the performance metric.

Capability assessment plays a crucial role in the demonstration and construction of equipment. To improve the accuracy and stability of capability assessment, we study the neural network learning algorithms in the field of capability assessment and index sensitivity. Aiming at the problem of over-fitting and parameter optimization in neural network learning, the paper proposes an improved machine learning algorithm—the Ensemble Learning Based on Policy Optimization Neural Networks (ELPONN) algorithm with the policy optimization and ensemble learning. This algorithm presents optimized neural network learning algorithm through different strategies evolution, and builds an ensemble learning model of multi-intelligent algorithms to assessment the capability and analyze the sensitivity of the indexes. Through the assessment of capabilities, the algorithm effectively avoids parameter optimization from entering the minimum point in performance to improve the accuracy of equipment capability assessment, which is significantly better than previous neural network assessment methods. The experimental results show that the mean relative error is 4.10%, which is better than BP, GABP, and early stopping. The ELPONN algorithm has better accuracy and stability performance, and meets the requirements of capability assessment.

With the depletion of shallow surface resources, the future mining work will develop towards the deep surface, and the objective conditions such as the mining environment will be more complex and dangerous than now, and the requirements for personnel and equipment will be higher and higher. The efficient exploitation of deep space cannot be separated from such mobile and flexible production and transportation equipment as scraper. In the new era, intelligence is the development trend of the LHD, and path tracking control technology is the key to the intelligent LHD, and it is also an urgent problem to be solved for its unmanned driving. This paper describes the realization of the automatic operation function of articulated LHD from two aspects of mathematical model and trajectory tracking control method, and focuses on the research of the path tracking control scheme in the field of unmanned driving, that is, LQR controller. On this basis, the parameters of the LQR controller are optimized by combining different intelligent cluster algorithms to find the optimal solution of the LQR controller.

This work aims to contribute to better understanding the use of public street spaces. (1) Background: In this sense, with a multidisciplinary approach, the objective of this work is to propose an experimental and reproducible method on a large scale. (2) Study area: The applied methodology uses artificial intelligence to analyze Google Street View (GSV) images at street level. (3) Method: The purpose is to validate a methodology that allows to characterize and quantify the use (pedestrians and cars) of some squares in Rome belonging to different historical periods. (4) Results: Through the use of machine vision techniques, typical of artificial intelligence and which use convolutional neural networks, a historical reading of some selected squares is proposed with the aim of interpreting the dynamics of use and identifying some critical issues in progress. (5) Conclusions: This work validated the usefulness of a method applied to the use of artificial intelligence for the analysis of GSV images at street level.

Artificial learning and machine learning is playing a pivotal role is the society especially in the field of medicinal chemistry and drug discovery. Particularly its algorithms, neural networks or other recurrent networks drive this area. In this review, we have taken into account the diverse use of AI in a number of pharmaceutical industries including discovery of drugs, repurposing, development of pharmaceutical drug and its clinical trials. In addition, the efficiency of these artificial or machine learning programs in achieving the target drugs in short time period, along with accurate dosage and cost effectively of the drug has also been discussed. Numerous applications of AI in property prediction such as ADMET have been used for prediction of strength of this technology in QSAR. In case of de-novo synthesis, it results in generation of novel drug molecules with unique design proving this a promising field fir drug design. Moreover, its involvement in synthetic planning, ease of synthesis and much more contribute to automated drug discovery in near future.

Lignocellulosic fibers are widely applied as composite reinforcement due to their properties. The thermal degradation behavior determines the maximum temperature in which the fiber can be applied without significant mass loss. It is possible to determine these temperatures using Thermogravimetric Analysis (TG). In particular, when curves are obtained at different heating rates, kinetic parameters can be determined and more detailed characteristics of the material are obtained. However, every curve obtained at a distinct heating rate demands material, cost, and time. Methods to predict thermogravimetric curves can be very useful in the materials science field and in this sense mathematical approaches are powerful tools if well employed. For this reason, in the present study, curaua TG curves were obtained at three different heating rates (5, 10, 20, and 40 °C.min-1) and Vyazovkin kinetic parameters were obtained. After, the experimental curves were fitted using an artificial neural network (ANN) approach followed by a Surface Response Methodology (SRM). Curves at any heating rate between the minimum and maximum experimental heating rates were obtained with high reliability. Finally, Vyazovkin kinetic parameters were tested again with the new curves showing similar kinetic parameters from the experimental ones. In conclusion, due to the capability to learn from the own data, ANN combined with SRM seems to be an excellent alternative to predict TG curves that do not test experimentally, opening the range of applications.

Air pollution contributes to a critical environmental problem in various towns and cities. With the emergence of the smart cities concept, appropriate methods to curb associated with exposure to pollutants must have been a portion of appropriate urban development policy. This study presents a technologically driven air quality solution in smart cities to advertise energy-efficient and cleaner sequestration in these areas. It aims to address the issue of how to integrate the data-based strategies and artificial intelligence into efficient public sector pollution management in smart cities as a core part of the smart city definition. Exploratory research has been used in 152 smart cities, and environmental experts contributed to this study. It further addresses the technical criteria for implementing such a framework that the public administration uses to prepare the renovation of public buildings, minimize energy use and costs, and link these smart police stations to monitor air pollution as a part of integrated cities. Such a digital transition in resource management will increase public governance's energy performance, a higher standard of operation, and a healthier environment.

The diagnosis of papillary thyroid carcinoma has always been a concerned and challenging issue and it is very important and meaningful to have a definite diagnosis before the operation. In this study, we tried to use an artificial intelligence algorithm instead of medical statistics to analyze the genetic fingerprint from gene chip results to identify papillary thyroid carcinoma. We trained 20 artificial neural network models with differential genes and other important genes related to the cell metabolic cycle as the list of input features, and apply them to the diagnosis of papillary thyroid cancer in the independent validation data set. The results showed that when we used the DEGs and all genes lists as input features the models got the best diagnostic performance with AUC=98.97% and 99.37% and the accuracy were both 96%. This study revealed that the proposed artificial neural network models constructed with genetic fingerprints could achieve a prediction of papillary thyroid carcinoma. Such models can support clinicians to make more accurate clinical diagnoses. At the same time, it provides a novel idea for the application of artificial intelligence in clinical medicine.

Conventionally, partial differential equations (PDE) problems are solved numerically through discretization process by using finite difference approximations. The algebraic systems generated by this process are then finalized by using an iterative method. Recently, scientists invented a short cut approach, without discretization process, to solve the PDE problems, namely by using machine learning (ML). This is potential to make scientific machine learning as a new sub-field of research. Thus, given the interest in developing ML for solving PDEs, it makes an abundance of an easy-to-use methods that allows researchers to quickly set up and solve problems. In this review paper, we discussed at least three methods for solving high dimensional of PDEs, namely PyDEns, NeuroDiffEq, and Nangs, which are all based on artificial neural networks (ANNs). ANN is one of the methods under ML which proven to be a universal estimator function. Comparison of numerical results presented in solving the classical PDEs such as heat, wave, and Poisson equations, to look at the accuracy and efficiency of the methods. The results showed that the NeuroDiffEq and Nangs algorithms performed better to solve higher dimensional of PDEs than the PyDEns.

Coxiella burnetii (Derrick) Philip, the causative agent of Q fever, is mainly transmitted by aerosols, but ticks can also be a source of infection. Transstadial and transovarical transmission of C. burnetii by Hyalomma lusitanicum (Koch) has been suggested. There is a close relationship between this tick species, wild animals and C. burnetii but the transmission in a natural environment has not been demonstrated. In this study, we collected 80 engorged nymphs of H. lusitanicum from red deer and wild rabbits. They molt to adults under laboratory conditions and we feed them artificially through silicone membranes after a preconditioning period. C. burnetii DNA was tested in ticks, blood and feces samples using real-time PCR. The pathogen was found in 36.25% of fed adults demonstrating that transstadial transmission from nymph to adult occurs in nature. The presence of DNA in the 60% of blood samples confirms that adults transmit the bacteria during feeding. Further studied are needed about co-feeding and other possible transmission routes to define the role of this tick species in the cycle of C. burnetii.

The principal minimum horizontal stress plays an important role in the study of reservoir characteristics, modeling of oil and gas reservoir, drilling, production and stimulating wells. However, it is currently not possible to measure the minimum horizontal stress along the wellbore as a geophysical parameter logging. Minimum horizontal stress is measured by leak-off test (LOT) at only several points in a well. In order to have the values all along the wellbore, experimental formulas were established to determine the minimum horizontal stresses for different fields. Then these formulas must be calibrated with LOT data whose number is usually limited, even sometimes unavailable. On the other hand, the empirical formulas of one field might not be accurate for another. This study presents a new approach to solve the problem of minimum horizontal stress estimation by a combination of artificial intelligence and geostatistics. The method consists of using artificial neural network (ANN) to build a model of minimum horizontal stress estimation from relevant parameters such as true vertical depth, pore pressure and vertical stress, then combined with Kriging interpolation to obtain the distribution in space of the minimum horizontal stress. Hence, this method can estimate the minimum horizontal stress with a limited amount of available data and therefore we do not need to drill new wells or to find empirical formulas for each survey area. The method was then applied in a case study involved real geomechanical dataset of Hai Thach - Moc Tinh field in Nam Con Son basin, Vietnam.

Precision agriculture is a technology used by farmers to help food sustainability amidst growing population. One of the tools of precision agriculture is yield monitoring, which helps a farmer manage his production. Yield monitoring is usually done during harvest, however it could also be done early in the growing season. Early prediction of yield, specifically for fruit trees, aids the farmer in the marketing of their product and assists in managing production logistics such as labor requirement and storage needs. In this study, a machine vision system is developed to estimate fruit yield early in the season. The machine vision system uses a color camera to capture images of fruit trees during the full bloom period. An image segmentation algorithm based on an artificial neural network was developed to recognize and count the blossoms on the tree. The artificial neural network segmentation algorithm uses color information and position as input. The resulting correlation between the blossom count and the actual number of fruits on the tree shows the potential of this method to be used for early prediction of fruit yield.

Nature-inspired algorithms are very popular tools for solving optimization problems inspired by nature. However, there is no guarantee that optimal solution can be obtained using a randomly selected algorithm. As such, the problem can be addressed using trial and error via the use of different optimization algorithms. Therefore, the proposed study in this paper analyzes the time-complexity and efficacy of some nature-inspired algorithms which includes Artificial Bee Colony, Bat Algorithm and Particle Swarm Optimization. For each algorithm used, experiments were conducted several times with iterations and comparative analysis was made. The result obtained shows that Artificial Bee Colony outperformed other algorithms in terms of the quality of the solution, Particle Swarm Optimization is time efficient while Artificial Bee Colony yield a worst case scenario in terms of time complexity.

Nature-inspired algorithms are very popular tools for solving optimization problems inspired by nature. However, there is no guarantee that optimal solution can be obtained using a randomly selected algorithm. As such, the problem can be addressed using trial and error via the use of different optimization algorithms. Therefore, the proposed study in this paper analyzes the time-complexity and efficacy of some nature-inspired algorithms which includes Artificial Bee Colony, Bat Algorithm and Particle Swarm Optimization. For each algorithm used, experiments were conducted several times with iterations and comparative analysis was made. The result obtained shows that Artificial Bee Colony outperformed other algorithms in terms of the quality of the solution, Particle Swarm Optimization is time efficient while Artificial Bee Colony yield a worst case scenario in terms of time complexity.

The novel coronavirus disease (COVID-19) pandemic has impacted health and wellbeing globally. To strengthen preventive and clinical care amid this pandemic, technological innovations like artificial intelligence (AI) are increasingly used in different contexts. This bibliometric study aimed to assess the current scholarly development and prominent research domains in applications of AI technologies in COVID-19 research. A total of 105 articles were retrieved from MEDLINE database that emphasized on the use of AI in the context of COVID-19. Most articles had multiple authors with a collaboration index of 7.18. Moreover, most of the articles were produced from the USA (22.86%) and China (21.9%), whereas developing countries were underrepresented among the contributing nations. Furthermore, several research domains were identified, including prevention and control, diagnostics, epidemiological characteristics, therapeutics, psychological conditions, and different areas of data sciences related to COVID-19. The current bibliometric evidence shows the early stage of development in this field, which necessitates equitable applications of AI in COVID-19 research emphasizing on health disparities, socio-legal issues, vaccine development, and applied public health research in this pandemic.

Increase in the emission of Greenhouse Gases (GHS) is among the significant concerns of government, societies, and policymakers. Due to the highest share of carbon dioxide in the produced GHGs, it is necessary to assess the factors that influence its emission. Energy systems and economic activities noticeably influence the amount of carbon dioxide production of countries. In this article, Artificial Neural Network (ANN) in addition to a linear correlation used to predict carbon dioxide emission of four CIS countries, including Turkmenistan, Uzbekistan, Kazakhstan, and Azerbaijan based the consumption of various energy sources and GDP, as the economic indicator. According to the obtained data by the proposed models, carbon dioxide emission can be accurately estimated by utilizing the mentioned input data. Models’ R-squared value are 0.9997 and 0.9999 in the cases of applying the correlation and ANN-based model. Moreover, the average absolute relative deviations by utilizing the correlation and GMDH ANN are approximately 1.05% and 0.61%, respectively. These statistical values demonstrate more proper performance of the ANN-based model compared with the applied linear correlation.

The objective of this article was to forecast the ultimate failure load laminate stacking sequence combination on bonding joints which are exposed to tensile strength by using artificial neural networks. We have glass fiber composite materials with three different sequence combinations ([0°/90°], [±45°], [0°/90°/±45°]). Various adherend thicknesses and also ductile type adhesive was used in the experiment. The bonding geometry is a single lap and has four types of overlap angles 30°, 45°, 60°, 75° respectively. The experimental results demonstrate that composite laminate stacking sequence profoundly affects the bonding joints of failure load. Taking experimental results into account, Levenberg–Marquardt learning algorithm model was used by preferring a three layer forward on ANN so as to discipline network. In order to procure a precise ANN tool, an integrate methodology of experimental method has been used. The outcomes are used to ensure the experimental data’s to the ANN. The method of ANN permits surveying much adequately the probabilities of composite laminate stacking sequence combination using the prevalent ones which are [0°/90°], [±45°] and [0°/90°/±45°]. Testing data and training results were quite well 0.998, 0.997 and 0.998 in turn. Consequences acquired can be used by engineers who are interested in the composite material design to enhance failure load.

Soil conditions is a major aspect of interest for farmers due to the knowing of the physicochemical properties of the same can help with any necessary restoration of soil that guarantees the quality and the production of their crop. However, technology and analysis of the soil become of difficult access mainly in developing countries, by which the present paper shows the development of a system thought to estimate physicochemical variables of soils growing sugar cane through studies of spectroscopy. Its characteristic is that it is a portable system, with low cost, easy to use and can estimate physicochemical variables in-situ with the objective of knowing the degree of degradation present in the soil and through this help the farmers define possible strategies to restore it. The device uses the frequency response of the soil determining values of magnitude and phase, which are used by algorithms of artificial intelligence capable of getting an estimation of the physicochemical properties. The obtained results show errors below 8% in the estimation of the variables compared to the analysis results of the soil at laboratories.

In this paper, the influence of the various degradation conditions, on the molecular and supramolecular structure of polybutylene succinate (PBS) and polybutylene succinate adipate (PBSA) copolymer during degradation time is described. Experiment was carried out by the use injection molded samples and normalized conditions of biodegradation in soil, composting and artificial weathering. Materials were studied by using size-exclusion chromatography (SEC) coupled with multiangle laser light scattering (MALLS) detection and wide-angle X-ray diffraction (WAXD). Additionally, the physical and mechanical properties of the samples were determined. The performed experiments clearly show difference impact of selected degradation condition on the macroscopic, supramolecular and molecular parameters of studied aliphatic polyesters. The structural changes in PBS and PBSA explain the observed changes in the physical and mechanical properties of the obtained injection molded samples.

Background: Artificial intelligence can help improve the quality of healthcare by analyzing vast amounts of data and providing more effective and personalized treatment plans. Researchers are working on developing AI-powered solutions that can help improve the outcomes of patients. Objective: To explore the potential of AI in improving healthcare outcomes and patient experience. Results: The study suggests that AI can improve healthcare efficiency and patient outcomes but cannot fully replace human healthcare professionals. AI can assist healthcare professionals in their work, leading to better resource utilization and improved patient care. However, there is still a need for human healthcare professionals to oversee AI systems and provide empathy and personalized care to patients. Conclusion: While there is immense potential for AI in healthcare, it is not yet feasible to replace human healthcare workers. Instead, it should be viewed as a tool that can help improve the efficiency and effectiveness of human healthcare.

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables to provide equipment state monitoring services and to generate decision-making for equipment operations. In this paper, we propose two machine learning models: 1) to forecast the amount of pentane (C5) content in the final product mixture; 2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach by using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models.

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

Artificial Intelligence has transformed the cyber security industry by enabling organizations to systematize and enlarge outdated safety procedures. AI can provide more effective threat detection and response capabilities, enhance vulnerability management, and improve compliance and governance. AI technologies such as machine learning, natural language processing, behavioral analytics, and deep learning can enhance cyber security defenses and protect against a wide range of cyber threats, including malware, phishing attacks, and insider threats.Theoretical underpinnings of AI in cyber security, such as machine learning, natural language processing, behavioral analytics, and deep learning, are discussed. The advantages of using AI in cyber security are discussed including speed and accuracy, continuous learning and adaptation, and efficiency and scalability. It's important to note that AI is not a silver bullet for cyber security and should be used in conjunction with other security measures to provide a comprehensive defense strategy.AI has transformed the way cyber security operates in today's digital age. By analyzing vast amounts of data quickly and accurately it has become a valuable tool for organizations looking to protect their assets from cyber threats.

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

Although composting has many advantages in the treatment of organic waste, there are still many problems and challenges associated with emissions, like NH3, VOCs, and H2S, as well as greenhouse gases such as CO2, CH4, and N2O. One promising approach to enhancing composting conditions is used of novel analytical methods bad on artificial intelligence. To predict and optimize the emissions (CO, CO2, H2S, NH3) during composting process kinetics thought mathematical models (MM) and machine learning (ML) models were utilized. Data about everyday emissions from laboratory composting with compost’s biochar with different incubation (50, 60, 70 °C) and biochar doses (0, 3, 6, 9, 12, 15% d.m.) were used for MM and ML models selections and training. MM has not been very effective in predicting emissions, (R2 0.1 - 0.9), while ML models such as acritical neural network (ANN, Bayesian Regularized Neural Network; R2 accuracy CO:0,71, CO2:0,81, NH3:0,95, H2S:0,72)) and decision tree (DT, RPART; R2 accuracy CO:0,693, CO2:0,80, NH3:0,93, H2S:0,65) have demonstrated satisfactory results. For the first time the ML models to predict CO and H2S during composting were demonstrated. Further research in a semi-scale and field study composting with biochar is needed to improve the accuracy of developments models.

The dawn of Artificial Intelligence (AI) in healthcare stands as a milestone in medical innovation. A plethora of different medical fields are heavily involved and pediatric emergency medicine is no exception. These new tools do not merely provide more advanced and efficient systems for patients’ diagnosis, management and treatment. They rather concern a strict shift from traditional methods based upon broad categories towards a more personalized healthcare. AI offers many promises in pediatric emergency medicine with a wide range of applications involving clinical decision making, patients’ flows management and prioritization. Main barriers to a widespread diffusion involve technological challenges but also ethical issues and the paucity of extensive datasets in pediatric contexts. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies, pointing out the actual applications and challenges until future feasible research perspectives.

Autonomous mobile robots are essential to the industry, and human-robot interactions are becoming more common nowadays. These interactions require that the robots navigate scenarios with static and dynamic obstacles in a safely manner, avoiding collisions. This paper presents a physical implementation of a method for dynamic obstacle avoidance using a Long Short-Term Memory (LSTM) neural network that obtains information from the mobile robot’s LiDAR for it to be capable of navigating through scenarios with static and dynamic obstacles while avoiding collisions and reaching its goal. The model is implemented using a TurtleBot3 mobile robot within an OptiTrack Motion Capture (MoCap) system for obtaining its position at any given time. The user operates the robot through these scenarios, recording its LiDAR readings, target point, position inside the MoCap system, and its linear and angular velocities, all of which serve as the input for the LSTM network. The model is trained on data from multiple user-operated trajectories across five different scenarios, outputting the linear and angular velocities for the mobile robot. Physical experiments prove that the model is successful in allowing the mobile robot to reach the target point in each scenario while avoiding the dynamic obstacle, with a validation accuracy of 98.02%.

The opportunities and advantages of implementing Artificial Intelligence (AI) and Machine Learning (ML) to optimize the process control of wastewater treatment in the Oil and Gas industry are discussed in this work. Discussion is centered around five major points: 1) Various types of Oil and Gas wastewater (Sour, Oily, Produced) and industry standards/methods for treatment, including specific case studies and challenges when treating certain wastewater. This section aims at the common types of wastewater in the Oil and Gas industry and how they are generally treated. 2) How AI/ML has been implemented in wastewater treatment in various industries; summary of which AI/ML languages were used along with a brief overview of how they work. This article focused on an understanding of the specific case studies and AI explanations to understand the effectiveness of wastewater treatment using AI/ML. 3) AI that is currently implemented in Oil and Gas such as personal protective equipment (PPE), smartwatches radiofrequency identification etc. This showed that AI/ML already plays a successful role in the Oil and Gas industry and has proven to help optimize many processes and components. Many of these AI/ML implementations are now industry standards and have greatly benefitted the industry. This section demonstrates that the implementation and the use of AI/ML have been proven to be a successful endeavor and that expanding the role of AI/ML would be beneficial. 5) Using the established points from the previous sections the paper outlines why AI/ML is ideal for Oil and Gas wastewater treatment; this argument centers around fit-for-purpose treatment, and the possible uses of the treated water. A determination of the best-suited AI languages, possible implementation scenarios, and how this would shift the way Oil and Gas industries treat and approach wastewater is presented to the reader.

The healthcare situation in Africa is characterized by limited resources and a high burden of disease, presenting significant challenges to the healthcare systems in the region. The integration of Artificial Intelligence (AI) technologies in African healthcare holds immense potential for addressing these challenges and improving health outcomes. This paper aims to provide a comprehensive understanding of the current healthcare landscape in Africa and the urgent need for AI-driven solutions.In addition to discussing the potential applications of AI in African healthcare, such as disease diagnosis, treatment planning, and medical imaging analysis, this manuscript seeks to shed light on the specific challenges hindering the implementation of AI in the region. By providing detailed examples and case studies, we aim to offer insights into the obstacles that need to be overcome and potential solutions that can be pursued.By leveraging AI technologies, Africa has the opportunity to revolutionize its health systems, bridging the gap in access to quality healthcare across the continent. The objective of this review article is to not only highlight the potential benefits that AI can bring to African healthcare but also to emphasize the urgent need for action and collaboration in implementing and scaling up AI innovations in the region. By doing so, African countries can pave the way for improved health outcomes and ensure that their populations receive the healthcare services they deserve.

Dengue is one the most significant mosquito-borne viral diseases globally, with its prevalence extending across tropical and subtropical regions, exerting a considerable financial strain on health systems. Despite decades of research leading to the development of a vaccine implemented in endemic countries, its usage remains restricted to a fraction of the population at risk of contracting dengue. Due to these limitations, there is a growing focus on research aimed at unraveling the cellular mechanisms crucial for Dengue virus (DENV) infection to pinpoint potential host targets. Multiple studies propose a pivotal role of cytoskeletal remodeling in DENV infection. Specifically, DENV alters the organization of actin to facilitate viral entry, replication, and release. In this study, we investigated the involvement of the protein c-Abl, a kinase crucial in actin dynamics, in the context of DENV infection. Our findings suggest that either silencing or pharmacological inhibition of the kinase c-Abl leads to accumulation of dengue ENV protein in vitro, associated with actin remodeling. These results illuminate cellular mechanisms that could serve as potential therapeutic alternatives for dengue management.

Geopolitical situations, such as the Ukraine–Russia war, the Israel–Hamas war, and pandemics, can disrupt supply chains, such as the rerouting of oil tankers via South Africa to avoid the impact of the Israel–Hamas conflict. These events will probably occur again, but they are black swan events and difficult to predict. How can firms manage these events to minimise disruption to their supply chains? This paper proposes that a blockchain architecture has the potential to build resilience in supply chains and give them the flexibility to withstand the challenges of black swan events. The decentralisation ability of blockchain technology enables separation of the discrete elements of a sustainable supply chain so contagion does not impact the overall sustainable supply chain. To achieve flexibility in a sustainable supply chain ecosystem requires governance, leadership, and management. Also, artificial intelligence (AI), in conjunction with blockchain, is a technology that can enhance the flexibility and resilience of the supply chain. Thus, AI can act as a controller, co-ordinator, and an integrator in sustainable supply chain systems.

The aim of this study is to develop an African Neutral Network (ANN) model for predicting temperature and relative humidity in the greenhouse during the calendar year. Input data are ambient temperature and relative humidity outside of the greenhouse chosen for a typical meteorological year for the location of the object and the values of temperature and relative humidity of the air in the greenhouse, obtained as a result of a TRNSYS dynamic simulation of the thermal load of the facility. The goal was to show that our ANN model is capable of making predictions over extended time period. The TRNSYS model was created on a real object, taking into account all relevant parameters, in order to describe the dynamic behavior of the object as good as possible. The ANN model is formed of 3 layers, where the number of neurons in the hidden layer is obtained by training different types in order to reach the optimal number of neurons, and the values of the learning rate and epochs are also varied. The activation functions were respectively the hyperbolic tangent in the hidden layer and the linear function in the output layer. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Correlation Coefficient were chosen as the statistical criteria for measuring of the network performance. MAE and RMSE showed that the model with 6 neurons in the hidden layer gave the best results of data prediction in relation to the input data. Statistical analysis of output shows that, the RMSE and Mean Absolute Error (MAE) between the measured and predicted temperature was 0.3166 °C and 0.1002°, and the relative humidity RMSE and MAE was 5.9% and 3.4%, respectively, which can satisfy the demand of greenhouse climate control. The results of the study showed that the model can be used with satisfactory accuracy to predict the parameters of the internal environment, in relation to the complex simulation of the thermal load of the object.

The prediction of hydrological flows (rainfall-depth or rainfall-discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of lake Nokoue in Benin. This paper aims to provide an effective and reliable method enable of reproducing the future daily water level of Lake Nokoue, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Nokoué Lake up to a forecast horizon of t+10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash-Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t+3 days. The values of these metrics remain stable for forecast horizons of t+1 days, t+2 days, and t+3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Nokoué Lake basin. Based on the evaluation indices used to assess the model's performance for the appropriate forecast horizon of water level in the Nokoué lake basin, the forecast horizon of t+3 days is chosen for predicting future daily water levels.

Nowadays the dynamism induced by the constant change of strategic decisions on the markets produces an extra difficulty in the management of an organization. The strategic decisions made by managers can become obsolete in a short period of time. One of the major difficulties in managing a commercial organization is to predict, with some precision degree, the impact some strategic decisions have on the financial results. Business Intelligence (BI) is an area widely used to help managers to make strategic decisions. But the methods used, behind the scenes, to achieve the conclusions are kept secret by BI company-based services. Modelling the environment can be a good option to study the impact of an action, provided by a strategic decision, in a real environment. A good model should give an approximate result of an action applied to a previous state of the environment. Artificial Neural Networks (ANNs) are proven to be excellent in modeling environments with some level of data noise. The same strategic action can have different results in different organizations. A tool that allows an evaluation of a strategic action applied to an environment should have a major importance in the management scope. Modeling the environment should save time and money for the organization to improve the performance of the strategic plan. If one evaluates the state of the environment after a strategic action being applied, it can be possible to mitigate the risk of failure of the strategic plan. As we will be able to verify, it is possible to use ANNs to model strategic environments that should allow the prediction of sales and operating results by the used strategies with some precision, although the level of success will be directly related with the user’s necessity.

In the dynamic landscape of modern education, characterized by an increasingly active involvement of IT technologies in learning, the imperative to transfer to students the skills necessary to integrate Artificial Intelligence (AI) into the process represents an important goal. This paper presents a novel framework for knowledge transfer, diverging from traditional programming language-centric approaches by integrating PSoC 6 microcontroller technology. This framework establishes a cyclical learning cycle encompassing theoretical fundamentals and practical experimentation, fostering AI literacy at the edge. Through a structured combination of theoretical instruction and hands-on experimentation, students develop proficiency in understanding and harnessing AI capabilities. Emphasizing critical thinking, problem-solving, and creativity, this approach equips students with the tools to navigate the complexities of real-world AI applications effectively. By leveraging PSoC 6 as an educational tool, a new generation of individuals is efficiently cultivated with essential AI skills. These individuals are adept at leveraging AI technologies to address societal challenges and drive innovation, thereby contributing to long-term sustainability initiatives. Specific strategies for experiential learning, curriculum recommendations, and results of knowledge application are presented, aimed at preparing students to excel in a future where AI will be omnipresent and indispensable.

Artificial Intelligence (AI) emerges as a powerful catalyst poised to reshape the global sustaina-bility landscape by facilitating the attainment of Sustainable Development Goals (SDGs). This comprehensive Delphi study meticulously probes the insights of domain experts, shedding light on the strategic utilization of AI to advance these critical sustainability objectives. Employing rigorous statistical techniques, encompassing measures of central tendency and interquartile de-viation, this research scrutinizes consensus dynamics among experts and elucidates potential variations in their viewpoints. The findings resoundingly convey experts' collective positive perspective regarding AI's pivotal role in propelling the SDGs forward. Through two iterative rounds of extensive discussions, a compelling consensus crystallizes—AI indeed exerts an overall positive impact, exemplified by a robust mean score of 78.8%. Intriguingly, distinct SDGs manifest varied propensities toward AI intervention, with Goals 6, 7, 8, 9, 11, 13, 14 and 15 basking in the radiance of highly positive impacts. Goals 1, 2, 3, 4, 5, 10 and 12 exhibit positive impact scores, indicating a juncture ripe for positive advancements. Meanwhile, Goal 16 and Goal 17 languish with neutral scores, signifying a juncture demanding nuanced deliberations about AI's impact on peace, justice, and strong institutions as well as on partnerships for the goals respectively. This paper underscores AI as a formidable instrument poised to address humanity's most pressing challenges while harmonizing seamlessly with the overarching SDG objectives. It gracefully dovetails into established practices across pivotal domains such as health, education, and resilient infrastructures, amplifying the collective global endeavor to navigate the path toward a more sustainable future.

Accurate estimations of actual evapotranspiration (ETa) are fundamental for various environmental issues. Artificial Intelligence-based models are considered a promising alternative to the most common direct ETa estimation techniques and indirect methods by remote sensing (RS)-based surface energy balance models. Artificial Neural Networks (ANN) are proven to be suitable for predicting reference evapotranspiration and ETa based on RS data. The aim of this study is to develop a methodology based on ANNs for estimating daily ETa values using NDVI and land surface temperature, coupled with limited site-specific climatic variables at a large irrigation catchment. Two scenarios were implemented by the ANN model. Data from only the 38 days of satellite overpass dates was selected in Scenario-I, while the 769-day data included the satellite overpass dates and other days located between two satellite overpass acquisitions in Scenario-II. An irrigation scheme in the Mediterranean region of Turkiye was selected, and a total of 38 Landsat images, and local climatic data were used in 2021 and 2022. Results showed that R2 values in Scenario-I and II were acknowledgeably high for training (0.79 and 0.86), testing (0.75 and 0.81), and the entire dataset (0.76 and 0.84), respectively. Results of the new model in two scenarios showed acceptable agreement with ETa-METRIC values. The proposed ANN model demonstrated the potential of obtaining daily ETa using limited climatic data and RS imagery.

Background: The growth of arthroplasty procedures requires innovative strategies to reduce inpatients Length of Stay (LOS). This study aims to develop a machine learning prediction model that may aid to predict LOS after hip or knee arthroplasties. Methods: A collection of all the clinical notes of patients undergoing elective primary or revision arthroplasty from 1 January 2019 to 31 December 2019 was performed. The hospitalization has been classified as “Short LOS” if it was less than or equal to 6 days and “Long LOS” if it was greater than 7 days. Clinical data coming from pre-operative laboratory analysis, vital parameters, demographic characteristics of patients were screened. Final data have been used to train a Logistic Regression model with the aim of predicting short or long LOS. Results: Final dataset was composed of 1517 patients (795 “LONG”, 722 “SHORT”, p = 0.3196) with a total of 1541 admissions (729 “LONG”, 812 “SHORT”, p < 0.000). Complete model had a prediction efficacy of 78,99% (AUC 0.7899). Conclusions: Machine learning may facilitate day-by-day clinical practice predicting which patients are suitable for a shorter LOS, from those with a longer LOS in which a cautious approach could be recommended.

The accurate and timely assessment of lymph node involvement is paramount in the management of patients with malignant tumors, owing to its direct correlation with cancer staging, therapeutic strategy formulation, and prognostication. Dual-energy computed tomography (DECT), as a burgeoning imaging modality, has shown promising results in the diagnosis and prediction of preoperative metastatic lymph nodes in recent years. This article aims to explore the application of DECT in identifying metastatic lymph nodes (LNs) across various cancer types, including but not limited to thyroid carcinoma (focusing on papillary thyroid carcinoma), lung cancer, and colorectal cancer. Through this narrative review, we aim to elucidate the clinical relevance and utility of DECT in the detection and predictive assessment of lymph node metastasis in malignant tumors, thereby contributing to the broader academic discourse in oncologic radiology and diagnostic precision.

Breast cancer has become the most diagnosed cancer in women globally with 2.3 million new diagnosis each year. Accurate early staging is essential for improving survival rates with metastatic spread from loco regional to distant metastasis decreasing rates by 50%. Current guidelines do not advice the routine use of Positron Emission Tomography (PET)-Computed Tomography (CT) in staging of early breast cancer in the absence of symptoms. However, there is a growing body of evidence to suggest that PET-CT used in this early stage can benefit the patient by improving staging and as a result treatment and outcome as well as psychological burden without increasing costs to the health service. Ongoing research in PET radiomics and artificial intelligence is showing promising future prospects in its use in diagnosis, staging, prognosticatication and assessing response to treatment in breast cancer. Furthermore ongoing research to address current limitations of PET-CT by improving techniques and tracers is encouraging.

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.

The effect of climate change plays a significant role on groundwater level variations and crop yield. The change in climate leading to increased temperatures, decreased rainfalls and extreme drought conditions ultimately cause low groundwater level and crop yield. Judicious groundwater management technology viz., water recharge options, need based irrigation, crop selection, must be adapted for further increasing the water table level in soil ecosystem. Additionally, climate change impacts combined with changes in agricultural water use can affect groundwater dynamics. Increased irrigation demand and decreased summer precipitation can lower groundwater levels, impacting crop production. Overall, climate change affects both groundwater resources and crop yield highlighting the need for sustainable water management practices and consideration of soil properties in agricultural modelling. The authors carefully selected relevant research articles addressing the impact of climate change on ground water level in Asian countries. The review highlights the use of machine learning method to ground water level and crop yield modelling. According to the study, machine learning techniques have made significant contributions to predicting groundwater level changes and crop yield with higher accuracies, high performance and less running time.

The purpose of this study is to ​​explore the use of a custom Transformer model in brain-computer interfaces (BCIs) that translate the neural activity present when an individual with limited verbal and fine-motor skills attempts to handwrite. As found in previous studies, Transformers have performed better than recurrent neural networks (RNNs) in translation tasks which are similar to its usage here in decoding neural signals into intended handwritten text. Due to the known benefits of a Transformer, the hypothesis was that the Transformer would show promise in the context of a BCI through the recorded metrics. The neural signals of a tetraplegic individual, when they attempted to handwrite, were provided by existing research. Four trials were conducted using data with or without augmentation which the model used when training to separately minimize training loss and validation loss. When comparing the results of a BCI with the implementation of a Transformer model with the original RNN BCI (the original data source), the Transformer model performed less favorably across all four trials. Although the results do not indicate that the Transformer model currently outperforms an RNN BCI, it is important to note that further testing of the model's capabilities (such as training it with a larger and more preferable dataset and/or for longer, comparing training times between the RNN and Transformer, and/or seeing how the Transformer is improved with an offline autocorrect feature) is necessary before determining whether Transformers can enhance communication in this manner.

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.

The present work fronts the human pose estimation problem, developing a method that enables automated ergonomic risk assessment. A research methodology is developed to calculate joint angles from digital snapshots or videos by using computer vision and machine learning techniques to get a more accurate ergonomic risk assessment. Starting from an ergonomic analysis, the study explores the use of a semi-supervised training method to detect the skeletons of workers. The research methodology developed aims to infer the positions and angles of the joints, to calculate the criticality index based on the RULA scores and fuzzy rules. Then, to prevent work-related musculoskeletal disorders (WMSD), improve production capacity and decrease the ergonomic risk, the system uses joint with a collaborative robot to support the worker in carrying out the most critical operations. The method has been tested on a real industrial case in which manual assembly activities of electrical components are conducted. The approach developed can overcome the limitations of recent developments based on computer vision or wearable measurement, sensors by performing an assessment with an objective and flexible ap-proach to postural analysis development.

The aims of this paper can be traced back to goals number seven, twelve and thirteen introduced by Agenda 2030, namely "affordable and clean energy", “responsible consumption and production” and "climate action”, respectively. This is due to the fact that the present work supports decarbonization processes acting in a direct way on the electricity consumption achieved by plants pertaining to the waste industry; in fact, this study aims at the realization of a machine learning model for predicting the energy consumption achieved by a mechanical biological treatment (MBT) waste plant located in central Italy, given the distribution of the entering waste. This model is implemented in MATLAB. The model can be used to tune the distribution of the entering waste in order to adapt the plant energy consumption to the capability of the energy sources and can serve as a play-ground model for other energy transformation plants. The results of the study, which feed the literature on the application of artificial intelligence to real industrial plants, could be used to determine energy efficiency actions that could be incorporated into Property’s strategic planning.

Wirobond C is a commercial dental casting alloy suitable for the fabrication of crowns, bridges as well as metal-ceramic restorations. This work aims with the study of the effect of ready-to-use Listerine® and Meridol® mouthwashes and sodium fluoride on corrosion resistance of the CoCrMo dental alloy to electrochemical corrosion in artificial saliva at 37 °C. SEM, EDS, SKP, and microhardness investigations were carried out to characterize the material under study. In vitro corrosion resistance of the CoCrMo alloy was conducted by the open circuit potential method, electrochemical impedance spectroscopy, and anodic polarization curves. The presence of Co 59.8(8) wt.%, Cr 31.5(4) wt.%, and Mo 8.8(6) wt.% was confirmed. The CoCrMo alloy was characterized by Vickers microhardness of 445(31) µHV0.3. Based on EIS data, the capacitive behavior and high corrosion resistance of CoCrMo alloy was revealed. The kinetics of pitting corrosion in the artificial saliva was lower after the modification with NaF and Listerine and Meridol mouthwashes. The potentiodynamic characteristics revealed the passive behavior of CoCrMo alloy in all solutions. Based on the SKP measurements of the CoCrMo alloy after corrosion tests, the effect of artificial saliva modification on electronic properties of Bego Wirobond C dental alloy was found.

The combined efect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong detterminant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web-platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and non-local complementarity (EC) in PPIs using the Complementarity Plot. It further derives an AI-based ∆G_binding with a prediction accuracy comparable to the state-of-the-art. This book-chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.

Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Traditionally, experts have relied on manual EEG signal analyses for seizure detection, which is labor-intensive and prone to hu-man error. Recognizing this limitation, the rise of deep learning methods has been heralded as a promising avenue, offering more refined diagnostic precision. On the other hand, the prevailing challenge in many models is their constrained emphasis on specific domains, potentially diminishing their robustness and precision in complex real-world environments. This paper presents a novel model that seamlessly integrates the salient features from the time-frequency domain alongside pivotal statistical attributes derived from EEG signals. This fusion process involves the integration of essential statistics, including the mean, median, and variance, combined with the rich data from compressed time-frequency (CWT) images processed using auto-encoders. This multidimensional feature set provides a robust foundation for subsequent analytic steps. A long short-term memory (LSTM) network, meticulously optimized for the renowned Bonn Epilepsy dataset, was used to enhance the capability of the proposed model. Preliminary evaluations underscore the prowess of the proposed model: remarkable 100% accuracy in most of the binary classifications, impressive performance exceeding 95% for three-class and four-class challenges, and a commendable rate exceeding 93.5% for the five-class classification.

The rapid growth of Artificial Intelligence (AI) has led to considerable progress in Automated Fact Verification (AFV). This process involves collecting evidence for a statement, assessing its relevance, and predicting its accuracy. Recently, research has begun to explore automatic explanations as an integral part of the accuracy analysis process. However, the explainability within AFV is lagging compared to the wider field of explainable AI (XAI), which aims at making AI decisions more transparent. This study looks at the notion of explainability as a topic in the field of XAI, with a focus on how it applies to the specific task of Automatic Fact Verification. It examines the explainability of AFV, taking into account architectural, methodological, and dataset-related elements, with the aim of making AI more comprehensible and acceptable to the general society. Although there is a general consensus on the need for AI systems to be explainable, there a dearth of systems and processes to achieve it. In this research, we investigate the concept of explainable AI in general and demonstrate its various aspects through the particular task of Automated Fact Verification. We explore topic of faithfulness in the context of local and global explainability and how these correspond with architectural, methodological and data-based ways of achieving it. We examine these concepts for the specific case of AFV and analyze the current datasets used for AFV and how they can be adapted to further the identified aims on XAI. The paper concludes by highlighting the gaps and limitations in current data science practices and possible recommendations for modifications in architectural and data curation processes that would further the goals of XAI.

This study uses explainable artificial intelligence to analyze major predictors of poor self-rated health, dementia and their comorbidity, which include various aspects of social support. Data came from the Korean Longitudinal Study of Ageing (2016-2018), with 5527 participants aged 56 or more. The dependent variables were: (1) poor self-rated health (SRH) with 5 categories (very good, good, middle, poor, very poor); (2) dementia (no, yes); and (3) their comorbidity with 4 categories (SRH very good, good or middle, dementia no; SRH very good, good or middle, dementia yes; SRH poor or very poor, dementia no; SRH poor or very poor, dementia yes). The 49 demographic, socioeconomic and health-related predictors were included. The accuracy and area under the receiver operating characteristic curve (AUC) were compared for logistic regression and the random forest. Logistic regression and the random forest registered similar AUCs with the range of 0.80-0.87. Based on random forest variable importance, 18 out of top-20 predictors were identical for SRH, dementia and their comorbidity: previous SRH, life satisfaction - health, age, body mass index, income, chronic diseases, life satisfaction - economic, life satisfaction - overall, grandchildren alive, friendship meeting, brothers/sisters alive, class identity, children alive, social activity - friendship, education, children in weekly contact, religion and drinker. However, some variations were found among the three dependent variables, i.e., previous SRH for comorbidity (1st) and SRH (1st) compared to dementia (11th), grandchildren alive for SRH (6th) and dementia (4th) compared to comorbidity (9th), chronic diseases for comorbidity (6th) compared to SRH (10th) and dementia (10th). In terms of Shapley Additive Explanation (SHAP) values, the probability of dementia is expected to decrease by 5.57% in case a predictor grandchildren alive is included to the model. This study highlights the importance of social support for the prediction of poor self-rated health, dementia and their comorbidity. Specifically, the promotion of family support and friendship activity for elders would be vital for the prevention and management of their self-rated health and dementia.

The definition of artificial intelligence (AI) is the capacity of a computer or machine to mimic or simulate human intelligence. AI has been applied in different areas, including medicine. In cardiology, AI techniques have revolutionized the field of cardiac diagnosis, enabling the detection and prediction of cardiovascular diseases to be more accurate and efficient. This article provides an overview of machine learning, deep learning, and neural networks as they pertain to cardiac diagnosis. We will investigate how these techniques harness the power of data and algorithms to analyze complex patterns in cardiac data, thereby facilitating early detection, risk assessment, and treatment decision-making.

Generative artificial intelligence (AI), in particular large language models such as ChatGPT have reached public consciousness with a wide-ranging discussion of their capabilities and suitability for various professions. The extant literature on the ethics of generative AI revolves around its usage and application, rather than the ethical framework of the responses provided. In the education sector, concerns have been raised with regard to the ability of these language models to aid in student assignment writing with the potentially concomitant student misconduct of such work is submitted for assessment. Based on a series of ‘conversations’ with multiple replicates, using a range of discussion prompts, this paper examines the capability of ChatGPT to provide advice on how to cheat in assessments. Since its public release in November 2022, numerous authors have developed ‘jailbreaking’ techniques to trick ChatGPT into answering questions in ways other than the default mode. While the default mode activates a safety awareness mechanism that prevents ChatGPT from providing unethical advice, other modes partially or fully bypass the this mechanism and elicit answers that are outside expected ethical boundaries. ChatGPT provided a wide range of suggestions on how to best cheat in university assignments, with some solutions common to most replicates (‘plausible deniability,’ language adjustment of contract written text’). Some of ChatGPT’s solutions to avoid cheating being detected were cunning, if not slightly devious. The implications of these findings are discussed.