In this paper, we present a framework that automatically labels Latent Dirichlet Allocation (LDA) generated topics using sentiment and aspect terms from COVID-19 tweets to help the end-users by minimizing the cognitive overhead of identifying key topics labels. Social media platforms especially Twitter are considered as one of the most influential sources of information for providing public opinion related to a critical situation like the COVID-19 pandemic. LDA is a popular topic modelling algorithm that extracts hidden themes of documents without assigning a specific label. Thus automatic labelling of LDA-generated topics from COVID-19 tweets is a great challenge instead of following the manual labelling approach to get an overview of wider public opinion. To overcome this problem, in this paper, we propose a framework named \texttt{SATLabel} that effectively identifies significant topic labels using \textit{top unigrams features of sentiment terms and aspect terms clusters from LDA generated topics} of COVID-19 related tweets to uncover various issues related to the COVID-19 pandemic. The experimental results show that our methodology is more effective, simpler, and traces better topic labels compare to the manual topic labelling approach.

Wire-driven hyper-redundant continuum manipulators are gaining more popularity and finding more applications in industry and in minimally invasive surgery. Unlike traditional rigid link manipulators, continuum robots with a flexible backbone structure are able to work in a highly constrained workspace and in an unstructured environment. However, in spite of a possible wide range of reachability, continuum manipulators have some issues related to payload capacity, accuracy and control. Therefore, in this research, we propose a novel hyper-redundant continuum robot with a passive sliding disc mechanism to improve payload capacity and accuracy. To prove the sliding mechanism concept, we demonstrate a comparison analysis with a conventional non-sliding continuum robot arm in a payload test, a bending test and a reachability test. Moreover, with this novel design, we are proposing robot kinematics and kinetic formulation and simulation results to validate the effectiveness of the sliding disc mechanism.

Total cloud cover (TCC) retrieval from ground-based optical imagery is a problem being tackled by a few generations of researchers. The number of human-designed algorithms for the estimation of TCC grows every year. However, there is not very much progress in terms of quality, mostly due to the lack of systematic approach to the design of the algorithms, to the assessment of their generalization ability, and to the assessment of the TCC retrieval quality. In this study, we discuss the optimization nature of data-driven schemes for TCC retrieval. In order to compare the algorithms, we propose the framework for the assessment of the algorithms characteristics. We present several new algorithms that are based on deep learning techniques: a model for outliers filtering, and a few models for TCC retrieval from all-sky imagery. For training and assessment of data-driven algorithms of this study, we present the Dataset of All-Sky Imagery over the Ocean (DASIO) containing over one million of all-sky optical images of visible sky dome taken in various regions of the World Ocean. The research campaigns contributed to DASIO collection took place in the Atlantic ocean, the Indian ocean the Red and Mediterranean seas, and also in the Arctic ocean. Optical imagery collected during these missions are accompanied by standard meteorological observations of cloudiness characteristics made by experienced observers. We assess the generalization ability of the presented models in several scenarios that differ in terms of the regions selected for the train and validation subsets. As a result, we demonstrate that our models based on convolutional neural networks deliver superior quality compared to all previously published schemes. As a key result, we demonstrate considerable drop of the ability to generalize the training data in case of strong covariate shift between training and validation subsets of imagery which may take place in case of region-aware subsampling.

Nowadays, the research and development of on-chip LiDAR sensors for vehicle collision avoidance is growing very fast. Therefore, the assessment of the reliability in obstacle detection using the information provided by LiDAR sensors has become a key issue to be explored by the scientific community. This paper presents the design and implementation of a self-tuning method in order to maximize the reliability of an Internet-of-Things sensors network and to minimize the number of sensors to localize with the required accuracy obstacles by a detection threshold. In order to achieve this goal, models that predict accuracy (i.e., prediction error) for object localization using data collected by LIDAR sensors are designed and implemented in Webots Automobile 3D simulation tool. The approach is based on combining different techniques. Firstly, point-cloud clustering technique and an error prediction model library composed by a multilayer perceptron neural network with backpropagation, k-nearest neighbors and linear regression are explored. Secondly the above-mentioned techniques for modeling are also combined with a supervised and reinforcement machine learning technique, Q-learning in order to minimize the detection threshold. In addition, a IoT driving assistance simulated scenario with a LiDAR sensor network is designed in order to validate the prediction model and the optimal configuration of the sensor network to guarantee reliability in obstacle localization. The results demonstrate that the self-tuning method is appropriate to increase the reliability of the sensor network whereas minimizing the detection threshold

Tsetlin machine (TM) is a novel machine learning approach that implements propositional logic to perform different tasks like classification and regression. The TM can not only achieves competitive accuracy in these tasks but it is also able to provide results that are explainable and easy to implement using simple hardware. The TM learns using clauses based off the features of the data. Final classification is done using a combination of the clauses. In this paper, we propose the novel idea of adding regularizers to TM, called as Regularized TM (RegTM), to improve generalization. Regularizers have been widely used in machine learning to improve accuracy. We explore different regularization strategies and how they would influence performance. We show the feasibility of our methodology by different experiments on benchmark datasets.

Artificial intelligence (AI) is revolutionizing the textile industry by improving the prediction of fabric properties and handfeel, which are essential for assessing textile quality and performance. However, the practical application and translation of AI-predicted results into real-world textile production remain unclear, posing challenges for widespread adoption. This paper systematically reviews AI-driven techniques for predicting these characteristics by focusing on model mechanisms, dataset diversity, and prediction accuracy. Among 811 papers initially identified, 26 were selected for in-depth analysis through both bibliometric and content analysis. The review categorizes and evaluates various AI approaches, including machine learning, deep learning, and hybrid models, across different types of fabric. Despite significant advances, challenges remain, such as ensuring model generalization and managing complex fabric behavior. Future research should focus on developing more robust models, integrating sustainability, and refining feature extraction techniques. This review highlights the critical gaps in the literature and provides practical insights to enhance AI-driven prediction of fabric properties, thus guiding future textile innovations.

The recent growth of wearable medical device technology in the form of fitness trackers, smart watches, smartphone apps, and patient monitoring systems has created people-generated health data (PGHD) that may benefit medical science with large amounts of continuous real-world data. The prevalence of these devices speaks to their broad popularity and user-friendliness and may lead us one day to a more fully “connected healthcare system.” Meanwhile, data security, confidentiality, and privacy issues have emerged in these hackable systems. Despite the promise of anonymized data, data can sometimes be re-identified, but even without that step, data breaches may reveal information (name, address, date of birth, social security number, and so on) sufficient for identity theft. Clinicians are often asked about the utility and value of wearable devices or monitors, but most are unaware that data from these systems may be transmitted, stored, and even sold without the user’s specific knowledge. Despite the confidentiality of medical information, cybersecurity surrounding wearables and monitors remains relatively lax, making them comparatively easy targets for cyber-villains. It is also important that efforts be taken to make PGHD more secure, since medical data may have great value to telehealth applications and AI-physician assistants. Clinicians should take an active role in informing patients about both risks and benefits of wearables and similar devices.

The utilization of artificial intelligence in education can contribute to sustainable development goals in various ways. AI in education can effectively convey sustainability issues to students. Through simulations and virtual environments, learners can experience sustainable practices firsthand. On the other hand artificial intelligence can assist administrators and educators in making data-informed decisions. This allows for more effective shaping of educational policies. This study explores how teachers and artificial intelligence (AI) address ethical dilemmas in edu-cation, comparing their decisions and examining the influence of variables such as gender, years of service, and education level. A total of 141 teachers from public schools in Turkey participated, and their responses were compared to AI-generated decisions using Yin's nested multiple-case de-sign. The scenarios were analyzed through five ethical frameworks: virtue ethics, deontological ethics, utilitarianism, social justice ethics, and situation ethics. Results showed that AI provided the same answer as the majority of teachers in six out of eight dilemmas but differed in two. AI favored a more analytical, result-oriented approach, while teachers emphasized empathy and rule adherence. Gender differences revealed that males leaned towards utilitarianism, while females favored social justice and situation ethics. Less experienced teachers preferred deontological and social justice ethics, while more experienced teachers leaned towards virtue ethics and utilitarian-ism. Elementary teachers emphasized virtue and social justice ethics, while middle and high school teachers favored deontological ethics and utilitarianism. The findings provide insights into ethical decision-making in education by highlighting the differences between solutions proposed by artificial intelligence and those offered by teachers. Additionally, they emphasize the im-portance of adapting artificial intelligence to everyday life to foster a sustainable world.

The purpose of this paper is to construct an evaluation system for AI painting software based on generative adversarial network technology, which optimizes the performance of the related software in terms of functionality, ease of use, system performance and safety. The results of the questionnaires are statistically analyzed. In addition, exploratory factor analysis was supported to extract the data of the study, which was ultimately used to calculate the weight and importance of each index through fuzzy hierarchical analysis method. The study constructed an evaluation system for AI painting software based on generative adversarial network technology, including 16 indicators of functionality, 16 indicators of ease of use, 7 indicators of system performance, and 8 indicators of safety respectively that their alpha coefficients were 0.882, 0.962, 0.932, 0.932, and 0.932, respectively. In addition, the accumulated explanatory variances of their coefficients were 84.405%, 84.897%, 84.013%, and 72.606%, respectively, 73.013%, and 72.606%, respectively. It is clear that the items included in each of the indicators are homogeneous, with a high degree of internal consistency. This paper suggests that the development of AI painting software focusing on functionality, ease of use, system performance and safety can enhance the market competitiveness of the software.

Artificial Intelligence (AI) is increasingly becoming integral to medical practice, potentially enhancing outcomes in thoracic surgery. AI-driven models have shown significant accuracy in diagnosing non-small cell lung cancer (NSCLC), predicting lymph node metastasis, and aiding in the efficient extraction of electronic medical records (EMR) data. Moreover, AI applications in robotic-assisted thoracic surgery (RATS) and perioperative management reveal potential in improving surgical precision, patient safety, and overall care efficiency. Despite these advancements, challenges such as data privacy, biases, and ethical concerns remain. This manuscript explores AI applications, particularly machine learning (ML) and natural language processing (NLP), in thoracic surgery, emphasizing their role in diagnosis and peri-operative management. It also provides a comprehensive overview of the current state, benefits, and limitations of AI in thoracic surgery, highlighting future directions in the field.

AI integration is becoming more important in today’s world and could potentially be very useful in the life sciences and biotechnology. This study evaluates the effectiveness of AI systems in teaching molecular cloning techniques to newcomers in molecular biology, focusing on a comparison between Gemini Advanced (powered by Gemini Ultra 1.0) and ChatGPT (GPT-4o). Molecular or DNA cloning is a lab technique for creating multiple copies of a specific DNA segment. It involves isolating the DNA fragment, inserting it into a vector, and introducing it into a host cell, typically bacteria, which then generates numerous copies of the DNA. This essential technique underpins genetic engineering, allowing for gene study, protein expression, and the creation of genetically modified organisms. The primary objective of this study was to assess whether individuals with limited laboratory experience could effectively acquire a specific molecular biology skill set solely through an AI system, rather than relying on the more traditional ‘learning by shadowing’ approach. Participants with limited lab experience completed these tasks using both Gemini and GPT-4o, and evaluated on quality of instructions, conciseness, scientific accuracy, and user satisfaction. Results indicated that both AI systems demonstrated strengths in delivering clear and comprehensive instructions, but in different ways. Gemini, although more interactive with images and diagrams lacked directness and had a harder time keeping past steps in its “memory”. On the other hand, GPT-4o was much more direct and concise in the steps listed and had more accurate results akin to what is performed in the lab. This study underscores the potential of AI-driven tools to simplify complex biotechnological procedures, making them accessible for learners at all levels.

Large language models (LLMs) have shown impressive performance and versatility. However, their billions of parameters and high computational costs hinder the development of personalized and privacy-preserving AI assistants operating locally on user devices. In this work, we explored the potential of pruning LLMs to create lightweight models suitable for user devices, using the moderate-sized Llama-2 7B model as an example. By adopting a simple yet effective pruning method, we found that up to 60% of the weights in the Llama-2 7B model could be pruned without significantly impairing its language modeling capabilities. Furthermore, despite occasional factual inaccuracies, the pruned model at the sparsity limit generated fluent and helpful answers to daily queries, demonstrating its feasibility of on-device AI assistants. These inaccuracies might originate from forgetting or hallucination due to pruning. We proposed a simple protocol to distinguish between the two mechanisms, as well as future directions to improve the pruned models for local AI assistants.

The familiar stirred tank reactor poses an inherent challenge of variability. Some inflowing chemicals spend too little time in the reactor, and others stay too long, harming the uniformity of the end product. This issue can be addressed by replacing the continuous operation with a flow of individually treated fluid quanta. Ideally one would devise a universal fluid handling apparatus that can be adjusted to take care of all the needed chemical engineering unit operations. Proposing a cylinder shape capsule containing a rotating piston that moves back and forth between the edges of the capsule. The piston is cut through with certain fluid pass ways (holes) which may be shifting between being open, being closed, and anything in between. The combined movement of holes-drilled rotating piston will allow this universal unit to adjust for pumping, mixing and reacting fluid, and fluid components. By lining up the inner sides of the piston holes with fluid impact materials (FIM) the same generic unit can be used for catalytic reactions and for separation challenges. These generic units are linked "Lego like" to form a compound process, replacing the familiar continuous operation. The multi-mode piston (MMP) unit is completely specified by a digital representation of the shift position , the rotational position and the open/closed positions of the fluid pass ways. This allows for an efficient imposition of control strategy and for optimized manufacturing. The resultant digital representation renders the control strategy to become AI friendly, consistent with supervised learning tools for improving the efficiency of many chemical processes that transform from continuous mode to quantum fluid mode. While appearing promising, the concept is in its preliminary stages and its prospective impact is not yet well determined.

This research evaluates the feasibility of addressing computer vision problems with limited resources, particularly in the context of medical data where patient privacy concerns restrict data availability. (1) Background: The study focuses on diagnosing skin diseases using five distinct transfer learning models based on convolutional neural networks. Two versions of the dataset were created, one imbalanced (4092 samples) and the other balanced (5182 samples), using simple data augmentation techniques. Preprocessing techniques were employed to enhance the quality and utility of the data, including image resizing, noise removal, and blur techniques. The performance of each model was assessed using fresh data after preprocessing. According to the research findings, (2) Methods: the VGG-19 model achieved an accuracy of 95.00% on the imbalanced dataset. After applying augmentation on the balanced data, the best-performing model was VGG-16-Aug with an accuracy of 97.07%. (3) Results: These results suggest that low-resource approaches, coupled with preprocessing techniques, can effectively identify skin diseases, particularly when utilizing the VGG-16-Aug model with a balanced dataset. (4) Conclusions: The study addresses rare skin disorders that have received limited attention in past research, including acne, vitiligo, hyperpigmentation, nail psoriasis, and SJS-TEN. The findings highlight the potential of simple data augmentation techniques moreover, explainable AI: Grad-CAM interpreted the model outcome by showing image contours visually as well as identifying uncommon skin conditions and overcoming the data scarcity challenge. The implications of these research findings are significant for the development of machine learning-based diagnostic systems in the medical field. Further investigation is necessary to explore the generalizability of these findings to other medical datasets.

In today's highly digitized and globalized financial environment, the need to protect payment systems from risk is more urgent than ever. Artificial intelligence (AI) technology is rapidly becoming a key tool in this space, with machine learning algorithms, big data analytics, and real-time monitoring enabling AI to effectively detect and prevent fraudulent activity, optimize risk management processes, and deliver intelligent services. AI not only improves the security and efficiency of payment systems, but also significantly improves customer satisfaction and loyalty. With the continuous development of AI technology and financial payment technology, the financial payment industry will usher in more innovations and changes in the future, further optimizing the payment process, improving payment efficiency, and ensuring payment security.

This qualitative research explores the integration of digital technologies to enhance supplier engagement in e-commerce, examining the transformative impact of AI, blockchain, and IoT on supplier relationship management (SRM). Through semi-structured interviews and documentary analysis with 15 industry experts, the study reveals that digital technologies enable organizations to optimize supply chain operations, improve transparency, and foster collaborative partnerships with suppliers. Strategic initiatives such as co-development projects and joint innovation efforts are pivotal in accelerating product development cycles and enhancing market competitiveness. Emotional intelligence (EI) emerges as critical in cultivating trust, empathy, and effective communication, underscoring the human dimension of supplier relationships amidst technological advancements. Challenges identified include technological complexity, data privacy concerns, and resistance to change, necessitating robust cybersecurity measures and proactive compliance strategies. Economically, organizations benefit from significant cost savings, improved efficiency, and enhanced profitability through digital transformation initiatives in SRM. By leveraging digital platforms and data-driven insights, organizations can optimize procurement processes, streamline operations, and mitigate risks to achieve sustainable growth and resilience.

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

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

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

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

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

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

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

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

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

This study explores the limitations of current AI systems, which predominantly function in the digital realm, and investigates the transformative potential of embodied AI. Embodied AI involves AI agents equipped with physical bodies, enabling them to interact directly with the physical world. This research focuses on how embodied AI can bridge the "embodiment gap" and enhance human-machine collaboration and learning in dynamic environments. The research examines the fundamental differences between traditional AI systems and embodied AI, emphasizing the importance of physical interaction for contextual understanding, adaptive learning, and intuitive human-machine collaboration. It explores various applications of embodied AI, including robotics, autonomous vehicles, and assistive technologies, demonstrating how physical embodiment can improve performance, safety, and user experience. Through experimental studies and real-world case analyses, the study highlights the advantages of embodied AI in tasks requiring situational awareness, dexterity, and real-time decision-making. It also addresses the challenges associated with developing and deploying embodied AI systems, such as sensor integration, real-time processing, and human-machine interface design. Findings indicate that embodied AI can significantly improve the efficacy of AI systems in dynamic and unpredictable environments. By leveraging physical embodiment, AI agents can better understand and respond to their surroundings, facilitating more natural and effective interactions with humans. The research concludes with recommendations for advancing embodied AI, including interdisciplinary collaboration, investment in sensor and actuator technologies, and the development of standardized frameworks for embodied intelligence.

Keywords: Artificial Intelligence in Implantology; AI in Dental Implantology; Machine Learning in Dental Implantology; Deep Learning in Implant Dentistry; Robotics in Dental Implantology; AI-Based Implant Planning.

This research assesses the effectiveness of the Socio-Technical Assistance (STA) program combined with Owner-Driven Housing Reconstruction for rural private housing recovery after Nepal’s 2015 earthquake, particularly targeting vulnerable households. Through a quantitative 304-questionnaire survey, the study reveals that 96% of households credited STA activities for accelerating reconstruction, with 95% acknowledging its significance and 78% emphasizing its necessity. Notably,89% expressed dependency on STA for reconstruction, and 85% believed it heightened disaster risk reduction awareness. In conclusion, the study establishes that STA activities significantly contributed to the successful reconstruction of vulnerable households, addressing critical aspects such as financial support, technical assistance, housing accessibility, earthquake-resilient construction, improved livelihoods, and safety enhancements. The field study presents crucial recommendations to enhance the effectiveness of Socio-Technical Assistance (STA) activities in post-earthquake rural private housing reconstruction. Emphasizing the need for tailored, demand-driven interventions, the study cautions against relying solely on an Owner-driven Reconstruction model, challenging the one-size-fits-all strategy. The study proposes integrating tailored interventions into overarching recovery strategies, advocating for coordinated efforts to enhance Disaster Risk Reduction (DRR) awareness and cultivate resilient communities in particularly vulnerable households. Aligned with United Nations Sustainable Development Goal 11, which focuses on sustainable cities and communities. This research aims to enhance the literature on post-disaster humanitarian shelter and settlement by emphasizing the significance of inclusive and comprehensive approaches to recovery and reconstruction.

The article is dedicated to addressing the current challenge of augmenting the load- bearing ca-pability of pile foundations. This predicament is most effectively addressed by employing piles with unconventional geometries, along with atypical methodologies for installing these foun-dation piles. The primary objective of research is to examine the impact of various fill materials (including both soil and rigid substances) on the energy consumption during pile driving and the resistance to static loads by piles with multiple shaft expansions. The findings derived from model-based investigations demonstrate that the load-bearing capacity of piles with shaft ex-pansions, installed with bulk material filling, surpasses that of conventional piles (prismatic and pyramidal) by a factor ranging from 1.5 to 4.6. Furthermore, the research outcomes also indicate greater energy consumption (1.14-1.66 times) and enhanced load-bearing capacity (1.15-1.68 times) for piles with shaft expansions driven with backfill, in comparison to piles installed without backfill. It is noteworthy that the type of backfill material significantly influences the energy consumption during pile driving and their stability under axial static loads. The correla-tion relationships can be applied to approximate projection of the energy-related and structural parameters of piles with shaft expansions embedded with the addition of bulk materials.

Deep Neutral Networks (DNNs) were initially proposed towards the midst of the 20th century, motivated by the neural structure and mechanism of the human brain. Thanks to major ad-vancements in computational resources, during the past decade DNN-based systems have demonstrated unprecedented performance in terms of accuracy and speed. However, recent work has shown that such models may not be sufficiently robust during the inference process. Furthermore, due to the data-driven learning nature of DNNs, designing interpretable and gen-eralizable networks is a major challenge, especially in critical applications, such as medical Computer Aided Diagnostics (CAD) and other medical imaging tasks, including classification, regression and reconstruction. Within this context, a line of physics-driven approaches for deep learning has recently emerged, aimed at improving the stability and generalization capacity of DNNs for medical imaging applications. In this paper, we review recent work focused on phys-ics-driven or prior-information learning for a variety of imaging modalities and medical applica-tions. We discuss how the inclusion of domain-related knowledge into the learning process and networks’ design supports their stability and generalization capability. In addition, we highlight current and future challenges within this scope.

Ultrasonic-guided lamb wave approach is an effective non-destructive testing (NDT) method used for detecting localized mechanical damage, corrosion, and welding defects experienced in metallic pipelines. Signal processing of the guided waves is often challenged due to the complexity of operational conditions and environments in the pipelines. Machine learning approaches in recent years, including convolutional neural networks (CNN) and long short-term memory (LSTM), have exhibited their advantages to overcome these challenges for signal process and data classification of complex systems, thus great potential for damage detection of critical oil/gas pipeline structures. In this study, a CNN-LSTM hybrid model is utilized for decoding ultrasonic guided waves for damage detection of metallic pipelines, and twenty-nine features are extracted as input to classify different types of defects from metallic pipes. The prediction capacity of the CNN-LSTM model is assessed by comparing it to the CNN and LSTM. The results demonstrate that the CNN-LSTM hybrid model exhibits much higher accuracy, with 94.8%, as compared to those of CNN and LSTM. Interestingly, the results also reveal that predetermined features, including time-, frequency-, and time-frequency domains, could significantly improve the robustness of the deep learning approaches, even though the deep learning approaches are often believed they include automated feature extraction, without the hand-crafted steps as the shallow learning do. Furthermore, the CNN-LSTM model displays higher performance when the noise level is relatively low (e.g., SNR=9 or higher), as compared to the other two models, but its prediction drops gradually with the increase of the noise.

The automotive industry is continuously seeking innovative solutions to improve the energy efficiency and sustainability of vehicles. Among the various energy-consuming systems in automobiles, air conditioning plays a crucial role. However, conventional vapour compression-based air conditioning systems are known for their high energy consumption and environmental impact. In recent years, adsorption technology has gained significant attention as a promising alternative due to its potential for higher energy efficiency and reduced greenhouse gas emissions. This review paper presents a comprehensive analysis of advancements in adsorption air conditioning technology specifically designed for automotive applications. The paper examines the recent research and development efforts in enhancing the performance and practicality of these systems, encompassing areas such as advanced materials selection, and system integration strategies. Furthermore, this review highlights the benefits and challenges associated with implementing adsorption air conditioning systems in vehicles. The environmental impact and potential for waste heat recovery are also discussed. Additionally, emerging trends, such as hybridization with conventional vapour compression systems, are explored to further optimize the energy efficiency and sustainability of automotive air conditioning. By critically evaluating the existing literature and research advancements, this review provides insights into the state-of-the-art thermally driven technologies for automotive applications.

We study in this work a dilute granular gas immersed in a thermal bath made of smaller particles with nonnegligible masses as compared with the granular ones. The kinetic theory for this system is developed and described by an Enskog--Fokker--Planck equation for the one-particle velocity distribution function. Granular particles are assumed to have inelastic and hard interactions, losing energy in collisions as accounted by a constant coefficient of normal restitution. The interaction with the thermal bath is based on a nonlinear drag force plus a white-noise stochastic force. To get explicit results of the temperature aging and steady states, Maxwellian and first Sonine approximations are developed. The latter takes into account the coupling of the excess kurtosis with the temperature. Theoretical predictions are compared with direct simulation Monte Carlo and event-driven molecular dynamics simulations. While good results for the granular temperature are obtained from the Maxwellian approximation, a much better agreement, especially as inelasticity and drag nonlinearity increase, is found when using the first Sonine approximation. The latter approximation is, additionally, crucial to account for memory effects like Mpemba and Kovacs-like ones.

Due to the digitization and Internet of Things revolutions, the present electronic world has a wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a growing concern in today's cyber security industry all over the world. Traditional security solutions are insufficient to address contemporary security issues due to the rapid proliferation of many sorts of cyber-attacks and threats. Utilizing artificial intelligence knowledge, especially machine learning technology, is essential to providing a dynamically enhanced, automated, and up-to-date security system through analyzing security data. In this paper, we provide an extensive view of machine learning algorithms, emphasizing how they can be employed for intelligent data analysis and automation in cybersecurity through their potential to extract valuable insights from cyber data. We also explore a number of potential real-world use cases where data-driven intelligence, automation, and decision-making enable next-generation cyber protection that is more proactive than traditional approaches. The future prospects of machine learning in cybersecurity are eventually emphasized based on our study, along with relevant research directions. Overall, our goal is to explore not only the current state of machine learning and relevant methodologies but also their applicability for future cybersecurity breakthroughs.

Cities are undergoing huge shifts in technology and operations in recent days, and `data science' is driving the change in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting insights or actionable knowledge from city data and building a corresponding data-driven model is the key to making a city system automated and intelligent. Data science is typically the study and analysis of actual happenings with historical data using a variety of scientific methodology, machine learning techniques, processes, and systems. In this paper, we concentrate on and explore ``Smart City Data Science", where city data collected from various sources like sensors and Internet-connected devices, is being mined for insights and hidden correlations to enhance decision-making processes and deliver better and more intelligent services to citizens. To achieve this goal, various machine learning analytical modeling can be employed to provide deeper knowledge about city data, which makes the computing process more actionable and intelligent in various real-world services of today's cities. Finally, we identify and highlight ten open research issues for future development and research in the context of data-driven smart cities. Overall, we aim to provide an insight into smart city data science conceptualization on a broad scale, which can be used as a reference guide for the researchers, professionals, as well as policy-makers of a country, particularly, from the technological point of view.

In the current age of the Fourth Industrial Revolution ($4IR$ or Industry $4.0$), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding real-world applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study's key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world applications areas, such as cybersecurity, smart cities, healthcare, business, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for not only the application developers but also the decision-makers and researchers in various real-world application areas, particularly from the technical point of view.

The activity of the mitochondrial Permeability Transition Pore, mPTP, a highly regulated multi-component mega-channel, is enhanced in aging and in aging-driven degenerative diseases. mPTP activity accelerates aging by releasing large amounts of cell-damaging Reactive Oxygen Species, Ca2+ and NAD+. The various pathways that control the channel activity, directly or indirectly, can therefore either, inhibit, or accelerate aging, retards, or enhance the progression of aging-driven degenerative diseases, and determine lifespan and healthspan. Autophagy, a catabolic process that removes and digests damaged proteins and organelles protects the cell against aging and disease. However, the protective effect of autophagy depends on mTORC2/SKG1 inhibition of mPTP. Autophagy is inhibited in aging cells. Mitophagy, a specialized form of autophagy, which retards aging by removing mitochondrial fragments with activated mPTP, is also inhibited in aging cells, and this inhibition leads to increased mPTP activation, which is a major contributor to neurodegenerative diseases, such as Alzheimer’s and Parkinson’s Diseases. The Increased activity of mPTP in aging turns autophagy/mitophagy into a destructive process leading to cell aging and death. Several drugs and lifestyle modifications that enhance healthspan and lifespan enhance autophagy and inhibit the activation of mPTP. Therefore, elucidating the intricate connections between pathways that activate and inhibit mPTP, in the context of aging and degenerative diseases, could enhance the discovery of new drugs and lifestyle modifications that slow aging and degenerative disease.

Background: Ensemble tree-based models such as Xgboost are highly prognostic in cardiovascular medicine, as measured by the Clinical Effectiveness Metric (CEM). However, their ability to handle correlated data, such as hospital-level effects, are limited. Objectives: The aim of this work is to develop a binary outcome mixed effects Xgboost (BME) model that integrates random effects at the hospital level. To ascertain how well the model handles correlated data in cardiovascular outcomes, we aims to assess its performance and compare it to fixed effects Xgboost and traditional logistic regression models. Methods: A total of 227,087 patients over 17 years of age, undergoing cardiac surgery from 42 UK hospitals between 1 Jan 2012 and 31 Mar 2019 were included. The dataset was split into two cohorts: Training/Validation (n = 157196; 2012-2016) and Holdout (n = 69891; 2017-2019). The outcome variable was 30 days mortality with hospitals considered as clustering variable. The logistic regression, mixed effects logistic regression, Xgboost and binary outcome mixed effects Xgboost (BME) were fitted to both standardized and unstandardized datasets across a range of sample sizes and the estimated prediction power metrics were compared to identify the best approach. Results: The exploratory study found high variability in hospital-related mortality across datasets, which supported the adoption of mixed effects models. Unstandardized Xgboost BME demonstrated marked improvements in predictor power over the Xgboost model at small sample size ranges, but performance differences decreased as dataset sizes increased. Generalized linear models (glm) and generalized linear mixed-effects models (glmer) models followed similar results, with Xgboost models excelling also at greater sample sizes. Conclusions: These findings suggest that integrating mixed effects into machine learning models can enhance their applicability in clinical settings where sample size is small such as rare conditions.

This study explores the impact of artificial intelligence (AI) on digital marketing strategies, emphasizing how AI technologies have transformed marketing practices. AI's ability to analyze vast amounts of data has significantly enhanced targeting and personalization, enabling marketers to deliver highly relevant and tailored content to specific consumer segments. The integration of AI in content creation has streamlined processes, improving efficiency while maintaining high standards of quality and relevance. Customer engagement has also been revolutionized through AI-driven tools such as chatbots and predictive assistance, which provide real-time, personalized support and enhance overall user satisfaction. Despite the numerous benefits, the adoption of AI in digital marketing presents challenges, including data privacy concerns, algorithmic bias, high implementation costs, and the complexity of integrating AI with existing marketing systems. The shortage of skilled professionals further complicates effective AI utilization. Looking forward, the potential for AI to drive innovation in digital marketing is substantial, with opportunities for advancements in personalization, predictive analytics, and the integration of emerging technologies like virtual and augmented reality. However, it is essential for businesses to navigate these developments carefully, addressing ethical considerations and ensuring responsible use of AI to maintain consumer trust and achieve sustained success. This study provides insights into the transformative effects of AI on digital marketing and offers a comprehensive overview of the associated benefits and challenges, highlighting the need for a balanced approach to leveraging AI technology.

The accelerated development of AI technology has brought about revolutionary changes in various fields of society. Recently, it has been emphasized that Fairness, Accountability, Transparency, Ethics (Explainability) (FATE) should be considered to support the reliability and validity of AI-based decision-making. However, in the case of autonomous driving technology, which is directly related to human life and requires real-time adaptation and response to various changes and risks in the real world, environmental adaptability must be considered in a more comprehensive and converged manner. In order to derive definitive evidence for each object in a convergent autonomous driving environment, it is necessary to transparently collect and provide various types of road environment information for driving objects and driving assistance, and to construct driving technology that is adaptable to various situations by considering all uncertainties in the real-time changing driving environment.This allows unbiased and fair results based on flexible contextual understanding, even in situations that do not conform to rules and patterns, by considering the convergent interactions and dynamic situations of various objects that are possible in a real-time road environment. The transparent, environmentally adaptive, and fairness-based outcomes provide the basis for the decision-making process and support clear interpretation and explainability of decisions. All of these processes enable autonomous vehicles to draw reliable conclusions and take responsibility for their decisions in autonomous driving situations. Therefore, this paper proposes an Adaptability, Fairness, Transparency, Explainability and Accountability (AFTEA) Framework to build a stable and reliable autonomous driving environment in dynamic situations. This paper explains the definition, role, and necessity of AFTEA in artificial intelligence technology and highlights its value when applied and integrated into autonomous driving technology. The AFTEA framework with environmental adaptability will support the establishment of a sustainable autonomous driving environment in dynamic environments, and aims to provide a direction for establishing a stable and reliable AI system that adapts to various real-world scenarios.

During tensile testing of materials, strain measurement is conducted using either contact or non-contact methods. Contact methods offer high accuracy and precision but are limited by the specimen's thickness and dimensions, whereas non-contact methods minimize damage to thin specimens and allow measurements in various environments, though they require longer prep-aration and calculation times. This paper proposes a circular grid marking pattern and a strain prediction algorithm using artificial intelligence (AI), which simplifies the preparation process and allows strain prediction without additional equipment. The circular grid pattern can be ar-ranged in various configurations from 1×5 to 5×7, and a laser marker, which requires minimal time, was used to engrave the pattern on the specimen to shorten the preparation time. The AI model, trained on image-based data, enables strain calculation regardless of the specimen's gauge length and size, and allows measurement of local strain as well as gauge-length strain. The reliability of this concept was verified by applying it to tensile testing.

Interaction with social agents employing natural language have been discussed especially since Weizenbaum (1966) observed the social effects of his chatbot named 'Eliza', which imitated a cli-ent-centered conversation. With Nass and Reeves’ media equation theory and computers as social actors paradigm, a well-tested framework has been introduced to the study of human-computer interaction. In the 1990s, there was a renewed focus on natural language analysis, which has be-come a crucial area of research in machine learning. Recent advancements in data processing and storage capacities have led to the development of AI-generated utterances that closely resemble human utterances. As a result, AI-based systems can now mimic social relationships to a signifi-cant extent. In light of these technological developments, the article explores whether quasi-social relationships should be considered within the field of social psychology and examines the poten-tial advantages and challenges of this approach.

This qualitative study explores the transformative role of digital technologies in enhancing supply chain efficiency and marketing effectiveness across diverse industries. The integration of Internet of Things (IoT), artificial intelligence (AI), blockchain, and advanced analytics has reshaped organizational practices, enabling real-time data collection, analysis, and decision-making in supply chain management (SCM) and marketing. Through semi-structured interviews with 25 professionals, including supply chain managers, marketing executives, and IT specialists, insights were gathered into the adoption, integration, and impact of digital technologies within organizational contexts. Key findings highlight IoT's contribution to enhancing supply chain visibility, predictive maintenance, and operational efficiency through continuous monitoring and data-driven insights. AI technologies support demand forecasting, inventory optimization, and personalized marketing strategies, improving customer engagement and satisfaction. Blockchain enhances supply chain transparency, traceability, and security, reducing risks associated with fraud and ensuring compliance with regulatory standards. Advanced analytics provide organizations with actionable insights into consumer behavior and market trends, guiding strategic decision-making and optimizing marketing campaigns. Despite these benefits, organizations face challenges such as technological complexity, integration issues, data privacy concerns, and organizational resistance to change. Strategic planning, leadership support, and investment in digital infrastructure are essential for overcoming these challenges and maximizing the potential of digital technologies. Future research directions include exploring sustainability initiatives in digital SCM, advancing AI-driven analytics, and understanding digital transformation in emerging markets.

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

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

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

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

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

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

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

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

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

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

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

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

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

In the dynamic realm of agribusiness construction, firms are striving to bolster their operational efficiency and profitability amidst the global shift from traditional farming to commercial agriculture. This transition has intensified the demand for sophisticated infrastructure development, presenting new challenges for commercial managers. A critical hurdle is the accurate estimation of profitability for prospective contracts, a task often reliant on intuition rather than data-driven methods. To address this, we propose the development of a mathematical model utilising machine learning techniques to predict contract profitability and identify influential factors. This model aims to aid in bid decision-making, financial forecasting, and enhancing competitiveness in the marketplace. Furthermore, it would provide valuable insights into how altering specific contract attributes might affect predicted profitability. The implementation of such a system necessitates close collaboration between IT professionals and construction executives. This study delineates the development of this predictive model, outlining the data analysis process and the application of machine learning algorithms to tackle this complex commercial challenge. The ultimate goal is to bridge the gap between intuitive estimation and data-driven prediction, thereby enhancing the financial performance and strategic decision-making capabilities of firms in the agribusiness construction sector.

In this study, we address the need for improved landslide forecasting by exploring innovative machine learning approaches to classify shallow landslide events. Focusing on Ometepe Island, Nicaragua, known for its susceptibility to hydrometeorological incidents and seismic activities, we constructed a comprehensive shallow landslide database using coherence layers derived from SAR images. We applied coherence thresholds ranging from 0.9 to 0.99, in conjunction with 7-day aggregated spatio-temporal precipitation data. By employing machine learning models such as Logistic Regression, Random Forest, and Support Vector Classifier, we established a correlation between precipitation patterns and landslide occurrences. The Support Vector Classifier with a Sigmoidal kernel proved to be the most effective, achieving an F1 score of 0.67. Our results demonstrate a linkage between precipitation and landslide events, confirming the critical role of rainfall over 7-day periods in triggering these events. The study also indicates that, while rainfall is a crucial factor, it is not the sole trigger for landslides, as seismic activities also play a significant role. This research contributes valuable insights into landslide prediction and risk assessment, underscoring the potential of machine learning in enhancing environmental hazard analysis.

Accurate, rapid, and stable prediction of electrical energy consumption is essential for decision-making, energy management, efficient planning, and reliable power system operation. Errors in forecasting can lead to electricity shortages, wasted resources, power supply interruptions, and even grid failures. Accurate forecasting enables timely decisions for secure energy management. However, predicting future consumption is challenging due to the variable behavior of customers, requiring flexible models that capture random and complex patterns. Existing forecasting methods, both traditional and modern, have limitations and do not fully meet accuracy expectations. To address these issues, this research introduces a hybrid models that combine FCRBM based forecaster, and GWDO based optimizer, namely FS-FCRBM-GWDO approach to enhance the model performance in STLF have been developed. While some models excel in accuracy and others in convergence rate, both aspects are crucial. The main objective is to create a forecasting model that provides reliable, consistent, and precise predictions for effective energy management. This led to the development of a novel two-stage hybrid model. The first stage predicts electrical energy usage through four modules using deep learning, support vector machines, and optimization algorithms. The second stage optimizes energy management based on predicted consumption, focusing on reducing costs, managing demand surges, and balancing electricity expenses with customer inconvenience. This approach benefits both consumers and electricity corporations by lowering bills and enhancing power system stability. Simulation results validate the proposed model's efficacy and efficiency compared to benchmark models.

This research explores the adoption of advanced analytics in supply chain decision-making, emphasizing its transformative impact on organizational efficiency and strategic advantage. The study investigates the key drivers behind the adoption of advanced analytics, including the growing complexity of data, the need for agility, and the pursuit of competitive advantage and cost reduction. It identifies significant challenges such as data integration difficulties, high implementation costs, and a shortage of skilled professionals, which organizations face when integrating these sophisticated tools. Despite these challenges, the research highlights the substantial benefits of advanced analytics, including enhanced forecasting accuracy, improved supply chain visibility, optimized processes, and cost savings. The findings indicate that while the implementation of advanced analytics varies across different sectors—such as retail, manufacturing, logistics, and healthcare—the overarching themes of improved efficiency and better decision-making are consistently observed. The study also discusses future trends, noting the integration of emerging technologies such as artificial intelligence, machine learning, and blockchain, which promise to further enhance analytics capabilities and accessibility. The successful adoption of advanced analytics relies not only on technological advancements but also on fostering a data-driven organizational culture, investing in continuous learning, and ensuring robust data governance. As supply chain management continues to evolve, advanced analytics will play a crucial role in driving strategic success and maintaining a competitive edge in a complex and dynamic market environment.

Digitalization plays a crucial role in improving the performance of chemical companies. In this context, different modeling, simulation, and optimization techniques like MILP, discrete-event simulation (DES), and data-driven (DD) models are being used. Due to their heterogeneity, these techniques must be executed individually, and holistic optimization is a manual and time-consuming process. We propose Benders decomposition to combine these techniques into one rigorous optimization procedure. The main idea is that heterogeneous models can simultaneously be optimized as Benders subproblems. We illustrate this concept with the distributed permutation flow shop scheduling problem (DPFSP) and assume that a MILP, DES, and DD model exist for three flow shops. Our approach can compute bounds and report gap information on the optimal makespan for five medium-sized literature instances. The approach is promising because it enables the optimization of heterogeneous models and makes it possible to build optimization capabilities on an existing model and tool landscape in chemical companies.

Smart Transformer (ST)-based Meshed Hybrid AC/DC Microgrids (MHM) are a feasible alternative to increase the performance of conventional microgrids (MG) and to increase the penetration of Distributed Energy Resource (DER) at the same time, active and reactive power dispatch on the system. Despite this, MHMs present challenges in managing resources under uncertainties and controlling electronic converters associated with the ST and DER, making it complex to achieve optimal system performance. In this paper, a Data-Driven Distributionally Robust Optimization (DDDRO) for Day-Ahead Operation Planning of an ST-based MHM considering the reactive power dispatch of the DER that minimizes network losses, voltage deviations, and operation cost simultaneously, considering uncertainties of Photovoltaic Generators (PVG) and demand, is proposed. The Column and Constraint Generation (C&CG) algorithm and Duality-Free Decomposition (DFD) method are adopted. Besides, the original mixed-integer non-linear planning problem is converted into a Mixed-Integer (MI) Second-Order Cone Programming (SOCP) problem through second-order cone relaxation and positive octagonal constraint method. Simulation experiments and results with a connected MHM system demonstrate the effectiveness and performance of the proposed model. Finally, we observe the effect of the meshed structure of MG and the positive impact of integrating the ST to form MHM, taking advantage of the degrees of freedom of this multi-stage converter for optimal energy management under uncertain conditions.

The ankle is a complex joint with a high injury incidence. Rehabilitation Robotics applied to the ankle is a very active research field. We present cable-driven reconfigurable robot kinematics and statics. We studied how the tendons pull mid-foot bones around the talocrural and subtalar axes. Likewise, we propose a hybrid serial-parallel mechanism analogous to the ankle. Then, using screw theory, we synthesized a cable-driven robot with the human ankle in the closed-loop kinematics. We incorporate a draw-wire sensor to measure the axes’ pose and compute the product of exponentials. We reconfigure the cables to balance the tension and pressure forces using the axis projection on the base and platform planes. Likewise, we also computed the workspace to show that the reconfigurable design fits several sizes. The data used is from anthropometry and statistics. Finally, we validated the robot’s statics with MuJoCo for various cable length groups corresponding to the axes’ range of motion. We suggested a platform adjusting system and an alignment method. The design is lightweight, and the cable-driven robot has advantages over rigid parallel robots, such as Stewart platforms. We will use compliant actuators.

The COVID-19 led to changes modifying the role of digital transformation and unleashing unparalleled opportunities to improve business production processes in all three dimensions of sustainability - economic, social, and environmental through data-driven innovative service implications that significantly impact restaurant business, resulting in an increased competitive advantage. This paper identifies the innovative service implications and summarizes sustainable social systems measures in the context of strategic management in the restaurant industry triggered by COVID-19. Based on in-depth interviews with sixteen key experts in the restaurant industry, the paper clarifies and concretizes the rapidly transforming problem of identifying the main changes in the restaurant business, using three dimensions: consumer demand, corporate strategy optimization, and the use of the company's innovative potential. The findings reveal how companies need to expand their digital strategies and adapt their management approach to all stakeholders bringing sustainable business development in the post-pandemic period into focus. This paper serves as a framework for understanding the digital transformation role in resilient business management within restaurant social systems, with friction occurring along the workflow from kitchen to customer.

This paper focuses on quantifying the economic and financial viability of NB-IoT and LoRaWAN technologies, two low-power wide-area network (LPWAN) technologies with unique characteristics that make them suitable for IoT applications. The purpose of the study is to propose an artefact for performing life cycle cost analysis and demonstrate its application to these technologies. The methodology uses pragmatic computational tools to facilitate the analysis and considers all relevant economic and financial factors, such as operating costs, equipment costs, and revenue potential. The main finding of the study is that NB-IoT and LoRaWAN technologies have different cost structures and revenue potentials, which may affect their economic and financial viability for different IoT applications. Ultimately, the study concludes that a comprehensive life cycle cost analysis is critical to making informed decisions about technology adoption, and that the proposed methodology can be applied to other IoT technologies to gain insight into their economic and financial viability.

Avian sex chromosomes evolved after the divergence birds and crocodilians from their common ancestor, so they are much younger than the better-studied chromosomes of mammals. It has long been recognized that there may have been several stages to the evolution of avian sex chromosomes. For example, the CHD1 undergoes recombination in paleognaths but not neognaths. Genome assemblies have suggested there may be variation in the timing of barriers to recombination among Neognathae, but there remains little understanding of the extent of this variability. Here, we look at partial sequences of ATP5F1A, which is on the avian Z and W chromosomes. It is known that recombination of this gene has independently ceased in Galliformes, Anseriformes, and at least five neoavian orders, but whether there are other independent cessations of recombination among Neoaves is not understood. We used a combination of data extracted from published chromosomal-level genomes with data collected using PCR and cloning to identify Z and W copies in 22 orders. Our results suggest there may be at least 19 independent cessations of recombination within Neognathae, and 3 clades that may still be undergoing recombination (or have only recently ceased recombination). Analyses of ATP5F1A protein sequences revealed an increased amino acid substitution rate for W chromosome gametologs, suggesting relaxed purifying selection on the W chromosome. Supporting this hypothesis, we found that the increased substitution rate was particularly pronounced for buried residues, which are expected to be more strongly constrained by purifying selection. This highlights the dynamic nature of avian sex chromosomes, and that this level of variation among clades means they should be a good system to understand sex chromosome evolution.

Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal, and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling and control become challenging problems. On one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behaviour. Therefore, the development of modelling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of development of robust control strategies when applied to a simulated wind turbine plant. Experiments with the wind turbine simulator and the Monte–Carlo tools represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model-reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics.

Increasingly, there is a focus on utilising renewable energy resources in a bid to fulfil increasing energy requirements and mitigate the climate change impacts of fossil fuels. While most renewable resources are free, the technology used to usefully convert such resources is not and there is an increasing focus on improving the conversion economy and efficiency. To this end, advanced control technologies can have a significant impact and is already a relatively mature technology for wind turbines. Though hydroelectric plants can use simple regulation systems, significant benefits have been shown to accrue from the appropriate use of the same control methods designed for wind turbine plants. This represents the key point of the paper. In fact, to date, the application communities connected with wind and hydraulic energies have had little communication, resulting in little cross fertilisation of control ideas and experience, particularly from the more mature wind area to hydrodynamic systems. Therefore, this paper examines the models and the application of control technology across both domains, both from a comparative and contrasting point of view, with the aim of identifying commonalities in models and control objectives, as well as potential solutions. Key comparative reference points include the articulation of the exployed models, specification of control objectives, development of high--fidelity simulators, and development of solution concepts. Not least, in terms of realistic system requirements are the set of physical and constraints under which such renewable energy systems must operate, and the need to provide reliable and robust control solutions, which respect the often remote and relatively inaccessible location of many onshore and offshore deployments.

The rapid increase in unstructured data across various fields has made multi-document comprehension and summarization a critical task. Traditional approaches often fail to capture relevant context, maintain logical consistency, and extract essential information from lengthy documents. This paper explores the use of Long-context Large Language Models (LLMs) for multi-document summarization, demonstrating their exceptional capacity to grasp extensive connections, provide cohesive summaries, and adapt to various industry domains and integration with enterprise applications/systems. The paper discusses the workflow of multi-document summarization for effectively deploying long-context LLMs, supported by case studies in legal applications, enterprise functions such as HR, finance, and sourcing, as well as in the medical and news domains. These case studies show notable enhancements in both efficiency and accuracy. Technical obstacles, such as dataset diversity, model scalability, and ethical considerations like bias mitigation and factual accuracy, are carefully analyzed. Prospective research avenues are suggested to augment the functionalities and applications of long-context LLMs, establishing them as pivotal tools for transforming information processing across diverse sectors and enterprise applications.

This study examines the feasibility and efficacy of utilizing generative AI technology to create inclusive online dating educational materials, focusing on integrating easy-to-read principles to enhance accessibility while improving efficiency and reducing the cost of material production. As online dating becomes increasingly prevalent, providing easily comprehensible safety education for special populations, such as individuals with disabilities and adolescents, is of paramount importance. This research developed an innovative AI-generated educational framework that combines easy-to-read principles with generative AI technology, aiming to enhance both the accessibility and production efficiency of educational materials. The study compared three AI generation strategies: independent generation by ChatGPT, independent generation by Claude, and Claude utilizing materials generated by ChatGPT. We evaluated 36 educational units through a mixed-methods approach for readability and accessibility. Results indicate that ChatGPT performed optimally in generating easily readable content (average compliance rate of 93.00%), followed by the strategy Claude using ChatGPT-generated materials (91.67%). Statistical analysis revealed significant differences among these strategies (p < 0.05). The research also found that employing AI generation technology significantly reduced material production time and costs, providing educators with a more efficient tool. The findings of this study underscore the significant potential of generative AI technology in creating inclusive educational resources. This not only opens up new avenues for enhancing online safety awareness and educational accessibility for special populations but also provides material developers with more efficient, cost-effective production methods. The empirical evidence presented here for the application of AI in inclusive education is a significant step forward. The study also proposes recommendations for future research and practice, aiming to further improve the accessibility, effectiveness, and production efficiency of online dating education.

The rapid advancement of artificial intelligence (AI) brought about significant opportunities and challenges in educational environment. It is disputable whether the application of AI to education is beneficial or not regarding human learning and teaching. This study aims at exploring the implications of the evolving educational landscape by comparing and discussing human and AI learning processes. Even though AI is not a conscient agent, it does not spell out any words randomly but provide meaningful texts with any reasoning. It helps us to understand human learning better by comparing learning in biological and artificial intelligences. Moreover, the new concepts in learning in AI come about the change of learning paradigm such as connectionism/connectivism. After all, AI will empower the abilities of learning and teaching for students as well as teachers. This study will give some implications for the future education with AI.

Human-elephant conflict has become a conservation challenge in Asia and Africa resulting in considerable human fatalities annually. We deployed an AI-embedded camera-alert system in a conflict hotspot in West Bengal, India to determine if AI algorithms could accurately detect elephants and if real-time alerts that triggered a rapid response could prevent serious incidents. The system successfully detected elephants near villages (266 events) and transmitted real-time alerts (x̄ = 42 seconds) permitting quick response by field personnel to prevent conflict (x̄ = 18.2, SD = 12.4 min to arrival). Matriarch herds of elephants were detected mostly at night when human traffic was minimal; lone adult bulls, the class most prone to conflict, were detected day and night. Frequent detections of humans (33,217 events) on routes used by elephants highlighted the value of real-time monitoring especially during daytime. Twelve human fatalities and eighteen serious injuries occurred in the study area between 2019-2023 but none during deployment of the AI system, supporting the hypothesis that alerts integrated with rapid response can reduce conflict. Loss of lives and livelihoods underscore the urgency of applying this approach to other elephant ranges and adapting AI models to conflict-prone species to provide early warning and promote coexistence.

This study explores the impact of artificial intelligence (AI) on financial inclusion satisfaction and recommendation, focusing on ethical dimensions and perceived algorithmic fairness. From the perspectives of organizational justice theory and the heuristic-systematic model, we examine how constructs of algorithm transparency, algorithm accountability, and algorithm legitimacy influence users' perceptions of fairness, and subsequently, their satisfaction with and recommendation of AI-driven financial inclusion. Through a survey-based quantitative analysis, our results indicate that perceived algorithmic fairness acts as a mediating factor between the ethical attributes of AI systems and user satisfaction as well as their recommendation. Findings reveal that higher levels of transparency, accountability, and legitimacy enhance customers' perceptions of fairness, which in turn significantly increases both their satisfaction with financial inclusion services facilitated by AI and their likelihood to recommend them. This research not only contributes to the literature on AI ethics by highlighting the critical role of transparent, accountable, and legitimate AI practices in fostering satisfaction and recommendation among users, but also fills a significant gap in understanding the ethical implications of AI in financial inclusion contexts.

Estimating the ocean’s subsurface thermohaline information from satellite measurements is essential for understanding ocean dynamics and El Niño phenomenon. This paper proposes an improved double-output Residual Neural Network (DO-ResNet) model to concurrently estimate the ocean subsurface temperature (OST) and ocean subsurface salinity (OSS) in the tropical Western Pacific using multi-source remote sensing data, including sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly(SSHA), sea surface wind (SSW), and geographical information (including longitude (LON) and latitude (LAT)). In the model experiment, Argo data were used to train and validate the model, and the root mean square error (RMSE), normalized root mean square error (NRMSE) and coefficient of determination (R²) were employed to evaluate the model's performance. The results showed that the sea surface parameters selected in this study have a positive effect on the estimation process, and the average RMSE and R² values for estimating OST (OSS) by the proposed model are 0.34 °C (0.05 psu) and 0.91 (0.95), respectively. Under the data conditions considered in this study, DO-ResNet demonstrates superior performance relative to the extreme gradient boosting model, random forest model, and artificial neural network model. Additionally, this study evaluates the model’s accuracy by comparing its estimations of OST and OSS across different depths with Argo-derived data, demonstrating the model's ability to effectively capture the most spatial features, and by comparing NRMSE across different depths and seasons, the model displays strong seasonal adaptability. In conclusion, this research introduces a novel artificial intelligence technique for estimating OST and OSS in the tropical Western Pacific Ocean.

Conversational artificial intelligence (AI) has shown strong capabilities in providing low-cost and timely interventions for a wide range of people and increasing well-being. Therefore, this study examined ChatGPT’s efficacy in conducting psychological counseling and its potential for providing accessible psychological interventions, especially in school settings. This study assessed the strengths and limitations of AI to inform future research. Data were generated from ChatGPT’s responses to 80 counseling questions that college students asked during a school counseling setting. All the responses generated during these simulated counseling sessions were then analyzed using three primary metrics—warmth, empathy, and acceptance—following APA guidelines. The analysis adopted several natural language processing methodologies for emotion detection and empathy measurement to quantify ChatGPT’s high efficacy in presenting the appropriate emotions and reactions for counseling. Some responses demonstrated randomness, which is crucial for consideration regarding the use of ChatGPT or relevant technology in psychological counseling. The Discussion section also highlights future directions in the application of conversational AI to enhance students’ mental health.

Given the widespread use of AI language models such as ChatGPT, there is an urgent need 1 to understand their political biases in different languages. This study aims to uncover such biases. To 2 investigate this issue, we applied IDRLabs’ Political Coordinates Test to ChatGPT based on GPT-4 3 in four languages - English, French, German, and Italian. The results confirm previous studies that 4 identified a left-libertarian bias in ChatGPT. There was remarkable consistency in political orientation 5 across all four languages, although minor variations were found. Differences in ChatGPT response 6 distributions across languages could indicate culturally determined differences in interpretation 7 of political questions. The results highlight the need to diversify and optimize data collection and 8 model training methods to ensure greater fairness and neutrality and minimize the influence of bias. 9 Our study highlights the importance of cultural differences in the processing and interpretation of 10 political information by AI models.

In this work, we explore the ability to dynamically group the Wind Turbine (WT) of a Wind Farm (WF) based on the behaviour of some of their Supervisory Control And Data Acquisition (SCADA) signals to detect the turbines that exhibit abnormal behaviour. We centre this study on a small WF of five WTs. We use the fact that the same signals from different turbines in the same WF coherently evolve temporally in a time domain, describing very similar waveforms. In this contribution, we use averaged signals from the SCADA system and omit maximums, minimums and standard deviations, focusing mainly on velocities and other slowly varying signals. For the temporal analysis, sliding windows of different temporal durations and overlapings are explored. The capability to automatically identify WTs whose signals differ from the group’s behaviour can alert and program preventive maintenance operations on such WTs before a major breakdown occurs.

The dynamic landscape of AI adoption and its impact on employment in Malaysia demands a multifaceted approach to navigate the challenges and harness the opportunities presented. While the government's commitment to fostering AI adoption through initiatives like the National AI Framework and the Malaysia Digital Economy Blueprint is commendable, it is imperative to address concerns regarding job displacement and ensure inclusive growth. This study identifies the pressing research problem of workforce readiness for the AI-driven future, emphasizing the need to explore strategies for reskilling and upskilling affected workers. By employing critical lenses such as Marxist, feminist, and postcolonial perspectives, scholars highlight the power dynamics and societal implications inherent in AI deployment, guiding the development of policies and interventions that prioritize workers' interests and address social inequalities. Moving forward, a comprehensive approach combining policy interventions, educational reforms, and inclusive strategies is essential to maximize the benefits of AI adoption while mitigating its potential risks. Longitudinal studies, interdisciplinary research, and comparative analyses will further advance our understanding and inform evidence-based policymaking in this evolving field. Ultimately, by prioritizing workforce preparedness, social equity, and sustainable development, Malaysia can position itself as a leader in the AI-driven global economy, fostering a resilient, inclusive, and prosperous society for all.

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

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

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

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

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

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

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

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

Digital marketing adoption in the multinational Fast-Moving Consumer Goods (FMCG) sector presents both challenges and opportunities that significantly impact organizational strategies and market competitiveness. This qualitative study explores the complexities surrounding digital marketing adoption through interviews with 45 participants from multinational FMCG companies. The research investigates key barriers such as organizational resistance, technological constraints, and concerns over data privacy and security, while also examining the benefits of enhanced consumer engagement, global market reach, and data-driven decision making. Findings reveal that organizational resistance remains a formidable barrier, driven by senior management's cautious approach to digital transformation and concerns over return on investment (ROI) amidst rapidly evolving digital landscapes. Technological challenges, including outdated systems and limited budgets for technology upgrades, further hinder comprehensive digital integration. Moreover, navigating regulatory frameworks and addressing consumer trust in data privacy emerge as critical considerations for FMCG companies seeking to leverage digital strategies effectively. Conversely, digital marketing adoption offers substantial opportunities. Enhanced consumer engagement through personalized and interactive campaigns allows companies to strengthen brand-consumer relationships and foster loyalty. The global reach facilitated by digital platforms enables multinational FMCG firms to expand market presence, penetrate new markets, and adapt marketing strategies to diverse consumer preferences worldwide. Data-driven decision making emerges as pivotal, leveraging analytics to optimize marketing strategies, measure campaign effectiveness, and anticipate consumer behaviors in real-time. Strategies for overcoming barriers emphasize building internal digital capabilities through training, recruitment, and cross-functional collaboration. Aligning digital strategies with overarching business objectives and integrating digital efforts with traditional marketing channels are crucial for creating cohesive and effective campaigns. Embracing innovation, flexibility, and agility in adapting to technological advancements are essential for maintaining competitiveness and relevance in a rapidly evolving digital landscape. Looking forward, emerging trends such as AI and machine learning integration for predictive analytics, sustainability-focused marketing initiatives, and the evolution towards omnichannel strategies are poised to reshape digital marketing practices in the FMCG sector. By aligning strategies with these trends, FMCG companies can harness digital technologies to drive sustainable growth, enhance market competitiveness, and meet evolving consumer expectations in a digitally-driven global marketplace.

Background: Learning arrangements in health care profession education are increasingly taking place in digital environments. VR in nursing education, as one of the digital elements, is the subject of controversial debate. On the one hand it supports the authenticity of case studies by adding realistic perspectives and information. On the other hand, the costs of developing and maintaining software and hardware hinder long-term implementation. Methods: In this paper, we describe our findings and insights from two different research projects focused on the incorporation of digital tools, particularly virtual reality (VR), into nursing education. Based on the German context, our aim is to promote the adoption of innovative digital methods in nursing education and to offer invaluable experiences from the field. Results: Starting with a brief recapitulation of the projects, we then elucidate the pedagogical strategies for embedding VR-driven scenarios in nursing education. Based on our experiences during the projects we identify various positive aspects, such as change of perspective and simulating acute situations. Key findings: Although potential drawbacks remain, we advocate long-term implementation and specific use of VR at the interface between theory and practice. Nevertheless it is crucial to establish regular evaluation observing the value of digitalisation, especially VR; for nursing education.

Business school curriculums are designed to improve business skills and a student’s eventual workplace performance. In addition to these business skill sets the emerging business environment demands softer skills associated with ethical decision-making and sustainable business practices. Understanding the key influencers of ethical orientation and attitudes towards the environment is the first critical step for curriculum planning designed to develop both ethical decision-making and environmental sensibilities of students in business schools. Using a bivariate regression analysis (OLS) that compared the established New Ecological Paradigm (NEP) scale and the newly introduced Ethical Orientation Scale (EOS), this study assesses environmental eco-consciousness and ethical orientation over time and across varying socio-demographic variables. The study shows first, that in addition to socio-cultural variables, situational factors influence ethical decision-making. Secondly, it illuminates that ethical orientations as measured by the EOS predicts beliefs about the environment as measured by the NEP scale. It further provides evidence of the ethical underpinnings of the New Ecological Paradigm as well as provides initial validation for the new EOS. These outcomes provide additional levers to assist business educators in the creation of high impact teaching strategies to measure and encourage ethical decision-making and sustainable business practices that protect the environment.

Validation and verification are the critical requirements in the knowledge acquisition method for the clinical decision support system (CDSS). After acquiring the medical knowledge from diverse sources, the rigorous validation and formal verification process are required before creating the final knowledge model. Previously, we have proposed a hybrid knowledge acquisition method for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data in the Smart CDSS for treatment of oral cavity cancer. The final knowledge model was created by combining knowledge models obtained from CPGs and patient data after passing through a rigorous validation process. However, detailed analysis shows that due to lack of formal verification process, it involves various inconsistencies in knowledge relevant to the formalism of knowledge, conformance to CPGs, quality of knowledge, and complexities of knowledge acquisition artifacts. Therefore, it is required to enhance a hybrid knowledge acquisition method that thwarts the inconsistencies using formal verification. This paper presents the verification process using the Z formal method and its outcome as an enhanced acquisition method – known as the refined knowledge acquisition (ReKA) method. The ReKA method adopted verification method and explored the mechanism of theorem proving using the Z notation. It enables to identify inconsistencies in the validation process used for hybrid knowledge acquisition. Additionally, it refines the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. Consequently, ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. The criteria ensure the validity of final knowledge model concerning formalism of knowledge, conformance to GPGs, quality of the knowledge, usage of stringent conditions and treatment plans, and inconsistencies possibly resulting from the complexities. Evaluation, using four medical knowledge acquisition scenarios, shows that newly added knowledge in CDSS due to the addition of criteria by ReKA method always produces a valid knowledge model. The final knowledge model was also evaluated with 1229 oral cavity patient cases, which outperformed with an accuracy of 72.57\% compared to a similar approach with an accuracy of 69.7\%. Furthermore, ReKA method identified a set of decision paths (about 47.8%) in the existing approach, which results in a final knowledge model with low quality, non-conformed from standard CPGs. In conclusion, ReKA is formally proved method which always yields valid knowledge model having high quality, supporting local practices, and influenced from standard guidelines.

The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, data--driven control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Some of the considered methods, such as fuzzy and adaptive self--tuning controllers, were already verified on wind turbine systems, and similar advantages may thus derive from their appropriate implementation and application to hydroelectric plants. These issues represent the key features of the work, which provides some guidelines on the design and the application of these control strategies to these energy conversion systems. The working conditions of these systems will be also taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many installations.

The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, self--tuning control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Some of the considered methods were already verified on wind turbine systems, and important advantages may thus derive from the appropriate implementation of the same control schemes for hydroelectric plants. This represents the key point of the work, which provides some guidelines on the design and the application of these control strategies to these energy conversion systems. In fact, it seems that investigations related with both wind and hydraulic energies present a reduced number of common aspects, thus leading to little exchange and share of possible common points. This consideration is particularly valid with reference to the more established wind area when compared to hydroelectric systems. In this way, this work recalls the models of wind turbine and hydroelectric system, and investigates the application of different control solutions. The scope is to analyse common points in the control objectives and the achievable results from the application of different solutions. Another important point of this investigation regards the analysis of the exploited benchmark models, their control objectives, and the development of the control solutions. The working conditions of these energy conversion systems will be also taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many installations.

The fault diagnosis of wind farms has been proven to be a challenging task and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model, that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.

By 2024, artificial intelligence (AI) will have had a revolutionary impact on cyber security, putting the sector at a critical turning point. This article explores the significant influence AI has had on cyber security procedures, outlining the progress made as well as the difficulties faced. artificial intelligence (AI) has transformed threat detection and response systems, allowing for proactive security tactics against a constantly changing array of cyber threats. By utilizing neural networks and machine learning algorithms, artificial intelligence (AI) systems can predict possible security breaches, detect unusual trends, and automatically reduce risks in real-time. These capabilities are unparalleled. Nevertheless, despite these impressive advancements, worries about data privacy, adversarial attacks, and ethical ramifications remain significant. Moreover, the never-ending game of cat and mouse between cyber attackers and defenders continues, requiring constant innovation and adaptability in cyber security strategies driven by Artificial Intelligence. In addition to providing insights into its implications for protecting digital assets and bolstering resilience against cyber threats in the changing cyber security scenario of 2024, this paper summarizes the current state of the artificial intelligence revolution in cyber security.

Reminding the courier through wearable artificial intelligence devices to prevent accidents is not only beneficial to the courier's safety but also will save a lot of insurance premiums for express companies, therefore, it is worth investigating what factors can influence the acceptance of wearable artificial intelligence devices by couriers. Push-pull-mooring (PPM) theory and affective event theory (AET) are integrated to test couriers’ adoption to wearable safety detection devices. Social influence, perceived security, personal innovativeness and affective event reaction are applied to research model. Questionnaires are distributed among several listed express companies and 263 valid questionnaires are used for empirical test. Empirical results indicated that social influence, perceived safety, personal innovativeness and affective event reaction are positively related to usage with coefficients 0.218, 0.301, 0.698 and 0.309. Personal innovativeness has positively moderating effects on relationships between affective event reaction, perceived security and usage with coefficients 0.145 and 0.106, whereas has no significant moderating effect on relationship between social influence and usage. The research aims to help support the proliferation and adoption of wearable artificial intelligence devices to optimize the current state of the express industry and improve the interaction between couriers and managers, creating an active management strategy that will allow express companies to thrive. This study can help express companies reduce insurance costs and therefore reduce the premium pricing of insurance companies for the courier industry.. This study also provides design suggestions for the application of wearable artificial intelligence devices based on chat GPT in various working conditions.

Sentiment analysis is a pivotal tool for gauging the public’s perception and understanding of human communication across digital social media platforms. However, due to linguistic complexities and limited resources, sentiment analysis is not well-represented in many African languages. While benchmark Africa-Centric Pre-trained Language Models (PLMs) have been developed for various Natural Language Processing (NLP) tasks, their applications in eXplainable Artificial Intelligence (XAI) remain unexplored. In this study, we introduce a novel approach that combines Africa-centric PLMs with XAI techniques for sentiment analysis. We demonstrate that applying attention mechanisms and visualisation techniques improves the transformer-based model’s transparency, trustworthiness, and decision-making abilities when making sentiment predictions. We then employ the SAfriSenti—a multilingual sentiment corpus for South African under-resourced languages. We use the corpus to perform various sentiment analysis experiments and also enable comprehensive evaluations, comparing the performance of Africa-centric models against mainstream PLMs. The Afro-XLMR model outperformed all models and achieved an average F1-score performance of 71.04% across the five tested languages and the lowest error rate among the evaluated models. Additionally, we incorporated techniques like Local Interpretive Model-Agnostic Interpretation (LIME) and Shapley Additive Interpretation (SHAP) in the sentiment classifier’s output to enhance the Afro-XLMR model’s interpretability and explainability. As a result, the use of XAI strategies ensures that sentiment predictions are not only accurate and interpretable but also understandable, fostering trust and reliability in the decision-making of AI-driven NLP technologies, particularly in the context of African languages.

This study focuses on the development and evaluation of an AI-based digital therapeutic prototype for adolescent mental health management and disaster response. The system integrates real-time monitoring, AI-driven conversation analysis, personalized psychological treatment programs, and multimodal data analysis. An algorithm was developed to detect gaslighting and verbal abuse using a BERT-based classification model, achieving 85% accuracy in gaslighting detection and 87% ac-curacy in verbal abuse detection. Additionally, a disaster psychological recovery support module was included, which demonstrated a 30% improvement in users' stress reduction rates in simulated disaster scenarios. The results of this study suggest that such digital therapeutic approaches can significantly contribute to early intervention in adolescent mental health issues and provide effec-tive support during disasters. The developed prototype demonstrates the potential of AI and digital technology to innovate mental health management and disaster response strategies.

Artificial intelligence (AI) has emerged as a transformative force in retail operations, promising significant advancements in efficiency, customer engagement, and competitive positioning. This research explores the integration of AI in retail, focusing on its potential benefits, challenges, and future trends. Through a comprehensive analysis, the study identifies key motivations for AI adoption, such as enhancing operational efficiency, optimizing inventory management, and personalizing customer interactions. Retailers are increasingly leveraging AI to streamline processes and gain insights into consumer behavior, which allows for more strategic decision-making and improved customer experiences. However, the implementation of AI is not without its obstacles. High costs, the need for specialized skills, and concerns about data privacy and ethical use of AI present substantial challenges. The impact of AI on employment, including job displacement and the necessity for reskilling, is also a critical consideration. Future trends suggest that AI will continue to drive innovation in retail, with developments in AI-powered analytics, autonomous retail solutions, and sustainability initiatives shaping the industry’s future. The study highlights the importance of addressing these challenges and ethical considerations to fully realize the potential of AI in retail. The findings emphasize the need for retailers to adopt a strategic approach to AI integration, balancing innovation with responsible practices to achieve long-term success. Overall, this research provides valuable insights into the evolving role of AI in retail and offers a foundation for understanding the complexities and opportunities associated with its adoption.

Smart grids represent the evolution of traditional electrical grids through the integration of digital and communication technologies. These grids are capable of monitoring and managing the distribution of electricity efficiently and resiliently. Artificial Intelligence (AI) plays a crucial role in this context, offering advanced solutions for optimization, forecasting, and energy management. This paper explores the main AI technologies applied to smart grids, highlighting their advantages, challenges, and impacts on energy efficiency and sustainability. The methodology employed includes a comprehensive literature review, analysis of current AI applications in smart grids, and examination of case studies to illustrate the practical implementation and benefits of AI technologies in this field.

The development of new turbomachinery designs requires numerous time-consuming and computationally intensive computational fluid dynamics (CFD) calculations. However, most of the generated high spatial resolution data remains unused at later development steps. That is also the case with automated optimization processes that use only a few integral values to determine objectives and constraints. Therefore the development of a data-driven AI model was initiated to ensure the potential of further use of the CFD data which is also used to train the AI model. The presented method subsequently provides a fast approximation of the 3D flow for new designs. In this paper, the structure of the developed AI model is presented and the approximation quality is analysed using a complex, state-of-the-art compressor test case. It is shown that the AI model can reproduce many characteristics of the 3D flow of new designs, and performance measures such as efficiency can be derived from these flow predictions. In addition, the complex test case revealed that greater design variation reduces the AI approximation quality which can lead to undesirable exploratory behaviour in an optimisation setup. Overall, the test case has shown promising results and has provided hints for further improvements of the AI model.