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

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.

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

Conventional cancer clinical trials can be time-consuming and expensive, often yielding results with limited applicability to real-world scenarios and presenting challenges for patient participation. Real-world data (RWD) studies offer a promising solution to address evidence gaps and provide essential information about the effects of cancer treatments in real-world settings. Defining RWD based on its original intent, which is the collection of data at the point of care for clinical purposes, can distinguish it from conventional clinical trial data. RWD can be generated using experimental designs that resemble those used in conventional clinical trials, but with several advantages, such as improved efficiency and a better balance between internal and external validity. RWD can support pharmacovigilance, insights into disease progression, and the development of external control groups. Prospective RWD collection enables evidence generation based on pragmatic clinical trials (PCTs) that support randomized study designs and expand clinical research to the point of care. To ensure the quality of real-world studies, it is crucial to implement auditable data abstraction methods and develop new incentives to capture clinically relevant data electronically at the point of care. The treatment landscape is constantly evolving, with the integration of front-line immune checkpoint inhibitors (ICIs), either alone or in combination with chemotherapy, affecting subsequent treatment lines. Real-world effectiveness and safety in underrepresented populations, such as the elderly, patients with poor performance status (PS), hepatitis, or human immunodeficiency virus-infected patients, are still largely unexplored. Similarly, the cost-effectiveness and sustainability of these innovative agents are important considerations in the real world.

(1) Background: Although diagnostic criteria for massive hemorrhage with organ dysfunction such as disseminated intravascular coagulation associated with delivery have been empirically established based on clinical findings, strict logic has yet to be used to establish numerical criteria. (2) Methods: A dataset of 107 deliveries with ≥2,000 g of blood loss, among 13,368 deliveries from nine national perinatal centers in Japan between 2020 and 2023 was obtained. Twenty-three patients had fibrinogen levels <170 mg/dl, which is the initiation of coagulation system failure per our previous reports. Three of these patients had hematuria. We used six machine learning methods to identify the borderline criteria dividing the fibrinogen/fibrin/fibrinogen degradation product (FDP) planes, using 15 coagulation fibrinolytic factors. (3) Results: The boundaries of hematuria development on a two-dimensional plane of fibrinogen and FDP were obtained. If the FDP–fibrinogen/3–60 (mg/dl) value is positive, this indicates hematuria; otherwise, the case is non-hematuria, as demonstrated by the support vector machine method that seemed the most appropriate. (4) Conclusions: The borderline criterion dividing the fibrinogen/FDP plane for patients with hematuria that could be considered organ dysfunction in massive hemorrhage during delivery was obtained using artificial intelligence, and this method seemed to be useful.

In cutting processes tool condition affects the quality of the manufactured parts. As such an essential component to prevent unplanned downtime and to assure machining quality is having information about the state of the cutting tool. The primary function of it is to alert the operator that the tool has reached or is reaching a level of ware beyond which behavior is unreliable. In this paper the tool condition is being monitored by analyzing the electric current on the main spindle via an artificial intelligence model utilizing a LSTM neural network. In the current study the tool is monitored while working on a cylindrical raw piece made of AA6013 aluminum alloy with a custom polycrystalline diamond tool, for the purposes of monitoring the ware of these tools. Spindle current characteristics were obtained using external measuring equipment to not influence the operation of the machine included in a larger production line. As a novel approach, an artificial intelligence model based on a LSTM neural network is utilized for the analysis of the spindle current obtained during a manufacturing cycle and assessing the tool ware range in real time. The neural network was designed and trained to notice significant char-acteristics of the captured current signal. The conducted research serves as a proof of concept for use of a LSTM neural network-based model as a method of monitoring the condition of cutting tools.

The rapid advancements and the merging of hybrid quantum-classical computing, artificial intelligence (AI), machine learning (ML), and deep learning (DL) pose a potentially unseen and significant threat to encryption that may impact Post-Quantum Cryptography (PQC) transition timelines. There is a direct hybrid quantum-classical computing threat on cryptography, and there is also a direct threat AI/ML on cryptography. However, the synergistic combination of these technologies presents known and unknown threats that need attention, focus action, and research. This paper reviews Grover's Adaptive Search (GAS), which combines Grover's Algorithm with adaptive techniques to optimize search further, potentially making it even more efficient for attacking encryption. This work also examines the quantum-accelerated Harrow-Hassidim-Lloyd (HHL) Algorithm, designed to solve systems of linear equations exponentially faster than classical algorithms in certain conditions. The HHL algorithm can solve some lattice-based problems, which have implications for lattice-based encryption. This technological confluence and its potential impact on cryptography and encryption necessitate a proactive and coordinated approach to developing and implementing quantum-resistant AI/ML cryptographic solutions. This paper reviews the technological confluence and its potential implications for classical cryptography and PQC transition timelines and calls for further research.

In recent years, the implementation of artificial intelligence (AI) in accounting has grown in many domains, including auditing. AI permits auditors to evaluate large data sets and rapidly identify discrepancies and sequences in audit. This means auditors can review all of a client's transactions via a low time and effort. This entails auditing firms investing heavily in technological infrastructure as well as training their human resources to use cutting-edge AI technologies. Thus, this research sought to investigate the impact of the use of AI techniques by Jordanian external auditors in fraud risk assessment, the moderating effect of auditor size. The descriptive analytical method was used by distributing 280 questionnaires electronically to auditors employed in auditing firms operating in Jordan, The result reveals that AI techniques are extremely beneficial in fraud risk assessment at all levels, and the relationship between the use of AI techniques and the assessment of fraud risk has improved as auditor size has increased.

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

In recent years the field of computer science has experienced great changes, due to the incredible advances in the field of artificial intelligence. Deep Learning models are responsible for most of them since the biggest milestones occurred in 2012 when AlexNet won the image classification challenge called ImageNet. These models have demonstrated great performances in different types of complex tasks like image restoration, medical diagnosis or object recognition. Their disadvantages are related to their high data dependency, which forces experts in the field to follow a precise methodology to obtain accurate models. In this paper, we describe a complete workflow that begins with the management of the raw data until the in-depth interpretation of the performance of the models. This should be taken as a high-level consultation document describing good practices that should be applied. Apart from the step-by-step methodology, we present different use cases that correspond to the two main problems of the field: classification and regression.

The main purpose of this article is to identify brain diseases such as Alzheimer's and Parkinson's in an intelligent way using Python software and processing the function of neurons in the neural network areas of the brain, which are classified into different components, and the data is based on the information obtained from FMRI images using algorithms artificial intelligence can intelligently diagnose the type of disease. Therefore, by using spatial clustering based on the deep learning method with noise algorithm (DBSCAN), fMRI brain image and fMRI brain surface complexity are classified, and then image preprocessing will be applied to fMRI images by using artificial intelligence neural networks (ANN) algorithm. became. Then, by using the ANN algorithm and Deep CNN topology of the brain neural network, the type of disease in the brain can be diagnosed by simulating the performance of an artificial neural network (ANN) in Python software. Deep machine learning-based methods, especially algorithms based on neural networks, have shown great success in medical image analysis for various tasks including brain tumor detection, segmentation, and classification, and Alzheimer's and epilepsy. In this research, we propose an integrated model based on a convolutional neural network for simultaneous recognition and classification of brain diseases for intelligent diagnosis of tumors and epilepsy. The average accuracy obtained from our proposed model is 97.89 percent accuracy and 91.66 percent specificity.

Recent advances in artificial intelligence combined with behavioral sciences have led to the development of cutting-edge tools for recognizing human emotions based on text, video, audio, and physiological data. However, these data sources are expensive, intrusive, and regulated, unlike plants which have been shown to be sensitive to human steps and sounds. A methodology is proposed to use plants as human emotion detectors. Electrical signals from plants are tracked and labeled based on video data. Labeled data are then used for classification. MLP, biLSTM, MFCC-CNN, MFCC-ResNet, and additionally, Random Forests, 1-Dimensional CNN, and biLSTM without windowing models are set using a grid search algorithm with cross-validation. Finally, best-parameterized models are trained and used with the test set for classification. The performance of this methodology is measured with a case study with 54 participants, who were watching an emotionally charged video, as ground truth their facial emotions were simultaneously measured using face emotion analysis. The Random Forest model shows the best performance, particularly in recognizing high-arousal emotions, achieving an overall weighted accuracy of 55.2% and demonstrating high weighted recall in emotions such as fear 61.0% and happiness 60.4%. The MFCC-ResNet model offers decently balanced results, with Accuracy(MFCC-ResNet)=0.318 and Recall (MFCC-ResNet)=0.324. With the MFCC-ResNet model fear and anger are recognized with 75% and 50% recall respectively. Thus, using plants as an emotion recognition tool seems worth investigating, addressing both cost and privacy concerns.

Generative artificial intelligence tools are flourishing across various sciences, particularly in the educational domain. ChatGPT, with its versions 3.5 and 4.0, stands out for its multilingual capabilities, catering to both students and educators. This research aimed to evaluate the performance differences between these versions, and importantly, to compare the accuracy of ChatGPT's responses in English versus Arabic. In our study, we presented ChatGPT with 39 chemistry problems related to 6th-7th grade curriculum, including twenty-five open questions and fourteen multiple-choice questions. Each response was categorized as accurate, partially accurate, or inaccurate. Our analysis, focused on trends across versions and languages, revealed significant enhancements in version 4.0, particularly in its ability to process Arabic. However, despite these improvements, our findings indicate that responses in English consistently outperformed those in Arabic in terms of accuracy. In light of this discrepancy, we suggest that ChatGPT either incorporates additional Arabic databases into its training regimen or develops a code that adopts the following strategy to ensure more accurate responses in science education: questions should first be translated from Arabic to English, addressed, and then retranslated into Arabic before being presented to the user. This approach takes advantage of the greater accuracy of responses in English to benefit Arabic-speaking users, and it may also improve outcomes for speakers of other languages who experience less precise answers in the fields of chemistry or science education.

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

Collaborative robotics is a major topic in current robotics research posing new challenges, especially in Human-Robot Interaction. The crucial aspect in this area of research focuses on understanding the behavior of robots when engaging with humans, where reinforcement learning is a key discipline that allows us to explore sophisticated emerging reactions. This review aims to delve into the relevance of different sensors and techniques, with special attention to EEG (electroencephalography data on brain activity) and its influence on the behavior of robots interacting with humans. In addition, mechanisms available to mitigate potential risks during the experimentation process such as virtual reality will also be addressed. In the final part of the paper, future lines of research combining the areas of collaborative robotics, reinforcement learning, virtual reality and human factors will be explored, as this last aspect is vital to ensure safe and effective Human-Robot Interactions.

Silica fly ash, Certyd aggregate, and an alkaline solution were used to produce lightweight geo-polymer concretes. The compressive strength, water absorption, bulk density and SEM photos showing the structure of the obtained composite were carried out. Tests conducted on the specifi-cation of lightweight geopolymer concretes have revealed significant chemical interactions between the ash aggregate and the geopolymer mortar, particularly when the coarse aggregate surface has been pre-treated with an alkaline solution. Statistical analysis of the experimental data, which in-vestigated the influence of three key variables on compressive strength, water absorption, and bulk density of Lightweight Geopolymer Concrete (LBG), identified the following factors as having the most substantial impact: the quantity of alkali used, the curing temperature, and the concentration of alkali in the mixture. The optimal test series exhibited a commendable compressive strength of 20.14 megapascals (MPa), accompanied by a water absorption rate of 14.72% and a bulk density of 1486.6 kilograms per cubic meter (kg/m³). These findings underscore the importance of alkali con-tent, curing temperature, and alkali concentration in tailoring the properties of lightweight geo-polymer concrete to meet specific performance requirements.

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

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

Neurology is a quickly evolving specialty that requires clinicians to make precise and prompt diagnoses and clinical decisions based on the latest evidence-based medicine practices. In all Neurology subspecialties, Stroke and Epilepsy in particular, clinical decisions affecting patient outcomes depend on neurologists accurately assessing patient disability. Artificial intelligence (AI) can predict the expected neurological impairment from an AIS (Acute Ischemic Stroke), the possibility of ICH (IntraCranial Hemorrhage) expansion, and the clinical outcomes of comatose patients. This review article informs readers of artificial intelligence principles and methods. The article introduces the basic terminology of artificial intelligence before reviewing current and developing AI applications in neurology practice. AI holds promise as a tool to ease a neurologist’s daily workflow and supply unique diagnostic insights by analyzing data simultaneously from several sources, including neurological history and examination, blood and CSF laboratory testing, CNS electrophysiologic evaluations, and CNS imaging studies. AI-based methods are poised to complement the other tools neurologists use to make prompt and precise decisions that lead to favorable patient outcomes.

The heavy transition metal tantalum (Ta) offers a wide range of applications, especially in the field of electronics, e.g. Ta capacitors with a comparatively long life-time. Therefore, electronic scrap containing these components is a potentially valuable secondary source of this metal. However, the recovery of Ta, as other critical raw materials, that are lost in pyrometallurgical slags can be a challenging process. As a way of overcome this, the "engineered artificial minerals" EnAM approach is proposed by first searching for suitable host minerals and characterizing them. A favorable prerequisite is the enrichment of the desired element(s) in compounds that have particularly suitable properties for this purpose, such as morphology, crystal size, surface or magnetic properties. Subsequently, the slag system is adapted (e.g. chemism, solidification curve, cooling and holding times) to optimize this compound. This paper presents the results of mineralogical characterization of Ta-containing iron (Fe)-rich synthetic slags in reducing environment under different cooling rates, and describes and discusses the incorporation of Ta into the different compounds detected by powder X-ray diffraction (PXRD) and electron beam microanalysis (EPMA). Additionally, the speciation of Fe and Ta was accessible through X-Ray absorption near edge structure (XANES) spectroscopy. EPMA also provides a first semi-quantitative assessment of the Ta distribution in these individual compounds. According to this, depending on the cooling rate the enrichment factor is highest in tantalite/perovskite-type oxides (FexTayO6, CaxFeyTazO3) with up to 60 wt.% Ta and ‘tantalomagnetite’ (FeII(FeIII(2-5/3xTax)O4) with a maximum of ~ 30 wt.% Ta (only fast cooling). This is followed by a perovskite-like silicon containing oxide (XYO3) with 12-15 wt.-% Ta (only slow cooling), and a hedenbergite-like compound (XYZ2O6) with varying content of 0.3 - 7 wt.-%. The Ta-concentration in pure Fe, Fe(1-x)O, hercynitic spinel and hematite is negligible. Since a larger fraction of potentially amorphous hedenbergitic matrix is formed at faster cooling, in this case the main Ta is enriched in this compound despite the significantly lower Ta content.

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

Applying the iterative methodology for dimensionality reduction/feature selection using categorical gradient boosted trees, as it has been defined in and has been successfully applied on similar datasets in and , on a dataset consisted of 12708 gene expressions coming from 5052 individuals from 105 studies, we classify whether a person has acute myeloid leukaemia (AML) or is healthy. A CatBoost model on a dataset with reduced dimensions of 72 genes reached a ROC-AUC score of 0.9973 using ten fold cross validation (10CV) and ROC-AUC: 0.9988 on an inference dataset. We further investigate the options of using less genes that potentially could be used in clinical practise and genes than have not been associated to AML yet, or to blood cancer in general. On the same folds of the 10CV and on the same inference dataset the performance of the tuned CatBoost models show that it could be the case that not all genes with an association to AML have been found yet and 19 genes could be enough to predict AML: CatBoost63 (ROC-AUC: 0.9941, Test: 0.9942), CatBoost19: (ROC-AUC: 0.9946, Test: 0.9941) and CatBoost15: (ROC-AUC: 0.9922, Test: 0.9900). In addition, our results verify that a gene diagnostic test for AML could be possible in the future as well as further research is needed on these 15 genes that it could lead to new and better drugs.

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

This paper proposes training improvement methods of artificial neural networks (ANN) trajectory predictors. First, a dynamic power system time-series trajectory is split into several different segments to simplify the original ANN training problem. Moreover, the time-derivative of the trajectory is included to obtain an augmented loss function. Compared to previous studies which mainly focused on increasing the prediction accuracy, the aim of these novel techniques is to reduce the computational burden where the ANN output performance is still acceptable. The effectiveness of the developed methods is validated based on the WSCC three-machine nine-bus and IEEE 39-bus system models. The mean absolute error (MAE) and trajectory prediction results are analyzed, in which the numbers of neurons, hidden layers, and training epochs are constrained during the ANN training process. Rotor-angle difference between generators and the system frequency are investigated as the dynamic trajectories of the power system models. It is revealed that when the ANN architecture and epochs are constrained, the MAE results can be reduced, and the ANN training results can better reflect the original trajectory using the improvement approaches.

The next-generation "Industrial Internet of Things" (IIoT) will support "Machine-to-Machine" (M2M) communications for smart Cyber-Physical-Systems and Industry 4.0, and require guaranteed cyber-security. This paper explores hardware-enforced cyber-security for critical infrastructures. It examines a Quantum-Safe "Software-Defined Deterministic IIoT" (SDD-IIoT), with a new forwarding-plane (sub-layer-3a) for deterministic M2M traffic flows. A "Software-Defined-Networking" (SDN) control-plane controls many "SDD Wide Area Networks" (SDD-WANs), realized with FPGAs. The SDN control-plane provides an "Admission-Control/Access-Control" system for network-bandwidth, using collaborating Artificial Intelligence (AI) rule-based "Zero Trust Architectures" (ZTAs). Hardware-enforced access-control eliminates all congestion, BufferBloat, and DoS/DDoS attacks in the forwarding-plane, reduces buffer-sizes by 100,000+ times, and supports ultra-reliable and uItra-low-latency communications in the SDD-WANs. The SDD-WANs can: (i) Encrypt/Authenticate M2M flows using Quantum-Safe ciphers, to withstand attacks by Quantum Computers; (ii) Implement "Guaranteed Intrusion Detection Systems" in FPGAs, to detect cyber-attacks embedded within billions of IIoT packets/second; (iii) Provide guaranteed immunity to external cyber-attacks against critical infrastructure, and exceptionally-strong immunity to internal cyber-attacks; (iv) Save $US100s of billions annually by exploiting FPGAs; and (v) Enable "Quantum Key Distribution" (QKD) Networks by providing a programmable forwarding-plane with "authenticated classical channels" and full-immunity to DoS/DDoS attacks. Extensive experimental results for an SDD-WAN over the European Union are reported.

In horses, quantitative assessment of gait parameters, as the use of inertial measurement units system (IMUs), might help in the decision-making process. However, it requires financial investment, is time-consuming, and lacks accuracy if displaced. An innovative Artificial Intelligence marker-less Motion Tracking System (AI-MTS) may overcome these limitations in the field. Our aim was to compare the level of agreement and accuracy between both systems and visual clinical assessment. Twenty horses underwent locomotion analysis by visual assessment, IMUs and AI- MTS systems, in the following conditions: straight hard (SH), straight soft (SS), left and right circle hard (LCH, RCH) and soft (LCS, RCS). A greater number of horses were considered sound by clinical examination, compared to those identified as symmetric by the two gait analysis systems. More limbs were considered asymmetric by the AI-MTS compared to IMUs, suggesting its greater sensitivity. The greatest agreement between the two systems was found for the difference between two minima in vertical head position in SH, while the lowest for the difference between two minima in vertical pelvis position in SS, reflecting the difficulties in assessing asymmetry of hindlimbs. Some degree of asymmetry may be clinically relevant, suggesting its regular use in training horses.

Oncology has emerged as a crucial field of study and treatment in the domain of medicine. Computed tomography has gained widespread adoption as a radiological modality for the identification and characterisation of pathologies, particularly in oncology, enabling precise identification of affected organs and tissues. However, achieving accurate liver segmentation in computed tomography scans remains a challenge due to the presence of artefacts and the varying densities of soft tissues and adjacent organs. This paper compares artificial intelligence algorithms and traditional medical image processing techniques to assist radiologists in liver segmentation on computed tomography scans, and evaluates their accuracy and efficiency. It is noteworthy that although studies have been conducted on liver segmentation in computed tomography scans, they often lack an intuitive and visual component that allows healthcare professionals to manipulate and observe the results obtained, thereby limiting interaction with the outcomes. From the literature review, challenges such as under-segmentation, over-segmentation, and poor boundary detection, as well as the selection of methods to improve the accuracy and efficiency of liver segmentation in computed tomography scanners, are highlighted as needs to be addressed. The importance of future research in understanding the essential features for the study, generating more datasets, improving segmentation efficiency, and developing lightweight artificial intelligence frameworks for liver segmentation is outlined.

Through the development of the metaverse concept from the Sumerian myth (5500 - 1800 BC) and mind-altering novel, “Snow Crash” in 1992, to today’s information age, human- and society-centred urban metaverse worlds, an extension of residents and urban society where the virtual and the physically real blend and are more organically integrated, are meant to mirror the fabric of urban life with no harm to their residents. The success of urban metaverse cybercommunities depends on the quality of data-driven Smart City (SC) Digital Twins (DTs), the seamless exchange of data between cyber and physical worlds (e.g. between residents and their counterpart “Avatars’’) and the processing of the data effectively and efficiently with no vicious interventions. The potential risks in this ecosystem that incorporates Web3 can be extremer than the ones in Web2 since users are immersed with multiple tightly coupled wearable sensor-rich devices perceiving the blend of the real and the virtual with possible imminent negative experiences. This study, by analysing potential cyberthreats in the urban metaverse cyberspaces, proposes a blockchain-based Decentralised Privacy-Preserving Machine Learning (DPPML) authentication and verification technique, which uses the metaverse immersive devices and can be instrumented effectively against identity impersonation and theft of credentials, identity, or avatars.

Artificial intelligence and Quantum science supports path-breaking technologies for main technological change. The investigation goal of this study is to examine their scientific and technological development to explain drivers of these disruptive technologies. Analyses of data, based on publications and patents in quantum science and artificial intelligence, with a technometric model show that that quantum science and technology has a growth rate of 1.07, artificial Intelligence ha a rate of growth of 1.37, whereas the interaction of quantum science and artificial intelligence generates a convergent technological trajectory that has an accelerated rate of growth of 1.58, higher than trends of these technologies taken individually. These findings here clarify one of the fundamental determinants of the evolution of path-breaking technologies that can support decision making of policymakers, technology analysts and R&D managers to direct R&D investments towards fruitful inter-relationships between radical technologies that foster scientific and technological change in knowledge economies.

In recent times, using image-based deep learning techniques has been very useful for classifying fruits. However, mango is a particular challenge because its shelf life is directly related to the degree of maturity at the time of harvest, which is determined mainly through the visual assessment of the operators when sorting them, doing it incorrectly, coupled with the blows and compressions exposed during storage and transport, trigger the deterioration in their quality, hence the importance of this operation. In this sense, the purpose of this study focuses on designing a computational model based on Deep Learning that uses a convolutional neural network (CNN) parameterized to select mangoes according to their color tone based on their maturity stage. A dataset was established from 201 labeled images, then a CNN architecture was built, obtaining a model on which 3 criteria were evaluated: the number of convolution and grouping layers, the resolution of the input images and the number of training epochs required. As a result, an input image resolution of 32 x 32 pixels was achieved, the process included a convolution layer followed by pooling that fed another layer that processed the results and ended in an output layer with 2 neurons under the One Hot Encoding representation. The model required 20 epochs to perfect its training, achieving 96.04 % accuracy in mango image selection. This study supports the potential of CNNs in quality analysis through mango color selection, even in uncertainty scenarios.

Exploring the fundamental mechanisms of locomotion extends beyond mere simulation and modeling. It necessitates the utilization of physical test benches to validate hypotheses regarding real-world applications of locomotion. This study introduces cost-effective modular robotic platforms designed specifically for investigating the intricacies of locomotion and control strategies. Expanding upon our prior research in Electric-Pneumatic Actuation (EPA), we present the mechanical and electrical designs of the latest developments in the EPA robot series. These include EPA Jumper, a human-sized segmented monopod robot, and its extension EPA Walker, a human-sized bipedal robot. Both replicate the human weight and inertia distributions, featuring co-actuation through electrical motors and pneumatic artificial muscles. These low-cost modular platforms, with considerations for degrees of freedom and redundant actuation, 1) provide opportunities to study different locomotor subfunctions— stance, swing, and balance; 2) help investigate the role of actuation schemes in tasks such as hopping and walking; and 3) allow testing hypotheses regarding biological locomotors in real-world physical test benches.

The paper outlines a methodology for developing a model to estimate energy consumption in electric vehicles (EVs). The most robust validation indicators were exhibited by an artificial intelligence method, specifically neural networks. Within this framework, two predictive models for EV energy consumption were developed for winter and summer conditions, based on actual driving cycles. Such models hold particular significance for microscale road analyses. The resultant model for test data in summer conditions demonstrates validation indicators with an R2 of 86% and an MSE of 1.4, while for winter conditions, the values are 89% and 2.8, respectively, confirming its high precision. The paper also presents exemplary applications of the developed models, utilizing both real and simulated microscale data. The results obtained and the presented methodology can be especially advantageous for decision-makers in city road management and infrastructure planners, aiding both cognitive understanding and better planning of charging infrastructure networks.

Incorporating AI tools like ChatGPT into higher educational settings has been beneficial for education, yet the extent of user satisfaction with the information provided by these tools, known as user information satisfaction (UIS), remains underexplored. Our study introduces a UIS model specifically designed for ChatGPT's application in the educational sector. Drawing from established UIS theory, we crafted a model centered around seven essential factors that influence the effective use of ChatGPT, aiming to guide both educators and learners in overcoming common challenges such as plagiarism and ensuring the ethical use of AI. We gathered data from Indonesian university participants and applied Structural Equation Modeling using Smart-PLS 4.0 for our data analysis. The results reveal that completeness, precision, timeliness, convenience, and the format of information as the most influential factors driving user satisfaction with ChatGPT. Interestingly, our research indicated that the accuracy and reliability of the information, typically deemed paramount, were not the primary concerns in the academic use of ChatGPT. Our findings lead to a recommendation for a cautious approach to the integration of ChatGPT in higher education. We advocate for a strategic use that recognizes its innovative potential while also acknowledging its limitations, thus ensuring a responsible and effective application in educational contexts. This balanced perspective is crucial for integrating AI tools into the academic fabric without compromising educational integrity or quality.

Background: This research examines how market knowledge and artificial intelligence (AI) interact in different market designs such as business-to-business (B2B) settings while taking emerging technologies and the changing digitalization landscape into account. Objective: The main goal is to understand how AI affects market knowledge in different market designs such as business-to-business (B2B) contexts, taking into account language barriers, practical difficulties, and the revolutionary effects on decision-making and customer interactions. Result: They underscore the transformative potential of artificial intelligence (AI) by highlighting how it shapes market knowledge, encourages customized approaches, and improves marketing efficacy in the business-to-business (B2B) space. Conclusion: In order to create a path for responsible AI integration in B2B marketing, the study concludes with recommendations for standardized terminology related to AI, practical insights into implementation challenges, and ethical issues.

Due to the major impact of climate change on the world's environment, political and economic systems, climate change mitigation has become a pressing priority for the international community and requires rapid action from the whole society. With the continuous advancement of artificial intelligence research, the integration of AI and other technologies makes it more and more used in the whole society. It has become a promising and innovative avenue in the field of climate change mitigation. This paper comprehensively considers the key role of AI technology in the field of climate change mitigation, such as climate modeling, the optimization of renewable energy systems, the development of intelligent solutions for sustainable practices and CSS technology, and affirms its future prospects. It also describes the challenges of AI in the field of climate change mitigation. As researchers, policymakers, and industries collaborate to refine AI methodologies and integrate them into practical applications, a concerted effort is required to establish ethical guidelines, transparency standards, and inclusive governance frameworks.

Machine learning models are increasingly being used in critical domains, but their complexity, lack of transparency, and poor interpretability remain problematic. Decision trees (DTs) and rule-based approaches are well-known examples of interpretable models, and numerous studies have investigated techniques for approximating tree ensembles using DTs or rule sets; however, tree ensemble approximators do not consider interpretability. These methods are known to generate three main types of rule sets: DT-based, unordered-based, and decision list-based. However, no known metric has been devised to distinguish and compare these rule sets. Therefore, the present study proposes an interpretability metric to allow comparisons of interpretability between different rule sets, such as decision list- and DT-based rule sets, and investigates the interpretability of the rules generated by the tree ensemble approximators. To provide new insights into the reasons why decision list-based and inspired classifiers do not work well for categorical datasets consisting of mainly nominal attributes, we compare objective metrics and rule sets generated by the tree ensemble approximators and the \textit{Recursive-Rule eXtraction algorithm (Re-RX) with J48graft}. The results indicated that \textit{Re-RX with J48graft} can handle categorical and numerical attributes separately, has simple rules, and achieves high interpretability, even when the number of rules is large.

As artificial intelligence takes an increasingly prominent role in healthcare, a growing body of research is being dedicated to its application in the investigation of communication sciences and disorders (CSD). This study conducts a bibliometric analysis of AI-based research in the field of CSD published up to December 2023. Utilizing the Web of Science and Scopus databases, we identified 15,035 publications, with 4,375 meeting our inclusion criteria. From 1985 onwards, there has been a consistent annual increase in publications, averaging at 16.51%, notably surging from 2012 to 2023. The primary communication disorders studied include autism, dysarthria, dementia, Parkinson’s, and aphasia. Noteworthy AI techniques employed in CSD research encompass machine learning, automatic speech recognition, support vector machine, and deep learning. Our analysis provides a comprehensive overview, serving as a valuable resource for researchers, developers, and professionals seeking to comprehend the evolving landscape of AI in CSD research. Moving forward, collaborative efforts need to prioritize addressing ethical concerns, ensuring data privacy, enhancing methodological enhancements, and considering ecological validity in the application of AI in studies related to communication disorders.

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

The introduction of ChatGPT in academia has raised significant concerns, particularly regarding issues like plagiarism and the ethical use of generative text for academic purposes. Existing literature has predominantly focused on ChatGPT adoption, leaving a notable gap in addressing the strategies users employ to mitigate these emerging challenges. To bridge this gap, this research utilizes the Uncertainty Reduction Theory (URT) to formulate user strategies for reducing uncertainty. These strategies include both interactive and passive approaches. Concurrently, the study identifies key sources of uncertainty, which include concerns related to transparency, information accuracy, and privacy. Additionally, it introduces the concepts of seeking clarification and consulting peer feedback as mediating roles to facilitate reduced uncertainty. We tested these hypotheses with a sample of Indonesian users (N = 566) using structural equation modeling via Smart-PLS 4.0 software. The results confirm that interactive Uncertainty Reduction Strategies (URS) are more effective in reducing uncertainty when using ChatGPT compared to passive URS. Transparency concerns, information accuracy, and privacy concerns are identified as factors that increase the level of uncertainty. In contrast, consulting peer feedback is the most effective strategy for reducing uncertainty compared to seeking clarification at the individual-system level. Insights from the mediating effects confirm that consulting peer feedback significantly mediates uncertainty sources to reduce uncertainty. The study discusses various strategies for the ethical use of ChatGPT by users in the educational context and contributes significantly to theoretical development.

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

Humans have a unique cognitive ability known as relational abstraction, which allows us to identify logical rules and patterns beyond basic perceptual characteristics. This ability represents a key difference between how humans and other animals learn and interact with the world. With current large language models rivaling human intelligence in many regards, we investigated whether relational abstraction was an emergent chat completion capacity in various models. We find that despite their impressive language processing skills, all tested language models failed the relational match-to-sample (RMTS) test, a benchmark for assessing relational abstraction. These results challenge the assumption that advanced language skills inherently confer the capacity for complex relational reasoning. The paper highlights the need for a broader evaluation of AI cognitive abilities, emphasizing that language proficiency alone may not be indicative of certain higher-order cognitive processes thought to be supported by language.

The integration of Artificial Intelligence (AI) in dairy farm management through biometric facial recognition for cows is a significant stride in livestock management. This review critically evaluates the evolution, applications, and challenges of AI-driven biometric facial recognition in dairy farming. It emphasizes the role of this technology in enhancing individual monitoring of dairy cows, providing accurate health, behavior, and productivity tracking. Originally derived from human facial recognition systems, this approach utilizes distinctive bovine facial features for essential, non-invasive, real-time monitoring in large-scale operations. The progression of AI from elementary pattern recognition to advanced Convolutional Neural Networks (CNNs) and deep learning models marks a shift toward data-driven farming. This study addresses key challenges such as environmental variability, data collection hurdles, ethical concerns, and technological limitations. It also contrasts various AI models, spotlighting their unique strengths and practical utility in dairy farming scenarios. Despite these challenges, facial recognition technology holds promise for improving farm efficiency, animal welfare, and sustainable practices, highlighting the need for continuous research and development. The review concludes by advocating for future research focused on environmental adaptability, ethical AI application, cross-breed compatibility, and integration with other farming technologies. Ultimately, it underscores AI's transformative potential in modernizing dairy farming towards a more data-oriented, responsible agricultural future.

Due to the ongoing global warming on the Earth, permafrost degradation has been extensively taking place, which poses a substantial threat to civil and industrial facilities and infrastructure elements, as well as to the utilization of natural resources in the Arctic and high-latitude regions. In order to prevent the negative consequences of permafrost thawing under the foundations of constructions, various geophysical techniques for monitoring permafrost have been proposed and applied so far: temperature, electrical, seismic and many others. At the same time, non-stationary electromagnetic methods seem to have significant potential and a number of important practical advantages. We propose a cross-borehole exploration system for the highest localization of target objects in the cryolithozone. A novel mathematical apparatus for three-dimensional modeling of transient electromagnetic (TEM) signals by the vector finite element method has been developed. The original combination of the latter, the Sumudu integral transform and artificial neural networks makes it possible to examine spatially heterogeneous objects of the cryolithozone with a high contrast of geoelectric parameters, significantly reducing computational costs. We have studied the sensitivity of the TEM signals to the boundary between thawed and frozen rocks depending on the inter-borehole distance and spatial orientations of the transmitters and receivers. Numerical simulation results of the TEM monitoring of industrial facilities located on permafrost are considered. The formation of a talik has been shown to significantly manifest itself in the measured electromagnetic responses, which enables timely prevention of industrial disasters and environmental catastrophes.

The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) has propelled technological innovation across various industries. This systematic literature review explores the current state and future trajectories of AI in IoT, with a particular focus on emerging trends in intelligent data analysis and privacy protection. The proliferation of IoT devices, marked by voluminous data generation, has reshaped data processing methods, providing actionable insights for informed decision-making. While previous reviews have offered valuable insights, they often fall short of comprehensively addressing the multifaceted dimensions within the AI-driven IoT landscape. This review aims to bridge this gap by systematically examining existing literature and acknowledging the limitations of past studies. To achieve this aim, the study uses a meticulous approach guided by established methodologies. The chosen methodology ensures the rigor and validity of the review, aligning with PRISMA guidelines for systematic reviews. This systematic literature review serves as a comprehensive guide for researchers, practitioners, and policymakers, offering insights into the current landscape and paving the way for future research directions. The identified trends and challenges provide a valuable resource for navigating the evolving domain of AI in IoT, fostering a balanced, secure, and sustainable advancement in this dynamic field.

This study focuses on predicting traffic speed using the simple Multilayer Perceptron (MLP) model, despite the availability of various models for traffic prediction, with artificial intelligence demonstrating superior predictive capabilities. Emphasizing that the quality of the data holds greater significance than the model itself, the research underscores the challenges posed by data containing significant errors and fluctuations. Unlike relying solely on criteria such as Mean Absolute Deviation (MAD) or coefficient of determination (r2), this study advocates for the use of MAD/range as a more robust metric for prediction accuracy, especially when dealing with data exhibiting substantial order.The study sets the speed limit and traffic value at 0.65 and 0.88, respectively. Notably, both criteria yield identical accuracy results of 13% for MAD/R in predicting speed and 14% for traffic. This parity suggests that the artificial intelligence model performs equally well for both variables, highlighting the importance of considering alternative evaluation metrics in the face of data complexities.

The objective of this study was to investigate the liquefaction resistance of chemically improved sandy soils in a straightforward and accurate manner. Using only the existing experimental databases and artificial intelligence, the goal was to make predictions without conducting physical experiments. Emphasis was placed on the significance of data from 20 loading cycles of cyclic undrained triaxial tests to determine the liquefaction resistance and the contribution of each explanatory variable. Different combinations of explanatory variables were considered. Regarding the predictive model, it was observed that a case with the liquefaction resistance ratio as the dependent variable and other parameters as explanatory variables yielded favorable results. In terms of exploring combinations of explanatory variables, it was found advantageous to include all variables as doing so consistently resulted in a high coefficient of determination. The inclusion of the liquefaction resistance ratio in the training data was found to improve the predictive accuracy. In addition, the results obtained when using a linear model for the prediction suggested the potential to accurately predict the liquefaction resistance using historical data.

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

Digital transformation systems generate a substantial volume of data, creating opportunities for potential innovation, particularly those driven by artificial intelligence. This study focuses on the intricate relationship between artificial intelligence and innovation as foundational elements in the digital transformation framework for sustained growth and operational excellence. This study provides a holistic perspective on the cultivation and pillars of AI-powered innovation, highlighting their pivotal role in revolutionizing industries, including healthcare, education, finance, manufacturing, transportation, and agriculture. The work emphasizes the key pillars essential for fostering AI-powered innovation, including monitoring performance measurement to use the power of the present, continuous learning and innovation, data analytics and insights, predictive analytics, and innovative product development. This study investigates how these pillars serve as the foundation for groundbreaking advancements, driving efficiency, enhancing decision-making processes, and fostering creativity within organizations. This study explores the significance of continuous learning, interdisciplinary collaboration, and industry partnerships in nurturing a thriving AI-powered innovation ecosystem. By understanding and harnessing these fundamental elements, businesses can navigate the complexities of the digital age, fostering innovation that not only optimizes processes but also enhances the overall human experience, ushering in a new era of technological excellence and societal progress.

Artificial intelligence (AI) has found extensive applications in the sustainability domain. The municipal solid waste incineration (MSWI) process can achieve waste-to-energy (WTE) goals through the assistance of automation and information technologies. In theory, AI can facilitate the intelligent operation of the MSWI process, simultaneously achieving low pollution emissions and high production efficiency. This study provides an overview of AI applications in the MSWI process. Addressing fundamental challenges, the optimal control of the MSWI process is categorized into four aspects: modeling, control, optimization, and maintenance. Subsequently, this study conducts a detailed analysis and discussion of AI applications in each of these technological domains. In conclusion, this paper outlines the future research directions and provides an outlook on the fundamental issues pertaining to AI applications in the optimal control of the MSWI process.

Objectives: Few studies have evaluated and compared artificial intelligence (AI) models for breast cancer detection on the same database. The purpose is to compare the performances of 3 artificial intelligence algorithms on the same dataset at a free operative threshold and to evaluate how the performance is impacted by the chosen threshold. Materials: In this retrospective single-center study, a dataset of 314 2D screening mammograms performed between 2012 and 2020 was established with a prevalence of 19.6% of histologically proven cancer. Three AI constructors using CE (European conformity) marking algorithms have agreed to take part in the study by submitting the cancer-enriched cohort to their algorithm. They chose a free operative threshold to distinguish benign and malignant mammograms. The most suspicious lesion was marked and correctly located by the AI algorithm. Statistical parameters were analyzed and compared between the algorithms. Results: Regarding both sensitivity and specificity, at the chosen threshold, AI 1 had the best compromise between sensitivity (74%) and specificity (79%). AI 2 had a statistically lower sensitivity (52%; p<0.05) with a higher specificity (98.4%) than the other AI algorithms. AI 3 had a nonsignificant sensitivity difference (69.9%) but a significantly lower specificity (45.3%; p<0.001) than the other two. The performances varied depending on the chosen threshold; when the AI 2 threshold was lowered, the sensitivity increased (69.9%), while a higher specificity (86.1%) was maintained. Conclusion: Two AI algorithms stood out in terms of performance when the threshold was optimized, resulting in an acceptable sensitivity and specificity.

Artificial Intelligence is developing at a rapid place, with examples of Machine Learning already utilized in aviation to improve efficiency. In the coming decade it is likely that Intelligent Assistants (IAs) or ‘Digital Colleagues’ will be developed and deployed to assist aviation personnel in the cockpit, the air traffic control centre and airports. This will be a game-changer and may herald the way forward for single pilot operations, AI-based management of Urban Mobility including fleets of drones and sky-taxis, and more carbon-efficient transport across global commercial transport networks. Yet in aviation there is a core underlying tenet that ‘people make safety’ and keep the skies and passengers safe, based on a robust industry-wide safety culture. The introduction of IAs into aviation could potentially undermine aviation’s hard-won track record in this area. This paper applies a scientifically validated safety culture tool to explore the potential impacts of the introduction of IAs into aviation. The results suggest that there are indeed potential negative outcomes, but also possible safety ‘affordances’ wherein AI could strengthen safety culture. Safeguards and mitigations are suggested for the key ‘risk owners’ in aviation organisations, to ensure safety remains a priority in the industry.

The term artificial intelligence refers to the field of computer science that deals with the design and implementation of computer systems that mimic elements of human behavior that imply basic intelligence: learning, adaptability, inference, contextual understanding, and problem solving among others. Artificial intelligence is a point of intersection between multiple sciences such as computer science, psychology, philosophy, neurology, linguistics and engineering science, with the aim of synthesizing intelligent behavior, with elements of reasoning, learning and adaptation to the environment, while it is usually applied to specially made machines or computers.

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

In the present article, the application of an artificial neural network (ANN) model is studied, whose function is the development of plastic instability maps of a medium carbon microalloyed steel during the hot forging process. And, secondly, we proceed to create another neural network capable of providing the recrystallized grain size in the steady state phase resulting from the deformation by hot forging, thus creating a methodology that can be applied to different types of processes and materials. In order to achieve this objective, we start from the experimental data of a medium carbon microalloy steel, obtained by hot compression tests, for strain rates that vary between 10-4 and 3 s-1 and in a range of temperatures between 900 oC and 1150 oC. These experimental data will be used to train the neural network that is intended to be created in this work. Once trained, it will be verified if the results of the test correspond to the experimental data and the fluency curves will be obtained. Finally, the processing maps will be developed applying the Dynamic Materials Model (DMM), according to which the safe domains to forge the material and the plastic instability domains are delineated, to be avoided during the forging process. The maps obtained by means of RNA will be compared with the experimental ones and it will be possible to verify that the optimal regions of forging in the maps obtained by RNA coincide with those obtained by means of experimental data. In addition, a study of the influence of the microstructure on the behavior of steel during hot forming will be carried out, since the experimental tests are carried out at austenitizing temperature, so the microstructure is different in each test.

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

This article offers a pragmatic ‘epistemology of wargaming’ which views wargames as immersive ‘thought experiments’ in which the human players involved use their experiential, empirical, and theoretical knowledge – together with whatever cognitive models they are able to deploy, or develop anew – to generate a conceptual, operational understanding of the adversarial scenario in which they are immersed; and exploit this understanding to craft tactical decisions designed to optimise the likelihood they will achieve their strategic objectives. From this perspective, contemporary interest in the use of ‘AI’-enabled tools to augment the validity of wargaming outputs – where these outputs constitute the decisions players make and the insights such decisions reveal – might most purposefully focus on: the design and implementation of wargames (to strengthen the architecture these provide to support immersive decision-making); and the analysis of players’ decisions (to better understand the cognitive models these involve and reflect). While the focus we suggest might disappoint those keen to replace human players with (semi-) autonomous decision-making machines, as long as the principal objectives of wargaming are to assess and enhance the decision-making capabilities of human players and human personnel, ‘AI’-enabled applications can only ever play a supporting role (albeit a potentially invaluable one) in the design, presentation, implementation, and analysis of wargames. As Irving Berlin might have it: ‘AI’ might soon be able to do most things better than us, but it can never replace humans when only a human will do.

Memristors are state-of-the-art, nano-sized, two-terminal, passive electronic elements with very good switching and memory characteristics. Owing to their very low power usage and a good compatibility to the existing CMOS ultra-high-density integrated circuits and chips, they are po-tentially applicable in artificial and spiking neural networks, memory arrays, and many other de-vices and circuits for artificial intelligence. In this paper, a complete electronic realization of ana-log circuit model of modified neural net with memristor-based synapses and transfer function with memristors and MOS transistors in LTSPICE is offered. Each synaptic weight is realized by only one memristor, providing enormously reduced circuit complexity. The summing and scaling implementation is founded on op-amps and memristors. The logarithmic-sigmoidal activation function is based on a simple scheme with MOS transistors and memristors. The functioning of the suggested memristor-based neural network for pulse input signals is evaluated both analytically in MATLAB-SIMULINK and in LTSPICE environment. The obtained results are compared one to another and are successfully verified. The realized memristor-based neural network is an im-portant step towards the forthcoming design of complex memristor-based neural networks for ar-tificial intelligence, for implementation in very high-density integrated circuits and chips.

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

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

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

This study develops a 7-layer Long Short-Term Memory (LSTM) model to enhance early diabetes detection in Oman, aligning with the theme of 'Artificial Intelligence in Healthcare'. The model focuses on addressing the increasing prevalence of Type 2 diabetes, projected to impact 23.8% of Oman's population by 2050. It employs LSTM neural networks to manage factors contributing to this rise, including obesity and genetic predispositions, and aims to bridge the gap in public health awareness and prevention. The model's performance is evaluated through various metrics. It achieves an accuracy of 99.40%, specificity and sensitivity of 100% for positive cases, a recall of 99.34% for negative cases, an F1 score of 96.24%, and an AUC score of 94.51%. These metrics indicate the model's capability in diabetes detection. The implementation of this LSTM model in Oman's healthcare system is proposed to enhance early detection and prevention of diabetes. This approach reflects an application of AI in addressing a significant health concern, with potential implications for similar healthcare challenges globally.st diagnostic capabilities, representing a significant leap forward in healthcare technology in Oman.

Artificial Intelligence (AI) made its first appearance in 1956, and since then it has progressively introduced itself in healthcare system and patients’ information and care. AI functions can be grouped under the following headings: Machine Learning (ML), Deep Learning (DL), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Computer Vision (CV). Chronic limb-threatening ischemia (CLTI) represents the last stage of peripheral artery disease (PAD), and has increased over recent years, together with the rise in prevalence of diabetes and population ageing. Nowadays AI grants the possibility of developing new diagnostic and treatment solutions in the vascular field, given the possibility of accessing clinical, biological, and imaging data. By assessing the vascular anatomy in every patient, as well as the burden of atherosclerosis, and classifying the level and degree of disease, sizing and planning the best endovascular treatment, defining the perioperative complications risk, integrating experiences and resources between different specialties, identifying latent PAD, thus offering evidence-based solutions and guiding surgeons in the choice of the best surgical technique, AI challenges the role of the physician’s experience in PAD treatment.

Smart cities, leveraging advanced data analytics, predictive models, and digital twin techniques, offer a transformative model for sustainable urban development. Predictive analytics plays a crucial role in proactive planning, enabling cities to adapt to evolving challenges. Concurrently, digital twin techniques provide a virtual replica of the urban environment, fostering real-time monitoring, simulation, and analysis of urban systems. This research underscores the significance of real-time monitoring, simulation, and analysis of urban systems to support test scenarios that identify bottlenecks and enhance smart city efficiency. The paper delves into the crucial roles of citizen report analytics, prediction, and digital twin technologies at the neighborhood level. The study integrates ETL/ELT processes, AI techniques, and a digital twin methodology to process and interpret urban data streams derived from citizen interactions with the city's coordinate-based problem mapping platform. By employing an interactive GeoDataFrame within the digital twin methodology, dynamic entities facilitate simulations based on various scenarios. This approach enables users to visualize, analyze, and predict the response of the urban system at the neighborhood level. Consequently, antecedent and predictive patterns, trends, and correlations are visualized at the physical level of each city area, leading to improvements in urban functionality, resilience, and resident quality of life.

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

Stress can reduce the accuracy of radiation therapy owing to muscle contraction and changes in breathing patterns. This study aimed to predict stress in patients before each treatment session using artificial intelligence (AI) from biological signals to enhance treatment accuracy. We measured 123 stress cases in 41 patients and calculated stress scores by analyzing seven stress-related features derived from heart rate variability obtained from photoplethysmography. The study analyzed stress score distribution and observed its trend changes throughout the treatment. Before-treatment information was used to predict changes in stress features during treatment. AI models included both non-pre-trained (Decision Tree, Random Forest, Support Vector Machine, Long Short-Term Memory (LSTM), Transformer) and pre-trained (ChatGPT) models. The performance was evaluated using 10-fold cross-validation, exact match ratio, accuracy, recall, precision, and F1 score. More than 90% of the patients experienced stress during radiation therapy. From all the classifications, LSTM and prompt engineering GPT4.0 had the highest accuracy (feature classification, LSTM: 0.703, GPT4.0: 0.659, stress classification: LSTM: 0.846, GPT4.0: 0.769). Our research pioneers the use of AI and biological signals for stress prediction in radiation therapy, potentially identifying patients needing psychological support, and suggesting methods to improve radiotherapy effectiveness through stress management.

Radiomics represents the science of extraction and analysis of a multitude of quantitative features from medical imaging, unrevealing quantitative potentiality of Radiologic images. This scientific review aims to provide radiologists a comprehensive understanding of radiomics, emphasizing its principles, applications, challenges, limits, and future prospects. Limits of standardization of actual scientific production are analyzed with the possible solutions proposed by some papers reported and commented. As the continuous evolution of medical images is ongoing, radiologists must be aware of new perspective to perform a central role in patients management.

Pancreatic ductal adenocarcinoma (PDAC) is the most common malignant tumor of the pancreas, carrying a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last 5 years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in detection of PDAC, segmentation of the lesion and classification algorithms used in differential diagnosis, prognosis, and histopathological and genomic prediction. The results show lack of multi-institutional collaboration and stresses the need for bigger datasets in order for AI models to be implemented in a clinically relevant manner.

The core motivation for this study emanates from Voronin S.'s seminal work on the universality of the zeta function. This universality positions the zeta function as a pivotal element in the conceptual framework of global artificial intelligence, suggesting its potential as a foundational component. A critical barrier to harnessing this promising universality has been the absence of efficient algorithms capable of actualizing this concept.

In conclusion, this review highlights the challenges in the current screening pathway and the potential for AI, particularly LLM, to revolutionize DR screening in Japan. The future direction will depend on leadership from ophthalmologists and stakeholders to address long-standing challenges in DR screening so that all people have access to accessible and effective screening.

In reservoir operations rule curves, it is necessary to apply rule curves to guide long-term reservoir management. This paper proposes an approach to optimize the reservoir operation rule curves (RORC) using intelligent optimization techniques from the Firefly Algorithm (FA) and a unique combination method utilizing the Artificial Bees Colony and Cultural Algorithm (ABC-CA). The aim is to establish a connection with the simulation model to determine the optimal RORC for flood control. The proposed model was used to determine the Nam-Oon Reservoir (NOR) is optimal flood control RORC in Northeastern Thailand. The minimum frequency and a minimum average of ex-cess water were given as an objective function for assessing the efficiency of the search process. The evaluation of the effectiveness of flood control RORC involved expressing water scarcity and excess water situations in terms of frequency, magnitude, and duration using historical inflow data syn-thesized from 1000 events. The results demonstrate that when using the obtained RORC to simulate the NOR system for reducing flooding in long-term operations, situations of excess water were smaller than those using the current RORC. The results indicated that situations of excess water using the obtained RORC from the proposed model were smaller than those using the current RORC situations. In decreasing flood situations, the newly acquired RORC from the suggested FA and ABC-CA models performed better than the current RORC.

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

This study analyzes the convergence between artificial intelligence (AI) and management accounting in hospital contexts. Management accounting plays a central role in the effectiveness of planning, control and decision-making in healthcare institutions such as hospitals. The application of AI in this scenario offers powerful tools to improve accounting processes, optimize operational efficiency and maximize the use of available resources, resulting in an improvement in the quality of care provided to patients. Through in-depth analysis, the article explores how AI is bringing about a revolution in hospital management accounting, presenting case studies illustrating its successful implementation, outlining the tangible benefits.

Tea infusions are the most consumed beverages in the world after water, their pleasant yet peculiar flavor profile drives consumer choice and acceptance and becomes a fundamental benchmark for industry. Any qualification method capable of objectifying the product's sensory features effectively supports industrial quality control laboratories guaranteeing high sample throughputs even without the human panel intervention. The current study presents an integrated analytical strategy acting as an Artificial Intelligence decision tool for black tea infusion aroma and taste blueprinting. Key markers validated by sensomics are accurately quantified in a wide dynamic range of concentrations. Thirteen key aromas are quantitatively assessed by standard addition with in-solution solid-phase microextraction sampling followed by GC-MS. On the other hand, nineteen key taste and quality markers are quantified by external standard calibration and LV-UV/DAD. The large dynamic range of concentration for sensory markers is reflected in the selection of seven high-quality teas from different geographical areas (Ceylon, Darjeeling Testa Valley and Castleton, Assam, Yunnan, Azores, and Kenya). The strategy acts as an AI smelling and taste machine predicting teas sensory features without the human panel intervention.

This research explores the transformation of learning methodologies from traditional to modern approaches, focusing on the development of a gamified educational application with an Artificial Intelligence (AI) mentor for secondary school graduates preparing for Croatian state graduation exams. Traditionally, education has been marked by a passive student role and teacher-centric transmission of knowledge. In contrast, modern learning tools, such as gamified applications, introduce interactive and engaging elements. The purpose of this study is to identify user-centric learning approaches through a comprehensive survey of graduating students, assessing their readiness for exams, learning styles, and preferences. The research explores issues such as financial burdens on parents for preparatory courses and copyright concerns with external educational materials. In summary, the learning application should prioritise sustained knowledge retention, motivation, and stress reduction, offering an alternative to external preparatory courses. Conclusions underscore the importance of fostering an engaging learning environment, incorporating teacher-like elements, and aligning with user expectations. As digital technology positively impacts the learning experience, understanding user preferences is crucial in designing educational apps that cater to learners' needs. The study provides insights for developing a gamified learning application that not only prepares students for exams but also enhances long-term knowledge acquisition and engagement.

The article describes machine learning using artificial neural networks (ANN) to develop the parameters of the friction stir welding (FSW) process for three types of aluminum joints (EN AW 7075). A total of 608 experimental data were used to build the ANN. Two types of networks were built: the first one was used to classify good/bad connections with the MLP 7-19-2 topology, the second one was used to regress the tensile load-bearing capacity with the MLP 7-19-1 topology. Selected parameters of the FSW process were used as input data for ANN training: rotational speed, welding speed, joint and tool geometry. Based on a case study, the quality of the FSW joint was assessed in terms of microstructure and mechanical properties. The usefulness of both trained neural networks has been demonstrated. For the regression network, the quality of the validation set was approximately 93.6%. In the case of confusion matrix for the test set, errors never exceeded 6%. Only 184 epochs were needed to train the regression network. The quality of the validation set was approximately 87.1%. On their basis, predictive maps were developed and presented in the work allowing for the selection of optimal parameters of the FSW process for three types of joints.

In this communication, we present an innovative approach leveraging advanced Machine Learning (ML) and Artificial Intelligence (AI) techniques, specifically the Non-Negative Matrix Factorization (NMF) method, to analyze downward and upward light spectra collected by Hyperspectral Ocean Color Radiometer (HyperOCR, HOCR) sensors in the water column. Our work focuses on the development of a robust and efficient tool for unraveling the structure and activities of natural microbial assemblages in the ocean. By applying the NMF method to HyperOCR data, we successfully extracted five spectral signatures, representing unique patterns in the data. These signatures were instrumental in predicting the abundances of various microbial components, including bacteria, heterotrophic nanoflagellates, and picoeukaryotes, showcasing the potential of ML and AI in advancing oceanographic studies. To validate the methods, the study area included a shallow coastal area under the influence of freshwater inflow and an open offshore area with a depth of 100 m. The study sites in coastal and offshore waters (Kaštela Bay and Stončica Vis, respectively) had significantly different hydrographic and microbiological characteristics. Kaštela Bay had lower temperatures and salinity than the site on Vis. We have proved by application of different AI and ML methods with specific HOCR sensors prediction of the structure of the microbial community.

The use of Artificial Intelligence (AI) applications in a growing number of domains in the latest years has put into focus Ethical Legal and Societal Aspects (ELSA) of these technologies and the relevant challenges they pose. In this paper, we propose an ELSA Curriculum for Data Scientists aiming to raise awareness about ELSA challenges in their work, provide them with a common language with the relevant domain experts to cooperate to find appropriate solutions, and finally, incorporate ELSA in the data science workflow and not be seen as an impediment or a superfluous artifact, rather than an integral part of the Data Science Project Lifecycle. The proposed curriculum uses the CRISP-DM model as a backbone to define a vertical partition, expressed in modules corresponding to the CRISP-DM phases. The horizontal partition includes knowledge units belonging to three strands that run through the phases, namely Ethical and Societal, Legal, and Technical Rendering Knowledge Units (KUs). In addition to the detailed description of the aforementioned KUs, we also discuss the implementation, issues such as duration, form, and evaluation of participants, as well as the variance of the knowledge level and needs of the target audience.

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

The aim of this study was to explore the perceived impact of artificial intelligence (AI) on radiographers’ activities and profession in Switzerland. A survey conducted in the UK, translated into French and German, was disseminated through professional bodies and social media. The participants were Swiss radiographers (clinical/educators/ researchers/students) and physicians working within the medical imaging profession (radiology/nuclear medicine/radiation-oncology). The survey covered five sections: demographics, AI-knowledge, skills, confidence, perceptions about the AI impact. Descriptive, association statistics and qualitative thematic analysis were conducted. A total of 242 responses were collected (89% radiographers; 11% physicians). AI is being used by 43% of participants in clinical practice, but 63.8% of them did not feel confident with AI terminology. Participants viewed AI as an opportunity (57.2%), while 18.5% considered it as a threat. The opportunities were associated with streamlining repetitive tasks, minimizing errors, increasing time towards patient-centred care, research, and patient safety. The significant threats identified were reduction on work positions (22.6%), decrease of the radiographers’ expertise level due to automation bias (16.4%). Participants (68.3%) did not feel well trained/prepared to implement AI in their practice, highlighting the non-availability of specific training (87.6%). 93% of the participants mentioned that AI education should be included at undergraduate education program. Although most participants perceive AI as an opportunity, this study identified areas for improvement including lack of knowledge, educational supports/training, and confidence in radiographers. Customised training needs to be implemented to improve clinical practice and understanding of how AI can benefit radiographers.

Structural health monitoring (SHM) has become paramount for developing cheaper and more reliable maintenance policies. The advantages coming from adopting such process have turned out to be particularly evident when dealing with plated structures. In this context, state-of-the-art methods are based on exciting and acquiring ultrasonic guided waves through a permanently installed sensor network. A baseline is registered when the structure is healthy, and newly acquired signals are compared to it to detect, localize and quantify damage. To this purpose, the performance of traditional methods based on tomographic algorithms has been overcome by machine learning approaches, which allow processing a larger amount of data without losing diagnostic information. However, to date, no diagnostic method can deal with varying environmental and operational conditions. This works aims to develop a framework for mitigating the impact of temperature variations on ultrasonic guided wave-based SHM through generative artificial intelligence. A variational autoencoder and singular value decomposition were combined to learn the influence of temperature on guided waves. After training, the generative part of the algorithm was used to reconstruct signals at new unseen temperatures. Moreover, a refined version of the algorithm called forced variational autoencoder was introduced to further improve the reconstruction capabilities. The accuracy of the proposed framework was demonstrated against real measurements on a composite plate.

Reservoir computing (RC) systems can efficiently forecast chaotic time series using nonlinear dynamical properties of an artificial neural network of random connections. The versatility of RC systems has motivated further research on both hardware counterparts of traditional RC algorithms and more efficient RC-like schemes. Inspired by the nonlinear processes in a living biological brain and using solitary waves excited on the surface of a flowing liquid film, in this paper we experimentally validate a physical RC system that substitutes the effect of randomness for a nonlinear transformation of input data. Carrying out all operations using a microcontroller with a minimal computational power, we demonstrate that the so-designed RC system serves as a technically simple hardware counterpart to the `next-generation’ improvement of the traditional RC algorithm.

In 2023, the technological landscape witnessed an unprecedented surge of transformative innovations, including Web 3.0 and machine learning, poised to revolutionize various aspects of our daily lives. At the forefront of this revolution stands artificial intelligence (AI), which has evolved from a concept once confined to science fiction into an indispensable force reshaping industries on a global scale. Notably, recent years have seen remarkable strides in AI functionalities, particularly in machine learning, computer vision, and natural language processing. These advancements have empowered AI to generate diverse interactions and media autonomously. This study ultimately provides invaluable insights into the dynamic intersection of AI and Advertising, con-tributing to a deeper comprehension of this evolving field. This study conducts a comprehensive review of Artificial Intelligence within the advertising domain, employing network analysis. By meticulously examining the evolution of AI through co-occurrences of terms, keywords, and co-authorship analysis, we aim to unveil the relationship of AI & Advertising through the lens of Generative models to identify pivotal trends, core concepts, and seminal studies that have profoundly influenced the industry. The analysis charts the trajectory leading to the emergence of Generative Artificial Intelligence (GenAI), a transformative development poised to revolutionize advertising practices and consumer engagement. In tandem with these objectives, the results highlight vital trends, emphasizing the growing prominence machine learning tools and techniques such as deep learning techniques and advanced natural language processing methods like word2vec, GANs, and more in shaping the future of advertising practices.

This manuscript examines the role of artificial intelligence in cancer imaging. It throws light on how artificial intelligence (AI) can significantly cut the wait time of patients and their clinicians in getting cancer diagnoses and assist health care providers by highlighting areas of an image where the interpreter needs to focus more for better result analysis and quality output. It also provides details on how the rise of AI attempts to standardize imaging results across providers by eliminating inter- and intra-observer variations among health care providers. Finally, it exposes the numerous limitations associated with AI use in cancer imaging, such as the need to digitize pathology laboratories and workflows, as well as transform our century-old tissue processing methods to fit into modern technological standards. In the field of radiology, the challenge of curating the enormous amount of data generated through MRIs, CT scans, PET scans, etc., poses a significant challenge to the ability of researchers to train relevant AI models to high levels of accuracy and reliability. In addition, over-reliance on AI by clinicians can deprive them of a common-sense approach to the health care issues of their patients and negatively impact doctor-patient relationships and confidentiality.

Background: Periprosthetic joint infection (PJI) following total hip and knee arthroplasty remains an extremely challenging and relatively high complication. This study aims to develop, validate and evaluate the use of machine learning (ML) algorithm to predict PJI in patients undergoing revision arthroplasties. Methods: A comprehensive review of patients undergoing hip or knee revision arthroplasty from 1 January 2015 to 31 March 2021 was conducted. Clinical data coming from preoperative patients history, laboratory analysis and demographic characteristics of patients were screened. Final data have been used to train a Logistic Regression model with the aim of predicting PJI preoperatively. Results: 1360 patients were enrolled, 1141 in the aseptic cohort and 219 in the infected cohort were included. ML demonstrated good discriminatory performance in predicting PJI in the selected patients (area under the curve 0.770 ± 0.006 in the training set and 0.720 ± 0.057 in the test set), and identified 3 significant predictors of PJI. Conclusion: ML algorithm trained using preoperative clinical data accurately predicted PJI. The incorporation of ML models into preoperative assessment of patients undergoing prosthetic revision procedures are useful in providing specific risk assessment to aid individualised counselling, shared decision making and presurgical optimization.

Anatomy education plays a critical role in medical practice, and the level of anatomical knowledge among students and physicians significantly impacts patient care. This article presents a pilot project conducted by the University of Milan aimed at exploring the effectiveness of the Area9's Rhapsode platform, an intelligent tutoring system that uses artificial intelligence to personalize learning and collect data on mastery acquisition. The study focused on liver anatomy and employed a flipped classroom approach, incorporating adaptive learning modules and an interactive in-class session. The project's execution, outcomes and preliminary pedagogical considerations are discussed. The results demonstrated improved learning quality, positive repurposing of study time, enhanced metacognitive awareness among students, with most of students demonstrating conscious mastery of the materials and a clear understanding of their level of competence. This approach, by providing valuable insights into the potential of artificial intelligence-based adaptive learning systems in anatomy education, could address the challenges posed by limited teaching hours, shortage of anatomist and the need for individualized instruction, with recommendations for future research and the expansion of adaptive learning modules to other anatomical districts.

Especially in the last two decades, due to the developments in durable mechanical circulatory support (MCS) technology and applications, left ventricular assist (LVAD) implantation has become used in more fragile end-stage heart failure patients with higher risk. Despite advances in technology and patient care processes, right heart failure remains the leading cause of morbidity and mortality after durable LVAD implantation. It is difficult to make decisions about the type and duration of MCS during preoperative evaluation, and many clinical, hemodynamic, biochemical and echocardiographic criteria must be taken into account when evaluating the right ventricle. Complex quantitative scoring systems, including measurements of different risk factors for postimplant right ventricular failure, and composite variables, including the anatomical and functional status of the right ventricle, have been used to decide between left or biventricular assist device implantation. The successful use of artificial intelligence (AI) and machine learning (ML) in various applications in the medical field has paved the way for the idea of using it to better predict possible conditions such as right heart failure after LVAD implantation. This review aims to evaluate the current situation of risk prediction models using AI and ML technology, which have become popular in recent years, and possible future developments in predicting right heart failure after the application of durable MCS systems.

Quantification of erythrocytes and leukocytes is an essential aspect of hemogram diagno- 23 sis in Veterinary Medicine. Flow cytometry analysis, laser scattering, and impedance detection are 24 standard laboratory techniques, verified by manual microscopy counting. Although single-cell scat- 25 tering is already used as a standard technology for differentiating cell counts in flow cytometry, it 26 requires capillary cell separation. The current study investigates the scattering characteristics of 27 whole blood to identify correlations with erythrocytes and leukocytes counts. The scattering infor- 28 mation present in blood samples can be classified into three types: i) geometrical scattering, which 29 occurs when non-absorbed light is reflected and scattered, ii) Mie scattering, which happens when 30 light is scattered by particles of a similar size to the wavelength, and iii) Rayleigh scattering, which occurs when light is scattered by particles that are smaller than the incident light wavelength. In 32 this study, we investigate the scattering correction coefficients of dog blood absorption spectra in 33 the visible-near infrared range, to establish direct correlations with erythrocytes and leukocytes 34 counts, using multivariate linear regression. Our findings demonstrate the possibility of using the 35 scattering properties of dog blood, which is a step towards the existence of a portable and miniatur- 36 ized hemogram diagnosis in Veterinary Clinics worldwide.

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

In today's digital landscape, cybersecurity has become a priority, with attacks becoming increasingly sophisticated. Traditional security approaches are no longer enough, requiring a more dynamic and advanced response. In this context, integrating Artificial Intelligence (AI) and Machine Learning (ML) appears key to addressing this growing threat. However, despite their high effectiveness, there is a developed problem: the integration of various data sources and technologies for comprehensive protection. This article presents an in-depth review of integrating Artificial Intelligence and Machine Learning in cybersecurity, focusing particularly on Open Extended Detection and Response (Open XDR) technology. The literature review in this article was meticulously conducted with specific criteria in mind. It focused on sourcing peer-reviewed journals, authoritative cybersecurity publications, and recent conferences that primarily concentrate on the advancements in AI and ML within the cybersecurity domain, particularly from 2018 to 2023. This timeframe was chosen to ensure the inclusion of the most current and relevant developments in Open XDR technology, Intrusion Detection Systems, Endpoint Detection and Response, and Security Information and Event Management systems. Additionally, the review gave special attention to studies and reports highlighting practical implementations and real-world applications of these technologies, thereby ensuring a comprehensive understanding of their impact and effectiveness in enhancing cybersecurity resilience. The methodology used is a detailed literature review, examining how various cybersecurity components interact and function. These components include Intrusion Detection Systems (IDS), which monitor networks for malicious activities; Endpoint Detection and Response (EDR), which focuses on detecting and investigating security incidents on endpoints; and Security Information and Event Management (SIEM), systems that provide real-time analysis of security alerts. The review also considers the role of Active Directory, a directory service for Windows domain networks, and the process of log forwarding, where log files are transmitted to a central server for analysis, in the context of AI and ML. The paper delves into the development of AI and ML, underscoring their roles in cybersecurity for advanced data processing, pattern recognition, and predicting threats. It explores both supervised (where the model is trained on labeled data) and unsupervised learning (where the model learns from unlabeled data) in ML, and how these techniques bolster cybersecurity measures. The article highlights the significance of Open XDR as a critical innovation that integrates data from multiple sources for comprehensive security analysis. Further, the review discusses how the integration of AI and ML into various cybersecurity tools, such as IDS, EDR, and SIEM, augments capabilities in threat detection and response. It addresses the challenges and opportunities that AI and ML present in the cybersecurity domain, focusing on ethical issues, data privacy concerns, and the necessity for ongoing professional development in this rapidly advancing field. The paper concludes by affirming the effectiveness of merging AI and ML with these cybersecurity tools within the Open XDR framework.

Cognitive Warfare (CogWar) is another form of warfare where the goal is to exploit cognition facets to disrupt, undermine, influence or modify human decisions. Famous examples include “Operation Gidlock” meddling with the US 2016 election, and CogWar campaigns assisting the annexation of Crimea. As Large Language Model(LLM)-based applications expand, reaching more users and their cognitive states, stealth Malicious Cognitive Behavioral Tactics (MCBT) can be embedded in trusted application sessions to systematically profile, then alter, each individual’s cognition over time. Successful MCBT can create more cases like Snowden, mass shootings, or January6. This short-form paper discusses a novel MCBT threat model, a novel kill chain, and an on-going prototype for demonstration.

In this study, we introduce a neural-learning based approach for the early detection of Parkinson Disease using DaT SPECT imaging, a promising technique in neurodegenerative disease diagnosis. Leveraging the advanced capabilities of deep learning, we developed a convolutional neural network model based on the Inception V3 architecture, which has been enhanced by modulating custom layers to fine-tune the model specifically for the analysis of DaT SPECT images. The imaging dataset was obtained through the Parkinson's Progression Markers Initiative (PPMI), and the model was trained to classify whether a patient had Parkinson Disease through a binary classification of their DaT SPECT Scan using the fine-tuned InceptionV3 model. This specialized adaptation allows for more accurate and nuanced interpretation of the scans, a crucial factor in early and reliable diagnosis of Parkinsonian syndromes. Our model underwent rigorous hyperparameter tuning, focusing on learning rates, the number of neurons in the dense layer, and dropout rates, to ensure optimal performance. Remarkably, the proposed model demonstrated a significant improvement in accuracy, achieving 74.65%, compared to a standard Sequential Model, which only reached an accuracy of 51.41%. Diagnosis of Parkinson Disease through DaT SPECT Scans is a promising approach not only as a more effective diagnostic tool but also as a beacon for future research in the application of deep learning to medical imaging for neurodegenerative diseases. By building layers on the InceptionV3 Architecture, we achieve a higher accuracy on DaT SPECT Scan diagnosis and pave the way for automated diagnosis of patients with Parkinson Disease, allowing for easier medical accessibility and paving the way for advancements in early diagnosis and personalized medicine strategies for Parkinson's disease and related disorders.

Viruses are silent enemies that intrude and take control of the plant cell's machinery for their own multiplication. Infection by viruses and the resulting damage is still a major challenge in the agriculture sector. Plants have the ability to fight back, but the ability of viruses to counteract and mutate at a fast rate helps them to hide from the host response. Therefore, classical approaches for introgressing resistance genes by breeding have obtained limited success in counteracting the virus menace. Genetic modification (GM) based strategies have been successful in engineering artificial resistance in plants. Several different approaches based on pathogen derived resistance, antisense constructs, hairpin RNA, double stranded RNA etc. have been used to enhance plant resistance to the virus. Recently, genome editing (GE) strategies mainly involving the CRISPR/Cas mediated modifications are being used for virus control. In this review, we discuss the developments and advancements in the GM and GE based methods in tackling virus infection in plants.

Along with the rapid development and wide application of Artificial Intelligence (AI), discussions regarding AI Sustainability have emerged. Employing a systematic mapping study, this paper reviews research about AI Sustainability from diverse fields and viewpoints, including three dimensions economic, social, and environmental. Our analysis indicates a transformative shift in research focus, evolving from fragmented approaches to a more holistic understanding of AI Sustainability. This maturation is reflected in the interplay between multiple dimensions and the integration of both "AI as a tool for sustainability" and "Sustainability of AI" perspectives. As the field continues to mature, researchers should embrace a comprehensive and unbiased exploration of AI Sustainability across all dimensions and approaches, ensuring a well-rounded foundation for future advancements in this rapidly evolving domain.

Social media has become more popular in the last few years. It has been used in public health development and healthcare settings to promote healthier lifestyles. Given its important role in today's culture, it is necessary to understand its current trends and future directions in public health. This review aims to describe and summarize how public health professionals have been using social media to improve population outcomes. Additionally, it discusses potential future directions on how social media can be used to improve population health. The findings showed how social media has been used as a tool for research, designing and implementing health campaigns, messaging, and health promotion. In addition, social media integration with Artificial Intelligence (AI) and Generative Pre-Trained transformers (GPT) can have a big impact and offer an innovative approach to tackle the problems and difficulties in health informatics. These innovations might improve patient participation, individualized assistance, and care experiences. The research showed how likely social media will keep growing and evolving and, if used effectively, have the potential to help close public health gaps across different cultures and improve population health.

This article summarizes Chinese framework for regulating artificial intelligence and integrates evolutionary game theory with cost-benefit analysis to establish a model and simulation. This framework is employed to analyze the behavioral trends among three distinct entities: governmental bodies, third-party independent institutions, and AI companies within the context of regulatory relationship. The findings indicate that: (1) The cost-benefit dynamics within the regulatory legal nexus significantly influence the behaviors of these entities; (2) Under the condition of normalized government regulation approaching full enforcement, the behavioral choices of third-party independent institutions and AI companies exhibit cyclical fluctuations.The paper draws two principal conclusions: (1) The regulatory framework need to be tailored to the specific risks presented by AI and the relative costs and benefits of legal enforcement in different jurisdictions. (2) From a cost-benefit standpoint, government intervention in AI regulation ought to be circumscribed, with government regulation focusing on critical legal risks. Other aspects of regulatory control should be delegated to cooperative legal framework that allows the participation of the independent third-party institution,which brings a nuanced and specialized approach to the governance of AI.

Smart home and building systems are popular solution that support maintaining comfort, safety and improving energy efficiency in buildings. However, dynamically developing distributed network technologies, in particular the Internet of Things, are increasingly entering the above-mentioned application areas of building automation, offering new functional possibilities. The result of these processes is the emergence of many different solutions that mix technologies of field-level and ICT networks in various configurations and architectures. New paradigms are also emerging, such as edge and fog computing, providing support for local monitoring and control networks in the implementation of advanced functions and algorithms, including machine learning and artificial intelligence mechanisms. This paper collects state-of-the-art information in these areas, along with a systematic review of the literature, case-studies and an analysis of selected development trends. The author systematized this information in the context of the potential development of building automation systems. Based on the conclusions of this analysis and discussion, a framework for the development of the Generic IoT paradigm in smart home and building applications has been proposed, along with a SWOT analysis of its usability. Future works are proposed as well.

In the dynamic and complex environment of industrial control rooms, operators are often inundated with a multitude of tasks and alerts, which can lead to an overwhelming situation known as task overload. This state can precipitate decision fatigue and a heavier reliance on cognitive biases, potentially compromising the decision-making process. To mitigate such risks, the implementation of decision support systems becomes crucial. These systems are designed to assist operators in making swift and well-informed decisions, particularly when they sense their own judgment may be faltering. Our research introduces an AI-based framework that leverages dynamic influence diagrams and reinforcement learning to construct an effective decision support system. The cornerstone of this framework is the creation of a robust, interpretable, and efficient tool that supports control room operators during critical process disturbances. By integrating expert knowledge, the dynamic influence diagram forms a comprehensive model that captures the uncertainties inherent in complex industrial processes. It is adept at anomaly detection and recommending optimal actions. Moreover, this model is enhanced through a strategic partnership with reinforcement learning algorithms, which fine-tune the recommendations to be more context-specific and precise. The ultimate goal of our framework is to provide operators with a live, reliable decision support system that can significantly improve their response during process upsets. This paper outlines the development of our AI framework and its application within a simulated control room environment. Our findings indicate that the use of decision support systems can lead to improved operator performance and reduced cognitive workload. However, it also reveals a trade-off with situation awareness, which tends to diminish as operators may become overly reliant on the system’s guidance. Trust emerges as a critical factor in the adoption and effectiveness of decision support systems. Our research underscores the importance of balancing the benefits of decision support with the need for maintaining operator engagement and comprehension during process operations.

In recent years, advancements in rooftop solar panel technology have sparked a revolution in the electricity markets. This has given rise to a new concept of energy exchange - the ability for consumers and producers to trade localized energy. This concept has been made possible by the emergence of blockchain technology, which has gained significant traction in the energy markets. Its unique ability to facilitate peer-to-peer (P2P) energy transactions has made it a promising solution for the trilemma of scalability, security, and decentralization. However, while blockchain technology has shown great potential, it is still in its early stages of development and has yet to reach its full potential. To fully understand the potential of blockchain technology in P2P energy trading, it is important to explore the trilemma in depth. This study proposes a blockchain-based scalability solution, with a focus on P2P energy trade. This strategy is supported by empirical modeling, utilizing data gathered from a trial case study. The results of this study demonstrate that the suggested technique outperforms base layer models in terms of scalability, while still maintaining the essential elements of security and decentralization. This proposed solution not only has the potential to revolutionize the energy markets, but also has broader implications. By providing a more secure and decentralized platform for P2P energy trading, it has the potential to address issues of energy distribution inequality and promote the adoption of renewable energy. With blockchain technology, individuals and communities have the opportunity to take control of their energy usage and production, reducing their reliance on traditional centralized energy systems. This not only has the potential to lower energy costs for consumers, but also contributes to the overall goal of reducing carbon emissions and mitigating the effects of climate change. The combination of rooftop solar panel technology and blockchain applications has the potential to create a significant shift towards decentralized and sustainable energy systems. This will not only benefit individuals and communities, but also have a positive impact on the environment and the global energy market as a whole. However, for this transition to occur, it is crucial that governments and energy companies continue to support and invest in these advancements.

Combustion parameters are the important aspects in the development and research of an internal combustion engines. Hence present work is an attempt to develop an artificial neural network (ANN) model for anticipating the combustion characteristics of computerized CI DI VCR, four strokes, single cylinder, and water cooled engine using mixture of two biodiesel and diesel blends as a fuel. The two biodiesel selected are Jatropha and Simarouba and mixed in the ratio of 75:25. Two biodiesel mixtures is used for the preparation of various blends with diesel. Experiments are carried out at different compression ratio, at different % load and blends B20, B40, B60and neat diesel (B00) fuel to accumulate the assorted combustion data. The input variables are % load, compression ratio, blends and crank angle. The outputs well thought-out are cylinder pressure (CP), net heat release rate (NHRR), cumulative heat release (CHR), rate of pressure rise (RPR), mass fraction burned (MFB) and maximum cylinder pressure. The assessment is focused on developing artificial neural network for the prediction and verifying the combustion characteristics of engine. MATLAB software is used for analysis of different combustion aspects of the engine. Multilayer neural network perceptions are used with Feed-Forward Back Propagation algorithm with performance function for mapping the input and output parameters. The experimental outcomes are compared with ANN estimated results for the analysis. The outcome from the study authenticate that experimental results are in match with ANN results. The accuracy of the ANN model shows the developed model can effectively predict the engine combustion characteristics with excellent regression coefficients ‘R’ and its values are close to unity and the mean square errors are extremely less. ANN model approach is competent towards predicting the combustion aspect of diesel engine with good accuracy

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