AI, Volume 7, Issue 5 (May 2026) – 31 articles

This research introduces a quantum machine learning (QML) approach for predicting heart disease (HD). The method combines preprocessing of data with quantum feature map (QFM) and quantum classification techniques. In the method, clinical data of HD are preprocessed, and then features are optimized using principal component analysis (PCA). After that, the resulting features are encoded into quantum states with five different QFM methods, namely angle encoding (AE), amplitude encoding (AmE), basis encoding (BE), Pauli encoding (PE), and ZZ feature map (ZZFM). Finally, four quantum classifiers, such as quantum support vector machine (QSVM), quantum k-nearest neighbor (QKNN), quantum random forest (QRF), and variational quantum circuit (VQC), are evaluated to predict the HD from the encoded states. Experimental results show that QSVM with AE achieved the best performance, with an overall accuracy of 90.26%, specificity of 83.42%, sensitivity of 92.16%, precision of 88.89%, F1-score of 89.68%, and kappa value of 0.7608. These results are superior to those from classical state-of-the-art methods. This research finding suggests QML methods can capture complex nonlinear relationships in clinical data effectively and thus improve diagnostic reliability. Full article

The Industrial Internet of Things (IIoT) is central to modern industrial automation, yet its growing connectivity exposes critical systems to evolving cyber threats. Traditional intrusion detection methods struggle in IIoT environments due to class imbalance and limited adaptability to zero-day attacks. This systematic review evaluates generative AI techniques for IIoT intrusion detection and identifies deployment requirements for industrial environments. We searched five databases (IEEE Xplore, ACM Digital Library, Springer, ScienceDirect, and arXiv) for studies published between January 2019 and December 2025, applying predefined inclusion criteria. Following a systematic selection process (identification plus three progressive screening stages) across 342 records, 42 primary studies were included for systematic synthesis. We examined four GenAI paradigms—Generative Adversarial Networks, Transformers, Diffusion Models, and Variational Autoencoders—analyzing nine state-of-the-art frameworks through comparative performance analysis. Hybrid Transformer architectures (e.g., Transformer-GAN-AE) achieve the most consistent detection performance, while diffusion-based models (e.g., Diff-IDS) provide computational advantages for edge deployments. However, substantial variability in evaluation methodologies and limited reporting of statistical rigor indicate important gaps in current research practices. These findings inform the development of GenAI-driven strategies tailored to industrial infrastructure constraints and highlight key directions for advancing IIoT cybersecurity. Full article

Measuring the impact of citizen-science projects is hard because inputs are heterogeneous, mostly categorical, and sparse. We present Alquimics, a compact supervised neural network trained on one-hot project descriptors to predict impacts across five domains (Environment, Economy, Governance, Science, and Society). Each project is encoded as a binary vector of length 4460 (223 questions × 20 options, flattened). The network employs a 4460–42–5 topology with logistic activations throughout; labels consist of five continuous targets in [0, 1] obtained by scaling expert domain scores in [1, 42]. We implement L2-regularised training in Octave using fmincg with MaxIter = 10 and lambda = 0.07. Leave-one-out cross-validation (LOOCV) over nine projects yields an overall RMSE = 10 and R² = 0.06 on the 1–42 scale, with Governance being the most predictable domain (RMSE = 6, R² = 0.3). We document the entire data pipeline, objective, and implementation, provide a minimal reproducible script, and discuss limitations arising from the small dataset (n = 9 projects). This establishes a transparent baseline that complements rule-based scoring and can be expanded as more labelled projects become available. Full article

Robust exercise recognition is essential for robot-assisted lower-limb rehabilitation, where misclassifications of sensor-derived movements can degrade therapy execution and supervision. This study proposes a novel hybrid weighted stacking ensemble to increase the efficiency of the intelligent module of the LegUp parallel robotic system for lower limb rehabilitation. The approach combines a Residual Multilayer Perceptron (ResMLP) and an optimized Kernel Extreme Learning Machine (KELM), where model hyperparameters are tuned using Optuna and the base-model probability outputs are fused through optimized weighting and a meta-learner. Experiments were conducted on a five-class dataset built from nine IMU orientation features acquired from three sensors placed on the healthy limb. Four meta-learners were evaluated (Logistic Regression, Random Forest, Gradient Boosting, and AdaBoost), with AdaBoost providing the best overall performance. To further assess the robustness and generalization capability of the proposed approach, a 5-fold cross-validation procedure was performed for the ResMLP, KELM, and the hybrid ensemble models. The proposed stacking hybrid ensemble consistently surpassed the performance of the strongest individual classifiers as well as the original LegUp Multilayer Perceptron model. These results indicate that combining residual learning with kernel-based classification in a weighted stacking framework yields a stable and high-performing solution for multi-class rehabilitation exercise recognition. Full article

Modern power systems are undergoing a structural transformation driven by the rapid integration of renewable energy sources, distributed energy resources, electrification, and increasing operational uncertainty. These developments expose the limitations of traditional centralized energy management and rule-based automation in highly distributed, data-intensive, and dynamically coupled energy infrastructures. In response, recent advances in artificial intelligence offer new opportunities for improving prediction, coordination, and adaptive control. This paper develops a reference architecture for Autonomous Energy Systems based on the integration of generative AI, agentic AI, digital twins, and distributed cyber–physical energy infrastructures. Rather than treating forecasting, control, simulation, and market coordination as separate research tracks, the paper organizes them within a common architectural perspective. Generative AI is positioned as a source of scenario intelligence, synthetic data generation, and uncertainty-aware forecasting, while agentic AI is framed as a bounded decision layer for perception, reasoning, planning, and coordinated action under operational constraints. The paper further clarifies the distinction between agentic AI, conventional multi-agent systems, and multi-agent reinforcement learning in energy applications. Representative application domains are discussed, including self-healing power grids, autonomous energy markets, and digital twin training environments. Major open challenges are identified in relation to scalability, physical consistency, safety verification, sim-to-real transfer, cybersecurity, interoperability with legacy infrastructures, and governance. The paper concludes by outlining a research agenda for the staged and safe development of increasingly autonomous energy systems. Full article

Artificial intelligence (AI) is transforming how organizations develop human potential, offering scalable and data-driven support for coaching and capability building. This study proposes and validates a conceptual framework for integrating AI into organizational coaching processes to enhance competence development and strategic alignment. AI-supported coaching in this research is treated as an emerging organizational technology whose potential organizational value depends less on model capability and more on governance design, decision rights, and auditable evaluation outputs. Following a mixed-methods, multi-phase design, the research combined a Systematic Literature Review (SLR) with the construction of a layered design architecture in which OSCAR serves as the primary coaching-process scaffold, complemented by KSA for competency specification, Situational Leadership for adaptive guidance, and KPIs for monitoring and governance. The framework structures AI-supported coaching across 10 interrelated phases, from contextual anchoring to review and measurement, while preserving iterative re-entry to earlier phases whenever review evidence, contextual change, or insufficient progress makes adjustment necessary. Prototyping demonstrated feasibility and coherence across models, while the focus group provided qualitative expert feedback on the framework’s clarity, governance needs, and perceived usefulness for competence development. At this stage, however, the KPI structures generated by the framework and the descriptive comparison across AI tools should be interpreted as prototype-level outputs rather than as empirically validated performance measures or evidence of added value over baseline approaches. Because the evaluation relied on two fictional prototyping scenarios and a small expert-oriented focus group (n = 6), the findings should be interpreted as evidence of prototype demonstration and qualitative refinement rather than of real-world effectiveness or organizational impact. The study also does not include a control group or comparison with traditional human coaching, so the added value of the AI-supported framework over alternative coaching arrangements remains a question for future empirical testing. Findings suggest that AI can usefully support organizational coaching by personalizing dialogue, structuring reflection, and generating auditable development artefacts, provided ethical safeguards and human oversight remain integral. The research contributes a preliminarily validated, ethics-informed, and governance-aware framework for AI adoption in organizational coaching and offers practical insights for embedding AI-enabled development in learning organizations. Full article

Evaluating large language models (LLMs) critically depends on human judgment. This article reviews and develops a conceptual framework for human-centered LLM evaluation, synthesizing research across evaluation methodology, psychometrics, cognitive science, and domain-specific applications. Four primary challenges are identified that limit current human evaluation practice: imperfect gold standards, evaluator fatigue and overload, shared and unique bias structures across humans and LLM judges, and the routine omission of uncertainty and dispersion estimates. To address these gaps, the STEP-V design framework is proposed: Stakes, Task-type, Evaluator availability, Purpose, and Volume, for selecting human and/or automated LLM evaluation methods under real-world constraints. An evaluator failure mode taxonomy is also proposed that analyzes human and LLM judges within a common error framework, clarifying where hybrid pipelines can compensate for weaknesses and where they might compound them. The framework motivates a more rigorous science of LLM evaluation, one that treats human judgment as a necessary but fallible measurement requiring explicit design, calibration, and uncertainty quantification. Full article

In the context of rapid technological evolution and increasing market uncertainty, technoparks have emerged as critical ecosystems for bridging scientific research and high-tech industrial production; however, their effectiveness is often constrained by limited flexibility, fragmented control mechanisms, and delayed decision-making processes. Motivated by these challenges, this article investigates the automation of control processes in research-driven and flexible manufacturing environments within technopark infrastructures, positioning automation as a strategic lever for enhancing operational adaptability and innovation throughput. The study conceptualizes control process automation as a multi-stage framework encompassing data acquisition, processing, intelligent analysis, and real-time decision execution and examines the role of enabling technologies such as artificial intelligence, the Internet of Things (IoT), and cyber-physical systems in supporting this paradigm. The analysis demonstrates that the integration of these technologies significantly improves production flexibility, resource optimization, and responsiveness to dynamic conditions, while simultaneously accelerating the transformation of scientific and research outputs into measurable economic value. By combining theoretical foundations with illustrative practical applications, the article substantiates the effectiveness of automated control systems and highlights their strategic relevance for increasing the competitiveness of technoparks, fostering sustainable technological innovation, and shaping resilient long-term development strategies. Full article

Machine vision plays a pivotal role in precision engineering for high-precision measurement that relies on high-resolution images. The highly reflective nature of metal surfaces and the need for high-quality images pose significant challenges in image processing. Although existing research has made significant progress in enhancing the resolution of natural images, super-resolution methods specifically tailored for multi-view metal images remain unexplored areas. To fill this gap, this paper focuses on developing a deep learning-based super-resolution algorithm, focusing on detail recovery on under multi-view metal images. The proposed super-resolution model utilizes a hybrid-resolution input that combines light field super-resolution at the image level and reference-based super-resolution at the feature level, demonstrating the effectiveness for achieving a large-scale multi-view metal image super-resolution. An experiment using a public metal object image dataset is conducted, and a comparison has been carried out with Bicubic, LFhybridSR and ERVSR. The proposed method demonstrates superior SSIM and achieves average PSNR improvements of 4.45 dB and 1.18 dB on synthetic data and real-world data. The results demonstrate that the method can improve the resolution and detail representation of metal images in terms of PSNR/SSIM and address the problem of super-resolution in multi-view metal images. Furthermore, applying the proposed SR method as preprocessing reduces the absolute relative error in depth estimation from approximately 0.5 to 0.1. Full article

Generating multiple text sequences and refining them through feedback is essential for improving the quality of outputs in many NLP tasks. While Large Language Models can leverage iterative feedback during inference, smaller models often lack this capability due to limited capacity and the absence of suitable training paradigms. In this paper, we propose a novel Feedback-Aware Inference approach that enables iterative sequence generation with integration of feedback signals. Our method allows models to generate multiple sequences, incorporate feedback from previous iterations, and refine outputs accordingly. This approach dynamically adjusts to different quality metrics, making it adaptable to various contexts and objectives. We evaluate our approach on two distinct tasks: Answer Selection for Question Generation and Keyword Generation, arguing for its generalizability and effectiveness. Results show that our method outperforms strong baselines, maintaining high performance across iterations and achieving superior results even with smaller, open-source models. Full article

Large language models (LLMs) have demonstrated remarkable capabilities in learning complex tasks purely from sequential data. To explore whether such models can internalize strategic world representations, We investigate whether generative transformer models can learn structured world representations from sequential data. Using the domain of Go life-and-death problems as a controlled micro-world, we train a GPT-style generative model to predict moves from serialized board states. Focusing on localized life-and-death (tsumego) scenarios, we train the model to predict valid next moves from serialized board states without providing any explicit Go rules or strategic supervision. Probing the model’s internal activations reveals structured representations aligned with liberties, eyes, and tactical group status. To interpret these representations, we introduce the Multi-Aspect World Probe (MAWP), a modular probing framework that disentangles tactical concepts into orthogonal dimensions. We further apply interventional techniques to manipulate internal representations and causally evaluate their impact on model predictions. Our results show that the proposed model achieves 94.7% accuracy in sequence correctness and 92.1% in outcome validity on life-and-death tasks. This work extends interpretability research into spatially structured domains and offers tools for understanding decision-making in sequence models. Full article

Remaining Useful Life (RUL) forecasting models are essential to enable predictive maintenance strategies. However, selecting the most appropriate model based solely on conventional accuracy metrics may be insufficient for practical decision making, where an adequate prediction horizon is required to plan maintenance activities. This study investigates the impact of prediction horizon on model performance and its implications for maintenance decision making. A multi-horizon evaluation approach is applied to assess model accuracy across different predictive horizons. The results show the fluctuation of accuracy and prediction error over different prediction horizons. Across both datasets, predictive accuracy was generally lowest at the long horizon (11.64–86.62%), remained variable at the medium horizon (18.13–82.04%), and was highest at the short horizon (30.29–98.25%). The results demonstrate that model performance varies significantly with the prediction horizon, highlighting a trade-off between prediction accuracy and the time available for operational planning. These findings emphasize that models with high short-term accuracy may not necessarily support effective maintenance decisions if sufficient lead time is not provided. The findings show how prediction horizon considerations shall be integrated into a risk-based evaluation framework, in which model performance is interpreted in relation to the operational consequences of prediction errors. A complete risk-based predictive maintenance framework is proposed to support a shift toward comprehensive, risk-based evaluation as a prerequisite for reliable and effective RUL prediction in predictive maintenance systems. Full article

At present, there are no easily understood explainable artificial intelligence (AI) methods for discrete token inputs, like text. Most explainable AI techniques do not extend well to token sequences, where both local and global features matter, because state-of-the-art models, like transformers, tend to focus on global connections. Therefore, existing explainable AI algorithms fail by (i) identifying disparate tokens of importance, or (ii) assigning a large number of tokens a low value of importance. This method for explainable AI for tokens-based classifiers generalizes a mask-based explainable AI algorithm designed originally for images. It starts with an Explainer neural network that is trained to create masks to hide information not relevant for classification. Then, the Hadamard product of the mask and the continuous values of the classifier’s embedding layer is taken and passed through the classifier, changing the magnitude of the embedding vector but keeping the orientation unchanged. The Explainer is trained for a taxonomic classifier for nucleotide sequences and it is shown that the masked segments are less relevant to classification than the unmasked ones. This method focused on the importance the token as a whole (i.e., a segment of the input sequence), producing a human-readable explanation. Full article

Artificial intelligence (AI), particularly large language models, is increasingly shaping how people access and engage with news. Guided by a multi-level digital divide framework, this exploratory study examines patterns of AI use for news consumption (AI-access) and perceptions of AI influence on social and political attitudes (AI-influence). The analysis is based on a quantitative online survey conducted among a diverse national sample of 515 participants in Israel. Measures captured self-reported AI-enabled news practices, including consuming, summarizing, and identifying fake news, as well as perceived influence and concerns about bias. Demographic indicators included age, gender, education, and income. The findings indicate a nuanced pattern that diverges somewhat from conventional digital divide expectations. Bivariate analyses suggest that older individuals, women, and those with lower levels of education report somewhat higher levels of AI use for news-related practices. However, multivariable regression analyses show that only age, gender, and education remain significant predictors, while income does not show consistent independent effects. Overall, the observed associations are relatively limited, suggesting that demographic variables explain only a small portion of the variance. At the same time, perceived AI influence shows a limited association with demographic characteristics. These results provide empirical insight into digital divide processes in the AI context and suggest that future research should examine additional explanatory mechanisms, including AI literacy, trust, perceived usefulness, and digital skills. Full article

Background and Objectives: Post-hepatectomy liver failure (PHLF) remains the leading cause of mortality following hepatic resection, with reported incidence rates ranging from 1.2% to 32%. Traditional scoring systems such as the Child–Pugh score, Model for End-Stage Liver Disease (MELD), and Albumin–Bilirubin (ALBI) grade have demonstrated limited predictive accuracy for PHLF. Machine learning (ML) algorithms have emerged as promising tools capable of integrating complex, multidimensional clinical data to improve predictive performance. This systematic review aims to evaluate the current evidence on ML-based prediction models for PHLF, assessing their predictive accuracy, methodological quality, clinical applicability, and the key variables utilized across models. Methods: A systematic literature search was conducted across PubMed, Embase, Web of Science, and the Cochrane Library from inception to January 2026. Studies that developed or validated ML models for predicting PHLF after hepatic resection were included. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to evaluate the risk of bias. Data on model performance, algorithms employed, sample sizes, predictor variables, and validation strategies were extracted. The review was conducted in accordance with the PRISMA 2020 guidelines and registered in PROSPERO. Results: Twelve PubMed-verified studies involving 6913 patients were retained in the final analysis. Publication years ranged from 2020 to 2025, with five studies published in 2025. Gradient boosting approaches (LightGBM/XGBoost or phase-specific boosting models) were the most frequent best-performing architectures, while ANN/deep learning, radiomics-integrated, and ensemble approaches also showed clinically relevant discrimination. Best reported non-training AUCs ranged from 0.7927 to 0.981 (median, 0.873). The strongest generalization signals came from studies with temporal, external, or prospective validation structures. Common predictor domains included bilirubin-based liver function measures, coagulation variables, platelet count, volumetry or extent of resection, imaging-derived radiomics features, and perioperative dynamic data. Conclusions: Machine learning models remain promising for PHLF prediction, but the evidence base is smaller and more heterogeneous than the original draft suggested. Performance is highest in studies that combine clinical liver-reserve markers with imaging or perioperative temporal data; however, widespread clinical adoption is still limited by retrospective design predominance, inconsistent outcome definitions, and incomplete external validation. Full article

In recent years, with the advancement of production technology in the manufacturing industry, the scheduling problems that rely on modeling in real-world scenarios have gradually evolved into complex process flows. Aiming at the limited problem modeling capabilities of existing scheduling problems, this study proposes Multi-Stage Flexible Job Shop Scheduling Problem (MS-FJSP). MS-FJSP alters the fixed operation processing sequence of jobs in conventional scheduling problems and introduces staged processing to incorporate flexible constraints on operation selection. Furthermore, MS-FJSP modifies the constraint of unique machine compatibility, enabling arbitrary adjustments to machine combinations according to processing requirements. To address the complex flexibility and large-scale solution space of MS-FJSP, we propose a particle swarm optimization algorithm based on double neighborhood tabu search (TS-PSO). Specifically, the PSO algorithm determines a superior neighborhood structure for this problem, while the TS algorithm improves and optimizes the solution quality within the neighborhood of this solution structure. We verify the algorithm’s performance using a dataset consisting of 12,000 MS-FJSP instances and an MS-FJSP instance modeled from a real-world scheduling scenario. Experimental results demonstrate that TS-PSO can achieve excellent solution quality within a reasonable time, and MS-FJSP possesses efficient modeling capability for real-world scheduling scenarios. Full article

Optical coherence tomography plays a critical role in diagnosing retinal diseases, yet automated deep learning classification is hindered by severe class imbalance in which rare pathologies are underrepresented and frequently misclassified, a limitation rarely exposed by the aggregate metrics reported in most prior work. We investigate a targeted intermediate-supervision framework, in which a secondary classifier head is attached to mid-level backbone features and jointly optimized with the primary classifier using inverse-frequency weighted loss. Unlike conventional deep supervision, which is primarily aimed at optimizing stability, the proposed formulation is used here to improve minority-class representation under severe OCT class imbalance. The method is evaluated on ResNet-18, ResNet-50, EfficientNet-B0, and ViT-B/16 using a four-class OCT dataset, with full per-class metrics reported across a systematic ablation of the auxiliary weight λ. EfficientNet-B0 achieved the best performance at λ = 0.3, attaining 97.78% accuracy, an AUROC of 0.995, and a Drusen F1-score of 93.51%, a gain of 2.64 percentage points over the unweighted baseline. Vision Transformers showed greater sensitivity to background padding artifacts than convolutional models. Grad-CAM and Attention Rollout analyses confirm that auxiliary supervision improves the localization of clinically relevant retinal structures, supporting its potential for interpretable, class-balanced automated OCT diagnosis. Full article

Machine learning has a wide range of applications, including classification, which categorizes elements based on their characteristics. This paper addresses the challenge of optimizing attribute weighting while assessing two crossover operators on differential evolution optimization and increasing the performance of the k-nearest neighbors classification algorithm (KNN). We use a differential evolution optimization method and assess the performance of both the differential evolution and the harmony crossover operators. Finally, the optimization method uses the accuracy of a KNN classification algorithm as a fitness function. The results show that the proposed method significantly enhances the KNN performance while proposing an alternative for other classification models such as neural networks and Random Forest. Full article

This paper presents an innovative methodology detecting out-of-distribution individuals based on a multimodal contrastive learning approach. The system combines voice and facial image data by projecting them into a shared representation in the embedding space, enable accurate identification of previously unseen individuals. This approach overcomes the limitations of traditional methods by providing more robust and consistent detection in dynamic scenarios, using advanced neural networks and optimized contrastive losses. Specifically, the main contribution of this work is the introduction of a multimodal contrastive framework that performs cross-modal consistency verification between facial and vocal representations, enabling reliable detection of out-of-distribution individuals without the need for identity gallery retrieval. Experimental results on multiple datasets highlight the effectiveness of the system, with accuracy above 90% in detecting in-distribution samples in all evaluated cases. Regarding the identification of out-of-distribution cases, the system maintains outstanding performance, achieving values close to 90% on average, with some datasets exceeding 95%. These results underscore its ability to recognize both known identities and handle unknown data, even under challenging conditions. This approach represents a significant advancement in the multimodal recognition of individuals, with potential applications in critical areas such as security, surveillance, and human–computer interaction. Full article

Technical documents differ from general text corpora in ways that complicate retrieval-augmented generation (RAG). Evidence for a single answer is often distributed across numbered clauses, tables, figures, captions, and ordered procedures rather than expressed in one passage. Standard RAG pipelines typically flatten these elements into independent chunks. This can break the document relations needed for exact evidence tracing. We introduce TechDocRAG, a relation-preserving framework for technical document question answering. The framework represents each document as a heterogeneous element graph and aligns three retrieval views for each element: technical identifiers, semantic summaries, and raw evidence. At query time, retrieval proceeds from identifier-aware recall to summary-level reranking and raw evidence bundling. We evaluate TechDocRAG on four benchmarks with more than 7500 evaluated question–answer pairs covering product manuals, engineering documents, and long multimodal PDFs. Across the suite, TechDocRAG improves the mean end-to-end score by 20.3 points over the strongest flat baseline and by 9.3 points over the strongest non-flat baseline. On the evidence-annotated subset, the strict raw evidence hit rate increases from 0.510 to 0.942. Resource profiling shows query time latency comparable to standard hybrid retrieval. Robustness tests show gradual degradation under relation loss, but clear sensitivity to severe identifier corruption. Overall, the results indicate that reliable RAG for technical documents depends less on retrieving more passages than on preserving the relations that make evidence interpretable. Full article

Artificial intelligence techniques have made notable progress in supporting learning processes, with increasing adoption across educational contexts. However, despite the increasing work on AI-assisted techniques, explicit and implicit learning mechanisms in AI educational assistants have not been systematically categorised. The study of how these techniques aid in and are implemented for learning remains underexplored. Therefore, a more systematic categorisation of how these techniques support learning through user interaction is needed. This paper presents a systematic review of 38 studies published between 2000 and 2024, spanning domains including programming education, cognitive skills, language learning, and the AI field. This review was conducted and reported in accordance with the PRISMA 2020 guidelines. In this review, we propose a taxonomy of explicit and implicit learning features. We analyse implementation aspects (e.g., knowledge representation, algorithms, and interaction modalities) and synthesise how prior work evaluates learning support capabilities. The findings show that (i) 79% of reviewed studies support explicit and 21% supported implicit learning through interaction; (ii) written interaction dominates (45%), followed by visualisation (34%), while voice-based interaction remains underrepresented (9%); (iii) some implementations lack details (e.g., knowledge bases and validation methods); and (iv) evaluation practices remain uneven, with most studies relying on experiment evaluation, highlighting the need for robust evaluation practices. Full article

Galaxy morphology combines measurable structural properties with subjective visual interpretation, limiting strictly hard-label classifications. This study proposes a framework designed to compare physically derived and human-based galaxy classifications while explicitly accounting for uncertainty and interpretability. Using photometric and structural features from the Sloan Digital Sky Survey (SDSS), physical groupings are obtained through Fuzzy C-Means clustering, enabling gradual transitions via soft memberships. Human clusters are constructed from Galaxy Zoo 2 debiased vote fractions, capturing aggregated perceptual judgments. Supervised models are trained to predict both physical and human cluster assignments from the same set of physical variables, providing a quantitative assessment of structural coherence and perceptual–physical alignment. SHAP-based explainability identifies the relative influence of color and concentration parameters in each scheme. Results show that physical clustering is driven by structural concentration and bulge dominance, while human classification exhibits smoother decision boundaries and greater sensitivity to photometric appearance. Discrepancies concentrate in transitional and orientation-sensitive systems. An interactive visualization layer supports traceable qualitative inspection. The framework provides a reproducible methodology for analyzing classification consistency, uncertainty, and human–model alignment. Full article

Higher technical and professional education is increasingly discussed in relation to workforce readiness, innovation, and societal inclusion. In Italy, the PNRR-funded Rome Technopole operates as a multi-institutional ecosystem in which universities, research organizations, industry, and public bodies interact through a Hub & Spoke model to support training and innovation activities. Among its components, Spoke 4 addresses professional higher technical education through the co-development of modular learning initiatives involving multiple stakeholders. This commentary examines the role and activities of the Italian National Institute of Health (ISS) within this context, with particular reference to the development of two pilot modules: one on Artificial Intelligence and Algorethics, and one on Accessibility and Assistive Technologies, including applications supported by AI. The paper combines a conceptual discussion of the approach with selected empirical insights derived from pilot implementation, including stakeholder engagement processes, structured evaluations, and thematic prioritization exercises. The findings suggest the perceived relevance of multi-stakeholder co-design, the use of flexible and modular learning formats, and the integration of technical and ethical dimensions in higher technical education. At the same time, they point to challenges related to coordination, scalability, and alignment across institutional actors. Rather than proposing a definitive model, the Spoke 4 experience is discussed as a context-specific case that may offer insights contributing to ongoing debates on the design and implementation of higher technical education in complex, multi-institutional settings. Full article

This study aims to develop and experimentally evaluate an artificial neural network-based temperature control strategy for hot-air carrot drying within an IoT-enabled cyber-physical system. The experimental setup employs an Arduino Mega 2560 equipped with AM2302 (air temperature sensor), MLX90614 (infrared surface temperature sensor), and SHT35 (relative humidity sensor), an HX711 load cell, and a WS68 anemometer, with cloud communication provided by an ESP8266 module for remote monitoring via Wi-Fi. The neural controller, implemented using the Arduino Neurona library, regulates the dryer temperature in real time, enabling drying kinetics analysis under ANN-based thermal control to investigate its capability to maintain thermal stability. Three initial loads (2, 4, and 6 kg) were analyzed to determine the thermal efficiency. In the dehydration experiments, the 2 kg load reached a final moisture content of 10% in 4.4 h, consuming 1390 kJ with a thermal efficiency of 83%. The 4 kg load exhibited the best time–energy balance (6.6 h, 1850.0 kJ, 88%), while the 6 kg load achieved the highest efficiency (8.1 h, 2250.0 kJ, 91%). These results demonstrate the effectiveness of neural-network-based control implemented on low-cost microcontrollers to enhance thermal efficiency in food dehydration processes. Full article

Semantic segmentation of high-resolution remote-sensing images is critical for urban planning, land-cover mapping, and ecological monitoring. However, existing methods face limitations in handling complex land-cover types, multi-scale objects, and modeling long-range dependencies. To address these challenges, we propose RFA2Net, an enhanced semantic segmentation model based on the DeepLabv3+ framework. The key innovations include the integration of the RFCSA-Conv module into the ResNet101 backbone to enhance feature representation and long-range dependency modeling, the design of the RFA-DASPP structure built upon the Dense ASPP framework with the novel RFCA-DConv dilated convolution module to reduce information loss during multi-scale feature fusion and enhance the model’s ability to perceive long-range directional structures, and the introduction of a Dual-Branch Fusion Network to improve segmentation accuracy for small-scale objects. Experimental results on the ISPRS Potsdam and LoveDA datasets demonstrate that RFA2Net outperforms several CNN and Transformer-based models, achieving 78.94% and 59.46% mean intersection over union (mIoU) on the ISPRS Potsdam and LoveDA datasets, respectively, with improvements of 3.19% and 3.08% over the original DeepLabv3+. Ablation studies and comparative experiments further confirm the model’s effectiveness, robustness, and practical applicability in high-resolution remote-sensing image segmentation, with particular relevance to environmental monitoring and sustainable energy applications. Full article

The integration of artificial intelligence (AI) into the diagnosis and prognosis of heart diseases is transforming cardiovascular and cardiac healthcare, improving predictive accuracy, and personalizing treatment plans. This review presents a novel contribution by providing a comprehensive overview of both diagnosis and prognosis in heart diseases through AI, covering ML and DL models. Following the PRISMA guidelines, a total of 84 recent research articles sourced from significant journals are reported. A bibliometric analysis using the VOSviewer tool was performed to map the impact of AI, enabling a detailed examination of academic connections and contributions. The findings reveal that DL models were employed 63% for diagnosis tasks, while ML models were utilized in 37% of the studies. Key recommendations include the incorporation of essential model evaluation metrics, as clinical validation indicators, integrating explainable artificial intelligence (XAI) to improve the transparency and interpretability of models, and adopting standardized frameworks to enable smooth clinical integration. This review highlights the potential of AI to improve cardiac and cardiovascular diagnosis and prognosis, providing an overview of its strengths, limitations, challenges and the possible application as AI-driven tools in patient monitoring and to support specialists in the decision-making process. Full article

In the past 20 years, Artificial Intelligence (AI) has made several advancements. Because of AI’s ability to process large datasets better than humans, it is thought to have a promising future in many fields. Despite their advantages, AI and specifically Machine Learning (ML) algorithms can have emergent behavior, which makes their adoption into safety-critical systems a challenge. Through an examination of the capabilities of AI systems at NASA, we see what AI is currently being used for and which algorithms are promising for future work. We also identify limitations in the potential impact of AI systems, noting that the majority of the reviewed papers focused on limitations in adopting AI systems rather than limitations in the technical abilities of AI systems. This review article provides insight into AI and ML algorithms, aviation, space and other AI-based platforms for automation. Full article

Building reliable vehicle detection models for intelligent transportation systems calls for large, well-annotated datasets—yet gathering and labelling real traffic data remains both costly and labour-intensive. This paper introduces Traffic Synth, an automated pipeline that generates synthetic training datasets by altering real traffic camera images rather than constructing entirely artificial scenes. The system begins by detecting vehicles through instance segmentation and removing them from the frame. It then places new vehicles directly into the cleared regions using diffusion-based inpainting, all while retaining the original road layout, lighting, and camera perspective. Doing so preserves the realistic scene context while broadening the visual variety of vehicles in the dataset. To ensure that the resulting traffic looks physically plausible, we incorporate a lane-aware prompting mechanism that matches each vehicle’s orientation to the direction of travel as seen from the camera. The system further draws on a weighted vehicle brand database that mirrors the makes and colours commonly found on European roads to better match actual deployment conditions. Class-specific mask processing—involving anisotropic scaling and relative dilation—rounds out the pipeline by improving generation quality across different vehicle size categories. The final output is a set of images with automatically generated annotations in a standard object detection format. Full article

Large Language Models (LLMs) are increasingly deployed across professional and societal domains, introducing security, privacy, and governance challenges beyond traditional software vulnerabilities. Despite extensive research on individual risk categories, a unified lifecycle-oriented perspective connecting architectural properties, adversarial threats, and governance implications remains limited. This review examines security and privacy risks associated with LLMs through a lifecycle framework covering data acquisition, model training, alignment procedures, deployment, and post-deployment interaction. The study synthesizes prior research to construct a taxonomy of threats including prompt injection, jailbreaking, adversarial manipulation, training-stage attacks, privacy leakage, and socio-technical misuse. Ethical issues such as hallucination, bias amplification, and malicious use are analyzed alongside governance and regulatory frameworks. Results indicate that vulnerabilities in LLM systems arise primarily from probabilistic generation mechanisms, large-scale data ingestion, and complex deployment ecosystems rather than isolated implementation defects. Classical software vulnerability models therefore provide only partial coverage of risks associated with generative AI systems. The review is grounded in the concept of the alignment gap to explain how discrepancies between training objectives and real-world interaction contribute to persistent vulnerabilities. The findings highlight the need for lifecycle-oriented defense-in-depth strategies combining technical safeguards, privacy-preserving training, runtime monitoring, and governance mechanisms to support responsible deployment of LLM-based systems. Full article

Multilingual learning is key in natural language processing, but is challenged by the transfer–interference trade-off, where positive transfer benefits certain languages, while negative interference affects others. Prior methods, including linguistic-based and embedding-based language clustering, have attempted to address this; yet, they remain constrained by their static design and lack of task-specific feedback. In this study, we propose a novel computational strategy inspired by molecular design that constructs molecules with targeted properties. Languages are modeled as nodes in an undirected graph, with edges representing the transfer strength. This language molecule is optimized via Reinforcement Learning to adjust edge connections and weights to enhance positive transfer and minimize interference, where graph clustering is applied, and clusters are then evaluated on the Named Entity Recognition and POS tagging tasks using 25 languages from the WikiANN dataset and 12 typologically diverse languages from the UDPOS dataset. Compared to linguistic and embedding-based language clustering baselines, our method yields substantial improvements, especially for low-resource languages, with some showing over 35% increase in F1 score, while high-resource languages benefit moderately, confirming reduced transfer–interference trade-off. Our atom–language model offers a novel path for multilingual learning, inspired by molecular principles from physical sciences. Full article