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The purpose of this study was to engage high school science teachers as co-design partners in refining and extending instructional frameworks to support multiple-document reading and writing in science classrooms. Using a participatory mixed-methods design, the project adapted the InSPECT framework for secondary science, developed professional development (PD) materials to introduce the framework, and explored the role of generative artificial intelligence (AI) in lesson planning. Five virtual focus group sessions guided the co-design of PD activities, followed by a pilot implementation in one biology classroom. Data included focus group and interview transcripts, surveys, and student work artifacts. Analyses examined teachers’ perceptions of PD features, framework usability, and student engagement. Teachers valued PD that was practical, relevant, and feasible within classroom constraints and described the frameworks as clear, stepwise structures that supported lesson design and literacy integration. Student work showed that paraphrasing was an accessible entry point, while bridging, elaboration, and source evaluation required additional modeling. Teachers viewed generative AI as a promising planning aid but expressed concerns about accuracy and ethics. Findings informed revisions emphasizing discipline-specific exemplars, scaffolds for higher-order strategies, and AI-literacy modules, illustrating how participatory design can yield feasible, teacher-centered PD. Full article

(This article belongs to the Special Issue Reading and Writing in the Digital Age: Supporting Language and Literacy Development for Students)

26 pages, 10862 KB

Open AccessArticle

Recurrent Neural Networks for Mexican Sign Language Interpretation in Healthcare Services

by Armando de Jesús Becerril-Carrillo, Héctor Julián Selley-Rojas and Elizabeth Guevara-Martínez

Sensors 2026, 26(1), 27; https://doi.org/10.3390/s26010027 (registering DOI) - 19 Dec 2025

Abstract

In Mexico, the Deaf community faces persistent communication barriers that restrict their integration and access to essential services, particularly in healthcare. Even though approximately two million individuals use Mexican Sign Language (MSL) as their primary form of communication, technological tools for supporting effective interaction remain limited. While recent research in sign-language recognition has led to important advances for several languages, work focused on MSL, particularly for healthcare scenarios, remains scarce. To address this gap, this study introduces a health-oriented dataset of 150 signs, with 800 synthetic video sequences per word, totaling more than 35 GB of data. This dataset was used to train recurrent neural networks with regularization and data augmentation. The best configuration achieved a maximum precision of 98.36% in isolated sign classification, minimizing false positives, which is an essential requirement in clinical applications. Beyond isolated recognition, the main contribution of this study is its exploratory evaluation of sequential narrative inference in MSL. Using short scripted narratives, the system achieved a global sequential recall of 45.45% under a realistic evaluation protocol that enforces temporal alignment. These results highlight both the potential of recurrent architectures in generalizing from isolated gestures to structured sequences and the substantial challenges posed by continuous signing, co-articulation, and signer-specific variation. While not intended for clinical deployment, the methodology, dataset, and open-source implementation presented here establish a reproducible baseline for future research. This work provides initial evidence, tools, and insights to support the long-term development of accessible technologies for the Deaf community in Mexico. Full article

(This article belongs to the Special Issue Deep Learning-Based Image and Signal Sensing and Processing: 2nd Edition)

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15 pages, 1265 KB

Open AccessReview

The Evolving Role of Artificial Intelligence in Medical Genetics: Advancing Healthcare, Research, and Biosafety Management

by Ying-Cheng Wu, Nan Tuo, Guoming Shi, Ka Li, Zhenju Song and Yanying Li

Genes 2026, 17(1), 6; https://doi.org/10.3390/genes17010006 (registering DOI) - 19 Dec 2025

Abstract

The integration of artificial intelligence (AI) with medical genetics is transforming healthcare by addressing the analytical challenges posed by the vast complexity of multi-omics data. This review explores the synergistic convergence of these fields, highlighting AI’s transformative role in enhancing diagnostic precision, enabling non-invasive molecular profiling through imaging-genetics, and advancing predictive and personalized medicine via polygenic risk scores and pharmacogenomics. AI is also emerging as a powerful generative tool in therapeutic design, accelerating drug discovery, protein engineering, and precision gene editing. However, this powerful synergy introduces significant ethical, regulatory, and biosecurity challenges, including data privacy, algorithmic bias, and the dual-use risks of AI-enabled genetic engineering. The future envisions a responsible co-evolution, with multimodal AI and the concept of the Digital Twin driving precision medicine, underpinned by interdisciplinary collaboration to ensure fairness, transparency, and societal trust. This article charts the current landscape and proposes actionable directions, emphasizing the need for robust governance to harness AI’s potential while mitigating its risks for the benefit of human health. Full article

(This article belongs to the Section Human Genomics and Genetic Diseases)

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16 pages, 6746 KB

Open AccessArticle

Cross-Attentive CNNs for Joint Specral and Pitch Feature Learning in Predominant Instrument Recognition from Polyphonic Music

by Lekshmi Chandrika Reghunath, Rajeev Rajan, Christian Napoli and Cristian Randieri

Technologies 2026, 14(1), 3; https://doi.org/10.3390/technologies14010003 (registering DOI) - 19 Dec 2025

Abstract

Identifying instruments in polyphonic audio is challenging due to overlapping spectra and variations in timbre and playing styles. This task is central to music information retrieval, with applications in transcription, recommendation, and indexing. We propose a dual-branch Convolutional Neural Network (CNN) that processes Mel-spectrograms and binary pitch masks, fused through a cross-attention mechanism to emphasize pitch-salient regions. On the IRMAS dataset, the model achieves competitive performance with state-of-the-art methods, reaching a micro F1 of 0.64 and a macro F1 of 0.57 with only 0.878M parameters. Ablation studies and t-SNE analyses further highlight the benefits of cross-modal attention for robust predominant instrument recognition. Full article

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21 pages, 918 KB

Open AccessCase Report

Isoechoic Renal Tumors: A Case Report and Literature Review

by Nicola Sinatra, Giulio Geraci, Roberto Palumbo, Gaspare Oddo, Giuseppe Zichittella, Emanuele Cirafici, Alessandra Sorce, Giuseppe Mulè and Caterina Carollo

Diagnostics 2026, 16(1), 14; https://doi.org/10.3390/diagnostics16010014 (registering DOI) - 19 Dec 2025

Abstract

Background and Clinical Significance: Isoechoic renal tumors, defined as masses demonstrating echogenicity similar to normal renal parenchyma, represent a significant diagnostic challenge in contemporary ultrasonographic practice. These lesions, occurring in 5–12% of all renal masses, frequently escape detection on conventional ultrasound, leading to delayed diagnosis and potentially adverse oncological outcomes. Isoechoic renal tumors encompass both benign and malignant entities, with clear cell renal cell carcinoma representing 65–70% of malignant cases. Conventional ultrasound shows limited sensitivity (48–67%) for detecting isoechoic masses, while contrast-enhanced ultrasound achieves detection rates of 94–98%. Multiparametric MRI and dual-energy CT provide superior characterization, with accuracy rates of 85–92% for differentiating benign from malignant lesions. Case Presentation: We describe the case of an 80-year-old male in whom a 2.4 cm isoechoic renal mass was incidentally detected during abdominal ultrasound performed for chronic kidney disease monitoring. Contrast-enhanced CT confirmed a solid, hypervascular lesion with wash-out characteristics. Given the patient’s age, comorbidities, and tumor characteristics, multidisciplinary evaluation led to an active surveillance strategy. At 6-month follow-up, the lesion remained stable. Conclusions: Isoechoic renal tumors require multimodal diagnostic approaches and individualized management strategies. Emerging technologies, including artificial intelligence-enhanced ultrasound systems and radiomic-based decision support tools, are undergoing clinical validation and may improve detection and characterization. Investigational approaches such as liquid biopsy and novel PET tracers targeting carbonic anhydrase IX are in early development. Translation of these technologies into clinical practice will require prospective validation, standardization of protocols, and demonstration of cost-effectiveness. Full article

(This article belongs to the Special Issue Ultrasound in Renal Diseases: A Useful Tool for Diagnostics and Therapy)

25 pages, 1881 KB

Open AccessArticle

When Droplets Can “Think”: Intelligent Testing in Digital Microfluidic Chips

by Zhijie Luo, Shaoxin Li, Wufa Long, Rui Chen and Jianhua Zheng

Biosensors 2026, 16(1), 3; https://doi.org/10.3390/bios16010003 (registering DOI) - 19 Dec 2025

Abstract

Digital microfluidic biochips (DMFBs) find extensive applications in biochemical experiments, medical diagnostics, and safety-critical domains, with their reliability dependent on efficient online testing technologies. However, traditional random search algorithms suffer from slow convergence and susceptibility to local optima under complex fluidic constraints. This paper proposes a hybrid optimization method based on priority strategy and an improved sparrow search algorithm for DMFB online test path planning. At the algorithmic level, the improved sparrow search algorithm incorporates three main components: tent chaotic mapping for population initialization, cosine adaptive weights together with Elite Opposition-based Learning (EOBL) to balance global exploration and local exploitation, and a Gaussian perturbation mechanism for fine-grained refinement of promising solutions. Concurrently, this paper proposes an intelligent rescue strategy that integrates global graph-theoretic pathfinding, local greedy heuristics, and space–time constraint verification to establish a closed-loop decision-making system. The experimental results show that the proposed algorithm is efficient. On the standard 7 × 7–15 × 15 DMFB benchmark chips, the shortest offline test path length obtained by the algorithm is equal to the length of the Euler path, indicating that, for these regular layouts, the shortest test path has reached the known optimal value. In both offline and online testing, the shortest paths found by the proposed method are better than or equal to those of existing mainstream algorithms. In particular, for the 15 × 15 chip under online testing, the proposed method reduces the path length from 543 and 471 to 446 compared with the IPSO and IACA algorithms, respectively, and reduces the standard deviation by 53.14% and 39.4% compared with IGWO in offline and online testing. Full article

(This article belongs to the Special Issue Intelligent Microfluidic Biosensing)

13 pages, 2987 KB

Open AccessArticle

Intelligent Modeling of Erosion-Corrosion in Polymer Composites: Integrating Fuzzy Logic and Machine Learning

by Hazzaa F. Alqurashi, Mohammed Y. Abdellah, Mubark Alshareef, Mohamed K. Hassan, Fadhel T. Alabdullah and Ahmed F. Moamed

Polymers 2026, 18(1), 9; https://doi.org/10.3390/polym18010009 (registering DOI) - 19 Dec 2025

Abstract

This study presents a novel hybrid intelligent framework integrating fuzzy logic and artificial neural networks (ANN) to model the erosion-corrosion behavior of glass-fiber-reinforced pipes (GRP) under harsh operating conditions. Experimental data encompassing multiple operational parameters—including abrasive sand concentrations (250 g, 400 g, 500 g), flow rates (0.0067 m³/min, 0.01 m³/min, 0.015 m³/min), chlorine content (0–10 wt.%), and exposure times (1–5 h)—were utilized to develop the computational models. The fuzzy logic system effectively captured qualitative expert knowledge and uncertainty in material degradation processes, while ANN models provided quantitative predictions of erosion and corrosion rates. Results demonstrated good prediction accuracy, with R² values of 0.81 for corrosion rate and moderate prediction accuracy 0.56 for erosion rate. The analysis revealed that flow rate (correlation: 0.6) and fuzzy severity (0.6) were the most influential parameters, followed by chlorine content (0.41) and sand concentration (0.32). The hybrid model identified optimal operating conditions to minimize material degradation: low sand concentration (250 g), low flow rate (0.0067 m³/min), absence of chlorine, and shorter exposure times. This intelligent modeling approach provides a powerful tool for predictive maintenance, operational optimization, and service life prediction of GRP systems in aggressive environments, bridging the gap between experimental data and computational intelligence for enhanced material performance assessment. Full article

(This article belongs to the Special Issue Advances in Polymer Molding and Processing)

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24 pages, 1829 KB

Open AccessArticle

SD-GASNet: Efficient Dual-Domain Multi-Scale Fusion Network with Self-Distillation for Surface Defect Detection

by Jiahao Fu, Zili Zhang, Tao Peng, Xinrong Hu and Jun Zhang

Sensors 2026, 26(1), 23; https://doi.org/10.3390/s26010023 - 19 Dec 2025

Abstract

Surface defect detection is vital in industrial quality control. While deep learning has largely automated inspection, accurately locating defects with large-scale variations or those difficult to distinguish from similar backgrounds remains challenging. Furthermore, achieving high-precision and real-time performance under limited computational resources in deployment environments complicates effective solutions. In this work, we propose SD-GASNet, a network based on a self-distillation model compression strategy. To identify subtle defects, we design an Alignment, Enhancement, and Synchronization Feature Pyramid Network (AES-FPN) fusion network incorporating the Frequency Domain Information Gathering-and-Allocation (FIGA) mechanism and the Channel Synchronization (CS) module for industrial images from different sensors. Specifically, FIGA refines features via the Multi-scale Feature Alignment (MFA) module, then the Frequency-Guided Perception Enhancement Module (FGPEM) extracts high- and low-frequency information to enhance spatial representation. The CS module compensates for information loss during feature fusion. Addressing computational constraints, we adopt self-distillation with an Enhanced KL divergence loss function to boost lightweight model performance. Extensive experiments on three public datasets (NEU-DET, PCB, and TILDA) demonstrate that SD-GASNet achieves state-of-the-art performance with excellent generalization, delivering superior accuracy and a competitive inference speed of 180 FPS, offering a robust and generalizable solution for sensor-based industrial imaging applications. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

23 pages, 94848 KB

Open AccessArticle

Reinforcement Learning-Based Sequence Training for Robust Vehicle Tracking in Dynamic Traffic Scenes

by Lili Pei and Zhao Yang

Appl. Sci. 2026, 16(1), 26; https://doi.org/10.3390/app16010026 - 19 Dec 2025

Abstract

Vehicle tracking is essential for autonomous driving, traffic surveillance, and intelligent transportation, yet most existing trackers rely on frame-level training that neglects temporal dependencies. This mismatch between training and testing leads to error propagation, mislocalization in challenging frames, and failure to re-identify vehicles after occlusion. We present a reinforcement learning (RL)-based sequence-level training framework that formulates tracking as a sequential decision process and directly incorporates evaluation metrics consistent with testing. Our approach enhances robustness in difficult frames and occlusion scenarios by leveraging temporal decision dependencies, and introduces a temporal data augmentation strategy based on sliding-window sampling to improve generalization across diverse motion patterns. Experiments on challenging benchmarks indicate that our method provides improved robustness and temporal continuity over frame-level training approaches, suggesting the benefits of incorporating sequence-level learning in vehicle tracking. Full article

17 pages, 4858 KB

Open AccessArticle

Retrieving Woody Components from Time-Series Gap-Fraction and Multispectral Satellite Observations over Deciduous Forests

by Woohyeok Kim, Jaese Lee, Yoojin Kang, Jungho Im, Bokyung Son and Jiwon Lee

Remote Sens. 2026, 18(1), 10; https://doi.org/10.3390/rs18010010 - 19 Dec 2025

Abstract

Leaf area index (LAI) is essential for understanding vegetation dynamics, ecosystem processes, and land–atmosphere interactions. Various measurement methods exist, but gap-fraction-based indirect methods are preferred due to their reduced labor and field survey time in comparison to direct methods. However, gap-fraction-based field observations, often referred to as plant area index (PAI), frequently overestimate LAI because they include woody components. To mitigate this issue, the woody-to-total-area ratio (α) can be utilized to exclude these woody components from PAI, yielding more accurate LAI estimates (hereafter referred to as LAI_adjusted). In this study, we demonstrate a novel method to estimate α using Sentinel-2-based normalized difference vegetation index (NDVI) and time-series PAI measurements. The α estimates effectively reduce the influence of woody components in PAI within deciduous broadleaf forests (DBF). Moreover, LAI_adjusted shows good agreement with the Sentinel-2 LAI, which represents effective LAI derived from the PROSAIL model. Notably, the spatial distribution of α effectively captured the expected seasonal dynamics across various forest types. In DBF, α values increased during winter due to leaf fall when compared to the growing season, while seasonal variations were relatively small in evergreen needleleaf forest (ENF). We further confirmed that our method demonstrates greater robustness with NDVI than with other vegetation indices that are more susceptible to topographic variation. Ultimately, this framework presents a promising pathway to mitigate biases in most gap-fraction-based PAI measurements, thereby enhancing the accuracy of vegetation structural assessments and supporting broader ecological and climate-related applications. Full article

20 pages, 412 KB

Open AccessArticle

Ethical Consumer Attitudes and Trust in Artificial Intelligence in the Digital Marketplace: An Empirical Analysis of Behavioral and Value-Driven Determinants

by Markou Vasiliki, Panagiotis Serdaris, Ioannis Antoniadis and Konstantinos Spinthiropoulos

Digital 2026, 6(1), 1; https://doi.org/10.3390/digital6010001 - 19 Dec 2025

Abstract

The rapid diffusion of artificial intelligence (AI) in marketing has reshaped how consumers interact with digital content and evaluate ethical aspects of firms. The present study examines how familiarity with and trust in AI shape consumers’ acceptance of AI-based advertising and, in turn, their ethical purchasing behavior. Data were collected from 505 Greek consumers through an online survey and analyzed using hierarchical and logistic regression models. Reliability and validity tests confirmed the robustness of the measurement instruments. The results show that familiarity with AI technologies significantly enhances trust and ethical confidence toward AI systems. In turn, trust in AI strongly predicts the consumers’ acceptance of AI-driven advertising, while acceptance positively affects ethical consumption intentions. The findings also confirm a mediating relationship, indicating that acceptance of AI-based advertising transmits the effect of AI rust to ethical consumption. By integrating ethical and technological dimensions within a single behavioral model, the study provides a more comprehensive view of how consumers form attitudes toward AI-enabled marketing. Overall, the findings highlight that transparent and responsible AI practices can strengthen brand credibility, foster ethical engagement, and support more sustainable consumer choices. Full article

(This article belongs to the Topic Consumer Behavior and Sustainable Marketing Strategy in the Digital Era)

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28 pages, 1328 KB

Open AccessArticle

A Hybrid Machine Learning Approach for Cyberattack Detection and Classification in SCADA Systems: A Hydroelectric Power Plant Application

by Mehmet Akif Özgül, Şevki Demirbaş and Seyfettin Vadi

Electronics 2026, 15(1), 10; https://doi.org/10.3390/electronics15010010 - 19 Dec 2025

Abstract

SCADA systems, widely used in critical infrastructure, are becoming increasingly vulnerable to complex cyber threats, which can compromise national security. This study presents an artificial intelligence-based approach aimed at the early and reliable detection of cyberattacks against SCADA systems. The study physically scaled the SCADA communication architecture of a hydroelectric power plant and created a suitable test environment. In this environment, in addition to the benign normal state, attack scenarios such as Man-in-the-Middle (MITM), Denial-of-Service (DoS), and Command Injection were implemented while the process created for the system’s operation was running continuously. While the scenarios were being implemented, the SCADA system was monitored, and network data flow was collected and stored for later analysis. Basic machine learning algorithms, including KNN, Naive Bayes, Decision Trees, and Logistic Regression, were applied to the obtained data. Also, different combinations of these methods have been tested. The analysis results showed that the hybrid model, consisting of a Decision Tree and Logistic Regression, achieved the most successful results, with a 98.29% accuracy rate, an Area Under the Curve (AUC) value of 0.998, and a reasonably short detection time. The results demonstrate that the proposed approach can accurately classify various types of attacks on SCADA systems, providing an effective early warning mechanism suitable for field applications. Full article

(This article belongs to the Topic Artificial Intelligence and Machine Learning in Cyber–Physical Systems)

31 pages, 1203 KB

Open AccessReview

Human Cardiac Organoids: Advances and Prospects from Construction to Preclinical Drug Evaluation

by Meng Chen, Tianyi Zhang, Sheng Yang, Yiru Niu, Yiling Ge, Zaozao Chen, Juan Zhang, Yuepu Pu, Zhongze Gu and Geyu Liang

Cells 2026, 15(1), 7; https://doi.org/10.3390/cells15010007 - 19 Dec 2025

Abstract

Drug-induced cardiotoxicity (DICT) severely hampers drug development and threatens patient safety. Together with the growing global burden of cardiovascular disease, there is an urgent need to establish more predictive preclinical models. Recently, human pluripotent stem cell-derived cardiac organoids (hCOs) have emerged as a promising three-dimensional in vitro model, achieving significant progress in simulating the complex structure and function of the human heart. However, existing reviews predominantly focus on technical construction or specific applications, lacking an integrated discussion of pathological model construction and their use under evolving regulatory frameworks. This review distinguishes itself by proposing a novel, holistic framework that bridges “construction technology,” “pathological modeling,” and “application evaluation.” We systematically categorize and summarize three major strategies for building hCO-based pathological models: patient-specific, gene-edited, and microenvironment-modulated approaches. Furthermore, we highlight the unique advantages of hCOs in preclinical drug assessment and detail their cutting-edge applications in early DICT warning, metabolism-related safety evaluation, and personalized drug evaluation. Finally, we address current challenges, including maturation and standardization, and outline future directions involving integration with organ-on-a-chip technology and artificial intelligence. This review aims to provide a theoretical foundation and roadmap toward more reliable and human-relevant drug development paradigms. Full article

(This article belongs to the Special Issue Advances in Human Pluripotent Stem Cells)

21 pages, 2847 KB

Open AccessArticle

Modeling and Solving Two-Sided Disassembly Line Balancing Problem Under Partial Disassembly of Parts

by Shuwei Wang, Huaizi Wang, Jia Liu, Guofeng Xu and Guoxun Xu

Symmetry 2026, 18(1), 4; https://doi.org/10.3390/sym18010004 - 19 Dec 2025

Abstract

In two-sided disassembly lines, stations are symmetrically arranged on both sides of the conveyor, which is suitable for large-sized waste products. During the disassembly process, evenly assigning parts to workstations while satisfying various constraints and optimizing some disassembly objectives is a challenging task. Therefore, this study deals with a two-sided partial disassembly line balancing problem, and a multi-objective mathematical model for this problem is built. While satisfying various constraints, four objectives, namely, the hazard index, profit, smoothness index, and demand index, are optimized. Due to the NP-hard nature of the problem, an improved discrete whale optimization algorithm is proposed. According to the characteristics of the feasible solutions, an encoding method based on a one-dimensional integer array is designed, which can effectively decrease the memory space and simplify the design of neighbor structures. In the three stages of encircling prey, random wandering, and bubble-net attacking, based on the search features of each stage, different neighbor operators and search strategies are designed to enhance the local exploitation and global exploration capabilities. Finally, the performance of the proposed algorithm was tested against other algorithms for different types of instances and a disassembly case. The results show that the proposed algorithm can not only solve various types of disassembly line balancing problems but also shows superior performance. Full article

(This article belongs to the Section Mathematics)

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40 pages, 4223 KB

Open AccessReview

Digital Twins for Clean Energy Systems: A State-of-the-Art Review of Applications, Integrated Technologies, and Key Challenges

by Myeongin Kim, Fatemeh Ghobadi, Amir Saman Tayerani Charmchi, Mihong Lee and Jungmin Lee

Sustainability 2026, 18(1), 43; https://doi.org/10.3390/su18010043 - 19 Dec 2025

Abstract

In the context of Industry 4.0, digital transformation is reshaping global energy systems. Among the key enabling technologies, Digital Twin (DT)—a dynamic, virtual replica of physical systems—has emerged as a critical tool for improving the performance, reliability, and safety of clean energy infrastructure. In line with the United Nations Sustainable Development Goals (SDGs)—particularly SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities)—the integration of DTs presents unprecedented opportunities to enhance operational efficiency and support proactive decision making. This state-of-the-art review, focused on studies published in 2020–2025, summarizes applications of DTs across the energy value chain, encompassing a broad spectrum of sectors—including solar, wind, hydropower, hydrogen, geothermal, bioenergy, nuclear, and tidal energy—and their critical role in building-to-grid integration. It synthesizes foundational concepts, assesses the evolution of the DT from a predictive tool to a system-level risk-management platform, and provides a critical analysis of its impact. Furthermore, this review discusses the key challenges hindering widespread adoption, including the critical need for interoperability across systems, ensuring the cybersecurity of socio-technical infrastructure, and addressing the complexities of the human-in-the-loop problem. Key research gaps are identified to guide future innovation. Ultimately, this study underscores the transformative potential of DTs as essential tools for accelerating the digital transformation of the energy sector, offering a robust framework for both methodological development and practical deployment. Full article

(This article belongs to the Special Issue The Synergistic Interaction of Artificial Intelligence and Digital Twin in the Sustainable Use of Natural Resources and Climate Change Solutions)

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