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35 pages, 1608 KB  
Article
The Predator-Prey Model of Tax Evasion: Foundations of a Dynamic Fiscal Ecology
by Miroslav Gombár, Nella Svetozarovová and Štefan Tóth
Mathematics 2026, 14(2), 337; https://doi.org/10.3390/math14020337 (registering DOI) - 19 Jan 2026
Abstract
Tax evasion is a dynamic process reflecting continuous interaction between taxpayers and regulatory institutions rather than a static deviation from fiscal equilibrium. This study introduces a predator-prey model of tax evasion that translates the Lotka-Volterra framework from biology into budgetary dynamics. The model [...] Read more.
Tax evasion is a dynamic process reflecting continuous interaction between taxpayers and regulatory institutions rather than a static deviation from fiscal equilibrium. This study introduces a predator-prey model of tax evasion that translates the Lotka-Volterra framework from biology into budgetary dynamics. The model captures the feedback between the volume of tax evasion and the intensity of regulation, incorporating nonlinearity, implicit reactive lag, and adaptive response. Theoretical derivation and numerical simulation identify three dynamic regimes—stable equilibrium, limit-cycle oscillation, and instability—that arise through a Hopf bifurcation. Bifurcation maps in the (r, a), (r, b), and (r, c) parameter spaces reveal how control efficiency, institutional inertia, and behavioral feedback jointly determine fiscal stability. Results show that excessive enforcement may destabilize the system by inducing regulatory fatigue, while weak control enables exponential growth in evasion. The model provides a dynamic analytical tool for evaluating fiscal policy efficiency and identifying stability thresholds. Its findings suggest that adaptive, feedback-based regulation is essential for maintaining long-term tax discipline. The study contributes to closing the research gap by providing a unified dynamic framework linking micro-behavioral decision-making with macro-fiscal stability, offering a foundation for future empirical calibration and behavioral extensions of fiscal systems. Full article
18 pages, 457 KB  
Review
Postmortem Microbiology in Forensic Diagnostics: Interpretation of Infectious Causes of Death and Emerging Applications
by Jessika Camatti, Maria Paola Bonasoni, Anna Laura Santunione, Rossana Cecchi, Erjon Radheshi and Edoardo Carretto
Diagnostics 2026, 16(2), 325; https://doi.org/10.3390/diagnostics16020325 (registering DOI) - 19 Jan 2026
Abstract
Background/Objectives: Postmortem microbiology has traditionally been regarded with caution in forensic practice due to concerns related to contamination, bacterial translocation, and postmortem microbial overgrowth. As a result, microbiological findings obtained after death have often been considered unreliable or of limited diagnostic value. [...] Read more.
Background/Objectives: Postmortem microbiology has traditionally been regarded with caution in forensic practice due to concerns related to contamination, bacterial translocation, and postmortem microbial overgrowth. As a result, microbiological findings obtained after death have often been considered unreliable or of limited diagnostic value. However, growing evidence indicates that, when appropriately interpreted and integrated with autopsy findings, histopathology, and circumstantial information, postmortem microbiology can provide crucial support for cause-of-death determination. This narrative review critically examines the current role of postmortem microbiology in forensic diagnostics, with a focus on its diagnostic applications, interpretative challenges, and future perspectives. Methods/Results: The transition from conventional culture-based techniques to molecular approaches—including polymerase chain reaction, microbiome analysis, and metagenomic methods—is discussed, highlighting both their potential advantages and inherent limitations within the forensic setting. Particular attention is devoted to key interpretative issues such as postmortem interval, sampling strategies, contamination, and bacterial translocation. In addition to cause-of-death attribution, emerging applications—including postmortem interval estimation, trace evidence analysis, and artificial intelligence–based models—are reviewed. Although these approaches show promising research potential, their routine forensic applicability remains limited by methodological heterogeneity, lack of standardization, and interpretative complexity. Conclusions: In conclusion, postmortem microbiology represents a valuable diagnostic tool when applied within a multidisciplinary forensic framework. Its effective use requires cautious interpretation and integration with pathological and contextual evidence, avoiding standalone or automated conclusions. Future progress will depend on standardized methodologies, multidisciplinary collaboration, and a clear distinction between experimental research and routine forensic practice. Full article
(This article belongs to the Special Issue Diagnostic Methods in Forensic Pathology, Third Edition)
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43 pages, 2844 KB  
Review
A Review of Gas-Sensitive Materials for Lithium-Ion Battery Thermal Runaway Monitoring
by Jian Zhang, Zhili Li and Lei Huang
Molecules 2026, 31(2), 347; https://doi.org/10.3390/molecules31020347 (registering DOI) - 19 Jan 2026
Abstract
Lithium-ion batteries (LIBs) face the safety hazard of thermal runaway (TR). Gas-sensing-based monitoring is one of the viable warning approaches for batteries during operation, and TR warning using semiconductor gas sensors has garnered widespread attention. This review presents a comprehensive analysis of the [...] Read more.
Lithium-ion batteries (LIBs) face the safety hazard of thermal runaway (TR). Gas-sensing-based monitoring is one of the viable warning approaches for batteries during operation, and TR warning using semiconductor gas sensors has garnered widespread attention. This review presents a comprehensive analysis of the latest advances in this field. It details the gas release characteristics during the TR failure process and identifies H2, electrolyte vapor, CO, CO2, and CH4 as effective TR warning markers. The core of this review lies in an in-depth critical analysis of gas-sensing materials designed for these target gases, systematically summarizing the design, performance, and application research of semiconductor gas-sensing materials for each aforementioned gas in battery monitoring. We further summarize the current challenges of this technology and provide an outlook on future development directions of gas-sensing materials, including improved selectivity, integration, and intelligent advancement. This review aims to provide a roadmap that directs the rational design of next-generation sensing materials and fast-tracks the implementation of gas-sensing technology for enhanced battery safety. Full article
(This article belongs to the Special Issue Nanochemistry in Asia)
31 pages, 4972 KB  
Article
Minutiae-Free Fingerprint Recognition via Vision Transformers: An Explainable Approach
by Bilgehan Arslan
Appl. Sci. 2026, 16(2), 1009; https://doi.org/10.3390/app16021009 (registering DOI) - 19 Jan 2026
Abstract
Fingerprint recognition systems have relied on fragile workflows based on minutiae extraction, which suffer from significant performance losses under real-world conditions such as sensor diversity and low image quality. This study introduces a fully minutiae-free fingerprint recognition framework based on self-supervised Vision Transformers. [...] Read more.
Fingerprint recognition systems have relied on fragile workflows based on minutiae extraction, which suffer from significant performance losses under real-world conditions such as sensor diversity and low image quality. This study introduces a fully minutiae-free fingerprint recognition framework based on self-supervised Vision Transformers. A systematic evaluation of multiple DINOv2 model variants is conducted, and the proposed system ultimately adopts the DINOv2-Base Vision Transformer as the primary configuration, as it offers the best generalization performance trade-off under conditions of limited fingerprint data. Larger variants are additionally analyzed to assess scalability and capacity limits. The DINOv2 pretrained network is fine-tuned using self-supervised domain adaptation on 64,801 fingerprint images, eliminating all classical enhancement, binarization, and minutiae extraction steps. Unlike the single-sensor protocols commonly used in the literature, the proposed approach is extensively evaluated in a heterogeneous testbed with a wide range of sensors, qualities, and acquisition methods, including 1631 unique fingers from 12 datasets. The achieved EER of 5.56% under these challenging conditions demonstrates clear cross-sensor superiority over traditional systems such as VeriFinger (26.90%) and SourceAFIS (41.95%) on the same testbed. A systematic comparison of different model capacities shows that moderate-scale ViT models provide optimal generalization under limited-data conditions. Explainability analyses indicate that the attention maps of the model trained without any minutiae information exhibit meaningful overlap with classical structural regions (IoU = 0.41 ± 0.07). Openly sharing the full implementation and evaluation infrastructure makes the study reproducible and provides a standardized benchmark for future research. Full article
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26 pages, 3598 KB  
Article
Unlocking Innovation in Tourism: A Bibliometric Analysis of Blockchain and Distributed Ledger Technology Trends, Hotspots, and Future Pathways
by Roberto A. Pava-Díaz, Juan M. Sánchez-Céspedes and Oscar Danilo Montoya
Digital 2026, 6(1), 7; https://doi.org/10.3390/digital6010007 (registering DOI) - 19 Jan 2026
Abstract
This article presents a comprehensive bibliometric analysis of the indexed academic literature on the application of distributed ledger technology (DLT) and blockchain in the tourism industry. Using the bibliometrix library within the RStudio environment, key bibliometric indicators were examined in order to characterize [...] Read more.
This article presents a comprehensive bibliometric analysis of the indexed academic literature on the application of distributed ledger technology (DLT) and blockchain in the tourism industry. Using the bibliometrix library within the RStudio environment, key bibliometric indicators were examined in order to characterize the evolution, structure, and thematic focus of this emerging field of research. The systematic literature review, which adhered to PRISMA guidelines, involved retrieving publications from the Web of Science and Scopus databases. A curated dataset of 100 relevant documents was identified and analyzed in terms of annual scientific production, leading journals, influential authors, and highly cited publications. The results indicate that blockchain technology dominates the literature, with a strong emphasis on its potential to enhance trust, transparency, and efficiency in tourism-related processes. In particular, identity management, secure transactions, and disintermediation emerge as central research themes, reflecting blockchain’s capacity to support decentralized, immutable, and privacy-preserving interactions between tourists and service providers. Overall, the findings reveal a rapidly growing and increasingly structured body of knowledge, highlighting emerging research directions and technological challenges for future studies on DLT applications in tourism. Full article
14 pages, 488 KB  
Article
The Evolution of Nanoparticle Regulation: A Meta-Analysis of Research Trends and Historical Parallels (2015–2025)
by Sung-Kwang Shin, Niti Sharma, Seong Soo A. An and Meyoung-Kon (Jerry) Kim
Nanomaterials 2026, 16(2), 134; https://doi.org/10.3390/nano16020134 (registering DOI) - 19 Jan 2026
Abstract
Objective: We analyzed nanoparticle regulation research to examine the evolution of regulatory frameworks, identify major thematic structures, and evaluate current challenges in the governance of rapidly advancing nanotechnologies. By drawing parallels with the historical development of radiation regulation, the study aimed to [...] Read more.
Objective: We analyzed nanoparticle regulation research to examine the evolution of regulatory frameworks, identify major thematic structures, and evaluate current challenges in the governance of rapidly advancing nanotechnologies. By drawing parallels with the historical development of radiation regulation, the study aimed to contextualize emerging regulatory strategies and derive lessons for future governance. Methods: A total of 9095 PubMed-indexed articles published between January 2015 and October 2025 were analyzed using text mining, keyword frequency analysis, and topic modeling. Preprocessed titles and abstracts were transformed into a TF-IDF (Term Frequency–Inverse Document Frequency) document–term matrix, and NMF (Non-negative Matrix Factorization) was applied to extract semantically coherent topics. Candidate topic numbers (K = 1–12) were evaluated using UMass coherence scores and qualitative interpretability criteria to determine the optimal topic structure. Results: Six major research topics were identified, spanning energy and sensor applications, metal oxide toxicity, antibacterial silver nanoparticles, cancer nano-therapy, and nanoparticle-enabled drug and mRNA delivery. Publication output increased markedly after 2019 with interdisciplinary journals driving much of the growth. Regulatory considerations were increasingly embedded within experimental and biomedical research, particularly in safety assessment and environmental impact analyses. Conclusions: Nanoparticle regulation matured into a dynamic multidisciplinary field. Regulatory efforts should prioritize adaptive, data-informed, and internationally harmonized frameworks that support innovation while ensuring human and environmental safety. These findings provide a data-driven overview of how regulatory thinking was evolved alongside scientific development and highlight areas where future governance efforts were most urgently needed. Full article
(This article belongs to the Section Environmental Nanoscience and Nanotechnology)
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34 pages, 1200 KB  
Review
The Role of Hydrogen in Energy Communities: Current Status, Challenges, and Future Developments
by Néstor Velaz-Acera, Cristina Sáez Blázquez, Víctor Casado-Lorenzo and Susana Lagüela
Hydrogen 2026, 7(1), 14; https://doi.org/10.3390/hydrogen7010014 - 19 Jan 2026
Abstract
Renewable hydrogen has become a versatile technology that can play a key role in the deployment of energy communities, although technological, economic, environmental, legal, and social challenges remain to be addressed. This study conducts a systematic review based on the Preferred Reporting Items [...] Read more.
Renewable hydrogen has become a versatile technology that can play a key role in the deployment of energy communities, although technological, economic, environmental, legal, and social challenges remain to be addressed. This study conducts a systematic review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology that analyzes the current state of technologies, the different applications, challenges and limitations, and future lines of research related to the enabling role of hydrogen in energy communities. Results from the bibliometric analysis show sustained growth in the number of publications over the last five years (2020–2025), with a predominance of applications in which hydrogen is combined with other energy carriers (58%). The versatility of hydrogen has prompted the evaluation of different applications, with particular emphasis on energy storage to capitalize on energy surpluses (51%), mobility (19%), and heating (20%). The main existing barriers come from the absence of stable long-term regulation, interoperability between components and technologies, and a lack of real data. Overcoming these challenges should be based on new technologies such as artificial intelligence and the construction and operation of pilot projects. In addition, a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis has been conducted building upon the SHARED-H2 SUDOE project, yielding particularly insightful results through the active involvement of stakeholders in the preparatory process. Based on all the points given above, the research concludes that it is necessary to improve long-term policies and increase training at all levels aimed at active end-user participation and a profound restructuring of the energy system. Full article
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46 pages, 1076 KB  
Review
Bio-Based Fertilizers from Waste: Nutrient Recovery, Soil Health, and Circular Economy Impacts
by Moses Akintayo Aborisade, Huazhan Long, Hongwei Rong, Akash Kumar, Baihui Cui, Olaide Ayodele Oladeji, Oluwaseun Princess Okimiji, Belay Tafa Oba and Dabin Guo
Toxics 2026, 14(1), 90; https://doi.org/10.3390/toxics14010090 (registering DOI) - 19 Jan 2026
Abstract
Bio-based fertilisers (BBFs) derived from waste streams represent a transformative approach to sustainable agriculture, addressing the dual challenges of waste management and food security. This comprehensive review examines recent advances in BBF production technologies, nutrient recovery mechanisms, soil health impacts, and the benefits [...] Read more.
Bio-based fertilisers (BBFs) derived from waste streams represent a transformative approach to sustainable agriculture, addressing the dual challenges of waste management and food security. This comprehensive review examines recent advances in BBF production technologies, nutrient recovery mechanisms, soil health impacts, and the benefits of a circular economy. This review, based on an analysis of peer-reviewed studies, demonstrates that BBFs consistently improve the physical, chemical, and biological properties of soil while reducing environmental impacts by 15–45% compared to synthetic alternatives. Advanced biological treatment technologies, including anaerobic digestion, vermicomposting, and biochar production, achieve nutrient recovery efficiencies of 60–95% in diverse waste streams. Market analysis reveals a rapidly expanding sector projected to grow from $2.53 billion (2024) to $6.3 billion by 2032, driven by regulatory support and circular economy policies. Critical research gaps remain in standardisation, long-term performance evaluation, and integration with precision agriculture systems. Future developments should focus on AI-driven optimisation, climate-adaptive formulations, and nanobioconjugate technologies. Full article
(This article belongs to the Special Issue Study on Biological Treatment Technology for Waste Management)
49 pages, 8938 KB  
Review
A Review of 3D-Printed Medical Devices for Cancer Radiation Therapy
by Radiah Pinckney, Santosh Kumar Parupelli, Peter Sandwall, Sha Chang and Salil Desai
Bioengineering 2026, 13(1), 115; https://doi.org/10.3390/bioengineering13010115 - 19 Jan 2026
Abstract
This review explores the transformative role of three-dimensional (3D) printing in radiation therapy for cancer treatment, emphasizing its potential to deliver patient-specific, cost-effective, and sustainable medical devices. The integration of 3D printing enables rapid fabrication of customized boluses, compensators, immobilization devices, and GRID [...] Read more.
This review explores the transformative role of three-dimensional (3D) printing in radiation therapy for cancer treatment, emphasizing its potential to deliver patient-specific, cost-effective, and sustainable medical devices. The integration of 3D printing enables rapid fabrication of customized boluses, compensators, immobilization devices, and GRID collimators tailored to individual anatomical and clinical requirements. Comparative analysis reveals that additive manufacturing surpasses conventional machining in design flexibility, lead time reduction, and material efficiency, while offering significant cost savings and recyclability benefits. Case studies demonstrate that 3D-printed GRID collimators achieve comparable dosimetric performance to traditional devices, with peak-to-valley dose ratios optimized for spatially fractionated radiation therapy. Furthermore, emerging applications of artificial intelligence (AI) in conjunction with 3D printing promise automated treatment planning, generative device design, and real-time quality assurance, and are paving the way for adaptive and intelligent radiotherapy solutions. Regulatory considerations, including FDA guidelines for additive manufacturing, are discussed to ensure compliance and patient safety. Despite challenges such as material variability, workflow standardization, and large-scale clinical validation, evidence indicates that 3D printing significantly enhances therapeutic precision, reduces toxicity, and improves patient outcomes. This review underscores the synergy between 3D printing and AI-driven innovations as a cornerstone for next-generation radiation oncology, offering a roadmap for clinical adoption and future research. Full article
(This article belongs to the Section Biomedical Engineering and Biomaterials)
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19 pages, 439 KB  
Review
Metabolomics for Preclinical Detection of Diabetic Kidney Disease: A Comprehensive Review
by Michael Garoufis, Sissy Foteini Sakkou, Christina E. Kostara, Eleni Bairaktari and Vasilios Tsimihodimos
Int. J. Mol. Sci. 2026, 27(2), 998; https://doi.org/10.3390/ijms27020998 (registering DOI) - 19 Jan 2026
Abstract
Diabetic kidney disease (DKD) affects up to 40% of individuals with diabetes and remains the leading cause of end-stage renal disease worldwide. Current biomarkers, such as albuminuria and estimated glomerular filtration rate, detect disease only after substantial kidney injury, limiting early intervention. Metabolomics [...] Read more.
Diabetic kidney disease (DKD) affects up to 40% of individuals with diabetes and remains the leading cause of end-stage renal disease worldwide. Current biomarkers, such as albuminuria and estimated glomerular filtration rate, detect disease only after substantial kidney injury, limiting early intervention. Metabolomics offers unique potential to identify early biochemical changes preceding the clinical onset of DKD. This review synthesizes evidence from animal and human studies in diabetes without overt kidney disease, highlighting early perturbations in energy metabolism (TCA cycle, beta-oxidation, glycolysis) as well as alterations in amino acid, nucleotide and urea cycle pathways associated with future DKD risk. We discuss methodological considerations, translational relevance, and current research gaps and outline strategies for integrating metabolomics into predictive diagnostics. Early, non-invasive metabolic biomarkers may enable more precise risk stratification and timely intervention to improve patient outcomes. Full article
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15 pages, 3968 KB  
Article
High-Resolution Integrative Delimitation of Intertidal Limpets via Multi-Locus Barcodes and SEM Morphology
by Jialong Liang, Kexin Zhao, Xiaonan Ma, Jiayi Zang, Wenxiao Guo and Ran Zhao
Diversity 2026, 18(1), 52; https://doi.org/10.3390/d18010052 - 19 Jan 2026
Abstract
Limpets are marine gastropod molluscs well adapted to intertidal rocky environments, yet their taxonomic resolution remains challenging due to extensive morphological convergence and the presence of cryptic species. In this study, we applied an integrative taxonomic framework combining multi-locus DNA barcoding and fine-scale [...] Read more.
Limpets are marine gastropod molluscs well adapted to intertidal rocky environments, yet their taxonomic resolution remains challenging due to extensive morphological convergence and the presence of cryptic species. In this study, we applied an integrative taxonomic framework combining multi-locus DNA barcoding and fine-scale morphological characterization to clarify species boundaries within three families of limpets—Nacellidae, Lottiidae, and Siphonariidae. A total of 132 individuals collected from six coastal sites in Shenzhen and adjacent areas of southern China were analyzed using four markers Cytochrome c oxidase subunit I (COI), 16S ribosomal RNA (16S rRNA), Cytochrome b (Cytb) and 28S ribosomal RNA (28S rRNA), together with scanning electron microscopy (SEM) observations of radular morphology. Molecular analyses identified nine distinct species across five genera. Kimura two-parameter distance analyses revealed clear barcode gaps in 16S rRNA, Cytb, and 28S rRNA genes, particularly among Cellana and Nipponacmea, whereas COI exhibited stronger discriminatory power within Siphonaria. Moreover, our study provides newly 16S, 28S references for Nipponacmea formosa and Cytb references for Nipponacmea formosa, Lottia luchuana, Siphonaria atra, Siphonaria sirius, Siphonaria sp. and Siphonaria sirius, enriching the public references and explaining the lack of corresponding records in previous BLAST searches. In addition, we identified misannotated COI references in NCBI which were labelled as Nipponacema schrenckii but belong to Cellana toreuma, highlighting inconsistencies in existing reference data rather than issues with our samples. SEM-based radular features displayed consistent interspecific variation that corroborated molecularly defined clades, offering comprehensive search of the NCBI reliable morphological evidence for species delimitation. Collectively, our findings highlight the value of integrating lineage-specific molecular markers with detailed morphological analyses to resolve taxonomic ambiguities in morphologically conservative marine gastropods. Furthermore, this approach strengthens molecular reference resources essential for future biodiversity and evolutionary research on intertidal limpets. Full article
(This article belongs to the Section Marine Diversity)
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33 pages, 7152 KB  
Article
DRADG: A Dynamic Risk-Adaptive Data Governance Framework for Modern Digital Ecosystems
by Jihane Gharib and Youssef Gahi
Information 2026, 17(1), 102; https://doi.org/10.3390/info17010102 - 19 Jan 2026
Abstract
In today’s volatile digital environments, conventional data governance practices fail to adequately address the dynamic, context-sensitive, and risk-hazardous nature of data use. This paper introduces DRADG (Dynamic Risk-Adaptive Data Governance), a new paradigm that unites risk-aware decision-making with adaptive data governance mechanisms to [...] Read more.
In today’s volatile digital environments, conventional data governance practices fail to adequately address the dynamic, context-sensitive, and risk-hazardous nature of data use. This paper introduces DRADG (Dynamic Risk-Adaptive Data Governance), a new paradigm that unites risk-aware decision-making with adaptive data governance mechanisms to enhance resilience, compliance, and trust in complex data environments. Drawing on the convergence of existing data governance models, best practice risk management (DAMA-DMBOK, NIST, and ISO 31000), and real-world enterprise experience, this framework provides a modular, expandable approach to dynamically aligning governance strategy with evolving contextual factors and threats in data management. The contribution is in the form of a multi-layered paradigm combining static policy with dynamic risk indicator through application of data sensitivity categorization, contextual risk scoring, and use of feedback loops to continuously adapt. The technical contribution is in the governance-risk matrix formulated, mapping data lifecycle stages (acquisition, storage, use, sharing, and archival) to corresponding risk mitigation mechanisms. This is embedded through a semi-automated rules-based engine capable of modifying governance controls based on predetermined thresholds and evolving data contexts. Validation was obtained through simulation-based training in cross-border data sharing, regulatory adherence, and cloud-based data management. Findings indicate that DRADG enhances governance responsiveness, reduces exposure to compliance risks, and provides a basis for sustainable data accountability. The research concludes by providing guidelines for implementation and avenues for future research in AI-driven governance automation and policy learning. DRADG sets a precedent for imbuing intelligence and responsiveness at the heart of data governance operations of modern-day digital enterprises. Full article
(This article belongs to the Special Issue Information Management and Decision-Making)
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20 pages, 445 KB  
Review
E-MOTE: A Conceptual Framework for Emotion-Aware Teacher Training Integrating FACS, AI and VR
by Rosa Pia D’Acri, Francesco Demarco and Alessandro Soranzo
Vision 2026, 10(1), 5; https://doi.org/10.3390/vision10010005 - 19 Jan 2026
Abstract
This paper proposes E-MOTE (Emotion-aware Teacher Education Framework), an ethically grounded conceptual model aimed at enhancing teacher education through the integrated use of the Facial Action Coding System (FACS), Artificial Intelligence (AI), and Virtual Reality (VR). As a conceptual and design-oriented proposal, E-MOTE [...] Read more.
This paper proposes E-MOTE (Emotion-aware Teacher Education Framework), an ethically grounded conceptual model aimed at enhancing teacher education through the integrated use of the Facial Action Coding System (FACS), Artificial Intelligence (AI), and Virtual Reality (VR). As a conceptual and design-oriented proposal, E-MOTE is presented as a structured blueprint for future development and empirical validation, not as an implemented or evaluated system. Grounded in neuroscientific and educational research, E-MOTE seeks to strengthen teachers’ emotional awareness, teacher noticing, and social–emotional learning competencies. Rather than reporting empirical findings, this article offers a theoretically structured framework and an operational blueprint for the design of emotion-aware teacher training environments, establishing a structured foundation for future empirical validation. E-MOTE articulates three core contributions: (1) it clarifies the multi-layered construct of emotion-aware teaching by distinguishing between emotion detection, perception, awareness, and regulation; (2) it proposes an integrated AI–FACS–VR architecture for real-time and post hoc feedback on teachers’ perceptual performance; and (3) it outlines a staged experimental blueprint for future empirical validation under ethically governed conditions. As a design-oriented proposal, E-MOTE provides a structured foundation for cultivating emotionally responsive pedagogy and inclusive classroom management, supporting the development of perceptual micro-skills in teacher practice. Its distinctive contribution lies in proposing a shift from predominantly macro-behavioral simulation toward the deliberate cultivation of perceptual micro-skills through FACS-informed analytics integrated with AI-driven simulations. Full article
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17 pages, 786 KB  
Review
Data Hidden in Sewage: Advanced Methods for Identification and Quantification of Synthetic Cannabinoids in Urban Wastewater
by Wiktoria Kurzeja, Mariola Kuczer, Jan Matysiak and Agnieszka Klupczyńska-Gabryszak
Molecules 2026, 31(2), 337; https://doi.org/10.3390/molecules31020337 - 19 Jan 2026
Abstract
Synthetic cannabinoids (SCs) represent one of the rapidly growing groups of new psychoactive substances (NPS) on the illicit drug market. SCs mimic the effects of Δ9-tetrahydrocannabinol, but they have a greater affinity to the receptors, resulting in more potent psychoactive effects [...] Read more.
Synthetic cannabinoids (SCs) represent one of the rapidly growing groups of new psychoactive substances (NPS) on the illicit drug market. SCs mimic the effects of Δ9-tetrahydrocannabinol, but they have a greater affinity to the receptors, resulting in more potent psychoactive effects than traditional substances. The toxicity and high abuse potential of SCs could pose serious health risks to their users. The challenges posed by the SCs require innovative monitoring strategies like the analysis of untreated wastewater, known as wastewater-based epidemiology (WBE). In this review article, we summarized the available literature on the detection and quantification of SCs in raw wastewater samples published between 2013 and 2025. We paid special attention to challenges related to different experimental stages of WBE analysis that hinder the accurate measurement of SCs and their metabolites. The reviewed studies show that wastewater analysis reflected the dynamic evolution of the illicit SCs market. As studies on the analysis of SCs in wastewater remain scarce, large monitoring campaigns and research performed in more locations are needed. Modern analytical hyphenated systems such as LC-MS are essential for the sensitive and accurate quantification of SC biomarkers in wastewater and their sound identification. Future studies should address further stability tests, investigation of SC metabolism, and careful selection of the effective SC extraction method from the complex environmental matrix. Full article
(This article belongs to the Section Analytical Chemistry)
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24 pages, 662 KB  
Article
Between Inclusion and Artificial Intelligence: A Study of the Training Gaps of University Teaching Staff in Spain
by Lina Higueras-Rodríguez, Johana Muñoz-López, Marta Medina-García and Carmen Lucena-Rodríguez
Educ. Sci. 2026, 16(1), 151; https://doi.org/10.3390/educsci16010151 - 19 Jan 2026
Abstract
This study analyzes how Spanish universities integrate inclusion, accessibility, digital competence, and artificial intelligence (AI) into the professional development of university teaching staff, in a context marked by rapid digital transformation. The research addresses the lack of comparative evidence on how these key [...] Read more.
This study analyzes how Spanish universities integrate inclusion, accessibility, digital competence, and artificial intelligence (AI) into the professional development of university teaching staff, in a context marked by rapid digital transformation. The research addresses the lack of comparative evidence on how these key dimensions of contemporary higher education are articulated, or remain disconnected, across institutions. Using a mixed-methods design, 83 training courses delivered between 2020 and 2025 in 24 public and private universities were examined through qualitative analysis, coding matrices, and hierarchical cluster analysis. The study adopts an explicitly exploratory and typological approach, aimed at mapping institutional patterns rather than establishing causal explanations. The results reveal a highly heterogeneous and weakly cohesive training landscape. Inclusion appears primarily as a normative discourse with limited pedagogical depth; accessibility is frequently reduced to technical compliance; and AI (particularly generative AI) is incorporated from instrumental, efficiency-oriented approaches. Ethical dimensions, algorithmic bias, and digital accessibility are virtually absent. The hierarchical cluster analysis identifies four institutional profiles: technocentric without inclusion, analogically inclusive, advanced hybrid, and low-density training models. These patterns show how institutional orientations shape the professional development trajectories of university teaching staff. The study highlights the need for comprehensive faculty development strategies that integrate inclusion, accessibility, and responsible AI use, and offers a structured typological baseline for future research assessing impact on teaching practice and student experience. Full article
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