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Since 2022, numerous H5N1 highly pathogenic avian influenza virus (HPAIV) detections have been reported in wild and domestic mammals in North America. Although H5N1 HPAIV detections in dogs are rare, hunting dogs that retrieve waterfowl are at increased exposure risk due to their physical contact with reservoirs (waterfowl) and contaminated environments. A cross-sectional survey of hunters was conducted during 2024 to characterize hunting procedures, disease prevention practices, and interactions between humans and their hunting dogs to identify potential risks for zoonotic disease transmission. Descriptive analysis (N = 112) indicated a majority of participants considered their hunting dog as part of the family (93.8%), and less than half considered their dog a pet (42.9%). Of the 112 individuals, 96.4% did not utilize personal protective equipment (PPE) when handling a sick dog and 81.3% did not use PPE when handling harvested birds. This research demonstrated complex, sustained physical and personal connections between individuals and their hunting dogs. Additional research utilizing a One Health approach is necessary to define H5N1 HPAIV risk factors in hunting dogs and the environment’s role in the transmission of viruses among wildlife and domestic animals. Understanding zoonotic disease transmission in these populations can inform approaches to mitigate viral exposure. Full article

(This article belongs to the Section Veterinary Food Safety and Zoonosis)

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20 pages, 598 KB

Open AccessArticle

Association of Social Determinants of Health with Primary and Cost-Related Medication Nonadherence Among Adult Patients with Diabetes

by Yamini Mallisetty, Shruti Chaudhary, Ashley W. Ellis, Rushin Shah and Satya Surbhi

Diabetology 2026, 7(5), 86; https://doi.org/10.3390/diabetology7050086 (registering DOI) - 2 May 2026

Abstract

Background/Objectives: To examine the association of social determinants of health (SDOHs) with primary and cost-related medication nonadherence among adults with diabetes. Methods: A retrospective cross-sectional analysis was conducted using 2021 data from the Medical Expenditure Panel Survey (MEPS), a nationally representative sample of the United States civilian noninstitutionalized population. Adults aged ≥ 18 years with a diagnosis of diabetes in 2021 were included. The outcomes include primary medication nonadherence (no antidiabetic prescriptions filled) and cost-related medication nonadherence (delaying prescriptions due to cost). The exposure variables include SDOHs such as financial stress, food insecurity, transportation barriers, social support, access to medical care in the neighborhood, and healthcare discrimination. Weighted multivariable logistic regression analyses were conducted to assess the association between SDOHs and medication nonadherence. Results: Among 21.9 million patients with diabetes, 6.5% reported cost-related nonadherence and 17.4% exhibited primary nonadherence. Difficulty paying rent or mortgage (OR 2.32, 95% CI: 1.27–4.23), food insecurity (OR 2.13, 95% CI: 1.27–3.58), and transportation barriers (OR = 2.15; 95% CI: 1.20–3.63) were significantly associated with cost-related nonadherence. In the Medicare subgroup, both difficulty paying rent or mortgage (OR = 2.41, 95% CI: 1.03–5.64) and food insecurity (OR = 2.16, 95% CI: 1.18–3.96) significantly increased cost-related nonadherence. Conclusions: Financial strain, food insecurity, and transportation barriers are associated with cost-related nonadherence. These findings suggest considering social and economic factors in strategies supporting diabetes medication adherence across populations, including Medicare beneficiaries. Full article

(This article belongs to the Special Issue Advances in Diabetes: Prevalence, Risk Factors, Complications, Costs and Disparities)

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25 pages, 14015 KB

Open AccessArticle

From Concept to Practice: Implementing a Knowledge-Driven Decision Support Platform for Sustainable Viticulture in Montenegro

by Tamara Racković, Kruna Ratković, Marko Simeunović, Nataša Kovač, Christoph Menz, Helder Fraga, Aureliano C. Malheiro, António Fernandes and João A. Santos

Sensors 2026, 26(9), 2843; https://doi.org/10.3390/s26092843 - 1 May 2026

Abstract

Viticulture is highly vulnerable to weather variability and climate change. Growers increasingly face risks associated with extreme weather events, water scarcity, and emerging pests and diseases. To address these challenges, this study presents the development and implementation of the first operational digital decision support platform (DSP) tailored to Montenegrin vineyards within the MONTEVITIS project. The platform integrates IoT sensor data, national meteorological records and high-resolution global climate datasets to provide real-time monitoring and climate projections for vineyard management. The system was piloted in four vineyards representing diverse microclimatic and soil conditions of Montenegro. Key functionalities include phenology, irrigation and disease alerts supported by a user-friendly dashboard, map-based visualisation tools and data export functions. The pilot deployment demonstrated that combining heterogeneous data streams increases the reliability of outputs and enables timely, site-specific recommendations. Challenges identified during implementation include connectivity limitations, gaps in data and variable levels of digital expertise among growers; however, lessons learned point to the importance of continuous stakeholder engagement and institutional support for sustained use. The MONTEVITIS experience demonstrates how digital agriculture tools can bridge tradition and innovation in viticulture. By fostering collaboration between growers, researchers and policy makers, the platform enables adaptive strategies for climate resilience and sustainable vineyard management. Although the platform has been successfully deployed and tested under pilot conditions, a comprehensive long-term validation of its performance and impact on vineyard decision-making remains part of ongoing future work. Full article

(This article belongs to the Special Issue Cutting-Edge Proximal and Remote Sensing Solutions for Precision Agriculture)

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33 pages, 2629 KB

Open AccessArticle

Research on Earthquake Demolition Rescue Robot Design Based on UXM–Kano–QFD Framework

by Wei Peng, Yuqi Xia, Yue Han, Haiqiang Wang, Yang Tang, Xinyu Liu and Yexin Chen

Appl. Sci. 2026, 16(9), 4456; https://doi.org/10.3390/app16094456 - 1 May 2026

Abstract

This study presents an integrated design methodology for earthquake demolition rescue robots by combining UXMs, Kano, and QFD to improve design rationality and performance in extreme rescue scenarios. It addresses key gaps in existing approaches, particularly the lack of systematic experiential data acquisition, quantitative requirement analysis, and effective design translation. UXMs are applied to reconstruct critical task scenarios and identify high-load nodes and user experience variations. The Kano model is used to prioritise and classify user requirements, which are then translated into engineering characteristics through QFD. Based on this framework, a conceptual robot design is developed using the FBS model and evaluated through process-level simulation and usability assessment. The results demonstrate that the proposed method enables structured requirement transformation and supports traceable design decisions. Simulation indicates the consistency of task workflows and coordination among functional modules at the process level. A System Usability Scale score of 80.22 indicates a relatively high level of perceived usability at the conceptual evaluation stage. The proposed methodology provides a structured and traceable conceptual design framework for earthquake rescue robots. While the current validation is based on conceptual-level evaluation, the methodology offers a traceable design pathway that may be extended to other high-risk emergency equipment with further empirical testing. Full article

(This article belongs to the Section Mechanical Engineering)

28 pages, 713 KB

Open AccessArticle

Degrees, Levels, and Profiles of Contextuality

by Ehtibar N. Dzhafarov and Víctor H. Cervantes

Entropy 2026, 28(5), 513; https://doi.org/10.3390/e28050513 - 1 May 2026

Abstract

We introduce a new notion, that of a contextuality profile of a system of random variables. Rather than characterizing a system’s contextuality by a single number, its overall degree of contextuality, we show how it can be characterized by a curve relating [...] Read more.

\begin{matrix} level & 1 & \dots & n - 1 & n > 1 & n + 1 & \dots & N \\ degree & 0 & \dots & 0 & d_{n} > 0 & d_{n + 1} \geq d_{n} & \dots & d_{N} \geq d_{N - 1} \end{matrix}

, where N is the maximum number of variables per system’s context. A system is represented at level n if one only considers the joint distributions with

k \leq n

variables, ignoring higher-order joint distributions. We show that the level-wise contextuality analysis can be used in conjunction with any well-constructed measure of contextuality. We present a method of concatenated systems to explore contextuality profiles systematically, and we apply it to the contextuality profiles for three major measures of contextuality proposed in the literature. Full article

(This article belongs to the Section Multidisciplinary Applications)

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21 pages, 4098 KB

Open AccessArticle

Carbon and Nitrogen Isotopic Signatures as Metabolic Biomarkers of Nodal Metastasis and Recurrence in Oral Squamous Cell Carcinoma

by Katarzyna Bogusiak, Zuzanna Popińska, Marcin Kozakiewicz, Piotr Paneth and Józef Kobos

Cancers 2026, 18(9), 1461; https://doi.org/10.3390/cancers18091461 - 1 May 2026

Abstract

Background/Objectives: Oral squamous cell carcinoma (OSCC) exhibits substantial biological heterogeneity, and current clinicopathological risk stratification incompletely reflects tumor metabolic behavior. Stable isotope ratio mass spectrometry enables the quantitative assessment of carbon and nitrogen isotopic composition, potentially capturing cumulative metabolic reprogramming associated with tumor aggressiveness. This study evaluated whether isotopic signatures of tumor tissue and surgical margins are associated with lymph node metastasis and survival outcomes in OSCC. Methods: In this prospective study, 54 consecutive patients undergoing primary surgical treatment for OSCC were enrolled. Paired samples derived from tumor tissue and surgical margins were analyzed using isotope ratio mass spectrometry to determine the relative abundance of nitrogen-15 and carbon-13 isotopes. The primary endpoint was pathological lymph node metastasis. Secondary endpoints included disease-free survival and overall survival. Paired comparisons were performed using Wilcoxon signed-rank tests with false discovery rate correction. Logistic regression models for nodal metastasis were constructed using Firth penalization with bootstrap internal validation, while survival outcomes were evaluated using Cox proportional hazards models with model complexity restricted according to the number of events. Results: Tumor tissues demonstrated significantly lower δ¹³C and δ¹⁵N values and higher nitrogen-to-carbon ratios compared with surgical margins (all adjusted p < 0.05). In multivariable analysis, tumor δ¹⁵N was independently associated with lymph node metastasis and modestly improved model discrimination. However, it was not independently associated with disease-free or overall survival. Exploratory analyses indicated that higher δ¹³C values in surgical margins were independently associated with shorter disease-free survival. Conclusions: These findings suggest that isotope ratio mass spectrometry-based isotopic profiling identifies reproducible metabolic differences between tumor and margin tissues in OSCC. Tumor δ¹⁵N is associated with lymph node metastasis, whereas margin δ¹³C may reflect recurrence risk and potentially capture metabolic field effects. These findings are hypothesis-generating and warrant validation in larger, independent cohorts. Full article

(This article belongs to the Section Cancer Biomarkers)

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18 pages, 2128 KB

Open AccessArticle

Effects of Dietary Squalene Supplementation on the Growth Performance and Disease Resistance of Largemouth Bass

by Shan Liu, Mengmeng Chen, Yan Meng, Mingyang Xue, Yong Zhou, Liping Zhang, Peng Chen, Yuding Fan, Yazhen Yang and Zhenyu Huang

Vet. Sci. 2026, 13(5), 448; https://doi.org/10.3390/vetsci13050448 - 1 May 2026

Abstract

Feed supplements play a crucial role in improving and maintaining fish health in modern aquaculture practices. Squalene is a functional lipid naturally present in fatty tissues, possessing numerous beneficial biological properties and wide applications in the food and pharmaceutical industries. In this study, the effects of 100 mg/kg (S1), 200 mg/kg (S2), 300 mg/kg (S3), and 400 mg/kg (S4) of dietary squalene supplementation over four weeks on growth performance, antioxidation, hepatoprotection, hypoxia tolerance, immune relative genes expression, and disease resistance of largemouth bass (Micropterus salmoides) were assessed. The results showed that squalene supplementation significantly increased the weight gain rate (WGR) and specific growth rate (SGR) of largemouth bass (p < 0.05). Serum glucose (GLU) levels were significantly decreased in all squalene-supplemented groups (p < 0.05). Squalene supplementation had minimal effect on serum triglyceride (TG) and total cholesterol in (TCHO) levels. A decrease in malondialdehyde (MDA) level, but accompanied by increases in superoxide dismutase (SOD) and hepatic catalase (CAT) activities, was observed in the S1 group supplemented with squalene. These suggest that squalene may mitigate free radical damage and promote health in largemouth bass. Dietary squalene supplementation enhanced intestinal enzyme activities (trypsin, lipase, and α-amylase) in largemouth bass without inducing any apparent hepatic or histopathological alterations. Squalene supplementation improved hypoxia tolerance and antiviral gene expression (mx, ifn-γ, and irf3) while suppressing the expression of inflammatory cytokine (il-1β, il-8, and tnf-α). The survival rate following LMBRaV infection was significantly higher in the S1 group (100 mg/kg group) compared to the control (p < 0.05). In conclusion, this study demonstrated that adding squalene into the diet of largemouth bass at an optimal level of 100 mg/kg effectively promotes growth performance, enhances digestive enzyme activity and hypoxia tolerance, and modulates lipid metabolism and immune gene expression, thereby contributing to improved resistance against LMBRaV. These findings confirm that squalene can serve as a beneficial functional feed additive in aquaculture. Full article

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11 pages, 766 KB

Open AccessCommunication

Comparative Antifungal Activity of Medicinal Plant Extracts and Essential Oils Against Clinical Isolates of Candida albicans from Denture Stomatitis Patients

by Nazanin Fathi, Joo-Hyun Hong, Farzaneh Lotfipour, Samin Ghaffari, Reza Abbasi, Parina Asgharian, Rana Attaran, Hamed Hamishehkar, Maryam Kouhsoltani and Ki Hyun Kim

Plants 2026, 15(9), 1392; https://doi.org/10.3390/plants15091392 - 1 May 2026

Abstract

In this study, we investigated the antifungal potential of methanolic extracts and essential oils obtained from five medicinal plants (Salvadora persica, Mentha spicata, Achillea millefolium, Matricaria chamomilla, and Zingiber officinale) against 25 clinical isolates of Candida albicans collected from patients with denture stomatitis. Antifungal susceptibility was assessed using broth microdilution as the primary method, with agar diffusion assays performed to provide complementary visual confirmation. Nystatin was included as a reference control. Across the tested samples, essential oils consistently showed stronger antifungal effects than the corresponding methanolic extracts. Notably, Z. officinale essential oil exhibited the highest level of activity, inhibiting 15 out of 25 isolates and, in several cases, demonstrating efficacy comparable to or exceeding that of nystatin. Chemical profiling by GC–MS indicated that the ginger essential oil was dominated by sesquiterpene and monoterpene hydrocarbons, with zingiberene (21.49%) being the major constituent, followed by β-sesquiphellandrene, α-curcumene, sabinene, and α-citral. This terpene-rich composition may contribute to the observed antifungal activity, potentially through the disruption of fungal cell membrane integrity. Taken together, these results suggest that Z. officinale essential oil represents a promising natural antifungal candidate for the management of denture-associated C. albicans infections. Further studies, including biofilm-based assays and in vivo evaluations, will be necessary to confirm its clinical applicability. To the best of our knowledge, this study is among the first to comparatively assess these five medicinal plants against clinical C. albicans isolates derived specifically from denture stomatitis patients. Full article

(This article belongs to the Special Issue Medicinal Properties and Biological Activity of Plant Extracts)

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45 pages, 3019 KB

Open AccessArticle

Demographic Dependency and the Future of the European Workforce: A Spatial–Temporal Forecasting Approach

by Cristina Lincaru, Adriana Grigorescu, Camelia Speranta Pirciog and Gabriela Tudose

Sustainability 2026, 18(9), 4468; https://doi.org/10.3390/su18094468 - 1 May 2026

Abstract

This research paper examines the spatial and time variation of demographic dependency in Europe in a 30-year horizon of the evolution of the demographic dividend regarding the economic dependency ratio (ADR1). We used the Curve Fit Forecast tool to estimate the trends of ADR1 in each of the EU Member States using data on Eurostat projections and a sophisticated geostatistical analysis tool developed in ArcGIS Pro 3.2.2. The findings indicate that the dependency in all countries has increased significantly in a statistically significant manner as the Gompertz function has appeared as the best curve in a third of the cases. It is an S-shaped asymptotic behaviour of this function that effectively describes the nonlinear patterns of acceleration and saturation of demographic ageing. As indicated in the analysis, the European regions are increasingly moving apart, with the southern and eastern nations such as Romania demonstrating the most alarming decline in ADR1. These trends highlight the need to reform labour market policies and social protection mechanisms to an ageing population. The paper combines the curve-fitting, descriptive statistics (median, skewness, interquartile range (IQR)) with time clustering (value, correlation, and Fourier) to provide an effective, replicable approach to early warning and policy prioritisation. Overall, the results highlight the importance of integrating predictive spatial modelling and demographic economics to support anticipatory and evidence-based policy decisions. The proposed approach proves to be a robust and transferable framework, applicable to a wide range of socio-economic phenomena characterised by inertia and structural change. Future research should extend the analysis to subnational levels, incorporate additional explanatory variables, and develop scenario-based simulations, including multivariate Gompertz-type models, to further enhance both predictive accuracy and policy relevance in the context of emerging structural labour scarcity. Full article

(This article belongs to the Section Sustainable Urban and Rural Development)

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25 pages, 2126 KB

Open AccessArticle

Crying Wolf in Cyberspace: A Cybersecurity Dynamics Study of Alarm Fatigue Attacks

by Enrico Barbierato

Information 2026, 17(5), 434; https://doi.org/10.3390/info17050434 - 1 May 2026

Abstract

Modern cyber–physical infrastructures rely heavily on alarm and notification systems to direct human attention when abnormal conditions occur. These mechanisms support timely and safe responses by informing operators and occupants about potential hazards. At the same time, research in human factors has shown that repeated or excessive alerts can weaken vigilance, slow reactions, and reduce confidence in warning systems. This behavioral pattern is commonly described as alarm fatigue. This paper examines how that vulnerability can be exploited intentionally. We refer to this adversarial strategy as alarm poisoning: the deliberate injection of false or misleading alerts in order to increase alarm pressure, erode trust in the monitoring infrastructure, and degrade organizational responsiveness over time. To study this process, we develop a stochastic Cybersecurity Dynamics model representing the interaction among attackers, defenders, alarm infrastructure, and a population of employees. Employee behavior is modeled through evolving trust and fatigue levels, while the overall system is formulated as a continuous–time Markov chain and simulated using the Gillespie Stochastic Simulation Algorithm. A Monte–Carlo campaign is used to analyze the resulting socio–technical dynamics under alternative attacker strategies. The study evaluates time-dependent trust, fatigue, and alarm-pressure trajectories, the distribution of times to behavioral collapse, and defender timing through Trust–Resilience–Agility–Mitigation (TRAM) metrics. The revised analysis also includes replication-sufficiency diagnostics, one-at-a-time sensitivity analysis, and threshold-robustness checks for the collapse criterion. The results show that false alarms with high perceived severity drive alarm pressure upward and degrade trust faster than nuisance-dominated campaigns, even when the total fake-alarm intensity is held constant across strategies. Collapse timing remains highly variable across stochastic realizations, and a non-negligible fraction of runs do not reach the collapse threshold within the simulation horizon. Sensitivity analysis indicates that the main qualitative ranking of attacker strategies is robust across most tested perturbations, with fatigue recovery and defender escalation emerging as particularly influential mechanisms. Overall, the findings support the view that alarm poisoning is a credible socio–technical attack vector and highlight the importance of rapid mitigation, robust alarm management, and human-centered defensive design in cyber–physical security systems. Full article

(This article belongs to the Special Issue Generative AI for Data Privacy and Anomaly Detection)

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28 pages, 3985 KB

Open AccessArticle

Analysis and Prediction of Vegetation Phenological Changes in Changbai Mountain Nature Reserve Based on MODIS and PSO-LSSVM

by Anqi He, Jie Zhang, Lv Zhou, Fei Yang, Yanzhao Yang, Xianbin Wang, Xin Wang and Jiasi Yan

Appl. Sci. 2026, 16(9), 4452; https://doi.org/10.3390/app16094452 - 1 May 2026

Abstract

Vegetation phenology is a key indicator of ecosystem responses to climate change. This study investigates the spatial-temporal dynamics of vegetation phenology in the Changbai Mountain Nature Reserve from 2001 to 2025 and projects future changes under CMIP6 scenarios using a particle swarm optimization–least squares support vector machine (PSO-LSSVM) model. The results show that SOS exhibits an advancing trend, while EOS is delayed, leading to an overall extension of LOS. Spatially, phenological patterns are strongly controlled by elevation, with higher elevations characterized by later SOS and shorter LOS. Correlation analysis indicates that SOS is primarily driven by spring temperature, whereas EOS is influenced by both temperature and precipitation, showing more complex responses. Notably, a negative relationship between autumn temperature and EOS suggests that factors other than temperature may play an important role. Future projections reveal that phenological changes intensify with increasing emission scenarios. By the end of the 21st century, SOS is projected to advance by 0.8–3.6 days, EOS to be delayed by 0.8–7.4 days, and LOS to extend by 1.6–11.8 days. Vegetation-type-based analysis further demonstrates significant heterogeneity in phenological responses. These findings improve the understanding of vegetation phenology in mountain ecosystems and provide a useful reference for assessing ecosystem responses under future climate change. Full article

25 pages, 7687 KB

Open AccessArticle

Dynamic Time-Division Multiplexing Control Method for Emergency Lane Opening on Freeways

by Linning Zhong, Hongzhao Dong, Pengjun Zheng, Yibing Wang and Yini She

Appl. Sci. 2026, 16(9), 4441; https://doi.org/10.3390/app16094441 - 1 May 2026

Abstract

To address the issue that static strategies, such as frequent closure or fixed-time opening, in emergency lane management on freeways struggle to adapt to dynamic traffic demands, a control method for Dynamic Time-Division Multiplexing of Emergency Lanes (DTMEL) is proposed. Based on real-time changes in the traffic status of social vehicles and emergency vehicles, the method designs a dynamic time slice division strategy to achieve dynamic response in road right allocation and ensure the priority of emergency vehicle. By constructing a dynamic spatial slice model that integrates driver decision-making behavior and different car-truck ratios, it provides a quantitative design basis for lane usage under the heterogeneity of social vehicles. Simulation experimental results show that in different car-truck ratios, the average speed of social vehicles under the DTMEL control method is 8.16–15.02% higher than that under the frequent closure strategy of emergency lanes and 2.15–3.29% higher than that under the fully open strategy control. In addition, compared to the fully opening strategy, the traffic capacity under DTMEL strategy control has increased by about 6.59–8.08%. The proposed method reduced the delay rate of emergency vehicles, verifying its feasibility in improving freeway operation efficiency and ensuring the priority of emergency vehicles. Full article

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14 pages, 3920 KB

Open AccessArticle

Evaluation of Mechanical Properties of Zirconia-Based Composites Designed for Biomedical Applications

by Agnieszka Wojteczko, Sebastian Komarek and Magdalena Ziąbka

Appl. Sci. 2026, 16(9), 4455; https://doi.org/10.3390/app16094455 - 1 May 2026

Abstract

In this study, bioceramic composites based on zirconia (ZrO₂) were synthesized and characterized in terms of mechanical properties. Two types of different-sized grains of zirconia powders were used to prepare the composites. A commercial zirconia micropowder (Tosoh) was used as a base for the composites modified with bioactive glass (BG), copper-doped bioactive glass (BGCu), and hexagonal boron nitride (hBN) with a sintering temperature of 1450 °C. The composites with the addition of hydroxyapatite, for which their sintering temperature was 1150 °C, were independently fabricated using a zirconia nanopowder prepared via co-precipitation and hydrothermal methods to achieve high densification and avoid hydroxyapatite decomposition. Mechanical performance of these composites was assessed with regard to biaxial flexural strength, Vickers hardness (HV), and fracture toughness (K_Ic). The reference 3Y-TZP material exhibited Vickers hardness (11.8 GPa) and fracture toughness (6.1 MPa∙m^1/2 values typical for dense tetragonal zirconia ceramics. The addition of all bioactive phases resulted in significant alterations in mechanical properties. Specifically, incorporating 20 wt.% HAp led to a threefold decrease in hardness and a 40% reduction in fracture toughness, while increasing the HAp content to 40 wt.% further reduced these properties. Nonetheless, the fracture toughness of these composites remained higher than that of pure hydroxyapatite materials. The incorporation of BG and BGCu reduced the hardness values by 45% and 30%, respectively, compared to 3Y-TZP. The most significant deterioration of the properties was observed for the 3Y-TZP-hBN composite. The 3Y-TZP–BGCu composite exhibited fracture toughness (5.9 MPa∙m^1/2) representing 95% of the toughness of pure zirconium dioxide, thereby showing the lowest weakness of all the other composites with bioactive additives. A slightly lower fracture toughness value (5.3 MPa∙m^1/2) was also observed in the composite with bioglass but lacking the copper additive. This factor, combined with a relatively small decrease in hardness in both cases, highlights high durability for implantology applications, thus marking the indicated materials the most promising among the composites studied. Full article

(This article belongs to the Special Issue Nanomaterials and Surface Science)

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31 pages, 772 KB

Open AccessSystematic Review

Explicit and Implicit Learning Mechanisms in AI Educational Assistants: A Systematic Review

by Fatmah Alqarni, Nada Alhirabi, Omer Rana and Charith Perera

AI 2026, 7(5), 160; https://doi.org/10.3390/ai7050160 - 1 May 2026

Abstract

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

(This article belongs to the Special Issue How Is AI Transforming Education?)

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34 pages, 3836 KB

Open AccessArticle

Blockchain Adoption and Demand Information Sharing Strategies in a Green Supply Chain

by Xiaodong Zhu and Shiying Chang

Sustainability 2026, 18(9), 4471; https://doi.org/10.3390/su18094471 - 1 May 2026

Abstract

This study investigates the interaction between a manufacturer’s blockchain adoption strategy and a retailer’s demand information sharing strategy in a green supply chain. For four strategy combinations, we establish a multi-stage game-theoretical model of a green supply chain consisting of a single manufacturer and a single retailer. We first derive the optimal pricing, greenness, service level, and profits, followed by sensitivity and comparative analyses. Next, by examining how consumer price sensitivity and the unit adoption cost of blockchain technology interact, we identify equilibrium strategy combinations. Finally, we validate the relevant findings through numerical analysis. The results demonstrate that adopting blockchain can mitigate the double marginalization effect when consumer price sensitivity is moderate, and can enhance product greenness and service level when the adoption cost remains low. Interestingly, the manufacturer is inclined to adopt blockchain irrespective of the degree of consumer skepticism. Meanwhile, the implementation of blockchain may motivate the retailer to share information when price sensitivity falls within a moderate range. These findings present actionable guidance for green supply chains regarding blockchain and information-sharing strategies. Full article

(This article belongs to the Section Sustainable Management)

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