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Aiming at the issues of low recognition accuracy and high model computational complexity for power quality disturbances (PQDs) in strong-noise environments, this paper proposes a novel lightweight PQD-recognition method that integrates a hybrid architecture of variational mode decomposition (VMD), convolutional neural network (CNN), and transformer. Firstly, a hybrid optimization algorithm named the monkey–genetic hybrid optimization algorithm (MGHOA) is proposed to optimize VMD parameters for denoising disturbance signals, thereby enhancing recognition accuracy in noisy environments. Secondly, to fully extract disturbance signal features and reduce the computational complexity of the model, a lightweight CNN–transformer model is designed. Depthwise separable convolution (DSC) is employed to extract local features and the multi-head attention mechanism of transformer is utilized to mine the long-distance dependence and global features, thereby enhancing the feature representation. Thirdly, a multitask joint-learning method is proposed to collaboratively optimize classification accuracy and temporal localization tasks, enhancing the discrimination of similar disturbances. Additionally, a dual-pooling global feature fusion strategy is designed to further enhance the model’s ability to discriminate complex disturbances. Comparative experiments on 16 typical PQD types demonstrate that the proposed method achieves excellent performance in recognition accuracy, model robustness, and computational efficiency. The integration of the MGHOA–VMD module improves recognition accuracy by 1.08%, while the multitask joint-learning method contributes an additional 0.55% improvement. When achieving recognition accuracy comparable to complex models, the training time of the proposed method is 36.51% of that required by DeepCNN and merely 5.90% of that required by bidirectional long short-term memory (BiLSTM), with a 31.22% reduction in parameter scale. This work provides a novel solution for intelligent power quality disturbance recognition. Full article

(This article belongs to the Section Power Electronics)

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28 pages, 3801 KB

Open AccessArticle

From Delays to Opportunities: Data-Driven Strategies for Bus Priority at Signalized Intersections

by Fabio Borghetti, Alessandro Giani, Nicoletta Matera and Michela Longo

Sustainability 2026, 18(9), 4288; https://doi.org/10.3390/su18094288 (registering DOI) - 26 Apr 2026

Abstract

Never has the analysis of bus travel times been so essential to transit planning: travelers complain about a decline in service quality, urban congestion is on the rise, and public transport companies struggle with a structural driver shortage. This research paper aims to address the urgent need to explore new tools to increase commercial speed on transit lines while avoiding costly, potentially inefficient technological investments. A data-driven, cost-neutral, and replicable methodological framework is proposed to provide a first-order estimation of the potential benefits of Transit Signal Priority (TSP) at signalized intersections. The approach is based on Automatic Vehicle Monitoring (AVM) data analysis, which is underpinned by a lean network representation logic built from origin/destination pairs of stops located upstream and downstream of signalized intersections. Bus travel inter-times across network arcs are compared between peak and off-peak periods through a two-level analytical process that progressively refines the estimation of recoverable delay. The methodology is applied to the urban bus network of Pavia (Italy), operated by Autoguidovie S.p.A. (one of the most important Local Public Transport companies in Italy), with a specific focus on the high-frequency PV3 line. Results indicate a potential reduction of up to approximately 6 h and 45 min of operating time per day at the line level (−13.5% of total driving time), and up to 2 min per trip along a 2 km corridor (−6% along the single corridor selected). The procedure integrates both infrastructural and operational perspectives, supporting preliminary decision-making on TSP implementation using only data already collected by transit agencies. Full article

(This article belongs to the Special Issue Sustainable and Smart Transportation Systems)

41 pages, 647 KB

Open AccessArticle

Educational Factor of Human Capital: Key Point for Sustainable Development and Strategic Competitiveness in Kazakhstan’s Metallurgical Enterprise

by Sergey Polevoy, Mariana Petrova and Assiya Atabayeva

Sustainability 2026, 18(9), 4287; https://doi.org/10.3390/su18094287 (registering DOI) - 26 Apr 2026

Abstract

This research analyzes “educational” factors influencing the human capital of a labor collective from Karaganda Metallurgical Plant from the perspective of the enterprise’s potential qualitative development. The purpose of this article is to consider the importance of educational factors in understanding the concept of “human capital” by employees of the enterprise. An additional goal is the description of various factors, influencing satisfaction with the educational component. The authors tested the following hypothesis: the factor of education directly affects employees’ satisfaction with the level of human capital development at the enterprise. The research results are based on a personnel survey about level of educational satisfaction, its quality and its accessibility. The main analysis tool to verify the stated hypothesis was structural modeling in the Smart PLS. The analysis of the research results revealed dissatisfaction from a significant portion of the respondents with the approaches to vocational education established at the enterprise, despite the fact that a high importance of the educational aspect in human capital formation was confirmed by the respondents during the hypothesis testing procedure for same survey’s results. The identified problems were explained by a discrepancy between the manufacturing tasks and the skills and knowledge acquired by the respondents. This research has the potential for development within the framework of company policy analysis in the field of education. Full article

(This article belongs to the Special Issue Advancing Management Practices Through Digital Marketing and Sustainable Innovation)

38 pages, 837 KB

Open AccessReview

Targeting Mycotoxin Toxicity: From Molecular Mechanisms to Nutritional Interventions

by Shirui Huang, Yiqin Gao, Thobela Louis Tyasi, Abdelkareem A. Ahmed, In Ho Kim, Hao-Yu Liu, Saber Y. Adam and Demin Cai

Vet. Sci. 2026, 13(5), 421; https://doi.org/10.3390/vetsci13050421 (registering DOI) - 26 Apr 2026

Abstract

Mycotoxin contamination is an important threat to food and feed safety as well as human and animal health, with particular emphasis on oxidative stress, apoptosis, autophagy, inflammation, and dysbiosis. Mycotoxins represent major health threats because they disturb cellular homeostasis and induce oxidative damage. Nutritional factors, such as dietary antioxidants and bioactive chemicals, can influence the body’s reaction to mycotoxin exposure, either reducing or increasing its effects. This study discusses how mycotoxins (aflatoxin B1, deoxynivalenol, and ochratoxin A) induce oxidative stress by producing reactive oxygen species (ROS)-mediated DNA damage, which induces cellular damage and activates apoptosis, an intended cell death process that is critical for tissue integrity. Furthermore, mycotoxins alter autophagy, a cellular degradation process that can be beneficial or destructive depending on the situation, affecting cell survival. The inflammatory response is particularly important because mycotoxin-induced oxidative stress and cell damage activate inflammatory pathways, which contribute to tissue injury and disease progression. Nutritional factors high in antioxidants, anti-inflammatory substances (Lycopene, Curcumin, Thyme oil, Gum Arabic, and Ginger), probiotics, and prebiotics show potential in mitigating these negative consequences by reducing oxidative stress and inflammation. Advances in molecular biology and omics technologies (transcriptomics, proteomics, metabolomics, and single-cell sequencing) can lead to better knowledge of the underlying pathways, allowing for more tailored nutritional recommendations and medicinal interventions. Finally, combining dietary modulation with mycotoxin risk management is a viable path for protecting health and increasing resilience to mycotoxin-related toxicities in animals. Full article

(This article belongs to the Special Issue Review Paper Special Issue: Nutritional Metabolism and Toxic Diseases in Veterinary Medicine)

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24 pages, 20745 KB

Open AccessArticle

pH-Responsive Bovine Serum Albumin Nanoparticles Encapsulating Doxorubicin-Based Complexes Induce Cuproptosis in Lung Cancer Cells

by Haiying Zhang, Xuanjia Chen, Shihui Qiao, Huanfeng Meng, Hui Long, Huamin Zhong, Yiheng Liu, Yun Song, Yanan Gao, Yan Liu and Lujia Mao

Pharmaceutics 2026, 18(5), 526; https://doi.org/10.3390/pharmaceutics18050526 (registering DOI) - 26 Apr 2026

Abstract

Background/Objectives: This study investigates the induction of cuproptosis in A549 lung cancer cells by doxorubicin (DOX) complexes and the development of pH-responsive bovine serum albumin (BSA)-based nanocarriers for their delivery. We successfully synthesized and characterized two novel complexes: DOX–Cu, where DOX acts as a ligand for Cu(II), and DOX–BTZ, a conjugate formed between DOX and the proteasome inhibitor bortezomib (BTZ). Methods: Spectroscopic and NMR analyses were performed to confirm the formation of the complexes. In vitro assays were conducted to evaluate cytotoxicity in A549 cells, alongside assessment of DLAT aggregation as a marker of cuproptosis. The formulation of DOX into BSA nanoparticles (DOX–Cu@BSA NPs and DOX–BTZ@BSA NPs) was carried out to evaluate potential alleviation of DOX-induced cytotoxicity in cardiomyocytes in vitro. Fluorescence quenching and molecular docking studies were employed to investigate the binding interactions between the complexes and BSA. Cellular uptake experiments were performed to assess nanoparticle internalization into A549 cells. Results: Both complexes exhibited superior cytotoxicity against A549 cells compared to individual components. This enhanced cell death was associated with significant aggregation of dihydrolipoamide S-acetyltransferase (DLAT), a key marker of cuproptosis, suggesting the involvement of this copper-dependent cell death pathway. The BSA nanoparticles displayed favorable characteristics, including uniform size (~190 nm), high encapsulation efficiency (~75–79%), and colloidal stability. Crucially, they exhibited a pH-responsive drug release profile, with significantly accelerated release under acidic conditions (pH 5.7) mimicking the tumor microenvironment. Fluorescence quenching and molecular docking studies revealed strong, spontaneous binding between the complexes and BSA, primarily driven by hydrophobic interactions. Cellular uptake experiments confirmed efficient internalization of the nanoparticles into A549 cells. Conclusions: Collectively, this work offers a proof-of-concept for a strategy of utilizing BSA-based multidrug delivery systems for cuproptosis induction, offering a potential avenue to enhance therapeutic efficacy while reducing systemic toxicity in lung cancer treatment. Full article

(This article belongs to the Special Issue New Insights into Nanomaterials for Cancer Therapy and Drug Delivery)

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14 pages, 628 KB

Open AccessArticle

The Environment Takes a Back Seat: A Content Analysis of Persuasive Appeals in Electric Vehicle Advertisements

by Abel Gustafson and Hayley R. Clark

Sustainability 2026, 18(9), 4286; https://doi.org/10.3390/su18094286 (registering DOI) - 26 Apr 2026

Abstract

Electric vehicles represent a promising path toward reducing transportation-related greenhouse gas emissions, but partisan polarization presents a significant barrier to their widespread adoption in the United States. This study provides a detailed look at the auto industry’s strategies for reframing electric vehicles (EVs) to resonate with mainstream American consumers, and it contributes to scholarly understanding of how sustainable products are framed to politically diverse audiences. Through a comprehensive content analysis, we analyze the persuasive strategies in all available EV video advertisements run in the U.S. from 2018 to 2023. Spanning 263 unique advertisements and 62 vehicle models, our analyses reveal the ways that nature and the environment are used in EV ads. Our data show that 90% of EV ads do not make any reference to sustainability, and 71% do not employ nature in any way. Instead, EV ads tend to emphasize vehicle features and performance, and they portray EVs as a futuristic transportation revolution. We situate these findings within the broader literature on partisan polarization of environmental issues, identity signaling in green consumer behavior, and green marketing strategy. We argue that the near-total absence of sustainability messaging in EV advertising reflects an industry-wide strategy to decouple electric vehicles from environmental identity and reframe them as mainstream consumer products. Full article

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18 pages, 5743 KB

Open AccessArticle

CFD Evaluation of Crop Presence and Evapotranspiration on Natural Ventilation and Thermal Stratification in a Tropical Tomato Greenhouse (OpenFOAM)

by Luis Humberto Martínez Palmeth, Nadia Brigitte Sanabria Méndez, Marlio Bedoya Cardoso, María Angélica González Carmona and Paula Andrea Cuervo Velásquez

Eng 2026, 7(5), 194; https://doi.org/10.3390/eng7050194 (registering DOI) - 26 Apr 2026

Abstract

This study used Computational Fluid Dynamics (CFD) with the Reynolds-Averaged Navier–Stokes (RANS) k-ω Shear Stress Transport (SST) model to evaluate how crop presence and evapotranspiration affect airflow and thermal stratification in a naturally ventilated tropical tomato greenhouse. Three configurations were simulated: SP-SC-R (No Plants—No crop thermal load—Radiation), CP-SC-R (Crop Present—No crop thermal load—Radiation), and CP-CC-R (Crop Present—Crop thermal load (233.68 W·m⁻²)—Radiation). Mesh independence analysis yielded numerical uncertainties of 1.58% (velocity) and 1 × 10⁻⁶ (temperature). Vegetation reduced canopy air velocity by 55% (from 4 m·s⁻¹ to values below 2 m·s⁻¹). Evapotranspiration enhanced buoyancy-driven mixing, decreasing temperature gradients by up to 1.5 °C, but thermal stratification persisted above 4.5 m in all cases (vertical gradients 0.31–0.42 °C·m⁻¹; maximum roof temperature 37.95 °C). Extreme wind speeds (greater than 20 m·s⁻¹) occurred in the leeward span but above the main foliage. Natural ventilation alone is insufficient for tomato cultivation under tropical conditions. Practical recommendations include increasing roof vent area, installing windbreak baffles, and adopting hybrid ventilation. Future work should use unsteady, RANS/large-eddy simulation (LES), porous media models based on leaf area density (LAI), and field validation. This study demonstrates that coupling crop geometry and evapotranspiration is essential for realistic greenhouse CFD modelling in warm climates. Full article

(This article belongs to the Section Chemical, Civil and Environmental Engineering)

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28 pages, 6628 KB

Open AccessArticle

Unified AI Framework for Decarbonization in Large-Scale Building Energy Systems: Integrating Acoustic-Vision Leak Detection and Schedule-Aware Machine Learning

by Mooyoung Yoo

Buildings 2026, 16(9), 1698; https://doi.org/10.3390/buildings16091698 (registering DOI) - 26 Apr 2026

Abstract

Compressed air systems (CASs) represent a significant portion of energy consumption in large-scale built environments and manufacturing facilities, suffering from both micro-level physical pipeline leaks and macro-level operational inefficiencies. This paper proposes a unified, dual-action artificial intelligence framework aimed at advancing building decarbonization by systematically integrating acoustic-vision leak quantification with schedule-aware machine learning. Specifically, the framework targets pneumatic pipe connection leaks, fitting leaks, and joint degradation faults within compressed air distribution networks, which are the primary sources of micro-level volumetric energy losses in industrial building systems. First, a probabilistic multimodal fusion algorithm (MPSF) using an ultrasonic camera is developed to detect and geometrically quantify physical leaks, successfully translating pixel areas into physical facility energy loss metrics (estimating 11.0 kW of wasted power from detected severe leaks). Second, to optimize the compressor’s supply matching the actual facility demand without risking data leakage from internal flow sensors, an eXtreme Gradient Boosting (XGBoost) model is proposed. By utilizing only external building environmental conditions and the real-time operational schedules of 13 distinct zones, the model achieves highly accurate dynamic power prediction (R² = 0.9698). Finally, comprehensive simulations based on real-world digital monitoring data from a facility-scale built environment demonstrate that only the concurrent application of both modules ensures stable end-point pressure. The integrated framework achieves a substantial system-wide building energy reduction of over 20% to 40% compared to baseline constant-pressure operations, yielding an estimated annual reduction of 116 tons of CO₂ emissions, thereby providing a direct pathway toward carbon-neutral building operations. Full article

(This article belongs to the Special Issue Built Environment and Building Energy for Decarbonization)

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14 pages, 4439 KB

Open AccessArticle

Study on Seismic Collapse Fragility of Corroded Platform Canopies with Different Fortification Intensities in China

by Haibing Liu, Junqi Lin and Jinlong Liu

Appl. Sci. 2026, 16(9), 4228; https://doi.org/10.3390/app16094228 (registering DOI) - 26 Apr 2026

Abstract

Twelve reinforced concrete (RC) railway platform canopies were designed for zones with different seismic fortification intensities (SFIs) in accordance with the Code for Seismic Design of Buildings (2024 Edition) GB/T 50011-2010. Numerical models were created in OpenSees for each structure under three conditions: no corrosion, 5% corrosion loss of reinforcement, and 15% corrosion loss of reinforcement, using the Modified Ibarra–Medina–Krawinkler (ModIMK) hysteretic model. Through IDA, seismic collapse fragility was assessed in accordance with the requirements of the Standard for Anti-collapse Design of Building Structures T/CECS 392-2021. The results are: (1) Double-column canopies strongly resist deterioration from reinforcement corrosion. Each structure with different SFIs meets the code’s collapse probability limit under all three corrosion levels when subjected to the maximum considered earthquake (MCE) and the extreme considered earthquake (ECE, an earthquake larger than MCE). (2) When subjected to MCE, Single-column canopies with different SFIs also meet the code’s collapse probability limit under the three corrosion levels. (3) When subjected to ECE, the collapse probability of single-column canopies with 5% corrosion increases compared to uncorroded structures at SFIs ranging from 6 to 8; for SFIs 8.5 and 9, the collapse probability decreases. The structure with SFI 8.5 has the highest risk and does not comply with the code. (4) When subjected to ECE, the collapse probability of the single-column canopy with 15% corrosion increases significantly compared to uncorroded structures at all SFIs. Structures with SFIs ranging from 7.5 to 9 fail to meet code requirements. This paper systematically investigates the collapse fragility of platform canopies with different seismic fortification intensities in China, examining three corrosion states: no corrosion, 5% corrosion, and 15% corrosion. It provides important guidance for the rational design of platform canopies and for analyzing the impact of corrosion levels on their collapse behavior. Full article

(This article belongs to the Section Civil Engineering)

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35 pages, 10652 KB

Open AccessArticle

Unveiling Long-Memory Dynamics in Turbulent Markets: A Novel Fractional-Order Attention-Based GRU-LSTM Framework with Multifractal Analysis

by Yangxin Wang and Yuxuan Zhang

Fractal Fract. 2026, 10(5), 293; https://doi.org/10.3390/fractalfract10050293 (registering DOI) - 26 Apr 2026

Abstract

Financial time series in turbulent markets exhibit complex long-memory dynamics and multifractal features that traditional deep learning models fail to capture due to inherent exponential forgetting mechanisms. To address this, we propose Frac-Attn-GL, a novel Fractional-order Spatiotemporal Attention-based GRU-LSTM framework. Grounded in the Fractal Market Hypothesis, the model embeds Grünwald–Letnikov fractional-order operators into a dual-channel architecture (FracLSTM and FracGRU) to characterize long-range memory with rigorous power-law decay priors. Furthermore, an extreme-aware asymmetric loss function is designed to drive a dynamic spatiotemporal routing mechanism, enabling adaptive shifts between long-term macro trends and short-term micro shocks. Empirical tests on major U.S. stock indices reveal three significant findings. First, the Frac-Attn-GL framework substantially reduces prediction errors, achieving up to a 93.1% RMSE reduction on the highly volatile NASDAQ index compared to standard baselines. Second, the adaptively learned fractional-order parameters exhibit a consistent quantitative alignment with the market’s empirical multifractal singularity spectrum, supporting the physical interpretability of the model’s endogenous memory mechanism. Finally, hybrid residual multifractal diagnostics indicate that the framework effectively captures deep long-range correlations, reducing the Hurst exponent of the prediction residuals from ~0.83 to approximately 0.50, a level consistent with the absence of significant long-range dependence. Full article

(This article belongs to the Special Issue Fractal Approaches and Machine Learning in Financial Markets)

23 pages, 3113 KB

Open AccessArticle

Microhabitat Primarily Structures Bacterial Communities, While Management History Shapes Functional Potential in Tomato-Associated Soils

by Santiago Adolfo Vio, Joaquín Rilling, Manuel Fernandez-Lopez, Milko Alberto Jorquera, Mariano Pistorio and María Flavia Luna

Diversity 2026, 18(5), 256; https://doi.org/10.3390/d18050256 (registering DOI) - 26 Apr 2026

Abstract

Intensive horticultural management modifies soil physicochemical conditions, yet its effects on microbial community assembly and functional organization remain poorly resolved. This study examined bulk soil (BS) and rhizosphere soil (Rh) bacterial communities associated with tomato plants grown in two contrasting commercial horticultural establishments: a long-term intensive monoculture (>10 years; MC) and a recently established system (FC). Total bacterial abundance and community structure were characterized using qPCR and 16S rRNA gene amplicon sequencing, respectively; the abundance and diversity of functional plant-growth-promoting (PGP) genes—nifH, phoD, and acdS—were assessed by qPCR and DGGE profiling. The MC system, associated with increased salinity, nutrient accumulation, and organic matter content, supported higher bacterial abundance, whereas the FC system showed a higher relative abundance of PGP genes. Amplicon sequencing revealed significant differentiation between BS and Rh, identifying microhabitat in tomato-associated soil as the primary driver of taxonomic structure, while site effects were weaker. In contrast, DGGE profiling supported differences in functional gene composition between management systems, whereas predicted pathway profiles inferred from 16S data were comparatively similar across samples. Overall, these results indicate that horticultural intensification is associated with shifts in predicted functional potential that are not paralleled by major changes in taxonomic structure. Full article

(This article belongs to the Special Issue Rhizosphere Microbial Community Diversity)

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21 pages, 480 KB

Open AccessArticle

From Injury to Recovery: A Six-Month Longitudinal Analysis of Quality of Life After Adult Trauma

by João Paulo de Melo Barros, Luís Manuel Mota Sousa, César João Vicente da Fonseca, Josiana de Oliveira Martins Duarte and Ana Lúcia da Silva João

J. Clin. Med. 2026, 15(9), 3295; https://doi.org/10.3390/jcm15093295 (registering DOI) - 26 Apr 2026

Abstract

Traumatic injuries are a major cause of disability in adults, with long-term consequences that extend beyond acute survival. Understanding the longitudinal trajectory of quality of life (QoL) following trauma is essential for optimising recovery pathways. This study aimed to evaluate changes in QoL over a six-month period after injury and to characterise the most affected health domains. Methods: A longitudinal observational study was conducted including 136 adult trauma patients. QoL was assessed using the EQ-5D-5L at three time points: retrospectively for the pre-trauma state, and prospectively at one and six months post-injury. Statistical analysis included Paired T-Tests and Cohen’s d to evaluate the significance and magnitude of changes across five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Results: The sample was predominantly male (57.4%), and falls were the most common mechanism of injury (57.4%). One month after trauma, a significant decline was observed across all EQ-5D dimensions (p < 0.001), with large effect sizes particularly in usual activities (d = 0.89) and self-care (d = 0.86). At six months, significant improvement was noted in all domains compared to the one-month assessment (p < 0.001). However, only mobility returned to pre-trauma levels (p = 0.137), while persistent impairments remained in pain/discomfort and anxiety/depression. The EQ-VAS score declined from a pre-trauma mean of 82.74 to 69.00 at one month and partially recovered to 77.29 at six months. Notably, only 15.4% of patients received specialized rehabilitation services. Conclusions: Trauma results in a profound immediate reduction in QoL. Although physical mobility tends to recover by six months, functional autonomy and psychological well-being remain compromised. The findings highlight the need for multidisciplinary post-discharge interventions, focusing on pain management and psychological support to bridge the gap in long-term recovery. Full article

(This article belongs to the Section Clinical Rehabilitation)

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21 pages, 10729 KB

Open AccessArticle

Detecting Dairy Cattle Protective Behaviors via a Multi-Stage Attention SlowFast Network

by Bo Zhang, Jia Li, Feilong Kang, Yongan Zhang, Yu Xia, Yanqiu Liu and Jian Zhao

Animals 2026, 16(9), 1321; https://doi.org/10.3390/ani16091321 (registering DOI) - 26 Apr 2026

Abstract

Protective behavior in dairy cattle is one of the important potential indicators of their health and welfare status, and the precise detection of this behavior is of great significance for improving pasture management. However, existing methods face challenges, including capturing rapid motions, excessive background interference, and sample imbalance in complex agricultural environments. In response to these challenges, we proposed a Multi-Stage Attention SlowFast (MSA-SlowFast) model based on the improved SlowFast network to explore the model’s ability to distinguish between normal and protective behavior of dairy cattle. It achieves performance improvement through three core modules: the Multi-Path Balanced Head (MPBHead) for alleviating category imbalance, the Spatio-Temporal Convolutional Block Attention Module (ST-CBAM) for enhancing key feature extraction, and the 7 (BAF) for promoting multi-path feature complementarity. Additionally, we proposed novel timing-aware oversampling methods and dynamic loss adjustment mechanisms to further improve the detection performance of minority-class protective behaviors. Finally, a spatio-temporal-oriented dairy cattle protective behaviors dataset is constructed. Experimental results demonstrate that the proposed MSA-SlowFast model achieves 79.41% mAP, surpassing the standard SlowFast (70.58%) and Slow-only (68.21%). Further validation shows that the model exhibits high detection confidence in four specific actions labeled as protective behavior: 0.97 for tail swaying, 0.90 for head shaking, 0.92 for ear flapping, and 0.90 for leg kicking. These preliminary results show that the method proposed in this study has certain feasibility and reference value for the detection of protective behavior of dairy cattle under our constructed dataset. Full article

(This article belongs to the Section Animal System and Management)

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11 pages, 4548 KB

Open AccessArticle

Preparation and Anodic Bonding Performance of (PEG)₁₀LiClO₄/NaAlOSiO Solid Electrolyte for Packaging

by Chao Du and Yali Zhao

Int. J. Mol. Sci. 2026, 27(9), 3837; https://doi.org/10.3390/ijms27093837 (registering DOI) - 26 Apr 2026

Abstract

In this study, a polyethylene glycol (PEG)-based solid electrolyte composite (PEG)₁₀LiClO₄/NaAlOSiO suitable for anodic bonding packaging was successfully fabricated via a combined ball milling and hot pressing process. The micromorphology, ion transport characteristics, and mechanical packaging properties of the composite were systematically investigated using characterization techniques including electrochemical impedance spectroscopy, X-ray diffraction, scanning electron microscopy, and anodic bonding performance tests. The results demonstrate that doping with NaAlOSiO molecular sieve can effectively reduce the crystallinity of the polymer matrix, construct more efficient carrier transport pathways, and simultaneously enhance the ionic conductivity and mechanical properties of the material. When the mass fraction of NaAlOSiO doping is 8 wt.%, the composite exhibits a room temperature ionic conductivity of up to 1.31 × 10⁻⁵ S·cm⁻¹. Under room temperature and a bonding voltage of 800 V, the sample with this doping ratio achieves the optimal anodic bonding with metallic Al, and the tensile strength of the bonding interface reaches 5.93 MPa, showing excellent application prospects in micro–nano-packaging. Full article

(This article belongs to the Special Issue Advanced Electrode and Electrolyte Materials: Molecular Scale Design, Synthesis, Mechanisms and Applications)

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26 pages, 2325 KB

Open AccessArticle

Vitamin E Intake Modulates the Effect of Selenomethionine on Sexual Function and Depressive Symptoms in Reproductive-Age Women with Euthyroid Autoimmune Thyroiditis: A Pilot Study

by Robert Krysiak, Karolina Kowalcze, Johannes Ott, Giovanni Cangelosi, Simona Zaami and Bogusław Okopień

Antioxidants 2026, 15(5), 549; https://doi.org/10.3390/antiox15050549 (registering DOI) - 26 Apr 2026

Abstract

Oxidative stress appears to be implicated in both the initiation and progression of autoimmune thyroiditis. Selenomethionine, which exhibits antioxidant properties, has been shown to reduce thyroid antibody titers in patients with autoimmune thyroiditis. Recent evidence suggests that vitamin E, a fat-soluble antioxidant, may protect against the development of autoimmune thyroiditis, and that its supplementation has been associated with improvements in female sexual function. The objective of the present pilot study was to determine whether vitamin E intake modulates the effects of selenomethionine on female sexual function and depressive symptoms in individuals with thyroid autoimmunity. The study enrolled three groups of reproductive-age women with euthyroid autoimmune thyroiditis, with 26 participants in each group. The groups were matched for age, thyroid peroxidase antibody titers, and TSH levels and differed according to vitamin E intake: adequate intake (group A), low intake (group B), and high intake (group C). All participants received selenomethionine supplementation (200 µg/day) for six months. Antibody titers and hormone levels were measured, and participants completed questionnaires assessing female sexual function (FSFI) and depressive symptoms (BDI-II). At baseline, no differences in biochemical outcomes were observed between the groups, except for testosterone levels. The study groups differed in sexual desire and arousal domain scores, which were higher in group A than in the other two groups. Total FSFI scores, the remaining FSFI domain scores, and BDI-II scores did not differ between groups at baseline. Across all groups, selenomethionine reduced thyroid peroxidase and thyroglobulin antibody titers and increased SPINA-GD and the ratio of free triiodothyronine to free thyroxine; however, the effects on antibody titers were most pronounced in group A. An increase in SPINA-GT and testosterone levels following selenomethionine supplementation was observed only in group A. In this group, selenomethionine also led to significant improvements in total FSFI scores and all individual domain scores. In contrast, in the remaining groups, the effects of supplementation were limited to increases in domain scores for lubrication, sexual satisfaction, and pain. A treatment-related reduction in total BDI-II scores was observed exclusively in women with adequate vitamin E intake. These findings suggest, for the first time, that dietary intake of a natural antioxidant may influence the effects of exogenous selenomethionine on sexual function and depressive symptoms in reproductive-age women with euthyroid autoimmune thyroiditis. Full article

(This article belongs to the Special Issue Dietary Antioxidants, Oxidative Stress, and Women’s Reproductive Health)

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