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Recent Articles  

27 pages, 7808 KiB  
Article
Phenology-Aware Transformer for Semantic Segmentation of Non-Food Crops from Multi-Source Remote Sensing Time Series
by Xiongwei Guan, Meiling Liu, Shi Cao and Jiale Jiang
Remote Sens. 2025, 17(14), 2346; https://doi.org/10.3390/rs17142346 (registering DOI) - 9 Jul 2025
Abstract
Accurate identification of non-food crops underpins food security by clarifying land-use dynamics, promoting sustainable farming, and guiding efficient resource allocation. Proper identification and management maintain the balance between food and non-food cropping, a prerequisite for ecological sustainability and a healthy agricultural economy. Distinguishing [...] Read more.
Accurate identification of non-food crops underpins food security by clarifying land-use dynamics, promoting sustainable farming, and guiding efficient resource allocation. Proper identification and management maintain the balance between food and non-food cropping, a prerequisite for ecological sustainability and a healthy agricultural economy. Distinguishing large-scale non-food crops—such as oilseed rape, tea, and cotton—remains challenging because their canopy reflectance spectra are similar. This study proposes a novel phenology-aware Vision Transformer Model (PVM) for accurate, large-scale non-food crop classification. PVM incorporates a Phenology-Aware Module (PAM) that fuses multi-source remote-sensing time series with crop-growth calendars. The study area is Hunan Province, China. We collected Sentinel-1 SAR and Sentinel-2 optical imagery (2021–2022) and corresponding ground-truth samples of non-food crops. The model uses a Vision Transformer (ViT) backbone integrated with PAM. PAM dynamically adjusts temporal attention using encoded phenological cues, enabling the network to focus on key growth stages. A parallel Multi-Task Attention Fusion (MTAF) mechanism adaptively combines Sentinel-1 and Sentinel-2 time-series data. The fusion exploits sensor complementarity and mitigates cloud-induced data gaps. The fused spatiotemporal features feed a Transformer-based decoder that performs multi-class semantic segmentation. On the Hunan dataset, PVM achieved an F1-score of 74.84% and an IoU of 61.38%, outperforming MTAF-TST and 2D-U-Net + CLSTM baselines. Cross-regional validation on the Canadian Cropland Dataset confirmed the model’s generalizability, with an F1-score of 71.93% and an IoU of 55.94%. Ablation experiments verified the contribution of each module. Adding PAM raised IoU by 8.3%, whereas including MTAF improved recall by 8.91%. Overall, PVM effectively integrates phenological knowledge with multi-source imagery, delivering accurate and scalable non-food crop classification. Full article
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11 pages, 7411 KiB  
Article
The Effects of Thermo-Mechanical Treatments on Microstructure and High-Temperature Mechanical Properties of a Nickel-Based Superalloy
by Zihan Kang, Yaxing Ma and Qian Lei
Crystals 2025, 15(7), 630; https://doi.org/10.3390/cryst15070630 (registering DOI) - 9 Jul 2025
Abstract
The effects of thermo-mechanical treatment and different annealing temperatures on the microstructure and mechanical properties of a nickel-based superalloy were investigated by metallographic microscope, scanning electron microscope, and mechanical properties measurements. The results demonstrated that the tensile strength and elongation of the hot-rolled [...] Read more.
The effects of thermo-mechanical treatment and different annealing temperatures on the microstructure and mechanical properties of a nickel-based superalloy were investigated by metallographic microscope, scanning electron microscope, and mechanical properties measurements. The results demonstrated that the tensile strength and elongation of the hot-rolled samples were higher than those of the annealed ones. The ultimate engineering stress and engineering strain of the studied samples solid solution treated at 1175 °C for 4 h were 709 ± 19.8 MPa and 87.2 ± 1.4%, and the product of strength times elongation (PSE) was 61.8 GPa·%. These findings indicated that the thermo-mechanical treatment was an effective method to improve both the strength and the ductility of the nickel-based superalloy. Full article
(This article belongs to the Special Issue Emerging Topics of High-Performance Alloys (2nd Edition))
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17 pages, 4293 KiB  
Article
Predicting Nitrogen Flavanol Index (NFI) in Mentha arvensis Using UAV Imaging and Machine Learning Techniques for Sustainable Agriculture
by Bhavneet Gulati, Zainab Zubair, Ankita Sinha, Nikita Sinha, Nupoor Prasad and Manoj Semwal
Drones 2025, 9(7), 483; https://doi.org/10.3390/drones9070483 (registering DOI) - 9 Jul 2025
Abstract
Crop growth monitoring at various growth stages is essential for optimizing agricultural inputs and enhancing crop yield. Nitrogen plays a critical role in plant development; however, its improper application can reduce productivity and, in the long term, degrade soil health. The aim of [...] Read more.
Crop growth monitoring at various growth stages is essential for optimizing agricultural inputs and enhancing crop yield. Nitrogen plays a critical role in plant development; however, its improper application can reduce productivity and, in the long term, degrade soil health. The aim of this study was to develop a non-invasive approach for nitrogen estimation through proxies (Nitrogen Flavanol Index) in Mentha arvensis using UAV-derived multispectral vegetation indices and machine learning models. Support Vector Regression, Random Forest, and Gradient Boosting were used to predict the Nitrogen Flavanol Index (NFI) across different growth stages. Among the tested models, Random Forest achieved the highest predictive accuracy (R2 = 0.86, RMSE = 0.32) at 75 days after planting (DAP), followed by Gradient Boosting (R2 = 0.75, RMSE = 0.43). Model performance was lowest during early growth stages (15–30 DAP) but improved markedly from mid to late growth stages (45–90 DAP). The findings highlight the significance of UAV-acquired data coupled with machine learning approaches for non-destructive nitrogen flavanol estimation, which can immensely contribute to improving real-time crop growth monitoring. Full article
(This article belongs to the Section Drones in Agriculture and Forestry)
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25 pages, 2464 KiB  
Systematic Review
Modulating the Gut Microbiota to Target Neuroinflammation, Cognition and Mood: A Systematic Review of Human Studies with Relevance to Fibromyalgia
by Gianna Dipalma, Grazia Marinelli, Laura Ferrante, Angela Di Noia, Claudio Carone, Valeria Colonna, Pierluigi Marotti, Francesco Inchingolo, Andrea Palermo, Gianluca Martino Tartaglia, Massimo Del Fabbro, Angelo Michele Inchingolo and Alessio Danilo Inchingolo
Nutrients 2025, 17(14), 2261; https://doi.org/10.3390/nu17142261 (registering DOI) - 9 Jul 2025
Abstract
Aim: This systematic review aims to evaluate the effectiveness of microbiota-modulating interventions (such as probiotics, prebiotics, and fecal microbiota transplantation) in reducing cognitive symptoms, pain, and neuroinflammation in human studies relevant to fibromyalgia (FM). The review will investigate the role of gut–brain axis [...] Read more.
Aim: This systematic review aims to evaluate the effectiveness of microbiota-modulating interventions (such as probiotics, prebiotics, and fecal microbiota transplantation) in reducing cognitive symptoms, pain, and neuroinflammation in human studies relevant to fibromyalgia (FM). The review will investigate the role of gut–brain axis modulation through these interventions and explore the potential therapeutic benefits for FM management. Materials and Methods: A comprehensive search was conducted in electronic databases including PubMed, Scopus, and the Cochrane Library for studies published from 1 January 2015 to 30 April 2025. Studies were eligible if they were randomized controlled trials (RCTs), pilot studies, or observational studies assessing the impact of microbiota-targeted interventions (probiotics, prebiotics, fecal microbiota transplantation) on cognitive function, pain, or neuroinflammation in patients with FM. Studies were excluded if they involved animal models, lacked relevant outcome measures, or were not peer-reviewed. Although only a subset of the included studies directly involved FM patients, all were selected for their relevance to symptom domains (e.g., pain, cognition, mood) and mechanisms (e.g., neuroinflammation, gut–brain axis dysfunction) that are central to FM. A total of 11 human studies were included in the final qualitative synthesis. Results: Preliminary findings from the included studies suggest that microbiota-targeted interventions, particularly probiotics and prebiotics, show promise in reducing cognitive symptoms, pain, and neuroinflammation in FM patients. Improvements in mood and quality of life were also reported, indicating potential benefits for overall well-being. However, heterogeneity in study designs, sample sizes, and outcome measures limit the ability to draw definitive conclusions. Conclusions: This systematic review highlights the potential of microbiota modulation as a therapeutic strategy for managing FM symptoms, particularly cognitive dysfunction and neuroinflammation. Full article
(This article belongs to the Special Issue Implications of Diet and the Gut Microbiome in Neuroinflammation)
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21 pages, 2170 KiB  
Article
IoT-Driven Intelligent Energy Management: Leveraging Smart Monitoring Applications and Artificial Neural Networks (ANN) for Sustainable Practices
by Azza Mohamed, Ibrahim Ismail and Mohammed AlDaraawi
Computers 2025, 14(7), 269; https://doi.org/10.3390/computers14070269 (registering DOI) - 9 Jul 2025
Abstract
The growing mismanagement of energy resources is a pressing issue that poses significant risks to both individuals and the environment. As energy consumption continues to rise, the ramifications become increasingly severe, necessitating urgent action. In response, the rapid expansion of Internet of Things [...] Read more.
The growing mismanagement of energy resources is a pressing issue that poses significant risks to both individuals and the environment. As energy consumption continues to rise, the ramifications become increasingly severe, necessitating urgent action. In response, the rapid expansion of Internet of Things (IoT) devices offers a promising and innovative solution due to their adaptability, low power consumption, and transformative potential in energy management. This study describes a novel, integrative strategy that integrates IoT and Artificial Neural Networks (ANNs) in a smart monitoring mobile application intended to optimize energy usage and promote sustainability in residential settings. While both IoT and ANN technologies have been investigated separately in previous research, the uniqueness of this work is the actual integration of both technologies into a real-time, user-adaptive framework. The application allows for continuous energy monitoring via modern IoT devices and wireless sensor networks, while ANN-based prediction models evaluate consumption data to dynamically optimize energy use and reduce environmental effect. The system’s key features include simulated consumption scenarios and adaptive user profiles, which account for differences in household behaviors and occupancy patterns, allowing for tailored recommendations and energy control techniques. The architecture allows for remote device control, real-time feedback, and scenario-based simulations, making the system suitable for a wide range of home contexts. The suggested system’s feasibility and effectiveness are proved through detailed simulations, highlighting its potential to increase energy efficiency and encourage sustainable habits. This study contributes to the rapidly evolving field of intelligent energy management by providing a scalable, integrated, and user-centric solution that bridges the gap between theoretical models and actual implementation. Full article
(This article belongs to the Special Issue Distributed Computing Paradigms for the Internet of Things: Exploring Cloud, Edge, and Fog Solutions)
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17 pages, 1820 KiB  
Article
A Federated Learning Architecture for Bird Species Classification in Wetlands
by David Mulero-Pérez, Javier Rodriguez-Juan, Tamai Ramirez-Gordillo, Manuel Benavent-Lledo, Pablo Ruiz-Ponce, David Ortiz-Perez, Hugo Hernandez-Lopez, Anatoli Iarovikov, Jose Garcia-Rodriguez, Esther Sebastián-González, Olamide Jogunola, Segun I. Popoola and Bamidele Adebisi
J. Sens. Actuator Netw. 2025, 14(4), 71; https://doi.org/10.3390/jsan14040071 (registering DOI) - 9 Jul 2025
Abstract
Federated learning allows models to be trained on edge devices with local data, eliminating the need to share data with a central server. This significantly reduces the amount of data transferred from edge devices to central servers, which is particularly important in rural [...] Read more.
Federated learning allows models to be trained on edge devices with local data, eliminating the need to share data with a central server. This significantly reduces the amount of data transferred from edge devices to central servers, which is particularly important in rural areas with limited bandwidth resources. Despite the potential of federated learning to fine-tune deep learning models using data collected from edge devices in low-resource environments, its application in the field of bird monitoring remains underexplored. This study proposes a federated learning pipeline tailored for bird species classification in wetlands. The proposed approach is based on lightweight convolutional neural networks optimized for use on resource-constrained devices. Since the performance of federated learning is strongly influenced by the models used and the experimental setting, this study conducts a comprehensive comparison of well-known lightweight models such as WideResNet, EfficientNetV2, MNASNet, GoogLeNet and ResNet in different training settings. The results demonstrate the importance of the training setting in federated learning architectures and the suitability of the different models for bird species recognition. This work contributes to the wider application of federated learning in ecological monitoring and highlights its potential to overcome challenges such as bandwidth limitations. Full article
(This article belongs to the Special Issue Federated Learning: Applications and Future Directions)
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31 pages, 1909 KiB  
Review
Centella asiatica: Advances in Extraction Technologies, Phytochemistry, and Therapeutic Applications
by Zaw Myo Hein, Prarthana Kalerammana Gopalakrishna, Anil Kumar Kanuri, Warren Thomas, Farida Hussan, Venkatesh R. Naik, Nisha Shantakumari, Muhammad Danial Che Ramli, Mohamad Aris Mohd Moklas, Che Mohd Nasril Che Mohd Nassir and Thirupathirao Vishnumukkala
Life 2025, 15(7), 1081; https://doi.org/10.3390/life15071081 (registering DOI) - 9 Jul 2025
Abstract
Centella asiatica (C. asiatica) has attracted significant scientific interest due to its extensive medicinal properties and long-established use in traditional medicine. This review synthesizes recent advances in the technological exploitation of C. asiatica, covering the extraction of bioactive constituents to [...] Read more.
Centella asiatica (C. asiatica) has attracted significant scientific interest due to its extensive medicinal properties and long-established use in traditional medicine. This review synthesizes recent advances in the technological exploitation of C. asiatica, covering the extraction of bioactive constituents to product development. Modern extraction techniques such as supercritical fluid extraction (SFE) and microwave-assisted extraction (MAE) have substantially improved the yield, selectivity, and preservation of key phytochemicals, particularly triterpenoids, saponins, and flavonoids. These compounds are now routinely characterized using advanced analytical platforms, ensuring product quality, consistency, and standardization. Moreover, the use of innovative formulation technologies and advanced delivery systems has facilitated the development of C. asiatica-based products tailored for various therapeutic areas, including pharmaceuticals, nutraceuticals, and cosmeceuticals targeting neuroprotection, wound healing, skin aging, and stress modulation. Alongside these developments, stringent quality control protocols, toxicological evaluations, and adherence to evolving regulatory standards enhance the safety and efficacy of C. asiatica-derived interventions. This review highlights the integration of traditional knowledge with modern science across the domains of extraction, analysis, formulation, and regulation. It serves as a comprehensive resource for researchers, formulators, and regulatory stakeholders aiming to develop high-quality, evidence-based C. asiatica products with improved bioavailability and therapeutic value. Full article
(This article belongs to the Special Issue Natural Bioactives: Exploring Their Therapeutic Potential—2nd Edition)
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24 pages, 3067 KiB  
Review
Integrated Management Strategies for Blackleg Disease of Canola Amidst Climate Change Challenges
by Khizar Razzaq, Luis E. Del Río Mendoza, Bita Babakhani, Abdolbaset Azizi, Hasnain Razzaq and Mahfuz Rahman
J. Fungi 2025, 11(7), 514; https://doi.org/10.3390/jof11070514 (registering DOI) - 9 Jul 2025
Abstract
Blackleg caused by a hemi-biotrophic fungus Plenodomus lingam (syn. Leptosphaeria maculans) poses a significant threat to global canola production. Changing climatic conditions further exacerbate the intensity and prevalence of blackleg epidemics. Shifts in temperature, humidity, and precipitation patterns can enhance pathogen virulence [...] Read more.
Blackleg caused by a hemi-biotrophic fungus Plenodomus lingam (syn. Leptosphaeria maculans) poses a significant threat to global canola production. Changing climatic conditions further exacerbate the intensity and prevalence of blackleg epidemics. Shifts in temperature, humidity, and precipitation patterns can enhance pathogen virulence and disease spread. This review synthesizes the knowledge on integrated disease management (IDM) approaches for blackleg, including crop rotation, resistant cultivars, and chemical and biological controls, with an emphasis on advanced strategies such as disease forecasting models, remote sensing, and climate-adapted breeding. Notably, bibliometric analysis reveals an increasing research focus on the intersection of blackleg, climate change, and sustainable disease management. However, critical research gaps remain, which include the lack of region-specific forecasting models, the limited availability of effective biological control agents, and underexplored socio-economic factors limiting farmer adoption of IDM. Additionally, the review identifies an urgent need for policy support and investment in breeding programs using emerging tools like AI-driven decision support systems, CRISPR/Cas9, and gene stacking to optimize fungicide use and resistance deployment. Overall, this review highlights the importance of coordinated, multidisciplinary efforts, integrating plant pathology, breeding, climate modeling, and socio-economic analysis to develop climate-resilient, locally adapted, and economically viable IDM strategies for sustainable canola production. Full article
(This article belongs to the Special Issue Integrated Management of Plant Fungal Diseases)
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11 pages, 805 KiB  
Article
Efficacy and Safety of OROSOL Spray for Oral Mucositis in Children: A Randomized, Double-Blind, Placebo-Controlled Trial
by Fatima-Zahra El Barche, Manon D’Almeida, Séverine Dameron and Rémi Shrivastava
Biomedicines 2025, 13(7), 1677; https://doi.org/10.3390/biomedicines13071677 (registering DOI) - 9 Jul 2025
Abstract
Background: Oral mucositis (OM) is a common and debilitating complication of cancer therapy, particularly in patients undergoing chemotherapy and radiotherapy. It significantly impairs quality of life and may necessitate the interruption of cancer treatment. This study aimed to evaluate the efficacy and [...] Read more.
Background: Oral mucositis (OM) is a common and debilitating complication of cancer therapy, particularly in patients undergoing chemotherapy and radiotherapy. It significantly impairs quality of life and may necessitate the interruption of cancer treatment. This study aimed to evaluate the efficacy and safety of OROSOL, an oral spray device, in managing oral mucositis in pediatric patients undergoing chemotherapy or radiotherapy. Methods: This randomized, double-blind, placebo-controlled clinical trial compared OROSOL to a placebo in children with oral mucositis aged 3 to 17 years. Participants were followed for 28 days with regular medical visits. The primary endpoints were changes in the Oral Assessment Guide (OAG) scores and key symptoms (mucositis score, difficulty in oral feeding, ulceration and erythema, and pain sensation). Safety was assessed via adverse events and local tolerability. Results: Both groups were demographically balanced at baseline (p > 0.6). OROSOL demonstrated significantly greater improvements in the mucositis score beginning on Day 7 (p = 0.0122) and maintained superiority through Day 28 (p = 0.0007). Notable reductions in mucositis severity were observed, with significantly faster relief in the OROSOL group compared to the placebo (p < 0.001 for most timepoints). Oral feeding difficulty also showed a marked decline, with significant improvements starting from Day 5 (p = 0.0153). Ulceration and erythema scores significantly decreased from Day 14 onwards (p = 0.0188). Pain sensation showed a marked reduction from Day 14 (p = 0.0014). No serious adverse events were reported, and tolerability was consistent across all participants. Conclusions: OROSOL has a significant impact on reducing mucositis severity, oral feeding difficulty, ulceration, erythema, and pain. Coupled with its excellent safety profile, it is a valuable therapeutic option. This treatment is particularly beneficial for pediatric patients, ensuring improved comfort and recovery without notable adverse effects. Full article
(This article belongs to the Section Drug Discovery, Development and Delivery)
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17 pages, 2928 KiB  
Article
Hybrid Machine Learning Model for Hurricane Power Outage Estimation from Satellite Night Light Data
by Laiyin Zhu and Steven M. Quiring
Remote Sens. 2025, 17(14), 2347; https://doi.org/10.3390/rs17142347 (registering DOI) - 9 Jul 2025
Abstract
Hurricanes can cause massive power outages and pose significant disruptions to society. Accurately monitoring hurricane power outages will improve predictive models and guide disaster emergency management. However, many challenges exist in obtaining high-quality data on hurricane power outages. We systematically evaluated machine learning [...] Read more.
Hurricanes can cause massive power outages and pose significant disruptions to society. Accurately monitoring hurricane power outages will improve predictive models and guide disaster emergency management. However, many challenges exist in obtaining high-quality data on hurricane power outages. We systematically evaluated machine learning (ML) approaches to reconstruct historical hurricane power outages based on high-resolution (1 km) satellite night light observations from the Defense Meteorological Satellite Program (DMSP) and other ancillary information. We found that the two-step hybrid model significantly improved model prediction performance by capturing a substantial portion of the uncertainty in the zero-inflated data. In general, the classification and regression tree-based machine learning models (XGBoost and random forest) demonstrated better performance than the logistic and CNN models in both binary classification and regression models. For example, the xgb+xgb model has 14% less RMSE than the log+cnn model, and the R-squared value is 25 times larger. The Interpretable ML (SHAP value) identified geographic locations, population, and stable and hurricane night light values as important variables in the XGBoost power outage model. These variables also exhibit meaningful physical relationships with power outages. Our study lays the groundwork for monitoring power outages caused by natural disasters using satellite data and machine learning (ML) approaches. Future work should aim to improve the accuracy of power outage estimations and incorporate more hurricanes from the recently available Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data. Full article
(This article belongs to the Special Issue Environment Observation Analysis Based on Remote Sensing and Geospatial Artificial Intelligence)
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23 pages, 9229 KiB  
Article
Magnetopause Boundary Detection Based on a Deep Image Prior Model Using Simulated Lobster-Eye Soft X-Ray Images
by Fei Wei, Zhihui Lyu, Songwu Peng, Rongcong Wang and Tianran Sun
Remote Sens. 2025, 17(14), 2348; https://doi.org/10.3390/rs17142348 (registering DOI) - 9 Jul 2025
Abstract
This study focuses on the problem of identifying and extracting the magnetopause boundary of the Earth’s magnetosphere using the Soft X-ray Imager (SXI) onboard the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) mission. The SXI employs lobster-eye optics to perform panoramic imaging of [...] Read more.
This study focuses on the problem of identifying and extracting the magnetopause boundary of the Earth’s magnetosphere using the Soft X-ray Imager (SXI) onboard the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) mission. The SXI employs lobster-eye optics to perform panoramic imaging of the magnetosphere based on the Solar Wind Charge Exchange (SWCX) mechanism. However, several factors are expected to hinder future in-orbit observations, including the intrinsically low signal-to-noise ratio (SNR) of soft-X-ray emission, pronounced vignetting, and the non-uniform effective-area distribution of lobster-eye optics. These limitations could severely constrain the accurate interpretation of magnetospheric structures—especially the magnetopause boundary. To address these challenges, a boundary detection approach is developed that combines image calibration with denoising based on deep image prior (DIP). The method begins with calibration procedures to correct for vignetting and effective area variations in the SXI images, thereby restoring the accurate brightness distribution and improving spatial uniformity. Subsequently, a DIP-based denoising technique is introduced, which leverages the structural prior inherent in convolutional neural networks to suppress high-frequency noise without pretraining. This enhances the continuity and recognizability of boundary structures within the image. Experiments use ideal magnetospheric images generated from magnetohydrodynamic (MHD) simulations as reference data. The results demonstrate that the proposed method significantly improves the accuracy of magnetopause boundary identification under medium and high solar wind number density conditions (N = 10–20 cm−3). The extracted boundary curves consistently achieve a normalized mean squared error (NMSE) below 0.05 compared to the reference models. Additionally, the DIP-processed images show notable improvements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), indicating enhanced image quality and structural fidelity. This method provides adequate technical support for the precise extraction of magnetopause boundary structures in soft X-ray observations and holds substantial scientific and practical value. Full article
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21 pages, 651 KiB  
Article
Validation of an Inventory of Sensitivity to Ideological Radicalization (ISIR-14) in a Mexican Sample
by Julio C. Penagos-Corzo and Isabel Govela
Soc. Sci. 2025, 14(7), 423; https://doi.org/10.3390/socsci14070423 (registering DOI) - 9 Jul 2025
Abstract
The development and validation of the Ideological Radicalization Sensitivity Inventory (ISIR-14) in a Mexican sample is presented. A total of 537 participants were assessed. Exploratory and confirmatory factor analyses supported a five-factor structure that explained 53.7% of the variance, with excellent model fit [...] Read more.
The development and validation of the Ideological Radicalization Sensitivity Inventory (ISIR-14) in a Mexican sample is presented. A total of 537 participants were assessed. Exploratory and confirmatory factor analyses supported a five-factor structure that explained 53.7% of the variance, with excellent model fit indices (CFI = 0.985, TLI = 0.978, RMSEA = 0.033). Evidence of concurrent validity was suggested through significant correlations with the Emotional Response to Unfairness Scale (ERU) and the Exposure to Violent Extremism Scale (EXPO-12). Reliability analyses indicated good internal consistency (ω = 0.819) for the instrument. Additionally, temporal stability, analyzed in a second study with 171 participants, showed moderate stability (r = 0.601). The study aimed to test the hypothesis that sensitivity to ideological radicalization can be reliably measured through a multidimensional instrument aligned with theoretically derived psychological risk factors, namely, inclination to seek redress, perceived social disconnection, ideological superiority, exposure to extreme ideologies, and collective/group identity. The results suggest that the ISIR-14 is a reliable and valid tool for assessing sensitivity to ideological radicalization. The scale provides a foundation for future research and interventions aimed at identifying and addressing factors associated with radicalization processes. Full article
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13 pages, 1871 KiB  
Article
Impact of Health Education on Infectious Disease Knowledge in Indigenous Communities in Northwestern Malaysia
by Barathan Muttiah, Wathiqah Wahid and Alfizah Hanafiah
Trop. Med. Infect. Dis. 2025, 10(7), 191; https://doi.org/10.3390/tropicalmed10070191 (registering DOI) - 9 Jul 2025
Abstract
Indigenous people possess unique health literacy issues and challenges with preventing infectious diseases. This research assessed the baseline knowledge and misinformation in the Semai indigenous subgroup in Perak state, Malaysia, and the impact of a culturally adapted health education intervention. A single-group pre-test/post-test [...] Read more.
Indigenous people possess unique health literacy issues and challenges with preventing infectious diseases. This research assessed the baseline knowledge and misinformation in the Semai indigenous subgroup in Perak state, Malaysia, and the impact of a culturally adapted health education intervention. A single-group pre-test/post-test design was used with 156 participants ranging from 7 to 69 years old, predominantly children. The survey addressed key issues of head lice, intestinal parasites, tuberculosis (TB), handwashing, and germ transmission. An interactive, multi-station health education session in the local language produced a significant increase in overall knowledge (mean score increased from 3.17 to 3.83 out of 5, p < 0.0001), with the largest increase among the adult group aged 31–50 years. This was most notable for handwashing knowledge, which had the greatest increase, and misconceptions about intestinal worms and head lice remained. Differences in outcome by age suggest the need for targeted educational strategies, particularly for teenagers and elderly individuals who achieved less gain. The results support the effectiveness of culturally tailored, community-based health education in promoting the awareness of disease among indigenous communities. The drawbacks are convenience sampling, the child dominance of the sample, and the short-term follow-up. Future emphasis should be placed on long-term, community-based intervention using culturally tailored content and digital media. Full article
(This article belongs to the Section Infectious Diseases)
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16 pages, 2086 KiB  
Article
High-Coverage Profiling of Hydroxyl and Amino Compounds in Sauce-Flavor Baijiu Using Bromine Isotope Labeling and Ultra-High Performance Liquid Chromatography–High-Resolution Mass Spectrometry
by Zixuan Wang, Youlan Sun, Tiantian Chen, Lili Jiang, Yuhao Shang, Xiaolong You, Feng Hu, Di Yu, Xinyu Liu, Bo Wan, Chunxiu Hu and Guowang Xu
Metabolites 2025, 15(7), 464; https://doi.org/10.3390/metabo15070464 (registering DOI) - 9 Jul 2025
Abstract
Background: Hydroxyl and amino compounds play a significant role in defining the flavor and quality of sauce-flavor Baijiu, yet their comprehensive analysis remains challenging due to limitations in detection sensitivity. In this study, we developed a novel bromine isotope labeling approach combined [...] Read more.
Background: Hydroxyl and amino compounds play a significant role in defining the flavor and quality of sauce-flavor Baijiu, yet their comprehensive analysis remains challenging due to limitations in detection sensitivity. In this study, we developed a novel bromine isotope labeling approach combined with ultra-high performance liquid chromatography–high-resolution mass spectrometry (UHPLC-HRMS) to achieve high-coverage profiling of these compounds in sauce-flavor Baijiu. Methods: The method employs 5-bromonicotinoyl chloride (BrNC) for rapid (30 s) and mild (room temperature) labeling of hydroxyl and amino functional groups, utilizing bromine’s natural isotopic pattern (Δm/z = 1.998 Da) for efficient screening. Annotation was performed hierarchically at five confidence levels by integrating retention time, accurate mass, and MS/MS spectra. Results: A total of 309 hydroxyl and amino compounds, including flavor substances (e.g., tyrosol and phenethyl alcohol) and bioactive compounds (e.g., 3-phenyllactic acid), were identified in sauce-flavor Baijiu. The method exhibited excellent analytical performance, with wide linearity (1–4 orders of magnitude), precision (RSD < 18.3%), and stability (RSD < 15% over 48 h). When applied to sauce-flavor Baijiu samples of different grades, distinct compositional patterns were observed: premium-grade products showed greater metabolite diversity and higher contents of bioactive compounds, whereas lower-grade samples exhibited elevated concentrations of acidic flavor compounds. Conclusions: These results demonstrate that the established method is efficient for the comprehensive analysis of hydroxyl and amino compounds in complex food matrices. The findings provide valuable insights for quality control and flavor modulation in sauce-flavor Baijiu production. Full article
(This article belongs to the Special Issue New Analytical Techniques and Applications of Metabolomics and Lipidomics)
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20 pages, 1338 KiB  
Article
Two-Dimensional Fuel Assembly Study for a Supercritical Water-Cooled Small Modular Reactor
by Valerio Giusti
J. Nucl. Eng. 2025, 6(3), 26; https://doi.org/10.3390/jne6030026 (registering DOI) - 9 Jul 2025
Abstract
Burnable poisoning and fuel enrichment zoning are two techniques often combined in order to optimize the fuel assembly behavior during the burnup cycle. In the present work, these two techniques will be applied to the 2D optimization of the fuel assembly conceptual design [...] Read more.
Burnable poisoning and fuel enrichment zoning are two techniques often combined in order to optimize the fuel assembly behavior during the burnup cycle. In the present work, these two techniques will be applied to the 2D optimization of the fuel assembly conceptual design for the supercritical water-cooled reactor developed in the framework of the Joint European Canadian Chinese development of Small Modular Reactor Technology project, funded within the Euratom Research and Training programme 2019–2020. The initial configuration of the fuel assembly does not include any burnable absorbers and uses a homogeneous fuel enrichment of 7.5% in 235U. The infinite multiplication factor, k∞, starts from approximately 1.32 and drops, almost linearly, to 1.0 after a burnup of 40.0 MWd·kg−1. The uniform enrichment is, however, responsible for a pin-power peaking factor that with fresh fuel starts from 1.32 and reduces to 1.08 at the end of the burnup cycle. A simplified analytical model is developed to assess the effect of different lumped burnable absorbers on the time dependence of the assembly k∞. It is shown that using an adequate number of B4C rods, positioned in the outer wall of the fuel assembly, together with a suitable distribution of six different 235U enrichments, it allows for obtaining an assembly k∞ factor that starts from 1.11 at the beginning of the cycle and remains quite constant over a large fraction of the burnup cycle. Moreover, the pin-power peaking factor is reduced to 1.03 at the beginning of the cycle and remains almost unchanged until the end of the burnup cycle. Full article
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11 pages, 2806 KiB  
Article
The Effect of Doping with Aluminum on the Optical, Structural, and Morphological Properties of Thin Films of SnO2 Semiconductors
by Isis Chetzyl Ballardo Rodriguez, U. Garduño Terán, A. I. Díaz Cano, B. El Filali and M. Badaoui
J. Compos. Sci. 2025, 9(7), 358; https://doi.org/10.3390/jcs9070358 (registering DOI) - 9 Jul 2025
Abstract
There is considerable interest in broadband nanomaterials, particularly transparent semiconductor oxides, within both fundamental research and technological applications. Historically, it has been considered that the variation in dopant concentration during the synthesis of semiconductor materials is a crucial factor in activating and/or modulating [...] Read more.
There is considerable interest in broadband nanomaterials, particularly transparent semiconductor oxides, within both fundamental research and technological applications. Historically, it has been considered that the variation in dopant concentration during the synthesis of semiconductor materials is a crucial factor in activating and/or modulating the optical and structural properties, particularly the bandgap and the parameters of the unit cell, of semiconductor oxides. Recently, tin oxide has emerged as a key material due to its excellent structural properties, optical transparency, and various promising applications in optoelectronics. This study utilized the ultrasonic spray pyrolysis technique to synthesize aluminum-doped tin oxide (ATO) thin films on quartz and polished single-crystal silicon substrates. The impact of varying aluminum doping levels (0, 2, 5, and 10 at. %) on morphology and structural and optical properties was examined. The ATO thin films were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), and transmittance spectroscopy. SEM images demonstrated a slight reduction in the size of ATO nanoparticles as the aluminum doping concentration increased. XRD analysis revealed a tetragonal crystalline structure with the space group P42/mnm, and a shift in the XRD peaks to higher angles was noted with increasing aluminum content, indicating a decrease in the crystalline lattice parameters of ATO. The transmittance of the ATO films varied between 75% and 85%. By employing the transmittance spectra and the established Tauc formula the optical bandgap values of ATO films were calculated, showing an increase in the bandgap with higher doping levels. These findings were thoroughly analyzed and discussed; additionally, an effort was made to clarify the contradictory analyses present in the literature and to identify a doping range that avoids the onset of a secondary phase. Full article
(This article belongs to the Special Issue Optical–Electric–Magnetic Multifunctional Composite Materials)
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31 pages, 3723 KiB  
Review
Chemical Profiling and Quality Assessment of Food Products Employing Magnetic Resonance Technologies
by Chandra Prakash and Rohit Mahar
Foods 2025, 14(14), 2417; https://doi.org/10.3390/foods14142417 (registering DOI) - 9 Jul 2025
Abstract
Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR [...] Read more.
Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR is widely applied for precise quantification of metabolites, authentication of food products, and monitoring of food quality. Low-field 1H-NMR relaxometry is an important technique for investigating the most abundant components of intact foodstuffs based on relaxation times and amplitude of the NMR signals. In particular, information on water compartments, diffusion, and movement can be obtained by detecting proton signals because of H2O in foodstuffs. Saffron adulterations with calendula, safflower, turmeric, sandalwood, and tartrazine have been analyzed using benchtop NMR, an alternative to the high-field NMR approach. The fraudulent addition of Robusta to Arabica coffee was investigated by 1H-NMR Spectroscopy and the marker of Robusta coffee can be detected in the 1H-NMR spectrum. MRI images can be a reliable tool for appreciating morphological differences in vegetables and fruits. In kiwifruit, the effects of water loss and the states of water were investigated using MRI. It provides informative images regarding the spin density distribution of water molecules and the relationship between water and cellular tissues. 1H-NMR spectra of aqueous extract of kiwifruits affected by elephantiasis show a higher number of small oligosaccharides than healthy fruits do. One of the frauds that has been detected in the olive oil sector reflects the addition of hazelnut oils to olive oils. However, using the NMR methodology, it is possible to distinguish the two types of oils, since, in hazelnut oils, linolenic fatty chains and squalene are absent, which is also indicated by the 1H-NMR spectrum. NMR has been applied to detect milk adulterations, such as bovine milk being spiked with known levels of whey, urea, synthetic urine, and synthetic milk. In particular, T2 relaxation time has been found to be significantly affected by adulteration as it increases with adulterant percentage. The 1H spectrum of honey samples from two botanical species shows the presence of signals due to the specific markers of two botanical species. NMR generates large datasets due to the complexity of food matrices and, to deal with this, chemometrics (multivariate analysis) can be applied to monitor the changes in the constituents of foodstuffs, assess the self-life, and determine the effects of storage conditions. Multivariate analysis could help in managing and interpreting complex NMR data by reducing dimensionality and identifying patterns. NMR spectroscopy followed by multivariate analysis can be channelized for evaluating the nutritional profile of food products by quantifying vitamins, sugars, fatty acids, amino acids, and other nutrients. In this review, we summarize the importance of NMR spectroscopy in chemical profiling and quality assessment of food products employing magnetic resonance technologies and multivariate statistical analysis. Full article
(This article belongs to the Special Issue Quantitative NMR and MRI Methods Applied for Foodstuffs)
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15 pages, 1758 KiB  
Article
Why Empirical Forgetting Curves Deviate from Actual Forgetting Rates: A Distribution Model of Forgetting
by Nate Kornell and Robert A. Bjork
Behav. Sci. 2025, 15(7), 924; https://doi.org/10.3390/bs15070924 (registering DOI) - 9 Jul 2025
Abstract
For over a century, forgetting research has shown that recall decreases along a power or exponential function over time. It is tempting to assume that empirical forgetting curves are equivalent to the rate at which individual memories are forgotten. This assumption would be [...] Read more.
For over a century, forgetting research has shown that recall decreases along a power or exponential function over time. It is tempting to assume that empirical forgetting curves are equivalent to the rate at which individual memories are forgotten. This assumption would be erroneous, because forgetting curves are influenced by an often-neglected factor: the distribution of memory strengths relative to a recall threshold. For example, if memories with normally distributed initial strengths were forgotten at a linear rate, percent correct would not be linear, it would decrease rapidly when the peak of the distribution was crossing the recall threshold and slowly when one of the tails was crossing the threshold. We describe a distribution model of memory that explains the divergence between forgetting curves and item forgetting rates. The model predicts that forgetting curves can be approximately linear (or even concave, like the right side of a frown) when percent correct is high. This prediction was supported by previous evidence and an experiment where participants learned word pairs to a criterion. Beyond its theoretical implications, the distribution model also has implications for education: Creating memories that are just above the threshold helps on short-term tests but does not form lasting memories. Full article
(This article belongs to the Special Issue Educational Applications of Cognitive Psychology)
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21 pages, 3738 KiB  
Article
Morphologic Pattern Differences in Reconstructive Tissue Repair of Bone Defects Mediated by Bioactive Ceramics and Hydrogels: A Microscopic Follow-Up Evaluation of Re-Ossification
by Róbert Boda, Viktória Hegedűs, Sándor Manó, Andrea Keczánné-Üveges, Balázs Dezső and Csaba Hegedűs
Gels 2025, 11(7), 529; https://doi.org/10.3390/gels11070529 (registering DOI) - 9 Jul 2025
Abstract
Although publications have documented the osteo-inductive effects of various bioactive materials on tissue sections, the associated morphologic patterns of tissue remodeling pathways at the cellular level have not been detailed. Therefore, we present a comparative histopathological follow-up evaluation of bone defect repair mediated [...] Read more.
Although publications have documented the osteo-inductive effects of various bioactive materials on tissue sections, the associated morphologic patterns of tissue remodeling pathways at the cellular level have not been detailed. Therefore, we present a comparative histopathological follow-up evaluation of bone defect repair mediated by silica aerogels and methacrylate hydrogels over a 6-month period, which is the widely accepted time course for complete resolution. Time-dependent microscopic analysis was conducted using the “critical size model”. In untreated rat calvaria bone defects (control), re-ossification exclusively started at the lateral regions from the edges of the remaining bone. At the 6th month, only a few new bones were formed, which were independent of the lateral ossification. The overall ossification resulted in a 57% osseous encroachment of the defect. In contrast, aerogels (AE), hydrogels (H), and their β-tricalcium-phosphate (βTCP)-containing counterparts, which were used to fill the bone defects, characteristically induced rapid early ossification starting from the 1st month. This was accompanied by fibrous granulomatous inflammation with multinucleated giant macrophages, which persisted in decreasing intensity throughout the observational time. In addition to lateral ossification, multiple and intense intralesional osseous foci developed as early as the 1st month, and grew progressively thereafter, reflecting the osteo-inductive effects of all compounds. However, both βTCP-containing bone substituents generated larger amounts and more mature new bones inside the defects. Nevertheless, only 72.8–76.9% of the bone defects treated with AE and H and 80.5–82.9% of those treated with βTCP-containing counterparts were re-ossified by the 6th month. Remarkably, by this time, some intra-osseous hydrogels were found, and traces of silica from AE were still detectable, indicating these as the causative agents for the persistent osseous–fibrous granulomatous inflammation. When silica or methacrylate-based bone substituents are used, chronic ossifying fibrous granulomatous inflammation develops. Although 100% re-ossification takes more than 6 months, by this time, the degree of osteo-fibrous solidification provides functionally well-suited bone repair. Full article
(This article belongs to the Special Issue Innovative Applications of Gelated Matrices in Biomedical and Environmental Engineering)
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3 pages, 157 KiB  
Editorial
Organic/Inorganic Nanocomposites Based on ‘Three Pillars’ (Organic Compounds, Metal Nanoparticles, and Carbon Nanomaterials)
by Tamara Basova
Int. J. Mol. Sci. 2025, 26(14), 6578; https://doi.org/10.3390/ijms26146578 (registering DOI) - 9 Jul 2025
Abstract
A wide variety of organic molecules, ranging from simple aromatic molecules to complexes with organic ligands and polymers, offer the possibility of creating both simple and complex structures with diverse physicochemical properties [...] Full article
(This article belongs to the Special Issue Organic/Inorganic Nanocomposites on the Basis of 'Three Pillars' (Organic Compounds, Metal Nanoparticles, and Carbon Nanomaterials))
19 pages, 11070 KiB  
Article
The Effect of the Finishing Deformation Temperature on the Microstructure of CrVNb Micro-Alloyed Steel
by Gholam Ali Baqeri, Chris Killmore, Lachlan Smillie and Elena Pereloma
Materials 2025, 18(14), 3234; https://doi.org/10.3390/ma18143234 (registering DOI) - 9 Jul 2025
Abstract
This study explored the effects of the finishing deformation temperature on the microstructure and properties of CrVNb micro-alloyed steel following thermomechanical processing (TMP). The investigation encompassed the influence of the deformation temperature on the ferrite grain size, precipitate characteristics, hardness and flow stress. [...] Read more.
This study explored the effects of the finishing deformation temperature on the microstructure and properties of CrVNb micro-alloyed steel following thermomechanical processing (TMP). The investigation encompassed the influence of the deformation temperature on the ferrite grain size, precipitate characteristics, hardness and flow stress. The microstructure characterization was performed using optical and electron microscopy techniques. The results show that decreasing the deformation temperature refined the ferrite grains, though a bimodal ferrite grain structure formed when the deformation temperature fell to about 100 °C below the Ar3 temperature. Additionally, lower deformation temperatures increased the number density of strain-induced precipitates (SIPs), whereas the density of finer precipitates (random and interphase precipitates (IPs)) decreased. The highest hardness was observed in a sample deformed at 950–850 °C temperatures. These findings highlight the impact of the finishing deformation temperatures on the microstructural and mechanical properties, providing valuable insights for optimizing steel processing conditions. Full article
(This article belongs to the Special Issue Characterization of Metallic Materials: Solidification, Deformation, Heat Treatment and Other Related Phenomena—Third Edition)
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19 pages, 1788 KiB  
Article
Impact of Whole-Fruit Storage Conditions on the Quality of Minimally Processed Pears
by Vanessa Cuozzo, Eva Torres, Yanina Pariani and Ana Cecilia Silveira
Plants 2025, 14(14), 2108; https://doi.org/10.3390/plants14142108 (registering DOI) - 9 Jul 2025
Abstract
The shelf life of minimally processed fresh (MPF) pears is affected by raw material characteristics and production factors. This study evaluated the effect of raw material storage (3 months in regular atmosphere [RA], 3 and 6 months in controlled atmosphere [CA]) on the [...] Read more.
The shelf life of minimally processed fresh (MPF) pears is affected by raw material characteristics and production factors. This study evaluated the effect of raw material storage (3 months in regular atmosphere [RA], 3 and 6 months in controlled atmosphere [CA]) on the organoleptic and functional quality of MPF pears packaged in polypropylene (PP) and low-density polyethylene (LDPE) for 0, 10, and 15 days at 0 °C. Wedges from 3-month CA showed the lowest respiratory activity (about 8.31 mg CO2 kg−1 h−1), and those from 6-mounth CA maintained higher firmness after 15 days. Lightness decreased during storage, less so in harvest samples, which also showed less browning. Nevertheless, polyphenol oxidase (PPO) activity increased fivefold after 15 days. Total polyphenol content decreased by about 50% during storage. Wedges in PP packaging exhibited higher total antioxidant capacity (TAC) measured by DPPH than those in LDPE (15.55 and 13.77 mg EAA 100 g−1 FW, respectively). In both, the contents were reduced after 15 days (15–38%). No differences in TAC were observed in the FRAP assay, where values remained unchanged. Significant correlations between PPO activity, TAC, and color variables suggest ongoing oxidative processes. In contrast to the effect of raw material storage, the type of packaging did not significantly affect any of the measured variables. Full article
(This article belongs to the Special Issue Postharvest Quality and Physiology of Vegetables and Fruits)
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16 pages, 907 KiB  
Review
The RhoGDIβ-Rac1-CARD9 Signaling Module Mediates Islet β-Cell Dysfunction Under Chronic Hyperglycemia
by Anjaneyulu Kowluru and Jie-Mei Wang
Cells 2025, 14(14), 1046; https://doi.org/10.3390/cells14141046 (registering DOI) - 9 Jul 2025
Abstract
Small (monomeric) GTP-binding proteins (smgs; Cdc42 and Rac1) play requisite roles in islet beta cell function, including glucose-stimulated insulin secretion. In addition, emerging evidence suggests that sustained (constitutive) activation of smgs (e.g., Rac1) culminates in the genesis of islet beta cell dysfunction under [...] Read more.
Small (monomeric) GTP-binding proteins (smgs; Cdc42 and Rac1) play requisite roles in islet beta cell function, including glucose-stimulated insulin secretion. In addition, emerging evidence suggests that sustained (constitutive) activation of smgs (e.g., Rac1) culminates in the genesis of islet beta cell dysfunction under the duress of chronic hyperglycemia. It is noteworthy that functions (i.e., activation–deactivation) of smgs in many cells, including the islet beta cell, have been shown to be under the regulatory control of at least three factors, namely the guanine nucleotide exchange factors (GEFs), the GTPase-activating proteins (GAPs), and the GDP-dissociation inhibitors (GDIs). The overall objective of this review is to highlight our current understanding of the regulatory roles of the RhoGDIβ-Rac1-CARD9 signalome in the pathology of beta cell dysfunction under chronic hyperglycemic stress. For brevity, this review is structured by an overview of smgs and their regulatory proteins/factors in the beta cell, followed by a discussion of potential roles of the RhoGDIβ-Rac1-CARD9 axis in the onset of cellular dysfunction under the duress of metabolic stress. Overall conclusions, potential knowledge gaps, and opportunities for future research in this field of islet biology are highlighted in the last section. Full article
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17 pages, 4340 KiB  
Article
Butylated Hydroxyanisole (BHA) Disrupts Brain Signalling in Embryo–Larval Stage of Zebrafish Leading to Attention Deficit Hyperactivity Disorder (ADHD)
by Kandhasamy Veshaal, Ramasamy Vasantharekha, Usha Rani Balu, Mahesh Vallabi Aayush, Saheshnu Sai Balaji Pillai, Winkins Santosh and Barathi Seetharaman
J. Xenobiot. 2025, 15(4), 116; https://doi.org/10.3390/jox15040116 (registering DOI) - 9 Jul 2025
Abstract
Background: Butylated hydroxyanisole (BHA) has been extensively used in several commercial industries as a preservative. It causes severe cellular and neurological damage affecting the developing fetus and might induce attention deficit hyperactivity disorder (ADHD). Methods: Zebrafish embryos were subjected to five distinct doses [...] Read more.
Background: Butylated hydroxyanisole (BHA) has been extensively used in several commercial industries as a preservative. It causes severe cellular and neurological damage affecting the developing fetus and might induce attention deficit hyperactivity disorder (ADHD). Methods: Zebrafish embryos were subjected to five distinct doses of BHA—0.5, 1, 2, 4, and 8 ppb up to 96 h post fertilization (hpf). Hatching rate, heart rate, and body malformations were assessed at 48 hpf, 72 hpf, and 48–96 hpf, respectively. After exposure, apoptotic activity, neurobehavioral evaluation, neurotransmitter assay, and antioxidant activity were assessed at 96 hpf. At 120 hpf, the expression of genes DRD4, COMT, 5-HTR1aa, and BDNF was evaluated by real-time PCR. Results: BHA exposure showed a delay in the hatching rate and a decrease in the heart rate of the embryo when compared with the control. Larvae exhibited developmental deformities such as bent spine, yolk sac, and pericardial edema. A higher density of apoptotic cells was observed in BHA-exposed larvae at 96 hpf. There was a decline in catalase (CAT), glutathione peroxidase (GPx), glutathione-S-transferase (GST), and superoxide dismutase (SOD) activity, indicating oxidative stress. There was a significant decrease in Acetylcholinesterase (AChE) activity and serotonin levels with an increase in concentration of BHA, leading to a dose-responsive increase in anxiety and impairment in memory. A significant decrease in gene expression was also observed for DRD4, COMT, 5-HTR1aa, and BDNF. Conclusions: Even at lower concentrations of BHA, zebrafish embryos suffered from developmental toxicity, anxiety, and impaired memory due to a decrease in AChE activity and serotonin levels and altered the expression of the mentioned genes. Full article
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19 pages, 265 KiB  
Article
The Climate Emergency and Place-Based Action: The Case of Climate Action Leeds, UK
by Paul Chatterton and Stella Darby
Sustainability 2025, 17(14), 6274; https://doi.org/10.3390/su17146274 (registering DOI) - 9 Jul 2025
Abstract
This paper is based on our engagement in a cross-sector network in Leeds, UK, taking local climate action. It draws on in-depth engagements with participants in this network, to explore how they negotiate being in, while at the same time wanting to push [...] Read more.
This paper is based on our engagement in a cross-sector network in Leeds, UK, taking local climate action. It draws on in-depth engagements with participants in this network, to explore how they negotiate being in, while at the same time wanting to push beyond, a climate emergency. We found three emergent trends: a reworked interpretation of the climate emergency through longer-term, holistic, historically grounded, and politicised definitions; novel forms of disruptive, collaborative place leadership that could help respond to this longer emergency; and a value-based focus on a reparative ethics of self-care, people-care, and Earth-care that foregrounds climate justice and accountability to frontline communities. We end by recommending that place-based actors can enhance the effectiveness of their collective action by broadening emergency definitions, developing politics and strategy, and supporting values-based climate justice and equity. Full article
23 pages, 1403 KiB  
Article
Stakeholder Insights and Presidential Capital: Leadership Turnover and Its Impact on Higher Education
by Trina Fletcher, Ahlam Alharbi and Lesia Crumpton-Young
Educ. Sci. 2025, 15(7), 876; https://doi.org/10.3390/educsci15070876 (registering DOI) - 9 Jul 2025
Abstract
Historically Black colleges and universities (HBCUs) in the United States have been experiencing a leadership turnover crisis, with 23 president and chancellor changes announced in 2022 and 41 in 2023. A survey of HBCU stakeholders at the 2023 White House Initiative on HBCUs [...] Read more.
Historically Black colleges and universities (HBCUs) in the United States have been experiencing a leadership turnover crisis, with 23 president and chancellor changes announced in 2022 and 41 in 2023. A survey of HBCU stakeholders at the 2023 White House Initiative on HBCUs was conducted to identify high-impact areas linked to this turnover, focusing on areas critical to the advancement and sustainment of HBCUs through the eyes of HBCU stakeholders. Additionally, it attempted to understand how campus dynamics and challenges can impact leaders using capital theory. The survey identified internal and external challenges, including engagement, morale, support, and retention across various stakeholders, suggesting that the turnover crisis needs to be viewed from the perspective of leaders’ turnover rather than leadership turnover. It was concluded that leaders’ forms of capital are compromised by misaligned campus dynamics, negatively impacting morale and engagement, leading to distrust, lack of support, pushback, and attrition. Therefore, leaders’ capitals can be depleted, leading to frustration, burnout, and ultimately voluntary resignation. The findings are crucial for institutions and leaders to understand and, most importantly, mitigate the impact of leader turnover on institutions, which demand stability. Full article
(This article belongs to the Special Issue Enhancing Educational Leadership and Management for Effective Education Policy Implementation)
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