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17 pages, 326 KB

Open AccessArticle

Indoor Environmental Determinants of Depression: A New Approach to Understanding Mental Health

by Gintare Kaliniene, Ruta Ustinaviciene, Rasa Zutautiene, Jolita Kirvaitiene, Abdonas Tamosiunas, Vaiva Lesauskaite and Dalia Luksiene

Medicina 2026, 62(3), 496; https://doi.org/10.3390/medicina62030496 (registering DOI) - 6 Mar 2026

Abstract

Background and Objectives: Depression has emerged in recent years as a significant global health issue, drawing considerable research interest and attention. The development of depression could be impacted by a range of environmental factors. Aim: To investigate the relationship between depressive symptoms and various [...] Read more.

Background and Objectives: Depression has emerged in recent years as a significant global health issue, drawing considerable research interest and attention. The development of depression could be impacted by a range of environmental factors. Aim: To investigate the relationship between depressive symptoms and various indoor environmental factors, such as microclimate, odors, mold, and room ventilation, in association with some sociodemographic and lifestyle factors. Materials and Methods: This epidemiological health survey of the study “Chronic diseases and their risk factors in the adult population” was performed during 2023–2024 in Kaunas city (Lithuania) following the methodology of the WHO MONICA study. A random sample of Kaunas inhabitants aged 25–69 years, stratified by sex and age, was randomly selected from the Lithuanian population register. The 3426 individuals were screened. The associations of various indoor environmental factors with depressive symptoms were investigated using binary logistic regression analysis. Results: Depressive symptoms were associated with sociodemographic, lifestyle, and indoor environmental factors. Poor microclimate conditions, unpleasant household odors, mold exposure, and insufficient room ventilation were associated with increased odds of depressive symptoms. The significance of these associations varied across sex, age, marital status, socioeconomic status, and physical activity of responders. Additional multivariable logistic regression analyses, including interaction terms between each indoor environmental factor and the stratification variables (sex, age groups, marital status, family economic situation, and physical activity), were performed. Significant interaction was found only between family status and room ventilation (p = 0.007). This indicates that the association between ventilation and depressive symptoms differed by family status. Conclusions: This study contributes to the cross-disciplinary understanding of the role of indoor environmental quality, sociodemographic, and lifestyle factors in the development of depression, adding to the evidence on the role of other factors in depression inequalities. Full article

(This article belongs to the Section Epidemiology & Public Health)

26 pages, 19729 KB

Open AccessArticle

Comparative Analysis of Different ZnO Particles as Additives of Bio-Based Andiroba, Copaiba, and Paraffinic Mineral Oils: Effects on Lubrication Properties

by Erickson Fabiano Moura Sousa Silva, Anielle Christine Almeida Silva, Vicente Afonso Ventrella, Victor Hugo Martins de Almeida, Ivan Bezerra Allaman, Thaís Marcelo Souza, Eli Jorge da Cruz Júnior and Aparecido Carlos Gonçalves

Sustainability 2026, 18(5), 2573; https://doi.org/10.3390/su18052573 (registering DOI) - 6 Mar 2026

Abstract

The growing demand for environmentally responsible lubricants motivates the use of bio-based base stocks and benign solid additives. This study assesses the tribological performance of two Amazonian vegetable oils, Carapa guianensis (andiroba) and Copaifera spp. (copaiba resin) and a paraffinic mineral oil (PNL30) [...] Read more.

The growing demand for environmentally responsible lubricants motivates the use of bio-based base stocks and benign solid additives. This study assesses the tribological performance of two Amazonian vegetable oils, Carapa guianensis (andiroba) and Copaifera spp. (copaiba resin) and a paraffinic mineral oil (PNL30) formulated with different zinc oxide (ZnO) particles, namely nanocrystals and microcrystals, at 0.01, 0.05, and 0.10 wt.%. Reciprocating sliding tests, coupled with 3D profilometry, viscosity, and sedimentation analyses, were used to link dispersion stability with friction and wear responses. A preliminary stability screening constrained the practical loading window to ≤0.10 wt.% for reproducible suspensions. Performance depended on the interplay between particle type and base-oil chemistry. Andiroba exhibited the most pronounced gains, with ZnO microcrystals near 0.05 wt.% delivering the best friction outcomes and the largest wear reductions (up to ~35%). In copaiba resin oil, nanocrystals produced small, sometimes non-significant improvements, whereas microcrystals tended to worsen wear consistent with abrasive third-body effects in a less polar matrix. In PNL30, the overall benefits were modest: nanocrystal additions generally increased wear, whereas microcrystals particularly at the highest loading 0.10 wt.% achieved a 36.4% reduction in SWR, representing a measurable and statistically significant improvement in wear resistance. These results highlight that eco-efficient lubricant design should co-optimize particle characteristics and dosage with base-oil polarity and film-forming tendencies, prioritizing dispersion stability alongside tribological targets. Full article

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26 pages, 1318 KB

Open AccessArticle

Governance and Fiscal Sustainability: Evidence from Developed and Emerging Economies

by Seydou Nourou Ndiaye, Zakari-yaou Doulla Harouna, Adama Sow Badji and Babacar Sène

Int. J. Financial Stud. 2026, 14(3), 70; https://doi.org/10.3390/ijfs14030070 (registering DOI) - 6 Mar 2026

Abstract

The quality of governance is a key driver of resource mobilisation in a context marked by successive shocks that exacerbate fiscal imbalances. This study aims to analyse the role of institutional quality in the relationship between public expenditure and tax revenue in a [...] Read more.

The quality of governance is a key driver of resource mobilisation in a context marked by successive shocks that exacerbate fiscal imbalances. This study aims to analyse the role of institutional quality in the relationship between public expenditure and tax revenue in a panel of 162 countries, broken down into developed and emerging economies between 2000 and 2023. Using causality tests and the cross-sectional autoregressive model with staggered lags (CS-ARDL) to control for cross-sectional heterogeneity and cross-dependence, the results reveal a bidirectional causality linking expenditure and revenue for the entire panel; emerging countries are more sensitive to fiscal policies; public expenditure significantly stimulates tax revenue in the short and long term, with an effect amplified by institutional quality; long-term sustainability depends crucially on the institutional framework. This study highlights the need for targeted institutional reforms and fiscal rules differentiated according to countries’ level of economic development. Full article

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20 pages, 9407 KB

Open AccessSystematic Review

A Systematic Review of River Discharge Measurement Methods: Evolution and Modern Applications in Water Management and Environmental Protection

by Oscar Abel González-Vergara, María Teresa Alarcón-Herrera, Ana Elizabeth Marín-Celestino, Armando Daniel Blanco-Jáquez, Joel García-Pazos, Samuel Villarreal-Rodríguez, Yolocuauhtli Salazar and Diego Armando Martínez-Cruz

Earth 2026, 7(2), 41; https://doi.org/10.3390/earth7020041 (registering DOI) - 6 Mar 2026

Abstract

Accurate river discharge estimation is fundamental for water resource management under increasingly variable hydrological conditions. While conventional in situ techniques remain hydrometric reference standards, their operational deployment is constrained by cost, accessibility, and limited spatial coverage. Advances in remote sensing and artificial intelligence [...] Read more.

Accurate river discharge estimation is fundamental for water resource management under increasingly variable hydrological conditions. While conventional in situ techniques remain hydrometric reference standards, their operational deployment is constrained by cost, accessibility, and limited spatial coverage. Advances in remote sensing and artificial intelligence (AI) have introduced non-contact discharge estimation frameworks based on image-derived observations. This systematic review, conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 reporting guidelines, examines the evolution of river discharge measurement methods between 2004 and 2024 through a structured two-stage design. An initial search in Web of Science and Scopus identified 2809 records, of which 249 were retained for first-stage synthesis. A focused second-stage screening isolated seven studies that directly integrate image-based data with machine learning or deep learning architectures for discharge estimation. The analysis reveals a methodological transition from instrument-based hydrometry toward computationally assisted, image-driven approaches. The retained studies employ close-range and satellite imagery combined with Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and related models. Although reported validation metrics indicate strong predictive capability under specific conditions, performance remains dependent on site-specific calibration and reference discharge records. Broader operational deployment requires improved transferability, uncertainty integration, and cross-basin validation. Full article

(This article belongs to the Topic Climate Change and Human Impact on Freshwater Water Resources: Rivers and Lakes, 2nd Edition)

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15 pages, 3209 KB

Open AccessArticle

An NMR-Based Protocol for Profiling the Endo- and Exo-Metabolomes in Aβ_1-42 Treated Human Astrocytes from Healthy and Alzheimer’s Disease Donors

by Elisa Bientinesi, Alessia Vignoli, Sara Ristori, Maria Salobehaj, Gianmarco Bertoni, Daniela Monti and Leonardo Tenori

Metabolites 2026, 16(3), 173; https://doi.org/10.3390/metabo16030173 (registering DOI) - 6 Mar 2026

Abstract

Background/Objectives: Astrocytes play a critical role in maintaining brain homeostasis and are increasingly recognized as active contributors to neurodegenerative processes. Metabolic dysfunction in astrocytes has been implicated in the onset and progression of Alzheimer’s disease (AD), yet the underlying metabolic alterations remain [...] Read more.

Background/Objectives: Astrocytes play a critical role in maintaining brain homeostasis and are increasingly recognized as active contributors to neurodegenerative processes. Metabolic dysfunction in astrocytes has been implicated in the onset and progression of Alzheimer’s disease (AD), yet the underlying metabolic alterations remain poorly characterized. Methods: We used an optimized protocol for untargeted metabolomic profiling of both intracellular and extracellular compartments of primary human astrocytes derived from AD patients and healthy subjects (HS) using ¹H nuclear magnetic resonance (NMR) spectroscopy. Cells were treated with oligomeric Aβ_1-42 to model pathological conditions. Results: Aβ_1-42 treatment induced intracellular metabolic alterations in both AD and HS astrocytes, including a consistent reduction in phosphocreatine, potentially indicating impaired energy-buffering capacity. Notably, a decrease in β-alanine was observed only in AD astrocytes, suggesting alterations in carnosine-related antioxidant defence. Analysis of conditioned media revealed differential responses between groups: AD astrocytes showed increased extracellular levels of 2-oxoglutarate, citrate, and glycine, whereas HS astrocytes exhibited reduced extracellular levels of leucine and isoleucine, suggesting distinct adaptive metabolic responses to Aβ-induced stress. However, none of these differences remained statistically significant after correction for multiple testing. Conclusions: These findings suggest that NMR-based metabolomics can detect subtle metabolic shifts in human astrocyte models of AD and HS exposed to amiloidogenic challenge. Given the limited sample size and the exploratory design adopted, the results should be interpreted as preliminary and require validation in larger, better-matched cohorts. Nevertheless, this study provides a methodological framework and generates biologically plausible hypotheses regarding astrocyte metabolic responses relevant to AD pathophysiology. Full article

(This article belongs to the Special Issue Advances in NMR- and MS-Based Metabolomics and Its Applications)

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17 pages, 1438 KB

Open AccessReview

Mapping High-Level Evidence in Neuroanesthesia: A Scoping Review of Multicenter Randomized Controlled Trials in Anesthesia for Neurosurgery

by Mouad Elganga, Abramo Aziz Rizk and Tumul Chowdhury

J. Clin. Med. 2026, 15(5), 2012; https://doi.org/10.3390/jcm15052012 (registering DOI) - 6 Mar 2026

Abstract

Background/Objectives: Anesthesia for intracranial neurosurgery presents unique challenges because of the sensitivity of the brain to perioperative physiological disturbances, yet neuroanesthetic practice remains highly variable and supported by a limited high-level evidence base. We conducted a scoping review to map and characterize [...] Read more.

Background/Objectives: Anesthesia for intracranial neurosurgery presents unique challenges because of the sensitivity of the brain to perioperative physiological disturbances, yet neuroanesthetic practice remains highly variable and supported by a limited high-level evidence base. We conducted a scoping review to map and characterize multicenter randomized controlled trials (RCTs) evaluating perioperative management strategies in adults undergoing intracranial neurosurgery. Methods: This scoping review was reported in accordance with the PRISMA extension for Scoping Reviews. MEDLINE, PubMed, EMBASE, Cochrane Central, and Web of Science were searched from inception to 25 June 2025. Multicenter RCTs enrolling adults undergoing intracranial neurosurgery and evaluating anesthetic, hemodynamic, ventilatory, or perioperative interventions were included. We prioritized mapping multicenter designs for their greater external validity and implementation potential. Data were extracted in duplicate and summarized descriptively. Results: Of 417 records identified, 13 multicenter trials (≥2 recruiting sites) involving 2765 participants across nine countries from 1997–2025 were included. Most trials evaluated anesthetic maintenance or opioid regimens (7/13), followed by post-craniotomy pain control (3/13), ventilation/brain relaxation strategies (1/13), antiemetic prophylaxis (1/13), and temperature management (1/13). Outcomes were predominantly short-term and process-based (hemodynamics 7/13, opioid use 7/13, emergence metrics 5/13). Patient-centered outcomes were rarely measured (mortality 1/13, functional neurological outcome 1/13, cognitive outcome 1/13; quality of life 0/13). Only one trial assessed outcomes at ≥72 h postoperatively. Over half of the included trials were judged at high risk of bias. Conclusions: Multicenter RCT activity in neuroanesthesia remains sparse and narrowly focused, highlighting the need for larger, methodologically robust trials targeting patient-centered and long-term outcomes. Full article

(This article belongs to the Special Issue Anesthesia and Intensive Care: Clinical Practices and Prospects)

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34 pages, 615 KB

Open AccessArticle

Selective Openness as a Sustainable Strategy: How Culinary MSMEs Organize Collaboration Across the Value Chain in an Emerging Economy

by Lia Senda Riyanti, Dina Dellyana, Sri Wahyuni, Kristi Kartika and Ahmad Basori

Sustainability 2026, 18(5), 2572; https://doi.org/10.3390/su18052572 (registering DOI) - 6 Mar 2026

Abstract

Culinary micro-, small-, and medium-sized enterprises (MSMEs) play a critical role in socio-economic sustainability, yet operate under heightened risks related to product quality, food safety, and business reputation. Although research on collaboration and co-creation often portrays openness as a desirable organizational orientation, limited [...] Read more.

Culinary micro-, small-, and medium-sized enterprises (MSMEs) play a critical role in socio-economic sustainability, yet operate under heightened risks related to product quality, food safety, and business reputation. Although research on collaboration and co-creation often portrays openness as a desirable organizational orientation, limited attention has been paid to how MSMEs strategically regulate openness across their value-chain activities. This study explores how culinary MSMEs organize collaboration and negotiate boundaries between openness and closure. Using a qualitative multi-case approach, the findings show that openness is not uniformly applied but governed by a logic of selective openness shaped by risk exposure, accountability demands, and organizational capacity constraints. Core production activities function as protected zones characterized by strong closure, whereas market-facing functions allow more curated forms of external involvement. By reframing openness as a risk-based governance practice rather than a default collaboration strategy, this study provides a process-level explanation of how MSMEs sustain collaboration without compromising quality control, accountability, and organizational viability. The findings contribute to theorizing boundary governance in small enterprises and offer practical insights for designing collaborative strategies under conditions of risk and constraint. Full article

(This article belongs to the Special Issue Sustainable Strategy in Entrepreneurship and in Small and Medium-Sized Enterprises)

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22 pages, 2411 KB

Open AccessReview

Granular Jamming in Soft Robotics: Simulation Frameworks and Emerging Possibilities—Review

by Stella Hrehova, Alexander Hošovský, Jozef Husár and Tibor Krenický

Biomimetics 2026, 11(3), 193; https://doi.org/10.3390/biomimetics11030193 (registering DOI) - 6 Mar 2026

Abstract

Soft robotics has become a dynamic field that emphasizes adaptability and safe interaction with complex environments. These structures utilize deformable materials and continuum mechanics to adapt their shape, absorb shocks, and perform tasks in unstructured environments. However, the design and optimization of these [...] Read more.

Soft robotics has become a dynamic field that emphasizes adaptability and safe interaction with complex environments. These structures utilize deformable materials and continuum mechanics to adapt their shape, absorb shocks, and perform tasks in unstructured environments. However, the design and optimization of these systems is challenging, primarily due to the nonlinear and discontinuous behavior of granular materials. In this paper, we address the role of simulation frames as an important tool for understanding, designing, and extending the functionality of software robotic devices utilizing granular jamming. The analysis suggests that DEM is essential for capturing particle-level mechanisms, while FEM is more effective for system-level optimization but tends to smooth out the transition of jamming. Hybrid FEM–DEM approaches provide the highest physical accuracy, albeit at an increased computational cost. Overall, the findings emphasize that the choice of framework must be application-oriented and that multiphysics coupling represents the future development. The review gives an up-do-date review of the simulation tools and approaches for granular-jamming-based systems with a specific focus on continuum arms with a granular-jamming-based central backbone. Such methods can be used for the optimization the back-bone geometry and its filling material (shape, porosity, granule size) with possible use in the real-time control of such arms. Full article

(This article belongs to the Section Locomotion and Bioinspired Robotics)

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13 pages, 1742 KB

Open AccessArticle

Automated Laser-Optical Setup for Seed Monitoring over Time

by José L. Contado, Dimitri Viana, Bruno Vicentini, Antônio A. A. Chepluki and Roberto A. Braga

AgriEngineering 2026, 8(3), 99; https://doi.org/10.3390/agriengineering8030099 (registering DOI) - 5 Mar 2026

Abstract

The biospeckle laser (BSL) technique is recognized as a sensitive method for detecting biological activity and has been successfully applied for seed vigor testing. Given these achievements, whether the integration of BSL into automated systems can provide complementary information on the seed imbibition [...] Read more.

The biospeckle laser (BSL) technique is recognized as a sensitive method for detecting biological activity and has been successfully applied for seed vigor testing. Given these achievements, whether the integration of BSL into automated systems can provide complementary information on the seed imbibition process remains limited. Addressing this gap represents a significant challenge with strong potential for technological innovation. This study presents an automated laser-optical system designed to monitor the imbibition process of multiple seeds over time using a mechanized carousel. The developed apparatus integrates all necessary components for the illumination and image acquisition of eight seeds across programmable time intervals, controlled by an industrial-grade programmable controller. Validation using maize seeds (Zea mays L.) over a 36-h period confirmed the system’s reliability. BSL indices enabled the characterization of internal biological activity throughout imbibition, revealing dynamic processes that remained undetected in previous discrete-time analyses. These results highlight the potential of the proposed system for more comprehensive and continuous seed monitoring. The successful automated laser-optical system with relative humidity control opens great potential in seeds research and daily industrial analysis. The test of the proposed system in other seeds is the next challenge, regarding the thick and colored coats. The design of larger carousels is a possible step forward, which will demand studies of the limits linked to the illumination and image acquisition time performed in each seed. Full article

(This article belongs to the Special Issue Digital Agriculture 5.0: A New Perspective in Agricultural Engineering)

24 pages, 720 KB

Open AccessArticle

Sustainability-Oriented Digital Transformation Under Industry 4.0: Managerial Perceptions of Digitalization and AI

by Claudia-Diana Sabău-Popa, Diana-Claudia Perțicaș, Adrian-Gheorghe Florea, Roxana Hatos and Hillary Wafula Juma

Sustainability 2026, 18(5), 2570; https://doi.org/10.3390/su18052570 (registering DOI) - 5 Mar 2026

Abstract

This study investigates managers’ perceptions of digitalization and artificial intelligence (AI) adoption within the framework of Industry 4.0, emphasizing the relationship between technological modernization, organizational culture, and sustainability. Drawing on empirical data collected in 2025 from 150 Romanian companies ’managers by applying a [...] Read more.

This study investigates managers’ perceptions of digitalization and artificial intelligence (AI) adoption within the framework of Industry 4.0, emphasizing the relationship between technological modernization, organizational culture, and sustainability. Drawing on empirical data collected in 2025 from 150 Romanian companies ’managers by applying a structured questionnaire, followed by a multivariate analytical approach supported by the Benjamini–Hochberg correction, the research identifies critical managerial perceptions that influence the success of digital transformation. The findings show that managers recognize digitalization as a strategic opportunity for process optimization and competitiveness. At the same time, they perceive it as a structural challenge driven by legacy systems, financial constraints, and limited digital competencies. Similarly, managers view AI as a valuable tool for data analysis and market forecasting, while also expressing concerns related to ethical, technical, and cybersecurity risks. The study further reveals managerial ambivalence toward Industry 4.0. Although automation and IoT are considered inevitable for maintaining competitiveness, their implementation remains constrained by logistical and cultural barriers. By integrating technological, organizational, and human dimensions, this research contributes to the literature on sustainable digital transformation. It provides an in-depth understanding of how managerial perceptions mediate the balance between innovation, responsibility, and long-term resilience. Finally, the results offer actionable insights for policymakers and business leaders seeking to align digitalization and AI initiatives with sustainable development objectives. Full article

(This article belongs to the Section Economic and Business Aspects of Sustainability)

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13 pages, 4984 KB

Open AccessArticle

Fieldwork in Physical Geography: A Quantitative Analysis, Perceptions, and Implications

by Néstor Campos and Adolfo Quesada-Román

Geographies 2026, 6(1), 28; https://doi.org/10.3390/geographies6010028 (registering DOI) - 5 Mar 2026

Abstract

Traditional fieldwork in Physical Geography courses is considered a key activity to fix concepts and ideas taught in class. Unfortunately, it is a complex and expensive activity. Over recent decades, with the advancement and emergence of new technological tools, part of the traditional [...] Read more.

Traditional fieldwork in Physical Geography courses is considered a key activity to fix concepts and ideas taught in class. Unfortunately, it is a complex and expensive activity. Over recent decades, with the advancement and emergence of new technological tools, part of the traditional fieldwork has been replaced by virtual fieldwork techniques. In this study, we analyzed and evaluated the perceptions of the students in relation to the traditional fieldwork, focusing on the reinforcement of the concepts taught in class. After several extensive fieldwork campaigns, we evaluated a group of Physical Geography students through tests, which assessed perceptions related to learning enhancement, skill acquisition, motivation and environmental awareness, and we confirmed that the traditional fieldwork allowed the students not only to reinforce their knowledge, but also to acquire new skills and improve their understanding of the importance of environmental conservation. Full article

(This article belongs to the Special Issue Geography as a Transdisciplinary Science in a Changing World)

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23 pages, 7862 KB

Open AccessArticle

UAV Cooperative Search Method Based on Asynchronous Collaborative Hybrid Architecture Under Urban Communication Constraints

by Xiefang Lin, Tingting Bai, Xiqiang Liu and Yong Liu

Drones 2026, 10(3), 179; https://doi.org/10.3390/drones10030179 (registering DOI) - 5 Mar 2026

Abstract

Cooperative searches by unmanned aerial vehicles (UAVs) have wide applications in urban environments. However, the dense obstacles and limited communication networks in urban settings often lead to repeated searches and inefficient information sharing among UAVs. To address these challenges, this article proposes a [...] Read more.

Cooperative searches by unmanned aerial vehicles (UAVs) have wide applications in urban environments. However, the dense obstacles and limited communication networks in urban settings often lead to repeated searches and inefficient information sharing among UAVs. To address these challenges, this article proposes a novel cooperative strategy named the Asynchronous Collaborative Hybrid Architecture (ACHA), which is tailored for urban flight. Specifically, a digital pheromone mechanism is devised to create artificial potential field to guide UAVs’ search efficiently within local communication constraints. Moreover, UAVs switch between the dual decision mode, namely Chain-Following Mode (CFM) and Tree Expansion Mode (TEM) based on the urban environmental topology. When UAVs arrive at a bifurcation node, the TEM is activated, asynchronously triggering the Collaborative-aware Pruning Search Tree (CPT) algorithm to generate subsequent paths, after which they switch back to CFM. Theoretically, it is demonstrated that the collaborative-aware pruning scheme can avoid the “cooperative benefit trap”, where there is a significant divergence between the actual and predicted cooperative benefits. The simulation results confirm that the proposed method outperforms existing approaches in terms of cooperative search accuracy, collision risk and convergence speed in complex urban search scenarios. Full article

(This article belongs to the Special Issue UAV Swarm Intelligent Control and Decision-Making)

28 pages, 3763 KB

Open AccessArticle

Directional Access to the Sky as a Criterion of Residential Environmental Quality in Sustainable Urban Design

by Zdzisław Pelczarski and Michał Pelczarski

Sustainability 2026, 18(5), 2569; https://doi.org/10.3390/su18052569 (registering DOI) - 5 Mar 2026

Abstract

Access to the sky is a key element of residential environmental quality. In densely built-up urban areas, exposure to the sky is often limited not only quantitatively but, above all, directionally. Traditional illuminance metrics, such as the Sky View Factor (SVF) or Daylight [...] Read more.

Access to the sky is a key element of residential environmental quality. In densely built-up urban areas, exposure to the sky is often limited not only quantitatively but, above all, directionally. Traditional illuminance metrics, such as the Sky View Factor (SVF) or Daylight Factor (DF), describe the proportion of visible sky or the amount of light in an averaged manner, without considering its relationship to the functional organisation of the human field of view.This article introduces the Relative Retinal Image (RRI) metric, which evaluates directional access to the sky through geometric analysis of viewing directions in relation to functional zones of the visual field, without reconstructing perceived images or simulating physiological processes. Within this geometric framework, human vision is interpreted as operating simultaneously in two visual cones: a narrow central cone responsible for acute, conscious vision (RRI-A), and a wider peripheral cone enabling the reception of low-resolution but spatially stable stimuli (RRI-B). For clarity, three concentric central ranges are distinguished: foveal (0–2.5°), sharp central (0–5°), and extended interpretative central vision (up to 10°). The proposed approach provides a geometry-based analytical tool that complements existing daylight metrics in the assessment of sustainable residential environments, without formulating normative or biological design prescriptions. Based on geometric and graphical analyses and a case study of the Józefowiec housing estate in Katowice, the results indicate that the directional structure of the sky view may be lost despite compliance with conventional planning criteria. Full article

(This article belongs to the Special Issue Shaping Sustainable Environments: Advances in Building and Infrastructure Design)

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30 pages, 1180 KB

Open AccessFeature PaperArticle

Solving the Road-Rail Intermodal Network Design Problem: A Novel 0-1 Nonlinear Model to Consider Carbon Emission Policies

by Yufei Meng, Zhenyu Wang and Boliang Lin

Mathematics 2026, 14(5), 893; https://doi.org/10.3390/math14050893 (registering DOI) - 5 Mar 2026

Abstract

In recent years, climate change has become increasingly urgent, and governments are intensifying efforts to regulate carbon-intensive industries through policy innovations. The transport sector faces particularly acute decarbonisation challenges due to its reliance on fossil fuels. This study investigates road-rail intermodal transport as [...] Read more.

In recent years, climate change has become increasingly urgent, and governments are intensifying efforts to regulate carbon-intensive industries through policy innovations. The transport sector faces particularly acute decarbonisation challenges due to its reliance on fossil fuels. This study investigates road-rail intermodal transport as a strategic solution that synergises the flexibility of trucking with the superior energy efficiency of rail. A novel arc-path 0-1 nonlinear model is developed, optimising profit maximisation while incorporating hard constraints on transport due dates. The predominant carbon emission policies—command-and-control regulations and carbon pricing mechanisms—are analysed, and the corresponding extended models are constructed. Next, the linearisation techniques are introduced. In the end, a numerical example is built to test the validity of the model and compare the optimisation decisions of the basic model and the extended models. Furthermore, a sensitivity analysis of key parameters is conducted to provide operational recommendations for enterprises to balance carbon emissions and profits. Full article

24 pages, 2554 KB

Open AccessArticle

An Adaptive Extraction Method for Knitted Patterns Based on Bayesian-Optimized Bilateral Filtering

by Xin Ru, Yanhao Wang, Laihu Peng and Jianqiang Li

Appl. Sci. 2026, 16(5), 2526; https://doi.org/10.3390/app16052526 (registering DOI) - 5 Mar 2026

Abstract

Extracting standardized digital design patterns from real knitted fabric images is critical for textile reverse engineering and digital archiving. Unlike smooth graphics, knitted fabrics exhibit high-frequency textures from yarn loop interlacing, introducing significant grayscale variations within same-color regions. Existing algorithms struggle to distinguish [...] Read more.

Extracting standardized digital design patterns from real knitted fabric images is critical for textile reverse engineering and digital archiving. Unlike smooth graphics, knitted fabrics exhibit high-frequency textures from yarn loop interlacing, introducing significant grayscale variations within same-color regions. Existing algorithms struggle to distinguish these from pattern edges, causing color quantization and segmentation failures. To suppress yarn texture while preserving edges between color blocks, we propose an adaptive pattern extraction method using Bayesian-optimized bilateral filtering. The primary contribution lies in providing a domain-specific, application-focused integrated framework. Specifically, (1) a knitting-texture-aware multidimensional evaluation parameter is constructed by integrating physical-cause-based texture features (gray-level co-occurrence matrix (GLCM) contrast, homogeneity, and Laplacian variance) with perception-based edge preservation metrics (the Sobel operator and the structural similarity index (SSIM)), enabling accurate discrimination between yarn-level texture noise and pattern-level color block boundaries—a distinction that generic image quality metrics cannot make. (2) Then, this domain-specific objective function is embedded within a Bayesian optimization framework to achieve automatic, zero-shot, per-image parameter adaptation across different knitting processes, without requiring any external training data. K-means color quantization maps in continuous tones to discrete classes, generating standardized patterns meeting knitting requirements. Experiments on 316 samples covering six processes show our method outperforms standard denoising and advanced algorithms like relative total variation (RTV), achieving an average SSIM of 0.83 and PSNR of 26.92 dB, reducing processing time from 15–30 min to 21 s per image, providing efficient automation for knitted Computer-Aided Design (CAD) systems. Full article

17 pages, 4802 KB

Open AccessArticle

miR-let-7 Targeting ZcCTL-S1 to Regulate Reproductive Development in Zeugodacus cucurbitae

by Yi-Kun Zhang, Guo-Feng Zhang, Li-Xiang Chen, Yu-Xue Zhang, Shi-Yuan Wang, Ke-Qing Deng, Lai-Wai Tun, Zhong-Shi Zhou and Lu Peng

Insects 2026, 17(3), 286; https://doi.org/10.3390/insects17030286 (registering DOI) - 5 Mar 2026

Abstract

The melon fly, Zeugodacus cucurbitae (Coquillett), is recognized as a globally significant quarantine pest, and it ranks among the most destructive insect species infesting cucurbit and solanaceous crops. However, the molecular mechanisms governing reproductive regulation in female Z. cucurbitae remain poorly characterized, [...] Read more.

The melon fly, Zeugodacus cucurbitae (Coquillett), is recognized as a globally significant quarantine pest, and it ranks among the most destructive insect species infesting cucurbit and solanaceous crops. However, the molecular mechanisms governing reproductive regulation in female Z. cucurbitae remain poorly characterized, particularly those underlying the reproductive processes mediated by microRNAs (miRNAs). In this study, we firstly identified the ovary-specific gene ZcCTL-S1 in Z. cucurbitae via transcriptomic analysis, and subsequently predicted its targeted miRNAs using bioinformatics approaches. Among these miRNAs, overexpression or inhibition of miR-971-1 and miR-let-7 led to corresponding inverse changes in the transcriptional level of ZcCTL-S1. Notably, only miR-let-7 displayed markedly elevated expression levels in Z. cucurbitae ovaries. Further analyses confirmed that miR-let-7 exhibited a direct targeting relationship with ZcCTL-S1, via a combinatorial approach involving in vivo RNA immunoprecipitation, in vitro dual-luciferase reporter assays, and site-directed mutagenesis techniques. Phenotypic analyses showed that both knockdown of ZcCTL-S1 and overexpression of miR-let-7 significantly inhibited egg hatchability, ultimately compromising the female reproductive capacity of Z. cucurbitae. Collectively, these findings identify a novel miRNA-gene regulatory module in the reproductive development of Z. cucurbitae, and provide novel insights for the development of gene- or miRNA-based pest control strategies. Full article

(This article belongs to the Section Insect Molecular Biology and Genomics)

24 pages, 2685 KB

Open AccessArticle

Research on an Intelligent Scheduling Method Based on GCN-AM-LSTM for Bus Passenger Flow Prediction

by Xiaolei Ji, Zhe Li, Zhiwei Guo, Haotian Li and Hongpeng Nie

Appl. Sci. 2026, 16(5), 2525; https://doi.org/10.3390/app16052525 (registering DOI) - 5 Mar 2026

Abstract

With the acceleration of urbanization, public transit systems face prominent challenges, including insufficient passenger flow prediction accuracy and low scheduling efficiency. This study analyzes passenger flow variation patterns from both spatial and temporal dimensions, constructs spatiotemporal matrices, and employs matrix dimensionality reduction methods [...] Read more.

With the acceleration of urbanization, public transit systems face prominent challenges, including insufficient passenger flow prediction accuracy and low scheduling efficiency. This study analyzes passenger flow variation patterns from both spatial and temporal dimensions, constructs spatiotemporal matrices, and employs matrix dimensionality reduction methods to extract key features. We propose a passenger flow prediction model based on GCN-AM-LSTM and a dynamic real-time intelligent scheduling strategy. For passenger flow prediction, the model first utilizes Graph Convolutional Networks (GCNs) to extract spatial features of the transit network, then employs Attention Mechanism-enhanced Long Short-Term Memory networks (AM-LSTM) to perform weighted extraction of temporal features, and finally integrates external factors such as weather conditions to generate prediction outputs. For scheduling optimization, a dynamic real-time scheduling mode is adopted: the foundational framework optimizes dynamic departure timetables using a multi-objective particle swarm optimization algorithm, which is then combined with real-time passenger flow data to adjust departure intervals at the route level and implement stop-skipping strategies at the station level. Validation was conducted using Xiamen BRT Line 1 as a case study. Experimental results demonstrate that the proposed GCN-AM-LSTM prediction model reduces Mean Absolute Error (MAE) by 14% and 22% compared to CNN and LSTM models, respectively, achieving significantly improved prediction accuracy. Regarding scheduling optimization, the number of departures decreased by 15.24%, passenger waiting time costs were reduced by 3.7%, and transit operating costs decreased by 3.19%, effectively balancing service quality and operational efficiency. Full article

(This article belongs to the Special Issue Research and Estimation of Traffic Flow Characteristics)

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27 pages, 1809 KB

Open AccessArticle

A Stability-Aware Adaptive Fractional-Order Speed Control Framework for IPMSM Electric Vehicles in Field-Weakening Operation

by Chih-Chung Chiu, Wei-Lung Mao and Feng-Chun Tai

Energies 2026, 19(5), 1326; https://doi.org/10.3390/en19051326 (registering DOI) - 5 Mar 2026

Abstract

High-performance speed regulation of interior permanent magnet synchronous motor (IPMSM) drives in electric vehicle (EV) applications becomes particularly challenging in the field-weakening region, where voltage constraints, parameter variations, and nonlinear aerodynamic loads significantly affect the closed-loop stability. To address these challenges, this paper [...] Read more.

High-performance speed regulation of interior permanent magnet synchronous motor (IPMSM) drives in electric vehicle (EV) applications becomes particularly challenging in the field-weakening region, where voltage constraints, parameter variations, and nonlinear aerodynamic loads significantly affect the closed-loop stability. To address these challenges, this paper proposes a stability-aware adaptive fractional-order speed control framework for EV traction systems. The framework integrates a fractional-order PI (FOPI) core to provide iso-damping robustness, a bounded fuzzy gain-scheduling mechanism for real-time adaptation, and an offline multi-objective optimization layer for systematic parameter tuning. A Lyapunov-based qualitative analysis is provided to justify closed-loop ultimate boundedness under adaptive gain modulation and field-weakening constraints. The fuzzy scheduler is explicitly structured to regulate the error energy dissipation rate by modulating the proportional and integral gains while preserving the gain boundedness. The controller parameters are optimized using a diversity-driven fractional-order multi-objective PSO algorithm to balance the tracking accuracy and control effort. The proposed framework was validated using a high-fidelity MATLAB/Simulink–CarSim 2023 co-simulation platform under the aggressive US06 driving cycle. The results demonstrated a zero-overshoot transient response, robustness against a 2.5× inertia mismatch, and sustained performance under flux-linkage and inductance variations in deep field-weakening operation. Compared with conventional PI-based strategies, the proposed approach reduced the speed RMSE by 82%, lowered the current THD from 18.5% to 3.2%, and reduced the cumulative DC-link current-squared index by 6.7%. These results validate the practical robustness and computational feasibility of the proposed stability-aware framework for EV traction control. Full article

(This article belongs to the Topic Advanced Electrical Machines and Drives Technologies, 3rd Edition)

31 pages, 2863 KB

Open AccessArticle

A Physics-Informed Hybrid Ensemble for Robust and High-Fidelity Temperature Forecasting in PMSMs

by Rifath Bin Hossain, Md Maruf Al Hasan, Md Imran Khan, Monzur Ahmed, Yuting Lin and Xuchao Pan

World Electr. Veh. J. 2026, 17(3), 133; https://doi.org/10.3390/wevj17030133 (registering DOI) - 5 Mar 2026

Abstract

The deployment of artificial intelligence in safety-critical industrial systems is hindered by a core trust deficit, as models trained via empirical risk minimization often fail catastrophically in out-of-distribution (OOD) scenarios. We address this challenge by developing a physics-informed hybrid ensemble that achieves state-of-the-art [...] Read more.

The deployment of artificial intelligence in safety-critical industrial systems is hindered by a core trust deficit, as models trained via empirical risk minimization often fail catastrophically in out-of-distribution (OOD) scenarios. We address this challenge by developing a physics-informed hybrid ensemble that achieves state-of-the-art accuracy and robustness for Permanent Magnet Synchronous Motor (PMSM) temperature forecasting. Our methodology first calibrates a Lumped-Parameter Thermal Network (LPTN) to serve as a physics engine for generating physically consistent data augmentations, which then pre-trains a Temporal Convolutional Network (TCN) encoder via self-supervision, with the final prediction assembled from the physics model’s baseline guess and a correction learned by an ensemble of gradient boosting models on a rich, multi-modal feature set. Evaluated against a suite of strong baselines, our hybrid ensemble achieves a state-of-the-art Root Mean Squared Error of 5.24 °C on a challenging OOD stress test composed of the most chaotic operational profiles. Most compellingly, our model’s performance improved by an unprecedented −10.68% under these extreme stress conditions where standard, purely data-driven models collapsed. This demonstrated robustness, combined with a statistically valid Coverage Under Shift (CUS) Gap of only 1.43%, provides a complete blueprint for building high-performance, trustworthy AI, enabling safer and more efficient control of critical cyber-physical systems and motivating future research into physics-guided pre-training for other industrial assets. Full article

(This article belongs to the Collection Feature Papers in Propulsion Systems and Components in Electric Vehicle)

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18 pages, 1283 KB

Open AccessArticle

Design and Performance Validation of 4D Radar ICP-Integrated Navigation with Stochastic Cloning Augmentation

by Hyeongseob Shin, Dongha Kwon and Sangkyung Sung

Sensors 2026, 26(5), 1660; https://doi.org/10.3390/s26051660 (registering DOI) - 5 Mar 2026

Abstract

Automotive radar has emerged as a pivotal technology for navigation in GNSS-denied environments, offering superior robustness to adverse weather and fluctuating lighting conditions compared to vision or LiDAR-based sensors. Despite these advantages, the inherent sparsity and noise of radar measurements often lead to [...] Read more.

Automotive radar has emerged as a pivotal technology for navigation in GNSS-denied environments, offering superior robustness to adverse weather and fluctuating lighting conditions compared to vision or LiDAR-based sensors. Despite these advantages, the inherent sparsity and noise of radar measurements often lead to degraded estimation accuracy and system reliability. To address these challenges, various radar-based localization frameworks have been explored, ranging from optimization-based and Extended Kalman Filter (EKF) approaches fused with Inertial Measurement Units (IMUs) to point cloud registration techniques like Iterative Closest Point (ICP). While filter-based methods are favored in multi-sensor fusion for their proven stability, ICP is widely utilized for high-precision pose estimation in point-cloud-centric systems. In this study, we propose a novel Radar-Inertial Odometry (RIO) framework that synergistically integrates ICP-based relative pose estimation with model-based sensor fusion. The proposed methodology leverages relative transformations derived from ICP alongside ego-velocity estimations obtained from radar Doppler measurements. To effectively incorporate relative ICP constraints, a stochastic cloning technique is implemented to augment previous states and their associated covariances, ensuring that the uncertainty of historical poses is explicitly accounted for. The performance of the proposed method is validated using public open-source datasets, demonstrating higher localization accuracy and more consistent performance compared to existing algorithms used for comparison. Full article

(This article belongs to the Section Navigation and Positioning)

34 pages, 5022 KB

Open AccessArticle

Evacuation Safety Evaluation for Deep Underground Railways Using Digital Twin Map Topology

by Jaemin Yoon, Dongwoo Song and Minkyu Park

Buildings 2026, 16(5), 1033; https://doi.org/10.3390/buildings16051033 (registering DOI) - 5 Mar 2026

Abstract

DUR (Deep Underground Railways) stations, such as Suseo Station in Korea, present unique evacuation challenges stemming from multi-level spatial depth, long vertical circulation paths, and rapid smoke spread dynamics. Conventional design guidelines often fail to capture these complexities, underscoring the need for advanced, [...] Read more.

DUR (Deep Underground Railways) stations, such as Suseo Station in Korea, present unique evacuation challenges stemming from multi-level spatial depth, long vertical circulation paths, and rapid smoke spread dynamics. Conventional design guidelines often fail to capture these complexities, underscoring the need for advanced, simulation-driven safety evaluation frameworks. This study proposes a comprehensive Digital Twin-based methodology that integrates spatial topology modeling, agent-based evacuation simulation, and dynamic hazard-aware routing. A multi-layer map topology was constructed from high-fidelity architectural geometry, decomposing the station into functional regions and encoding connectivity across platforms, concourses, corridors, and vertical circulation elements. Real-time hazard conditions were reflected through dynamic adjustments to edge weights, allowing evacuation paths to adapt to blocked exits, fire shutter operations, and smoke-infiltrated domains. Ten evacuation scenarios were developed to assess sensitivity to fire origin, exit availability, vertical circulation failures, and onboard passenger loads. Simulation results reveal that evacuation performance is primarily constrained by vertical circulation bottlenecks, with emergency stairways (E1 and E2) serving as critical choke points under high-density conditions. Cases involving exit closures or fire-compartment failures produced significant delays, frequently exceeding NFPA 130 and KRCODE performance criteria. Conversely, guided evacuation strategies demonstrated marked improvements, reducing congestion and enabling compliance with platform evacuation thresholds even in full-load scenarios. These findings highlight the necessity of transitioning from static design evaluations toward Digital Twin-enabled, predictive safety management. The proposed framework enables real-time visualization, intervention testing, and operator decision support, offering a scalable foundation for next-generation evacuation planning in extreme-depth railway infrastructures. Full article

(This article belongs to the Section Construction Management, and Computers & Digitization)

33 pages, 3309 KB

Open AccessArticle

A Multidimensional Traffic Accident Causation Index for Severity Modeling Using Explainable Machine Learning

by Halil İbrahim Şenol and Gencay Sarıışık

Systems 2026, 14(3), 282; https://doi.org/10.3390/systems14030282 (registering DOI) - 5 Mar 2026

Abstract

Road traffic accidents remain a major public health concern, and effective safety management requires interpretable tools that integrate multiple causal dimensions. This study proposes a Traffic Accident Causation Index (TACI) to provide a holistic representation of severity-related drivers by combining six theoretically grounded [...] Read more.

Road traffic accidents remain a major public health concern, and effective safety management requires interpretable tools that integrate multiple causal dimensions. This study proposes a Traffic Accident Causation Index (TACI) to provide a holistic representation of severity-related drivers by combining six theoretically grounded domains: Accident Infrastructure, Driver, Pedestrian, Road Condition, Emergency and Response, and Severity. Using a national police-reported dataset from Türkiye (N = 13,639), operational variables are mapped to normalized risk scores, aggregated into domain indices, and combined into a 0–100 composite TACI score. To assess the robustness and compatibility of the proposed index framework, we develop ensemble machine learning models (Random Forest, Gradient Boosting, LightGBM, XGBoost, and CatBoost) under two feature configurations: an Extended Feature Set (EFS) with the original variables and a Core Feature Set (CFS) consisting of the six domain indices. The results indicate that domain-level aggregation improves predictive stability, and the best-performing boosting models (XGBoost/CatBoost) achieve near-perfect agreement with the constructed index (test R² > 0.99) and very high classification performance (AUC > 0.999). SHAP-based explainability highlights pedestrian exposure and vulnerability as the dominant contributors, followed by lighting/visibility conditions, road surface quality, and adverse road–environment factors, whereas emergency-response and infrastructural attributes show comparatively indirect effects. Overall, the proposed framework supports interpretable, domain-oriented evidence for prioritizing safety interventions and monitoring high-risk accident conditions. Full article

(This article belongs to the Section Complex Systems and Cybernetics)

17 pages, 1795 KB

Open AccessArticle

A Nutraceutical Approach for Hypertension: Randomized Controlled Trial of Grape Pomace Extract and L-Arginine

by Federico Abate, Elisabetta Schiano, Mariano Stornaiuolo, Fabrizia Guerra, Anna Terracciano, Gaetano Piccinocchi, Eugenio Caradonna, Fulvio Ferrara, Gian Carlo Tenore and Ettore Novellino

Antioxidants 2026, 15(3), 329; https://doi.org/10.3390/antiox15030329 (registering DOI) - 5 Mar 2026

Abstract

Hypertension remains a major global health challenge, and pharmacological therapy is often constrained by tolerability issues. Adjunctive approaches targeting the nitric oxide synthase and soluble guanylate cyclase–cyclic guanosine monophosphate (sGC–cGMP) pathway may offer additional benefits. This study investigated the efficacy and safety of [...] Read more.

Hypertension remains a major global health challenge, and pharmacological therapy is often constrained by tolerability issues. Adjunctive approaches targeting the nitric oxide synthase and soluble guanylate cyclase–cyclic guanosine monophosphate (sGC–cGMP) pathway may offer additional benefits. This study investigated the efficacy and safety of a nutraceutical formulation combining grape pomace extract (Taurisolo^®) and L-arginine in patients with grade 1 and grade 2 hypertension. The formulation was designed to enhance nitric oxide (NO) bioavailability and support sGC–cGMP signaling. Taurisolo^®, a polyphenol-rich extract, is known for its antioxidant and endothelial-protective properties, while L-arginine serves as the physiological substrate for endothelial NO synthase. Clinical outcomes included blood pressure changes, renal function parameters, and health-related quality of life assessed through the SF-12 questionnaire. Supplementation with Taurisolo^® plus L-arginine resulted in significant and sustained reductions in systolic and diastolic blood pressure, with renal function remaining stable throughout the study. Participants also reported meaningful improvements in perceived health, emotional well-being, vitality, and social functioning. The intervention was well tolerated, with no major adverse effects. These findings support the potential of Taurisolo^® combined with L-arginine as a safe and effective adjunctive strategy to conventional antihypertensive therapy, warranting further mechanistic investigation. Full article

(This article belongs to the Special Issue Exploring New Frontiers in Oxidative Stress and Antioxidant Research: From Model Organisms to Human Disease)

34 pages, 2334 KB

Open AccessReview

Survey on Reconnaissance Autonomous Robotic Systems for Disaster Management

by Sahaj Sinha, Sinjae Lee and Saurabh Singh

Sensors 2026, 26(5), 1659; https://doi.org/10.3390/s26051659 (registering DOI) - 5 Mar 2026

Abstract

Systems that operate in dangerous environments are becoming essential in case of emergencies. This survey reviews the latest ground reconnaissance robots using computer vision (CV), machine learning (ML), MCU-based control, LoRa communication, DC motors, and dual-power systems. The analysis includes hardware and algorithms, [...] Read more.

Systems that operate in dangerous environments are becoming essential in case of emergencies. This survey reviews the latest ground reconnaissance robots using computer vision (CV), machine learning (ML), MCU-based control, LoRa communication, DC motors, and dual-power systems. The analysis includes hardware and algorithms, and their performance in the field and lab. There has been clear progress in navigation, sensor fusion, and situational awareness. The main challenges which remain include the use of energy and standardization of benchmarks. This survey focuses exclusively on Unmanned Ground Vehicles (UGVs) for disaster reconnaissance, examining recent advances in hardware, software, and autonomy. The survey highlights the improvements in navigation, sensor fusion, and intelligence, and identifies remaining challenges such as energy limitations, robustness in harsh conditions, and the lack of standardized benchmarks. The analysis synthesizes findings from over 190 recent studies (2020–2025) in ground-based disaster robotics, providing a comprehensive overview of current capabilities and research gaps. It encapsulates all issues with their remedy for future disaster-response systems. Full article

(This article belongs to the Special Issue Advanced Sensors and AI Integration for Human–Robot Teaming)

53 pages, 2913 KB

Open AccessArticle

SORA 2.5-Guided BVLOS UAS for Wildlife Conservation in Kenya: Reducing Friction Between Safety and Field Operations

by Guy Maalouf, Thomas Stuart Richardson, David Roy Guerin, Matthew Watson, Ulrik Pagh Schultz Lundquist, Blair R. Costelloe, Elzbieta Pastucha, Saadia Afridi, Edouard George Alain Rolland, Kilian Meier, Jes Hundevadt Jepsen, Thomas van der Sterren, Lucie Laporte-Devylder, Camille Rondeau Saint-Jean, Constanza Andrea Molina Catricheo, Vandita Shukla, Elena Iannino, Jenna Kline, Dat Nguyen Ngoc, William Njoroge and Kjeld Jensen Show full author list Hide full author list

Drones 2026, 10(3), 178; https://doi.org/10.3390/drones10030178 (registering DOI) - 5 Mar 2026

Abstract

Safe Beyond Visual Line of Sight (BVLOS) operations are increasingly required for wildlife monitoring and conservation, yet existing regulatory frameworks are rarely tailored to protected areas characterised by low population density and limited infrastructure. This paper presents a field-based use case illustrating how [...] Read more.

Safe Beyond Visual Line of Sight (BVLOS) operations are increasingly required for wildlife monitoring and conservation, yet existing regulatory frameworks are rarely tailored to protected areas characterised by low population density and limited infrastructure. This paper presents a field-based use case illustrating how the Specific Operations Risk Assessment (SORA) methodology can be applied to conservation-oriented BVLOS missions under Kenyan airspace conditions, including coordination within military-controlled airspace. We evaluate three population-density estimation approaches (qualitative, bottom-up, and top-down) against available ground truth, and compare tabulated and analytical SORA methods for deriving the Ground Risk Class. The work illustrates how SORA 2.5 structures ground and air risk reasoning in a conservation context, while retrospective review identifies limitations in containment, Operational Safety Objectives, and tactical mitigation performance requirements. Field trials involved five concurrent teams and 30 personnel conducting over 260 flights and more than 60 h of UAS activity across the Ol Pejeta Conservancy, providing insights into multi-team coordination under field conditions. Field implementation revealed areas of misalignment between prescribed safety requirements and operational realities, prompting iterative adaptation of workflows and procedures. Observed outcomes included reductions in team size (25–50%) and procedural steps (18%), derived from retrospective comparison of field procedures. A lightweight Uncrewed Traffic Management prototype was also trialled, revealing practical limitations in conservancy environments. Finally, we present a ten-step framework for developing field-ready safety procedures to support risk-informed decision-making in non-standard operational contexts. The findings provide empirically grounded guidance on applying SORA principles to conservation UAS missions, without proposing a new risk framework or generalised operational model. Full article

(This article belongs to the Special Issue UAVs for Nature Conservation Tasks in Complex Environments)

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19 pages, 2699 KB

Open AccessArticle

Extraction, Characterization, and Biological Evaluation of Atranorin Against Diabetes-Induced Reproductive Dysfunction Through Modulation of Oxidative Stress, Inflammatory Pathways and Key Reproductive Enzymes

by Walaa I. El-Sofany, Ahlam F. Alshammari, Mona Zaheed Alshammari, Hissah Khashman Alshammari, Nawal S. Alshammari, Najat Masood and Khaled Hamden

Int. J. Mol. Sci. 2026, 27(5), 2416; https://doi.org/10.3390/ijms27052416 (registering DOI) - 5 Mar 2026

Abstract

Male fertility is declining worldwide, with notable reductions in sperm counts, emphasizing the need for new therapeutic interventions. Atranorin (ATR), a lichen-derived secondary metabolite, exhibits strong antioxidant and anti-inflammatory activities. This study assessed the protective effects of ATR on type 1 diabetes (T1D)-induced [...] Read more.

Male fertility is declining worldwide, with notable reductions in sperm counts, emphasizing the need for new therapeutic interventions. Atranorin (ATR), a lichen-derived secondary metabolite, exhibits strong antioxidant and anti-inflammatory activities. This study assessed the protective effects of ATR on type 1 diabetes (T1D)-induced reproductive dysfunction in rats. T1D was induced in male Wistar rats via a single intraperitoneal injection of alloxan at 150 mg/kg body weight (bw). ATR significantly ameliorated T1D-related reproductive damage. At 170 mg/kg bw, ATR reduced hyperglycemia by 66% and attenuated seminal inflammation, decreasing leukocyte infiltration (−51%) and myeloperoxidase (MPO) activity (−68%). Oxidative balance improved, as evidenced by increased total antioxidant status (TAS) (+203%) and decreased thiobarbituric acid reactive substances (TBARS) (−73%), hydrogen peroxide (H₂O₂) (−45%), and total oxidant status (TOS) (−70%). Steroidogenesis was restored through enhanced 3β-hydroxysteroid dehydrogenase (3β-HSD) (+65%) and 17β-hydroxysteroid dehydrogenase (17β-HSD) (+102%) activities, resulting in a 90% recovery of testosterone levels. Seminal plasma function improved, with increased fructose levels (+71%), normalized pH (7.4), and enhanced hyaluronidase (HYAL) (+71%), adenosine triphosphatase (ATPase) (+71%), and prostatic acid phosphatase (PAP) (+79%) activities. Fertility biomarkers, such as adenosine deaminase (ADA) (+148%) and lactate dehydrogenase-C4 (LDH-C4) (+62%), increased, and essential minerals Zn²⁺ (+72%), Ca²⁺ (+96%), Mg²⁺ (+84%), and Se (+57%) were restored. Consequently, sperm density (+87%), viability (+69%), and motility (+189%) improved, while abnormalities declined (−46%). Histological findings confirmed the restoration of spermatogenesis and epididymal maturation. ATR effectively counteracts diabetes-induced reproductive dysfunction by reducing oxidative and inflammatory stress while improving hormonal and seminal parameters. Full article

(This article belongs to the Special Issue Advanced Research on Plant-Derived Products in the Service of Immune Regulation)

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