Search Results (515)

Photosystem II (PSII) contains several cofactors involved in light harvesting, charge separation, electron transfer, and catalysis. The initial charge separation in the reaction center of PSII creates the strongest known redox-cofactor oxidant in biology, a cationic radical distributed over a “special pair” of chlorophyll molecules (P680^•+). Two redox-active tyrosines, Y_Z and Y_D, located at opposite sides of the special pair, are the principal residues that reduce this cationic radical. Y_Z, in turn, oxidizes the manganese cluster of the oxygen-evolving complex to drive water oxidation, whereas Y_D forms a stable radical facilitated by local water translocation. The details of this asymmetry and the role of nearby protein residues in mediating branch-specific electron/hole-transfer pathways remain incompletely understood. Here, we investigate pathways for electron transfer (ET) from Y_Z and Y_D to P680^•+ and identify specific residues that are likely responsible for mediating ET. Graph-based analysis predicts aromatic residue-assisted pathways on both branches but also reveals a distinct tryptophan (D2-Trp191) that connects Y_D with P680^•+, whereas the corresponding D1-side position is occupied by a non-aromatic D2-Ile192. This suggests a possible role of this tryptophan as an ET mediator, thereby differentiating the nature of electronic connectivity between Y_Z/Y_D and the reaction center. Residue conservation analysis indicates retention of D2-Trp191 across various organisms. Molecular dynamics show that the predicted donor–mediator and mediator–acceptor contacts remain structurally persistent over the simulation, while QM/MM calculations show appreciable spin-density localization capacity, providing strong computational support for an ET mediator role of D2-Trp191. Together, these results suggest that ET between the redox-active tyrosines and the reaction-center chlorophylls occurs via distinct mechanisms—direct vs. mediated—with D2-Trp191 being a D2-specific mediator for the branch-selective electron/hole-transfer connectivity in PSII. Full article

Growing concerns regarding children’s disconnection from nature have increased interest in nature-based pedagogies within Early Childhood Education (ECE). This study examined how participation in a Forest School pilot project influenced children’s learning environments, play, human–nature relationships, and science process skills in three ECE settings in Northern Spain. This longitudinal study combined quantitative and qualitative data collection methods to follow 106 children aged 2–5 years throughout a complete academic year of regular Forest School sessions integrated within mainstream schooling. Data collection combined systematic observation scales, semi-structured interviews, environmental attitude measures, and family questionnaires to examine learning environments, children’s play, social interactions, environmental attitudes and science process skills. The findings indicate that forest environments provided richer opportunities for exploratory play, science process skills, and interaction with unstructured materials than conventional playgrounds. Group symbolic play occurred more frequently in forest settings, while lower levels of conflict were observed. Children progressively developed more ecocentric environmental attitudes, empathy toward living beings, and relational forms of environmental care through repeated interaction with nearby ecosystems. Teachers reported that children demonstrated increasing science process skills—including observation, exploration, representation, and communication—through direct interaction with natural phenomena. The study suggests that regular nature-based learning may support the development of science process skills, environmental care, and holistic child development within mainstream ECE contexts. Full article

In this publication we investigate in depth the design, architecture and implementation of a bio and cultural guiding system with augmented capabilities. Our platform will be able to provide a flexible and user-friendly application for smart mobile devices with Android OS, which will be able to provide to the user the ability to discover nearby places of interest such as museums, archeological sites, religious sites, natural beauty sites, etc. In addition, our application is able to offer users an enhanced, comprehensive and augmented tour experience, based on visual and audio smartphone services, using asynchronous and real-time user–server communication mechanisms, thus eliminating the need for a human tour guide operator in cases of a remote area, guiding service cost or unavailable time slot. The proposed platform integrates visual overlays, audio narration, Geolocation services, and cloud-based data management to enable users to explore cultural, historical, and natural points of interest without the need for a human tour guide. A modular, layered system architecture is developed, combining Firebase Realtime Database, RESTful web services, OpenStreetMap-based navigation, and Android-native components to ensure scalability, flexibility, and real-time responsiveness. UML modeling, including class and sequence diagrams, is employed to formally describe system structure and behavior, while a mathematical data model validates the consistency of the underlying database schema. The implementation demonstrates how AR guiding systems can enhance spatial understanding, accessibility, and user engagement while supporting sustainable tourism practices and efficient destination management. The results indicate that the proposed solution is technically feasible, user-centered, and adaptable to diverse bio-cultural contexts, contributing to the advancement of intelligent, inclusive, and sustainable digital tourism platforms. Full article

Accurate land cover mapping is a fundamental tool for environmental management and ecosystem monitoring. This study presents a comparative evaluation of four deep learning architectures, namely a Convolutional Neural Network (CNN), UNet++, ConvNeXt and Vision Transformer (ViT), for land cover classification into five primary classes: water, cropland, forest, low/natural vegetation and built-up. The broader Lake Kerkini basin was selected as the primary training and evaluation area. The multispectral input data were generated through Google Earth Engine and consisted of multi-seasonal Sentinel-2 composite mosaics for the 2021 mapping year, covering winter, spring, summer and autumn. To obtain a more reliable performance estimate and mitigate the effects of spatial autocorrelation, a four-fold spatial cross-validation approach was implemented. Under this spatial validation framework, the convolution-based architectures achieved the strongest performance. CNN obtained the highest numerical fold-mean performance, with an overall accuracy of 81.53% and a macro-averaged F1 score (Macro-F1) of 80.09%, followed closely by UNet++ and ConvNeXt. Non-parametric repeated-measures statistical testing indicated a significant overall architecture effect, with CNN, UNet++ and ConvNeXt showing broadly comparable fold-level Macro-F1 performance, while the tested ViT configuration trained from scratch ranked last across all spatial folds. Regional transferability was further evaluated in the nearby independent Lake Doirani region, where the convolutional architectures, particularly CNN and UNet++, showed strong agreement with the WorldCover-derived reference labels without fine-tuning. Finally, feature-importance analysis indicated that specific spectral-seasonal channels, especially the Blue band (B2) in winter and the Short-Wave Infrared band (B12) in summer, were consistently influential in the models’ predictions. Overall, under the tested 2021 Mediterranean case-study conditions, the results highlight the importance of spatially rigorous validation and show that the evaluated convolution-based configurations achieved stronger performance than the tested ViT configuration trained from scratch. Full article

Background/Objectives: Access to dental care is a multilevel challenge shaped by healthcare systems, individual characteristics, and broader social determinants. This study aimed to identify parent-reported barriers to dental care among a sample of children in Saudi Arabia using Levesque’s Conceptual Framework for Access to Healthcare. Methods: A cross-sectional survey was conducted between March and April 2023 among parents of children aged 1–18 years. The 44-item survey assessed sociodemographic, oral health, and dental care utilization, organized using Levesque’s access dimensions. Difficulty accessing needed dental care within the past 12 months served as the main outcome. The chi-square test was used to examine bivariate associations between potential barriers and difficulty accessing needed dental care. Multivariable logistic regression was used to identify factors independently associated with difficulty accessing needed dental care after adjustment for the other variables. Results: This study included 351 parents. The mean age of children was 10.7 ± 4.7 years, and 45.9% were male. One-third of parents reported difficulty accessing needed dental care for their children. The adjusted regression model identified fair/poor oral health (aOR = 13.39, 95% CI: 6.19–28.97), general health condition (aOR = 2.31, 95% CI: 1.11–4.82), and the absence of a nearby dental clinic (aOR = 2.08, 95% CI: 1.11–3.90) as factors independently associated with difficulty accessing dental care. Conclusions: Applying Levesque’s framework highlighted the multidimensional nature of barriers to accessing dental care for the study population. These findings provide preliminary evidence on parent-reported access barriers and may inform larger, representative studies and future policies to improve equitable pediatric oral healthcare in alignment with Saudi Vision 2030. Full article

Dissolved bio-essential trace metals (Cu, Zn, Co, Ni, and Fe) in seawater and 17 elements in surface sediments were studied to assess the metal contamination and associated ecological risks in the Sulaibikhat Bay Marine Protected Area (MPA) in the northwestern Arabian Gulf. Dissolved metals were mostly at low to moderate levels and within the limits of most international water quality standards. However, dissolved Cu concentrations (0.54 to 4.73 µg L⁻¹) in the MPA stations (MPA-1 and MPA-2) exceeded the Oslo–Paris Convention for the Protection of the Marine Environment of the North-East Atlantic (OSPAR) ecotoxicological assessment thresholds, suggesting possible adverse ecological effects. Sediment showed higher levels of Cd, Cr, Ni, V, Cu, and Zn at stations within the MPA, with Principal Component Analysis (PCA) linking these enrichments to nearby industrial and desalination discharge sources. The calculated enrichment factor (EF) and geoaccumulation index (I_geo) values confirmed moderate to substantial anthropogenic contributions for Cd, Cr, and Ni, while the major lithogenic elements were derived from natural mineralogical inputs. A pollution load index (PLI) > 1 at stations MPA-1, MPA-3 and MPA-4 in Sulaibikhat Bay indicated cumulative metal contamination. Ecological risk assessment criteria classified sediments within the MPA and Sulaibikhat Bay as having a moderate level of ecological risk, with Cd identified as the principal contributor. The toxicological indices, namely mean Effect Range Median Quotient (m-ERM-Q) and mean Probable Effect Level Quotient (m-PEL-Q), indicated a low-to-moderate probability of adverse biological effects due to Cu and Ni exposure in the MPA. In contrast, the reference station in the Kuwait Bay showed very low contamination and negligible ecological risk. These findings stress the need for continuous monitoring and effective source control to protect the ecosystem of this newly established MPA in Kuwait. Full article

Mining activities generate substantial socio-environmental impacts on local communities, often giving rise to conflicts that compromise the social and territorial viability of extractive projects. In the Coquimbo Region, however, empirical evidence that systematically identifies and prioritizes these issues from a community perspective remains limited. In response, this study aims to identify and prioritize the most critical socio-environmental issues perceived by communities located near mining operations using the Analytic Hierarchy Process (AHP). A total of 37 issues were identified through a structured literature review and organized into a hierarchical framework comprising five criteria and eight subcriteria, which were validated through expert judgment and a pilot test. Data were collected through 681 surveys administered across five communities in the Coquimbo Region, and global weights were estimated using Expert Choice^® software (version 11), incorporating consistency ratio (CR) assessment and sensitivity analysis. The results indicate that the issues with the highest global weights are the depletion of water sources (13.3%), followed by increases in the local cost of living (8.0%) and the alteration of natural streamflow regimes (7.5%), underscoring the central role of water-related and economic impacts in shaping community perceptions. Overall, the study provides a robust methodological approach for structuring and quantifying heterogeneous community perceptions within a consistent prioritization framework, thereby facilitating their integration into decision-making processes involving local communities and nearby mining projects in the Coquimbo Region. Full article

Child malnutrition remains a critical global health challenge, yet most existing studies rely on static risk estimates and overlook the spatial–temporal nature of environmental exposures and localized socioeconomic disparities. To address this gap, we integrated Earth observation-derived environmental indicators, geolocated conflict events, socioeconomic variables, and child health outcomes, and applied a Fixed Effects Two-Stage Least Squares Spatial Durbin Error Model (FE-2SLS-SDEM). We found distinct hotspots of joint vulnerability, where areas experiencing both high conflict intensity and recurrent droughts show significantly higher rates of childhood stunting. High conflict intensity, drought severity, diarrheal prevalence, and inadequate sanitation significantly increase stunting, while maternal and paternal education, improved sanitation, economic development (proxied by nighttime light intensity), and agricultural productivity reduce it. Among these determinants, female education demonstrated the most pronounced inverse relationship with childhood stunting. Additionally, exposure to both drought severity and high conflict intensity independently and in combination worsens childhood stunting not only within affected regions but also in nearby localities. Our results underscore the urgency of geographically targeted, multisectoral, and action-oriented policies aimed at strengthening community and health system capacities to mitigate the converging risks of climate change and conflict on child malnutrition. Full article

In this work, the structural, electronic, charge-transfer, thermal, and magnetic properties of pristine and Fe-doped silicon carbide nanosheets (SiCNShs) were systematically investigated using spin-polarized density functional theory (DFT) within the Local Spin Density Approximation including Hubbard correction (LSDA + U). A 4 × 4 SiCNSh supercell containing 80 atoms was considered, where Fe atoms were substitutionally introduced at carbon sites to evaluate dopant-induced modifications in the nanosheet. Structural optimization, energy convergence, force minimization, and stress evolution analyses confirm that Fe incorporation preserves the structural integrity of the SiCNSh and leads to energetically stable configurations. The calculated defect formation energy (−7.44 eV/atom) demonstrates the thermodynamic feasibility of Fe substitution, while ab initio molecular dynamics (AIMD) simulations at 300 K verify the thermal stability of the energetically favorable Fe-doped configuration. Electronic-structure calculations reveal that pristine SiCNSh exhibits a nonmagnetic semiconducting nature with a band gap of approximately 2.4 eV, whereas Fe incorporation significantly modifies the electronic structure through pronounced Fe–3d/C–2p/Si–3p orbital hybridization. The band gap is reduced to approximately 1.1 eV for the single-Fe-doped system and further decreases to 0.53/0.51 eV (spin-up/spin-down) in the double-Fe configuration, while preserving semiconducting behavior. Spin-polarized band structure and density of states analyses demonstrate clear spin asymmetry near the Fermi level, indicating strong dopant-induced spin polarization and exchange interactions. Charge-density difference and Bader charge analyses reveal substantial dopant-induced charge redistribution characterized by electron depletion around Fe atoms, enhanced electron accumulation on neighboring carbon atoms, and partial charge neutralization of nearby Si atoms, resulting in a more localized covalent Si–C–Fe bonding environment. Mulliken spin population analysis further demonstrates robust ferromagnetic ordering, where the Fe dopant acts as the dominant magnetic center with strong induced spin polarization extending into neighboring Si and C atoms. Comparison between ferromagnetic (FM) and antiferromagnetic (AFM) configurations confirms that the 2Fe@C-doped SiCNSh stabilizes in a ferromagnetic ground state, exhibiting a favorable FM–AFM energy difference of 0.216 eV. Based on the mean-field approximation, the Curie temperature was estimated to be approximately 837 K, indicating strong magnetic stability significantly above room temperature. The present findings collectively demonstrate that Fe incorporation effectively tailors the electronic and magnetic properties of SiCNSh through band-gap engineering, spin-symmetry breaking, and stabilization of high-temperature ferromagnetism. These combined characteristics establish Fe-doped SiCNShs as promising candidates for spintronic devices, magnetic semiconductors, spin injectors, spin filters, and non-volatile magnetic memory applications. Full article

Mankyua chejuense (Ophioglossaceae) is an endangered fern restricted to basaltic ephemeral wetlands on Jejudo-Island. However, its capacity to establish in transplantation habitats remains poorly understood. Its occurrence is shaped by rainfall-driven short-term inundation, rapid drying, basaltic microtopography, and seasonal light availability. We assessed its reintroduction potential through a two-year transplantation experiment using ramets collected from a natural population. Ramets were planted in two wetland-type recipient habitats: a small, microtopographically isolated wetland (WS) and a larger wetland with a steep inundation depth gradient (WL). During the 24-month monitoring period, transplanted ramets survived in both transplantation habitats, with establishment ratios of approximately 80% in WS and 55% in WL. Ramets maintained aboveground growth and reproductive structure formation, including stipe production, ramet height, vegetative pinna production, fertile segment formation, and reproductive ratio. Along the water-depth gradient in WL, deeper or fully inundated conditions tended to increase vegetative growth, particularly stipe production, whereas reproductive ratio was relatively lower under these conditions. These contrasting responses suggest a possible shift in growth–reproductive allocation along the inundation gradient. Overall, these findings indicate that M. chejuense can establish in environmentally suitable transplantation habitats when ramets are artificially introduced. Therefore, the absence of natural individuals in nearby unoccupied wetlands should not be interpreted as direct evidence of habitat unsuitability, but may instead reflect limitations in natural colonization processes, including dispersal arrival, germination, early establishment bottlenecks, and fine-scale microtopographic or hydrological filtering. Full article

The eruption of the Tajogaite volcano (Cumbre Vieja) on La Palma Island (Spain) generated a significant amount of volcanic ash (VA). This study evaluates the valorisation of VA, considered a “natural waste,” as a partial substitute for Portland cement or in combination with lime. By using this waste, this study aims to promote its valorisation and contribute to the circular economy on the island and in nearby areas. After the ash undergoes a drying and grinding process, various tests are conducted to assess its physical, mineralogical, and chemical properties. These tests include particle size distribution, powder X-ray diffraction, and field emission electron microscopy, among others. Methods such as the Frattini test, the R³ method, thermogravimetric analysis and calorimetry are used to measure pozzolanic reactivity. The values obtained using the Frattini and R³ methods indicate that VA has low-moderate reactivity. The mechanical properties of mortar specimens based on Portland cement blends and hydrated lime are analysed, where a portion of these binders is replaced with VA. It has been observed that the compressive strengths of the specimens with 15%, 25%, and 35% of cement replaced by VA in cement blends show favourable results after 90 and 365 days of curing. Mortars with a 25% replacement reach compressive strengths exceeding 40 MPa versus 57 MPa of the control after 28 days of curing, which is adequate for many applications in civil engineering. The study highlights the importance of exploring eco-friendly materials and believes that the addition of VA can be a valuable and effective enhancement for mortars. This research marks a significant endeavour in exploring the volcanic ash produced by the Tajogaite Volcano eruption, particularly in relation to its mechanical behaviour in lime-pozzolan mortars. Full article

Single-index greening trends can misrepresent post-mining recovery because they do not show whether disturbed surfaces are converging toward the spectral conditions of nearby stable vegetation. Here, we present a 22-year (2003–2024) Landsat-based assessment of the Nannihu molybdenum mine (Henan, China) by combining LandTrendr-based disturbance and recovery timing from annual NBR series with a benchmark-relative spectral recovery index (RSRI) and five-epoch random forest land-surface classification used as contextual support. The classifier was trained on 2024 samples and transferred to earlier epochs without independent validation at each epoch. Historical class labels should therefore be treated as approximate contextual support. A five-type recovery pathway typology showed that only 41.8% of mine-affected pixels followed vegetated recovery pathways, while 28.2% stabilized as non-vegetated surfaces and 25.0% remained under persistent disturbance. Even the combined vegetation recovery type had a mean RSRI of only 0.309 (SD = 0.143), suggesting that greening alone does not imply close benchmark-relative spectral proximity to the local stable-vegetation reference. Disturbance magnitude was the feature most strongly associated with RSRI variation (XGBoost SHAP mean, |SHAP| = 0.075). The RSRI quantifies benchmark-relative spectral proximity using local stable-vegetation benchmarks, and it does not measure species composition, biomass, or ecosystem function. This site-specific case study indicates that benchmark-relative spectral assessment can complement conventional greening metrics in retrospective mine monitoring using open-access Landsat archives, with field validation the natural next step toward linking these spectral findings to ecological or functional recovery. Full article

With growing interest in health and leisure, walking tourism has emerged as a significant segment of the tourism market. Coastal trails have gained prominence as attractive tourist attractions offering unique experiences that combine coastal and forest environments. Understanding the experiences of tourists using these trails is essential to their sustainability and the revitalization of nearby regions and tourist destinations. However, the sustainable management of coastal trails and the understanding of the perceptions and evaluations of tourists using them remain limited. Therefore, this study aims to analyze walking tourism experiences on coastal trails using online review data to identify tourists’ perceptions and evaluations. Three representative coastal trails in South Korea were selected as the study sites, and 21,289 reviews (including course information, titles, review content, and posting dates) were collected from Durunubi, a walking tourism application operated by the Korea Tourism Organization. The research methodology employed text mining and sentiment analysis in Python 3.12.13 and spatial analysis using GeoDa 1.22.0.20 and QGIS 3.40.11. This study explores the emotional geography of walking tourism experiences along Korean coastal trails by integrating the analysis of online review data using text mining, sentiment analysis, and spatial analysis. The analysis revealed that positive sentiments were associated with natural landscapes, while negative sentiments were associated with trail management. These emotional experiences exhibit distinct spatial clustering patterns. This finding has important implications for establishing sustainable trail management strategies. Full article

The intermittent nature of renewable energy systems and the mismatch between power generation and load demand necessitate the integration of efficient energy storage systems (ESSs). Among large-scale energy storage technologies, pumped hydro-energy storage systems (PHESs) are widely recognized as one of the most cost-effective and longest-lifetime storage solutions under favorable geographical conditions. This study proposes and optimizes a hybrid renewable energy system (HRES) for the Wadi Baish region in Saudi Arabia as a real case study, where the significant elevation difference between the nearby mountains and the existing lake provides favorable conditions for PHES implementation. A nested optimization framework is developed to determine the optimal sizing and operation of the HRES components. The external optimization loop employs the non-dominated sorting genetic algorithm II (NSGA-II) to optimize system sizing, while the internal optimization loop uses mixed-integer linear programming (MILP) to optimally dispatch the PHES, battery energy storage system (BESS), and hydrogen energy storage system (HESS). In addition, demand-side management (DSM) is coordinated with the MILP dispatch strategy to improve system performance and reliability. The results show that the optimized system can supply a 10 MW average load with a renewable energy penetration of 98.7%. The proposed configuration achieves a total lifecycle cost of USD 231.37 million and avoids approximately 898.58 kt of CO₂ emissions over the project lifetime. PHES operates as the primary bulk energy storage technology due to its high storage capacity and low degradation characteristics. Furthermore, the degradation-aware model predicts battery replacement every 12 years and HESS replacement every 5 years. Compared with rule-based control, the MILP-based dispatch strategy reduces grid dependency by 87%. The coordinated DSM and MILP operation also reduces the levelized cost of energy to USD 0.066/kWh while improving overall system reliability. These findings demonstrate the importance of coordinated energy management and accurate degradation modeling in the optimal design and operation of renewable-based HRES configurations. Full article

Accurate simulation modeling of the danger zone and real-time visualization of the toxic cloud spread during a fire and explosion at an industrial facility in a nearby urban area are in demand by rescue services conducting evacuation. Using a cellular automaton method allows us to create an optimal predictive model of the danger zone spread, combine modeling accuracy with computational speed, and consider multiple input variables and the cascading nature of an accident during visualization. The objective of this study was to develop a mobile application for calculating the parameters of the danger zone during an accident at an industrial facility caused by a toxic cloud spreading into an urban area, based on the selection of a cellular automaton algorithm. The primary objective of the study was a highly detailed visualization of the danger zone with several predicted values of toxic substance concentrations in the air. The authors developed a cellular automaton-based model, which forms the basis of the mobile application. It takes into account several variables characterizing chemicals in the explosion and fire zone, climate factors, occupancy, building parameters, and the availability of respiratory protection. The FireSoft Mobile app was developed using the Visual Studio 2022 development environment, C# 10.0, and .NET MAUI, adapted for Android 8.0 and higher. The mobile app was tested to visualize a cloud of toxic pollutants forming a hazardous zone in an urban agglomeration for cases involving an ammonia tank explosion and a large fire involving a large amount of polyvinyl chloride. The results demonstrate the app’s feasibility and effectiveness in predicting, planning, and managing evacuation measures during accidents at an industrial facility. Full article