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Introduction: Fish stock identification and delineation are fundamental requirements for preventing local depletion and promoting the sustainable exploitation of marine resources. The blackspot seabream, Pagellus bogaraveo, is the most commercially valuable sparid species across the Northeast Atlantic and the Mediterranean Sea. To effectively discriminate fish stocks, researchers increasingly rely on the use of natural tags, which reflect both environmental and genetic influences, providing critical information regarding fish movements and population structure. Objective: To broaden the understanding of P. bogaraveo stock structure, samples originally obtained for a parasite-based discrimination study were used to provide complementary insights through otolith shape and geochemical signatures. Methodology: A subset of 150 individuals (30 per location) collected across five Portuguese locations (Portugal mainland: Matosinhos, Figueira da Foz, and Sagres; and Archipelagos: Azores and Madeira) was selected for otolith analyses. Otolith contour phenotypic variation was quantified through Elliptical Fourier Descriptors (EFDs) and Shape Indices (SIs), while elemental signatures (element: Ca) were analyzed using solution-based inductively coupled plasma mass spectrometry (SB-ICP-MS). Statistical analyses involved both univariate (one-way ANOVA, followed by Tukey tests, if needed) and multivariate approaches (MANOVA and LDFA), considering both individual and combined datasets. Results: EFDs + SIs yielded the lowest discriminatory power, with an overall reclassification accuracy of 38%. In contrast, Ca signatures provided the highest discrimination at 79%. The combination of both markers resulted in a slightly lower overall accuracy of 75%, likely due to the higher variance associated with the morphological data. Conclusions: In agreement with the previous parasite assessment, these otolith-based approaches confirm that the Macaronesian archipelagos consist of distinct stocks, separate from the Portuguese continental shelf. Furthermore, significant differences in otolith geochemical signatures between Sagres and Figueira da Foz point to a further subdivision of stocks. These findings are consistent with recent genetic data identifying three distinct stocks along the western and southern Iberian Peninsula, reinforcing the need for localized management of P. bogaraveo populations to ensure long-term fishery sustainability. Full article

Walking tourism is an important form of thematic and sustainable tourism, especially in rural and naturally attractive destinations. It contributes to diversifying the tourist environments and improving destination management. This paper analyses the role of walking tourism in destination management and uses an integrated machine-learning model to predict tourist preferences. A key focus of this study is identifying the key factors influencing walking tourism preferences, including demographic, socioeconomic, behavioural, and activity-related variables. The methodology of this study is based on an integrated Machine Learning (ML) approach. CatBoostClassifier was used as the primary predictive model, and hyperparameter optimization was performed using Particle Swarm Optimization (PSO). Model interpretability was ensured through SHapley Additive exPlanations (SHAP) analysis, supported by CatBoost feature importance evaluation. This combination enables both high prediction accuracy and transparent explanation of variable influence. The research is based on 467 responses collected through an anonymous online survey. Results confirm that walking tourism is predominantly linked to natural and mountain experiences, which have strong implications for destination planning and tourism product development. The proposed model provides reliable predictions of tourist preferences under class imbalance conditions, achieving a macro-F1 score of 0.5114. Additionally, key factors influencing the choice of walking tours were identified, supporting destination managers in tourist segmentation, tourism product development, and sustainable allocation of destination resources. Full article

Cowpea (Vigna unguiculata (L.) Walp.) is a legume valued in several countries for its nutritional quality, low input requirements, and economic profitability. However, cowpea productivity is significantly constrained by anthracnose disease caused by Colletotrichum species. Enhancing host resistance through the introgression of resistance alleles from landraces and wild relatives offers a promising strategy for durable disease management. This study evaluated 20 cowpea landraces and 18 V. unguiculata ssp. dekindtiana accessions for resistance to anthracnose under natural field infection. The cultivars Ife Brown and V. vexillata line TVNu-428 were included as susceptible and resistant checks, respectively. The experiment was laid in an alpha lattice design (8 × 5) with three replications arranged. Anthracnose symptoms characterized by enlarged light-brown lesions surrounded by reddish-brown halos were produced on all the accessions and the symptoms progressed throughout the assessment period. Five V. unguiculata ssp. dekindtiana accessions (TVNu-113, TVNu-1506, TVNu-1222, TVNu-420, and TVNu-136) and three cowpea landrace accessions (TVu-14076, TVu-17556, and TVu-17567) were identified as promising sources of anthracnose resistance. These accessions represent valuable genetic resources for broadening the cowpea resistance gene pool and support the strategic utilization of wild cowpea germplasm in breeding programs. Full article

Small and medium enterprises (SMEs) in the entertainment sector face significant challenges managing seat assignments through manual processes that are error-prone and time-consuming. This paper presents an intelligent agent-based system that automates seat assignment, while providing natural language support for operational staff. The system integrates a large language model (Gemini 2.5 Flash) for conversational interaction with a constraint-based optimization algorithm that considers capacity, accessibility, revenue, and business priorities. A fuzzy matching engine combining spaCywith the fuzzy string matching library FuzzyWuzzy consolidates duplicate reservations from multiple channels. The cloud-based architecture leverages AWS managed serverless services (ECS Fargate for container orchestration and Lambda for event-driven pipelines) with PostgreSQL for data management. Technology Readiness Level 4 (TRL4) validation demonstrated 94% precision in duplicate detection, successful assignment of 87% of reservations with 82% average capacity utilization, and effective natural language query handling. The system reduces manual processing time by 65%, while improving assignment quality through systematic enforcement of constraints. This work demonstrates the feasibility of AI-powered operations management for resource-constrained SMEs, offering a practical reference architecture combining conversational AI with algorithmic optimization. Full article

Drought is a widespread natural hazard in China that can sequentially trigger meteorological, hydrological, agricultural, and socio-economic drought types, yet traditional drought indices typically focus on a single hydrologic component and cannot capture integrated water deficits across multiple compartments. This study aims to systematically characterize the spatiotemporal evolution of comprehensive drought across China’s ten major river basins and to identify and quantify the main natural and anthropogenic factors associated with drought dynamics. We utilized the Gravity Recovery and Climate Experiment (GRACE) Mascon dataset spanning the entire mission period (April 2002–June 2017), which provides a continuous 15-year observation window suitable for detecting decadal-scale trends and inter-annual variability. Given the documented asynchrony between precipitation and terrestrial water storage changes, a zoned index framework was applied: the Combined Climatologic Deviation Index (CCDI) for arid basins and the Drought Severity Index (DSI) for humid basins. The Theil–Sen estimator and Mann–Kendall test, both non-parametric and robust to outliers, were employed for trend detection, and Pearson correlation analysis was used to evaluate statistical associations between drought indices and potential influencing factors. The results reveal a clear “dry gets drier, wet gets wetter” pattern during 2002–2017: severe drought episodes in humid basins (e.g., the Yangtze) were concentrated in 2002–2006, whereas those in arid basins (e.g., the Haihe) occurred mainly in 2013–2017. Groundwater storage anomaly (GWSA) constituted the primary component of total water storage changes in most basins, with the most rapid depletion rate of −45 mm yr⁻¹ in the northern arid basins. Land use/cover change, especially urban expansion, showed a significant statistical association with drought intensification in arid regions, with its standardized contribution being comparable to that of natural factors such as runoff. This study provides a systematic cross-basin assessment and offers scientific insights for differentiated drought mitigation strategies and water resources management. Full article

Forest disturbances significantly affect the terrestrial carbon cycle, yet high-resolution detection, driver attribution, and carbon loss quantification remain challenging in cloudy and complex terrains. Here, we investigated the Northeast China and Southwest Hengduan Mountains forest regions from 2021 to 2024. We developed a Bayesian Model Averaging (BMA) framework integrating multi-source remote sensing (Sentinel-1/2, Landsat 8/9) and multi-algorithm ensembles (LandTrendr, CCDC, 1D-CNN) to extract 10 m disturbance features. Automated driver attribution and carbon loss quantification were achieved utilizing the Fire Information for Resource Management System (FIRMS), Dynamic World, and GEDI L4B LiDAR data. Validation yielded overall spatial accuracies of 91.15% in the Northeast and 89.62% in the Hengduan Mountains, with corresponding ensemble F1-Scores of 0.92 in both regions. Results indicated the disturbed area in the Northeast (1084.58 ha) significantly exceeded the Hengduan region (133.48 ha). Natural degradation dominated both regions (Northeast: 72.25%; Hengduan: 88.43%), though the Northeast experienced more wildfires and anthropogenic activities. Topographically, Northeast disturbances clustered on low-lying, gentle landscapes, whereas Hengduan events occurred on steep, high-altitude terrains. Due to denser per-pixel carbon storage, the Hengduan area exhibited higher carbon emission costs per unit area. Ultimately, this framework provides a quantitative technical foundation supporting high-resolution forest conservation and spatial evaluations for carbon neutrality commitments. Full article

The dynamic growth of tourism in Serbia has significantly reshaped the spatial structure of destinations, raising important issues related to sustainable development and spatial management. This study aims to examine the perceived effectiveness of spatial planning in tourism, based on tourists’ assessments of plan implementation and its visible implications for sustainable development. The research was conducted in Serbia in 2025 using a mixed-method approach, combining quantitative and qualitative analysis based on a survey of 208 valid respondents. The quantitative analysis included Spearman’s rank correlation and Z-test to examine relationships between variables and differences in attitudes, while qualitative insights were derived from open-ended responses. The results indicate a statistically significant positive correlation between the perceived implementation of spatial plans and the level of tourism development (Rs = 0.283, p < 0.001). However, the findings also reveal that 41.3% of respondents believe that tourism is only slightly considered in spatial plans, while 45.19% express negative attitudes toward the integration of tourism into planning processes. The study identifies key challenges, including weak cross-sector coordination, insufficient integration of tourism into spatial plans, and limited involvement of local communities. These findings highlight a critical gap between formal planning frameworks and their practical implementation. The main contribution of this research lies in providing empirical evidence from the perspective of tourists, a stakeholder group often overlooked in spatial planning studies, thereby addressing a gap in the literature on tourism–planning integration in Serbia. The results suggest that improving institutional coordination, strengthening participatory planning, and enhancing monitoring mechanisms are essential for achieving sustainable tourism development. The study offers targeted policy implications for aligning spatial planning with tourism development goals while preserving natural and cultural resources. Full article

Northern Colombian Creole-Antillean avocado constitutes a promising agroindustrial resource because of its lipid-rich composition and regional availability. Despite this potential, the industrial exploitation of this biomass remains limited, particularly regarding the technical assessment of large-scale oil production systems. In this study, an avocado oil production process was evaluated through computer-aided simulation combined with the Water–Energy–Product (WEP) methodology to assess operational behavior, resource utilization, and process efficiency from an integrated technical perspective. The evaluated system achieved an overall production yield of 9.43%, mainly affected by the elevated raw material requirements associated with oil generation. Nevertheless, the extraction stage exhibited favorable technical performance, reaching an oil recovery efficiency of 81.42%. Concerning water management, the process required 26.85 m³/t of freshwater and generated wastewater equivalent to 96.05% of the total water consumed, revealing important limitations related to water integration and recirculation within the process configuration. From an energy perspective, the system presented a specific energy intensity of 19,929 MJ/t, with natural gas representing the predominant energy source throughout the operation. Overall, the obtained results demonstrate that the proposed process is technically viable for avocado oil production while also identifying critical opportunities for improving resource utilization, decreasing water demand, and enhancing the operational sustainability of the system. Full article

Against the backdrop of rapid urbanization, urban land-resource use faces the dual challenge of improving efficiency while maintaining ecological sustainability. Enhancing urban land green use efficiency contributes to the achievement of the United Nations Sustainable Development Goals, particularly SDG 11 and SDG 15. As an emerging governance approach for urban green infrastructure, the National Sponge City Policy (NSCP) aims to address urban waterlogging through nature-based solutions while improving land multifunctionality and ecological carrying capacity. Based on city-level panel data from 2005 to 2022, this study employs a difference-in-differences (DID) approach to identify the policy effect of the NSCP on ULGUE and further examines three transmission channels: innovation effects, infrastructure-support effects, and population-agglomeration effects. The novelty of this study lies in integrating the NSCP into the analytical framework of urban land green use efficiency, extending previous research that mainly focused on waterlogging control, water-resource management, and ecological benefits, and further developing a “policy intervention-factor reallocation-ULGUE improvement” mechanism pathway. The empirical results show that the NSCP significantly improves land green use efficiency in pilot areas, and this conclusion remains valid across multiple robustness checks. The mechanism analysis indicates that strengthened green innovation capacity, improved green infrastructure, and population agglomeration are key channels through which the policy effect is realized. Heterogeneity analysis further reveals that the policy effect varies across regions, dominant industrial structures, and industrial-base types. Overall, the NSCP promotes green spatial governance and efficient resource utilization, providing important institutional experience for coordinating ecological protection and urban development. Full article

In the face of the raw materials crisis and environmental concerns, sustainable waste management has become a priority for current and future generations. Recycling waste from wastewater treatment plants in a closed loop protects natural resources, reduces landfill volumes, and lowers disposal costs. This paper presents the results of tests on the physical, filtration, and mechanical properties of mixtures of washed mineral waste (WMW) from grit chambers with fly ash from the thermal treatment of municipal sewage sludge (SSA) in a fluidized bed furnace. Additionally, radiological tests of the mixture components were conducted. Based on the conducted tests, the possibility of sustainable use in civil engineering, such as soil backfills and embankment construction materials, was assessed. The possibility of safely using waste materials in the indicated construction solutions was demonstrated for mixtures with dominant WMW content (90% and 70% by total weight). The waste mixtures correspond to poorly or medium-grade sands with a small amount of silt (uniformity coefficients of 3.33, 3.50, and 8.00). They are characterized by maximum dry densities of 1.542, 1.770, and 1.780 g/cm³; optimal moisture contents of 12.54, 12.86, and 20.25%; permeability coefficients of 0.08, 0.22, and 0.39 m/d; and internal friction angles of 38.4, 39.5, and 40.1°. The values of the determined parameters of some mixtures are similar to those of natural sands used as construction aggregates. All mixtures meet the geotechnical criteria for use in road embankments, below frost depth, and in flood embankment bodies. Mixtures with a 90% mass fraction of WMW were also approved for application as backfill for installation trenches. However, none of the mixtures met the hydraulic conductivity threshold required for the upper layers of embankments nor for backfill of abutments and retaining structures without the use of an additional binder (cement or lime), which is considered a prerequisite for these applications. Full article

Across semi-arid and environmentally vulnerable regions, intensifying climate pressures, land degradation, and resource scarcity are placing growing demands on institutions, communities, and land users. However, the knowledge and technical skills required to respond effectively remain uneven and often poorly aligned with local needs. This study presents a comparative skills audit in Kimberley, Upington, and Rietfontein in the Northern Cape, identifying capacity gaps, stakeholder-specific training priorities, and structural barriers in natural resource and sustainable land management. Using questionnaires, semi-structured interviews, participatory site visits, and multi-stakeholder consultations, competencies were assessed across GIS and remote sensing, climate resilience, soil and land restoration, water conservation, sustainable agriculture, and policy literacy. Results show significant disparities in skills proficiency. GIS and remote sensing (0.8) and climate resilience strategies (1.0) were weakest, while policy literacy (1.5) and soil management (2.0) were also limited. Sustainable agriculture (4.0) and water conservation (2.8) showed relatively stronger capacity. Training needs varied by stakeholder, with government prioritizing geospatial tools and governance, and farmers emphasizing climate adaptation and resource management. Key barriers include limited digital infrastructure (83%), insufficient government support (80%), high training costs (78%), and contextual mismatches (50%). Integrated, place-based capacity development is essential to strengthen adaptive governance and long-term resilience. Full article

Developing sustainable strategies for natural resource management and conservation under shifting climatic scenarios is increasingly necessary due to exacerbated abiotic stresses, such as salinity. Under salt stress, several negative effects are observed in plants, particularly in leafy vegetables such as lettuce (Lactuca sativa L.). To mitigate the effects of saline stress from brackish water, several strategies have been adopted, including hydroponic cultivation. Therefore, this study aimed to determine the effects of variations in nutrient solution concentration and volume per lettuce plant cultivated in a nutrient film technique (NFT) hydroponic system using brackish water. The experiment was conducted using a randomized complete block design in a 2 × 2 × 2 factorial scheme, combining two levels of water electrical conductivity (ECw of 0.3 and 5.0 dS m⁻¹), two nutrient solution concentrations (NSC of 50 and 100%), and two nutrient solution volumes (NSV of 1 and 2 L plant⁻¹), with four replications. Growth, production, and water productivity variables were evaluated at 20 and 25 days following the imposition of treatments. The responses of the variables to saline stress varied according to the evaluation period (20 and 25 days), depending on the NSC and NSV levels. At the end of the 25-day cycle, it can be concluded that for lettuce cultivation using brackish water, the NSC can be reduced to 50% and provide an NSV of 2 L plant⁻¹. Under these growing conditions, leaf fresh matter production loss was approximately 40% lower than under cultivation without saline stress, which yielded 144.11 g plant⁻¹ under 100% NSC and an NSV of 2 L plant⁻¹. In contrast, water productivity of fresh matter was similar, at 78.68 and 76.55 g L⁻¹, respectively. Full article

Using solid wastes to fabricate sustainable backfill materials for mining engineering is crucial for environmental sustainability worldwide. In this study, the use of coal gangue aggregates as a sustainable alternative to natural aggregates in geopolymer gel backfill materials was explored, which contributes to green mining development. Through uniaxial compression tests, the effects of fine gangue content, mass concentration, and the binder content of geopolymer backfill materials on the compressive behavior of coal gangue geopolymer gel backfill–rock combinations (CGBRC) were systematically evaluated. Digital Image Correlation (DIC) and acoustic emission (AE) techniques were employed to reveal the strain field evolution and damage progression of CGBRC. Results show that as the content of fine coal gangue increases, the compressive strength first increases and then decreases. Compared with the compressive strength at a 20% content, the compressive strength at a 40% content increased by 33.2%, while the elastic modulus increased by 11.2%. Meanwhile, with the increase in mass concentration and binder content, the compressive strength and elastic modulus of coal gangue geopolymer filling materials show an increasing trend, reaching peak values at 86% mass concentration and 32% binder content, respectively. The strain concentration zones mainly form near the backfill interface, with propagation paths governed by backfill strength. Damage evolution undergoes three stages including rapid accumulation during compaction, gradual development in the elastic-plastic stage, and abrupt acceleration at failure. The interfacial debonding behavior is primarily influenced by the strength difference between the backfill and surrounding rock. Specimen failure is dominated by brittle shear fracture, categorized into three modes based on crack paths relative to the backfill, which include penetrating backfill failure, axisymmetric interface failure, and centrally symmetric interface failure. These findings offer theoretical and technical support for coal gangue resource utilization and green mining practices, advancing sustainable solid waste management. Full article

Coastal wetlands, as sensitive ecological interfaces of land–sea interactions, provide regulating functions and ecosystem service values for maintaining regional ecological security. To achieve systematic restoration of ecological functions and intelligent management of resources in coastal wetlands, it is critical to deconstruct the evolution patterns of their landscape configurations across multiple spatiotemporal scales and precisely identify driving factors and ecological risk transmission mechanisms. This study constructs a trinity framework of “pattern evolution-driver analysis-risk assessment” for landscape ecological risk (LER) evaluation, integrating spatial statistical analyses (Standard Deviational Ellipse, Land Use Transition Matrix) and Geographically Weighted Regression (GWR) models to systematically analyze the spatiotemporal evolution characteristics and multidimensional driving mechanisms of landscape patterns in the Yellow River Delta (YRD), a typical coastal wetland, from 2000 to 2023. The results are as follows: (1) total wetland area initially declines followed by partial recovery, with natural wetlands decreasing persistently and artificial wetlands expanding; (2) Gross domestic product (GDP) and temperature (TMP) are identified as the primary drivers of wetland evolution; (3) Wetland LER levels significantly increase from 2015 to 2020, with the proportion of high-risk areas rising from 10% in 2015 to 23% in 2020; (4) LER is predominantly characterized by High-High (H-H) clustering, with Moran’s I values ranging from 0.493 to 0.672 (all p < 0.001), indicating significant positive spatial autocorrelation. The wetland LER assessment framework developed in this study, grounded in a land–sea integrated perspective, provides decision-making support and theoretical foundations for formulating differentiated wetland restoration strategies and optimizing coastal ecological security patterns. Full article

Climate change directly affects agricultural productivity, particularly in small island systems where ecosystems and livelihoods are highly exposed to climate variability. This study presents a comprehensive analysis of climate variability for the three districts North and Middle Andaman, South Andaman, and Nicobar, using a six-model CMIP6 ensemble under four SSP scenarios (SSP126, SSP245, SSP370, and SSP585), coupled with ensemble tree-based machine learning algorithms to project coconut yield responses. The historical data was analysed from 1981 to 2025 and the projection was from 2026 to 2100. Observed rainfall reveals a persistent north-to-south gradient, with South Andaman recording the highest mean annual rainfall (3408.40 mm) and Nicobar recording the lowest (2442.13 mm), alongside pronounced inter-annual variability and a discernible drying tendency post-2015. Nicobar consistently records the warmest mean Tmax (30.89 °C) and Tmin (24.11 °C), while North and Middle Andaman exhibit the greatest inter-annual temperature variability. Future projections indicate a robust and statistically significant warming across all districts and scenarios, with end-of-century Tmax increases reaching up to 4.05 °C (Nicobar, SSP585) and Tmin increases up to 3.73 °C (North and Middle Andaman, SSP585), accompanied by a progressive compression of the diurnal temperature range. Precipitation projections show modest wetting in the Andaman districts under most scenarios, while Nicobar exhibits a muted response, with SSP370 uniquely projecting a decline of approximately 69 mm below the observed baseline. Among the ten evaluated CMIP6 models, six (ACCESS-CM2, CMCC-ESM2, CNRM-ESM2-1, EC-Earth3-Veg-LR, GFDL-ESM4, and NorESM2-MM) were selected based on composite skill scores across rainfall, Tmax, and Tmin. Model selection was optimized independently for each district via Leave-One-Year-Out cross-validation with hyperparameter tuning, yielding district-specific best performers: GradientBoost for North and Middle Andaman (R² = 0.471), RandomForest for South Andaman (R² = 0.609), and ExtraTrees for Nicobar (R² = 0.289). K-Nearest Neighbours demonstrated competitive predictive skill in all three districts, confirming that instance-based learning can capture non-linear climate–yield relationships, though tree-based ensembles were preferred for their robustness and interpretability. Ensemble tree-based ML models and instance-based learning consistently outperformed all linear and kernel-based approaches, confirming the non-linear nature of climate–yield relationships in this setting. Coconut yield projections indicate above-baseline productivity gains of 3.4–21.5% in North and Middle Andaman and 24.6–36.8% in South Andaman, driven by favourable warming and precipitation trends, while Nicobar yields plateau at 7.7–13.7% above baseline, indicating thermal saturation of the climate yield response under already near-optimal thermal conditions. Notably, Nicobar exhibits a reversed yield–emission relationship wherein lower-emission pathways marginally outperform high-emission scenarios, likely reflecting avoidance of thermal stress thresholds. Inter-CMIP6-model uncertainty emerges as the dominant source of projection spread, exceeding scenario uncertainty across most districts, underscoring the critical importance of multi-model ensemble frameworks for robust agricultural climate impact assessments in data-sparse tropical island environments. Full article