Search Results (2,652)

This study investigates the structural optimization of lightweight three-layer panels made from industrial hemp shives (core) and hemp fibers (faces) as a sustainable alternative to wood-based materials in furniture manufacturing. Panels with target densities of 400–600 kg·m⁻³ and face-layer contents of 30%–50%were produced and tested to European standards. The optimal configuration—600 kg·m⁻³ with ~37%–41% face layers—achieved a modulus of elasticity up to 3750 N·mm⁻² and a bending strength (MOR) up to 21.57 N·mm⁻². Across the design space, water absorption ranged from ~83% to 162%, and the minimum thickness swelling was ~29%, indicating that while the mechanical properties meet the requirements for P2 particleboards (EN 312) and in some cases approach MDF benchmarks for dry use, thickness swelling remains above the EN 622-5 limit (12%) and thus precludes MDF classification. These findings demonstrate the technical feasibility of hemp shive–fiber panels and underscore the need to balance density and face-layer ratio to avoid loss of core densification at excessive face contents. From a sustainability perspective, the use of rapidly renewable hemp and agricultural residues highlights the potential of these composites to support resource-efficient, low-carbon furniture production, while future work should target improved water resistance through binder and process modifications. Full article

This study addresses the growing need for intelligent monitoring in aquaponic systems by developing a predictive system based on artificial intelligence and environmental sensing. The goal is to improve fish welfare through the early detection of adverse water conditions. The system integrates low-cost digital sensors to continuously measure key physicochemical variables—pH, dissolved oxygen, and temperature—using these as inputs for real-time classification of fish health status. Four supervised machine learning models were evaluated: linear discriminant analysis (LDA), support vector machines (SVMs), neural networks (NNs), and random forest (RF). A dataset of 1823 instances was collected over eight months from a red tilapia aquaponic setup. The random forest model yielded the highest classification accuracy (99%), followed by NN (98%) and SVM (97%). LDA achieved 82% accuracy. Performance was validated using 5-fold cross-validation and label permutation tests to confirm model robustness. These results demonstrate that sensor-based predictive models can reliably detect early signs of fish stress or mortality, supporting the implementation of intelligent environmental monitoring and automation strategies in sustainable aquaponic production. Full article

After the original support system in the auxiliary transportation roadway of the northern wing of the Zhaoxian Mine failed, the extent of damage and deformation varied significantly across different sections of the drift. A single support method could not meet the engineering requirements. Therefore, this paper conducted research on the classification of roadway damage and zoning repair. The overall damage characteristics of the roadway are described by three indicators: roadway deformation, development of rock mass fractures, and water seepage conditions. These are further refined into nine secondary indicators. In summary, a rock mass damage combination weighting evaluation model based on the FAHP–entropy weight TOPSIS method is proposed. According to this model, the degree of damage to the roadway is divided into five grades. After analyzing the damage conditions and support requirements at each grade, corresponding zoning repair plans are formulated by adjusting the parameters of bolts, cables, channel steel beams, and grouting materials. At the same time, the reliability of partition repair is verified using FLAC3D 6.0 numerical simulation software. Field monitoring results demonstrated that this approach not only met the support requirements for the roadway but also improved the utilization rate of support materials. This provides valuable guidance for the design of support systems for roadways with similar heterogeneous damage. Full article

Wetland mapping plays a crucial role in monitoring wetland ecosystems, water resource management, and habitat suitability assessment. Wetland classification remains significantly challenging due to the diverse types, intricate spatial patterns, and highly dynamic nature. This study proposed a dynamic hybrid method that integrated feature selection and object-oriented ensemble model construction to improve wetland mapping using Sentinel-1 and Sentinel-2 data. The proposed feature selection approach integrates the ReliefF and recursive feature elimination (RFE) algorithms with a feature evaluation criterion based on Shapley additive explanations (SHAP) values, aiming to optimize the feature set composed of various variables. During the construction of ensemble models (i.e., RF, XGBoost, and LightGBM) with features selected by RFE, hyperparameter tuning is subsequently conducted using Bayesian optimization (BO), ensuring that the selected optimal features and hyperparameters significantly enhance the accuracy and performance of the classifiers. The accuracy assessment demonstrates that the BO-LightGBM model with ReliefF-RFE-SHAP-selected features achieves superior performance to the RF and XGBoost models, achieving the highest overall accuracy of 89.4% and a kappa coefficient of 0.875. The object-oriented classification maps accurately depict the spatial distribution patterns of different wetland types. Furthermore, SHAP values offer global and local interpretations of the model to better understand the contribution of various features to wetland classification. The proposed dynamic hybrid method offers an effective tool for wetland mapping and contributes to wetland environmental monitoring and management. Full article

Seasonally dry tropical forests (SDTFs) are shaped by strong climatic and edaphic constraints, including pronounced rainfall seasonality, extended dry periods, and shallow karst soils with limited water retention. Understanding how tree species respond to these pressures is crucial for predicting ecosystem resilience under climate change. In the Yucatán Peninsula, we characterized sixteen tree species along a spatial and seasonal precipitation gradient, quantifying wood density, predawn and midday water potential, saturated and relative water content, and specific leaf area. Across sites, diameter classes, and seasons, we measured ≈4 individuals per species (n = 319), ensuring replication despite natural heterogeneity. Using a principal component analysis (PCA) based on individual-level data collected during the dry season, we identified five functional groups spanning a continuum from conservative hard-wood species, with high hydraulic safety and access to deep water sources, to acquisitive light-wood species that rely on stem water storage and drought avoidance. Intermediate-density species diverged into subgroups that employed contrasting strategies such as anisohydric tolerance, high leaf area efficiency, or strict stomatal regulation to maintain performance during the dry season. Functional traits were strongly associated with precipitation regimes, with wood density emerging as a key predictor of water storage capacity and specific leaf area responding plastically to spatial and seasonal variability. These findings refine functional group classifications in heterogeneous karst landscapes and highlight the value of trait-based approaches for predicting drought resilience and informing restoration strategies under climate change. Full article

Spectral detection technology offers non-destructive, in situ, and high-speed capabilities, making it widely applicable for detecting biological and chemical samples and quantifying their concentrations. Water resources, essential to life on Earth, are widely distributed across the planet. The application of spectral technology to underwater environments is useful for wide-area water resource monitoring. Although spectral detection technology is well-established, its underwater application presents challenges, including waterproof housing design, power supply, and data transmission, which limit widespread application of underwater spectral detection. Furthermore, underwater spectral detection necessitates the development of compatible computational methods for sample classification or regression analysis. Focusing on underwater spectral detection, this work involved the construction of a suitable hardware system. A compact spectrometer and LEDs (400 nm–800 nm) were employed as the detection and light source modules, respectively, resulting in a compact system architecture. Extensive tests confirmed that the miniaturized design-maintained system performance. Further, this study addressed the estimation of total phosphorus (TP) concentration in water using spectral data. Samples with varying TP concentrations were prepared and calibrated against standard detection instruments. Subsequently, classification algorithms applied to the acquired spectral data enabled the in situ underwater determination of TP concentration in these samples. This work demonstrates the feasibility of underwater spectral detection for future in situ, high-speed monitoring of aquatic biochemical indicators. In the future, after adding UV LED light source, more water quality parameter information can be obtained. Full article

Ochetobius elongatus, a critically endangered (CR) fish species of the Yangtze River Basin in China, has experienced a severe decline in its wild population. Understanding its mechanisms of phenotypic variation is essential for developing effective conservation and restoration strategies. Using geometric morphometrics based on 14 landmarks, we examined the phenotypic difference among five populations from the mainstem, the tributary, and the river-connected lakes of the middle Yangtze River. The results showed that significant phenotypic divergence was detected between river and lake populations. River individuals exhibited a more elongated body, smaller head, inferior mouth position, larger operculum, and narrower caudal peduncle, whereas lake individuals showed a deeper body, and anterior shift in the origin of pelvic fin. The first canonical variable effectively distinguished river and lake populations, with the accuracy of both original and cross-validation classification exceeding 90%, indicating that habitat heterogeneity was the primary driver of phenotypic differentiation. No significant correlation was found between morphological distance and geographical distance. Water temperature, flow velocity, water depth, and food abundance significantly influenced phenotypic variation, but their individual effects were limited, which suggested that environmental shaping of morphology depended more on synergistic effects. Our findings provide important insights into the adaptive evolution of this critically endangered species and offer a scientific basis for conservation efforts. Full article

This research assesses how land-cover transitions from 2012 to 2022 have impacted the value of ecosystem services in Denton County, Texas. Using remote sensing and spatial analysis, this study quantitatively links land-use change to its ecological and economic consequences. Full-county Landsat data were analyzed in ArcGIS Pro through supervised classification and categorical change detection. To quantify the impact of these changes, an accuracy assessment was performed, and a benefit-transfer method using both global and Texas-specific coefficients was applied to estimate the change in Ecosystem Service Value (ESV). Results revealed a complex dynamic: while the county experienced significant urban expansion, it also saw substantial greening as large areas of bare land transitioned to vegetation. However, this greening was not enough to offset the economic impact of losing high-value ecosystems. The analysis shows a net loss in total ESV over the decade, estimated between USD 24 million and USD 95 million per year, primarily driven by the significant reduction of water bodies. This study provides a replicable framework for policymakers to assess the environmental trade-offs of development and highlights the critical importance of preserving existing high-value ecosystems alongside urban greening initiatives. Full article

This study explores the detection and classification of aguadas (ponds) in the Bajo El Laberinto region, in the Calakmul Biosphere Reserve, Campeche, Mexico, using remote sensing techniques. Lidar-derived digital elevation models (DEMs), orthophotos and satellite imagery from multiple sources were employed to identify and characterize these water reservoirs, which played a crucial role in ancient Maya water management and continued to be vital for contemporary wildlife. By comparing different visualization techniques and imagery sources, the study demonstrates that while lidar data provides superior topographic detail, satellite imagery—particularly with nominal 3 m, or finer, spatial resolution with a near-infrared band—offers valuable complementary data including present-day hydrological and vegetative characteristics. In this study, 350 aguadas were identified in the broader region. The shapes, canopy cover, and topographic positions of these aguadas were documented, and the anthropogenic origin of most features was emphasized. The paper’s conclusion states that combining various remote sensing datasets enhances the identification and understanding of aguadas, providing insights into ancient Mayan adaptive strategies and contributing to ongoing archaeological and ecological research. Full article

Classification of island coastal landscapes is a challenge to incorporate both the terrestrial and the aquatic environment characteristics, and place biological diversity in a regional and insular context. The Coastal and Marine Ecological Classification Standard (CMECS) was developed for use in the United States and incorporates geomorphic data, substrate data, biological information, as well as water column characteristics. The CMECS framework was applied to the island of Great Exuma, The Bahamas. The classification used data from existing studies to include oceanographic data, seawater temperature, salinity, benthic invertebrate surveys, sediment analysis, marine plant surveys, and coastal geomorphology. The information generated is a multi-dimensional description of benthic and shoreline biotopes characterized by dominant species. Biotopes were both mapped and described in hierarchical classification schemes that captured unique components of diversity in the mosaic of coastal natural communities. Natural community classification into biotopes is a useful tool to quantify ecological landscapes as a basis to develop monitoring over time for biotic community response to climate change and human alteration of the coastal zone. Full article

Coffee processing generates large volumes of spent coffee grounds (SCGs), which contain 30–40% hemicellulose, 8.6–13.3% cellulose, and 25–33% lignin, making them a promising lignin-rich filler for biocomposites. Conventional wood composites rely on urea-formaldehyde (UF), melamine–urea–formaldehyde (MUF), and phenol–formaldehyde resins (PF), which dominate 95% of the market. Although formaldehyde emissions from these resins can be mitigated through strict hygiene standards and technological measures, concerns remain due to their classification as category 1B carcinogens under EU regulations. In this study, fiber-based biocomposites were fabricated from thermomechanical wood fibers, SCGs, and ammonium lignosulfonate (ALS). SCGs and ALS were mixed in a 1:1 ratio and incorporated at 40–75% of the oven-dry fiber mass. Hot pressing was performed at 150 °C under 1.1–1.8 MPa to produce panels with a nominal density of 750 kg m⁻³, and we subsequently tested them for their physical properties (density, water absorption (WA), and thickness swelling (TS)), mechanical properties (modulus of elasticity (MOE), modulus of rupture (MOR), and internal bond (IB) strength), and thermal behavior and biodegradation performance. A binder content of 50% yielded MOE ≈ 2707 N mm⁻² and MOR ≈ 22.6 N mm⁻², comparable to UF-bonded medium-density fiberboards (MDFs) for dry-use applications. Higher binder contents resulted in reduced strength and increased WA values. Thermogravimetric analysis (TGA/DTG) revealed an inorganic residue of 2.9–8.5% and slower burning compared to the UF-bonded panels. These results demonstrate that SCGs and ALS can be co-utilized as a renewable, formaldehyde-free adhesive system for manufacturing wood fiber composites, achieving adequate performance for value-added practical applications while advancing sustainable material development. Full article

Using Landsat series imagery and the deep learning model CITNet, this study conducted high-accuracy classification and spatiotemporal change analysis of wetlands on Chongming Island from 2000–2020 and explored the driving mechanisms by integrating climatic and anthropogenic factors. The results demonstrate that the total wetland area decreased by approximately 125.5 km² over the two decades. Among natural wetlands, tidal mudflats and shallow seawater zones continuously shrank, while herbaceous marshes exhibited a “decline recovery” trajectory. Artificial wetlands expanded before 2005 but contracted significantly thereafter, mainly due to aquaculture pond reduction. Wetland transformation was dominated by wetland-to-non-wetland conversions, peaking during 2005–2010. Driving factor analysis revealed a “human pressure dominated, climate modulated” pattern: nighttime light index (NTL) and GDP demonstrated strong negative correlations with wetland extent, while minimum temperature and the Palmer Drought Severity Index (PDSI) promoted herbaceous marsh expansion and accelerated artificial wetland contraction, respectively. The findings indicate that wetland changes on Chongming Island result from the combined effects of policy, economic growth, and ecological processes. Sustainable management should focus on restricting urban expansion in ecologically sensitive zones, optimizing water resource allocation under drought conditions, and incorporating climate adaptation and invasive species control into restoration programs to maintain both the extent and ecological quality of wetlands. Full article

Background/Objectives: This study aimed to develop a novel amorphous solid dispersion (ASD) platform using poly(2-ethyl-2-oxazoline) (PEtOx) for the solubility enhancement of poorly water-soluble drugs. Fenofibrate (FB), a Biopharmaceutics Classification System (BCS) Class II drug, was selected as the model drug. The novelty of this work lies in the formulation of dual-matrix systems by blending PEtOx of varying molecular weights (50 kDa, 200 kDa, 500 kDa) with solubility-enhancing polymers, Soluplus^® and Kollidon^® VA64, to investigate component compatibility, synergistic solubility enhancement, and the influence of PEtOx molecular weight on drug release. Methods: ASDs were prepared via hot-melt extrusion (HME) and characterized using differential scanning calorimetry (DSC), scanning electron microscopy (SEM), powder X-ray diffraction (PXRD), and Fourier transform–infrared spectroscopy (FTIR) to confirm FB amorphization and evaluate drug–polymer interactions. In vitro dissolution testing was performed to assess drug release performance, and stability studies were conducted at ambient conditions for one month to evaluate physical stability. Results: DSC, PXRD, and FTIR confirmed the successful amorphization of FB and good miscibility between PEtOx and the selected excipients. In vitro dissolution studies showed an 8–12-fold increase in FB release from ASDs compared to crystalline drug. Lower-molecular-weight PEtOx grades yielded faster release profiles, while binary blends with Soluplus^® or Kollidon^® VA64 enabled tailored drug release. Stability testing indicated that all formulations maintained their amorphous state over one month. Conclusions: PEtOx-based ASDs represent a versatile platform for enhancing the solubility and dissolution of poorly water-soluble drugs. By adjusting polymer molecular weight and combining with complementary excipients, release profiles can be optimized to achieve improved performance and stability. Full article

Surface water resources are under growing pressure from urbanization, industrial activity, and agriculture, making effective monitoring essential for safeguarding ecological integrity and human use. Conventional monitoring methods, which rely on manual sampling and rigid Water Quality Index (WQI) categories, often provide delayed feedback and oversimplify conditions near classification thresholds, limiting their usefulness for timely management. To overcome these shortcomings, we have developed an interactive fuzzy logic-based water quality monitoring interface or dashboard that integrates the WQI developed by Malaysia’s Department of Environment with the National Water Quality Standards (NWQS) Class I–V framework. The interface combines conventional WQI computation with advanced visualization tools such as dynamic gauges, parameter tables, fuzzy membership graphs, scatter plots, heatmaps, and bar charts. Then, triangular membership functions map six key parameters to NWQS classes, providing smoother and more nuanced interpretation compared to rigid thresholds. In addition to that, the dashboard enables clearer communication of trends, supports timely decision-making, and demonstrates adaptability for broader applications since it is implemented on the Replit platform. Finally, evaluation results show that the fuzzy interface improves interpretability by resolving ambiguities in over 15% of cases near class boundaries and facilitates faster assessment of pollution trends compared to conventional reporting. Thus, these contributions highlight the necessity and value of the research on advancing Malaysia’s national water quality monitoring and providing a scalable framework for international contexts. Full article

The increasing droughts and climate change effects and their frequencies worldwide are a critical threat, especially to regions facing water scarcity and wars. Therefore, comprehensive drought evaluation and trend analysis are crucial for water resources management, climate change, and drought mitigation plans. Classical drought evaluation methods predominantly rely on in situ observations, often limited or unavailable in many regions, particularly in developing countries such as Palestine. This study investigates the temporal and spatial characteristics and trends of drought across Palestine between 1940 and 2025. To the best of our knowledge, for the first time in the literature, bias-corrected ERA5 precipitation data are employed alongside ground-based observations to assess drought using the Standardized Precipitation Index (SPI) at multiple timescales (1-, 6-, and 12-month). Trend detection was performed through conventional statistical approaches, including the Mann–Kendall test, Spearman’s Rho, and Sen’s slope (SS), as well as the Frequency-Innovative Trend Analysis (F-ITA) method. Furthermore, the performance of the original and bias-corrected ERA5 precipitation datasets was evaluated against observational data using statistical metrics. The main findings indicated that the bias correction significantly improves the accuracy of the ERA5 precipitation data. Also, droughts in SPI-1 and SPI-6 ranged from 4 to 5 months, the minimum at which a drought can be classified. In addition, the average drought duration at a 12-month timescale ranged between 14 and 16 months. At short (SPI-1) and medium (SPI-6) timescales, no significant trends were found, whereas at the long timescale (SPI-12) all stations showed a significant decreasing SPI trend, such as −5.611 in Jenin, reflecting intensifying drought conditions. For F-ITA, the frequencies of extreme drought classification increased from 0.4% in the first period to 2.18% in the second period. The findings of this research have important implications for drought management, water policy planning, and climate adaptation in Palestine. Full article