Search Results (32,922)

The management and efficient use of water resources are crucial to the sustainable development of agriculture in arid regions. The Manas River Basin faces severe water shortages due to its arid climate and heavy reliance on irrigation water. Therefore, based on water footprint theory, this study comprehensively utilized the CROPWAT model, pathway analysis, and CMIP6 data to construct an integrated “assessment–driving–prediction” framework for crop water footprints, with the aim of revealing the evolution patterns and driving mechanisms of water footprints in river basins. The results showed that the cultivated area of crops in the Manas River Basin exhibited a nonlinear expansion trend from 1990 to 2020, with a total increase of 143.56% over the 30-year period. Among all crops, cotton occupied the largest cultivated area, accounting for 60.34% of the total. During the study period, the crop water footprint, crop blue water footprint, and crop green water footprint in the Manas River Basin showed overall upward trends, increasing by 1.07 × 10⁹ m³, 1.04 × 10⁹ m³, and 3.0 × 10⁷ m³, respectively. Total agricultural machinery power and per capita grain production are the main factors influencing changes in crop water footprint. Under future climate scenarios, the crop water footprint in the Manas River Basin is projected to follow the order SSP2-4.5 > SSP5-8.5 > SSP1-2.6. By 2100, the crop water footprint under the SSP2-4.5 scenario is expected to increase by 37.01% relative to 2020, posing substantial challenges to agricultural water resource management in the basin. In contrast, the crop water footprint under the SSP1-2.6 scenario remains relatively stable, indicating a more sustainable development pathway. Full article

Air pollution from particulate matter (PM2.5 and PM10) poses a serious public health risk in urban environments, particularly in areas with heavy vehicular traffic. Against this backdrop, the present study proposes an Internet of Things (IoT)-based system designed to support air quality monitoring and evidence-based decision-making regarding PM2.5 and PM10 concentrations, integrating low-cost sensors with a machine learning prediction module. The study follows an experimental-applied design with a quantitative–comparative approach. Its scientific contribution is organized around an integrated IoT-ML framework addressing a concrete gap in the literature: the lack of local empirical evidence regarding which family of machine learning algorithms delivers the greatest accuracy, stability, and computational efficiency for particulate matter forecasting in mid-altitude urban environments using low-cost sensors. On one hand, the framework proposes and deploys a four-node IoT network for continuous PM2.5 and PM10 monitoring in high-traffic urban microenvironments—representing one of the first sustained deployments with low-cost, high-temporal-resolution sensors (10-minute intervals) in Arequipa, Peru. On the other hand, the study presents the most extensive benchmarking reported in the local literature: a systematic evaluation of 44 machine learning algorithms under homogeneous experimental conditions, covering classical statistical models, traditional machine learning techniques, deep learning architectures, and hybrid approaches, along with an analysis of ensemble learning strategies using Ridge stacking and K-Fold cross-validation. This unified comparative analysis—applying consistent metrics (MAE, RMSE, R², and MAPE), the same prediction horizon, and a shared dataset—provides replicable empirical evidence that had not previously been reported for the urban context of Arequipa. The results show that traditional statistical models perform poorly overall, while tree-based and boosting algorithms consistently achieve R² values above 0.90 for both pollutants. Ensemble models, particularly stacking with Ridge regression and cross-validation, yielded the strongest overall performance, demonstrating greater robustness and prediction stability. Explainability criteria were also incorporated, enabling an assessment of each base model’s individual contribution and identifying the variables most relevant to the prediction process. The methodological contribution provides future researchers with a rigorous reference framework for algorithm selection in environmental IoT systems. Taken together, the findings demonstrate that combining low-cost IoT networks with advanced machine learning and ensemble learning techniques constitutes an effective, scalable, and cost-efficient alternative for air quality monitoring, predictive analysis, and the support of informed mitigation strategies in urban environments. Full article

In our day-to-day lives, we are regularly exposed to a wide spectrum of dangerous gases. Their origins vary, ranging from industrial activities to objects found within our very homes. Naturally, there is an interest in developing cost-efficient and durable devices that can successfully track these gases within our environment. One such candidate is represented by chemoresistive gas sensors based on metal oxides. This is due to their simple architecture and the possibility of scaling down their size, making them valid contenders for future advancements in portable gas sensors. This review focuses on chemoresistive gas sensors that have been obtained through different Physical Vapor Deposition (PVD) methods, which are easily scalable for potential technological transfer towards commercialization or are already exploited at the industrial level, and how varying different deposition parameters impacts the structure of the active material, thus modifying the gas sensing properties of the device. In this review, we report results obtained for different metal oxides: WO₃, ZnO, CeO₂, TiO₂, NiO, and SnO₂. The main findings of these studies revealed that the sensor’s response was highly impacted by oxygen deficiencies within the deposited material, the specific surface area, and the thickness of the film. Moreover, this study also delves into different strategies of functionalization that result in improved gas sensing properties. Thus, we herein report how tailoring functional properties modifies the gas sensing performance of different metal oxides. Full article

The high speed and manoeuvrability of cross-domain unmanned aerial vehicles (CDUAVs) significantly reduce the handover window between mid-terminal guidance stages, challenging reliable target acquisition. To address this, we propose an optimisation method for interceptor selection and field-of-view (FOV) cooperative coverage based on high-probability region (HPR) modelling. First, a predictive error covariance propagation model is constructed based on error propagation theory, and the HPR is established via eigenvalue decomposition. Second, the cooperative detection by heterogeneous interceptor seekers is formulated as a coverage optimisation problem with mixed FOV, and a cost minimisation model under complete coverage constraints is established. Finally, an improved genetic algorithm (IGA) is employed for solution, and a coverage area ratio screening mechanism based on two-dimensional close-packing theory is designed to enhance optimisation efficiency. Simulation results demonstrate that the probabilistic modelling approach for CDUAV HPR can accurately characterise the anisotropic distribution of target position uncertainty; the algorithmic mechanism reduces redundant computational load by 62.5% and shortens the optimisation time by 36.7%; the intelligent coverage optimisation framework provides a more generalisable solution for cooperative detection by heterogeneous interceptors’ seekers under conditions of target localisation uncertainty. Full article

Sustainable agricultural development in natural heritage sites poses a challenge, requiring food security without compromising the conservation of ecosystems and their outstanding universal values (OUV). The Galapagos Islands, recognized as a Natural World Heritage, have problems of scarce water and arable land, compounded by anthropogenic pressures such as high population and tourism growth and dependence on food imports. The objective of this research is to evaluate the environmental impacts of implementing agricultural greenhouses in the Galapagos by applying a traditional environmental matrix alongside a UNESCO World Heritage approach, integrated with a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, to formulate strategies for strengthening local agriculture without compromising ecosystems. This study employed a semi-quantitative methodological approach, integrating three key aspects: (i) a baseline of agricultural information and water availability on the islands; (ii) an integrated Environmental Impact Assessment (EIA) approach to greenhouse implementation; and (iii) sustainable agricultural development and environmental impact mitigation strategies. The results of the traditional EIA and the UNESCO approach through the OUV showed negative impacts classified as insignificant to moderately significant. For the evaluated design, these impacts can be managed through the active participation of academia, the community, and government entities. However, their scalability depends on a more in-depth analysis of the potential long-term risks associated with the availability of natural resources, microplastic pollution, and the use of agrochemicals. Among the proposed strategies, the importance of monitoring water and soil quality and of agricultural and environmental education campaigns in the community was highlighted. This study presents agricultural greenhouses as well-known alternatives for food self-sufficiency, adapted to the realities of the island territory and the objectives of ecosystem conservation. The proposed methodological approach can be applied in protected areas to promote conservation and sustainable agricultural production. Full article

PFAS form a class of synthetic chemicals that has become an area of increasing concern because of its impact on the environment and the threat it poses to human health. The structure of PFAS makes them highly resistant to degradation. As a result, they are highly effective at bioaccumulation. Certain water treatment technologies have been proven to remove PFAS from contaminated water sources. This study reviews the most promising treatment technologies used for the treatment of PFAS-contaminated waters. Well-established treatment technologies, such as granular activated carbon, ion exchange resin, reverse osmosis, and nanofiltration, were quantitatively compared. The removal efficiency was assessed by collecting the data of individual PFAS species from the literature and grouping them into five groups: PFAS (all species), PFSA, PFCA, long chain, and short chain. The results identified that, for all PFAS groups, the most effective treatment technologies were in the following order: reverse osmosis, nanofiltration, ion exchange resin, and granular activated carbon. The performance of reverse osmosis and nanofiltration did not appear to significantly differ between the different PFAS groups, as opposed to ion exchange resin and granular activated carbon, where there was a greater degree of variation in performance between different PFAS groups. Overall, it was identified that membrane technologies outperformed adsorbent technologies. However, the cost associated with membrane technologies may limit its economic viability when compared with adsorbent technologies, which are typically a more viable option except under specific circumstances. For example, contaminated water with high concentrations of other contaminants that need to be treated simultaneously. Lack of standardised experimental and operational conditions limited the available data. While this work provides guidance on which treatment is more likely to be appropriate based on the concentration and composition of different species of PFAS, more data are needed to conduct a more accurate statistical analysis and enable accurate modelling of treatment performance. Full article

To address the inherent drawbacks of peroxymonosulfate advanced oxidation processes (PMS-AOPs), including the low efficiency of reactive species production, short radical half-lives, and restricted pollutant degradation performance, sodium salt-assisted modification was adopted to fabricate ZIF-8-derived carbon. In this study, sodium salt-assisted modification was adopted to treat ZIF-8, and the chlorine-doped derived carbon materials HNC-T_x-Cl were prepared for peroxymonosulfate activation and tetracycline degradation in water. Compared with NC-800 fabricated by direct calcination of ZIF-8 at 800 °C, HNC-800-Cl synthesized via NaCl-assisted calcination exhibits more abundant pore structures and richer carbon defects, with a specific surface area of 1115 m²/g and a high graphitic defect ratio I_D/I_G of 1.20. Catalytic tests reveal that HNC-800-Cl achieves 93.39% tetracycline removal within 90 min at a catalyst dosage of 0.05 g L⁻¹ and PMS concentration of 0.1 mM. The system possesses a strong anti-interference ability toward complex water environments, maintaining a favorable degradation performance in the presence of coexisting anions, natural organic matter and actual water matrices. It also exhibits outstanding cycling stability, retaining a removal rate of 80.34% after five recycling runs. Radical quenching experiments and EPR characterization verify that superoxide radical (·O₂⁻) is the dominant reactive species during tetracycline degradation. Both the radical and non-radical pathways are clarified to illustrate the mechanisms of PMS activation and pollutant degradation. This work provides a novel catalytic material strategy to overcome the deficiencies of conventional PMS-AOPs, and offers a new perspective for structural regulation and non-metallic doping modification of ZIF-8-derived carbon materials. Full article

The practical performance of building-integrated solar systems in cold climates is strongly governed by temperature-grade matching between solar energy output and space-heating demand. However, year-round field evidence comparing solar thermal collectors, photovoltaic systems, and photovoltaic/thermal systems under the same building, climatic, and heating-network boundary conditions remains limited. This study conducted a year-round field evaluation of solar collector (SC), photovoltaic (PV), and photovoltaic/thermal (PVT) systems installed in an office building in Tianjin, China. Continuous operating data collected from November 2022 to October 2023 were used to assess seasonal thermal output, electricity generation, effective heat supply, solar utilization efficiency, carbon reduction, and payback period. During the heating season, SC exhibited the strongest direct-heating capability among the investigated systems, delivering 817.50 MJ/m² of useful heat. In contrast, under the investigated system configuration without heat-pump assistance, the outlet temperature of the PVT subsystem remained below the 45 °C direct-heating threshold, and its thermal output could not be directly utilized for winter space heating. This result is specific to the investigated operating conditions and does not exclude the potential application of PVT systems coupled with heat pumps or low-temperature heating terminals. During the non-heating season, the investigated PVT subsystem simultaneously produced electricity and usable low-temperature heat, with heat and electricity accounting for 61.3% and 38.7% of its useful output, respectively, indicating its potential for combined energy harvesting. Under the investigated climatic, system, cost, and energy-demand conditions, the entropy-weighted TOPSIS assessment ranked SC highest when non-heating-season heat demand was present, whereas PV was more suitable when such heat demand was absent. Furthermore, a demand–output matching method was developed to support SC/PV area allocation for different building types. Under the investigated climatic and energy-demand assumptions, the recommended PV area ratios were 54.5%, 67.4%, and 79.7% for residential, office, and commercial buildings, respectively. These results provide field evidence for effective heat evaluation, temperature-grade matching, and component selection in solar-assisted heating systems for cold-climate buildings. Full article

Drying of Californian red worms (CRW) (Eisenia foetida) was optimized using the refractance window (RW) method. Response surface methodology was used to evaluate the effects of drying temperature (65–85 °C) and mass per unit area (W/A) (0.256–0.367 g/cm²) g/cm²) on the response variables related to the meal’s protein quality and drying parameters. The response variables were digestibility (D), sulfhydryl bonds (SH), protein digestibility-corrected amino acid score (PDCAAS), final moisture content (FMC), and the water effective diffusion coefficient (D_eff). The results showed that as temperature increases, D_eff also increases, while FMC decreases. Conversely, as the W/A ratio increases, SH increases, FMC increases, and D_eff decreases. Furthermore, the optimization of the obtained models yielded values for water temperature (79.9 °C) and W/A (0.367 g/cm²), as well as predicted values for the response variables D (96.87%), FMC (4.12%), D_eff (1.041 × 10⁻¹⁰ m²/s), SH (19.11 μmol/g protein), and PDCAAS (0.459); with a desirability of 0.734. The experimental values obtained for optimal conditions were D: 97.14%, FMC: 6.8%, D_eff: 6. × 10⁻¹¹ m²/s, SH: 14.84 μmol/g protein, and PDCAAS: 0.780. This drying method proved optimal and efficient for dehydrating Californian red worm meat, as it preserves the protein quality of the worm meal while ensuring appropriate drying conditions. Full article

This study aims to map and compare the demand for green skills across four selected African countries by analyzing online job vacancies. Accordingly, the study addresses four research questions: (RQ1) how green skills demand has evolved over time; (RQ2) which sectors exhibit the highest demand for green skills; (RQ3) which occupations are most associated with green skills; and (RQ4) which green competencies are most frequently requested by employers. A Big Data and Labour Market Intelligence approach is employed based on secondary data provided by the online job vacancies (OJV). The results reveal a general upward trend in green skills demand, although with significant cross-country variation. Sectorally, sustainable energy dominates across all countries, followed by more context-specific areas such as agriculture, tourism, and production. At the occupational level, environmental engineers and other technical professions are most strongly associated with green skills. The thematic analysis highlights renewable energy, energy efficiency, and environmental sustainability as the most prominent skill domains, alongside emerging competencies in sustainable mobility, circular economy, and green digital skills. The study contributes to the literature by providing empirical evidence from an underexplored African context and demonstrating the value of online job vacancy data for monitoring labour market transformations. Full article

Currently, the global manufacturing industry faces significant challenges due to increasingly competitive and constantly changing markets. Therefore, adapting to customer needs and improving efficiency and productivity are essential to compete internationally. Plant design and layout play a crucial role in production, material handling, time, and operational costs. The objective of this research was to implement the Systematic Layout Planning (SLP) methodology, supported by CAD and quality tools, to free up 280 m² for production processes in a horizontal blind manufacturing company. AutoCAD was used to model the facilities and visualize pre- and post-improvement scenarios, while ABC classification and root cause analysis supported problem identification in inventory areas. Results show a released expansion area of 340 m², corresponding to 21.5% above the initial space requirement, and a reduction in material travel distance from 317 m to 109 m, equivalent to 65.6%. These improvements enhanced workflow continuity and operational efficiency. The integration of SLP with CAD and quality tools provides a replicable framework for layout optimization in manufacturing environments, while future research should validate the approach under dynamic production conditions. Full article

Assessing the sustainability of urban development, including road changes, is increasing from year to year and requires clear indicators for robust decision-making tools to gain knowledge across regions. This study conducts the selection of transportation routes over a longer period as an example to evaluate the sustainability of historical official routes in achieving economically cost-efficient operation and maintenance. Official ways in the Qiantang River Basin connected the Jiangnan region, the economic center of China, with surrounding provinces were assessed. During the past six hundred years, the official road network in this area gradually simplified, evolving from valley roads to river banks, which covered longer distances. However, this transformation lacks a systematic explanation. By applying the analytic hierarchy process (AHP) with geographic information system (GIS), quantitative analysis was gained and the results are as follows: (1) Among the influencing factors, the weights of transportation cost and population related to economic needs are 39.54% and 29.52% respectively, with a combined total of 69.06%. (2) The official road network is often designed for governing the people, but in places such as the Qiantang River Basin, economic logic superseded political imperatives, becoming the dominant factor in reshaping the official ways. (3) In the pre-industrial era characterized by limited technological capacity, the physical environment had a greater impact on economic costs, ultimately reshaping the spatial configuration of official route networks. Overall, the evolution of official routes reflects the decline in their military-political function, driven by sustained peace and long-term decline in strategic position. The evolution of the official ways in the Qiantang River Basin reveals the importance of economic benefits in road selection. Full article

Three-dimensional surface reconstruction is essential for accurately acquiring the external quality parameters of watermelons, such as size, volume, and defect area. Binocular stereo vision provides a low-cost and easily deployable solution for the single-view 3D reconstruction of watermelons. However, watermelons present highly similar surface textures, and as typical spheroid-like objects, the excessive angle between surface normals of edge regions and the camera optical axis leads to insufficient feature representation. Consequently, directly applying existing stereo matching algorithms often introduces matching ambiguities, and lightweight networks struggle to balance real-time performance with matching accuracy. This study focuses on the high-precision single-view point cloud generation of Kirin watermelons. To address these issues, we first construct a cross-modal, high-precision Kirin watermelon stereo matching dataset. Building upon the Fast-ACVNet+ architecture, we then propose MI-ACVNet, a lightweight stereo matching network tailored for high-precision watermelon point cloud acquisition. In the feature extraction stage, a Multi-Scale Stereo Feature Extraction (MSFE) module is adapted. By incorporating the re-parameterized network MobileOne and Epipolar-Enhanced Coordinate Attention (E2CA), MSFE improves the discriminative capability for weak and similar textures without compromising inference speed. For cost computation, a Coarse-to-Fine Cascaded Residual Correction (C2F-CRC) strategy is incorporated to construct a fine-grained cost volume via sub-pixel interpolation, enhancing the network’s ability to capture subtle surface fluctuations. Furthermore, a Semantics-Guided Region-Aware Loss (SGRA-Loss) is formulated, leveraging semantic masks to apply differentiated supervision weights across edge, center, and background regions to significantly improve edge matching accuracy. Ablation studies validate the effectiveness of the MSFE, C2F-CRC, and SGRA-Loss components. Compared to the baseline model, the full MI-ACVNet reduces the End-Point Error (EPE) by 19.5% and the Bad-0.5 error rate by 34.5% in the watermelon region. Furthermore, when compared against five mainstream algorithms (StereoNet, AANet, HSMNet, LightStereo-L, and NMRF-swint), MI-ACVNet achieves state-of-the-art performance: EPE and Bad-0.5 are reduced to 0.091 pixels and 1.159%, respectively, with a single-frame inference time of only 46 ms. The average depth error of the reconstructed point clouds is merely 0.26 mm. By ensuring both real-time efficiency and high-precision depth estimation, this method demonstrates promising potential for deployment in industrial Kirin watermelon sorting lines, driving sorting equipment toward higher precision and intelligence. Full article

This paper reports a systematic study of low-rank adaptation (LoRA)-based fine-tuning applied to Qwen3 language models (4B, 8B, and 14B parameters) for the task of legal question answering within the jurisdiction of the Republic of Kazakhstan. The bilingual dataset comprises 63,114 question–answer pairs (76.2% Russian, 23.8% Kazakh) covering 11 legal domains. Models are evaluated through both automated metrics (BERTScore, citation accuracy, and hallucination rate) and blind expert assessment by a panel of two practising legal experts. Key findings: (1) all fine-tuned models reach BERTScore F1 close to 90% (89.6–90.2%) versus 82.2–83.1% for untuned base models; (2) fine-tuned models outperform GPT-4o (87.2%) and GPT-4o-mini (86.7%) on semantic similarity while exhibiting far lower hallucination rates (27–29% vs. 83–90%); (3) blind expert assessment confirms the advantage of fine-tuned models, with panel mean completeness scores of 4.28/5 versus 1.95/5 for base models (quadratically weighted Cohen’s κ = 0.80–0.95 across rating dimensions, indicating substantial to almost perfect inter-rater agreement); and (4) we identify a practical scaling plateau: paired Wilcoxon tests (n = 500) detect statistically significant but practically small differences across the 4B, 8B, and 14B fine-tuned variants (largest mean gap 0.67 pp on BERTScore F1; Cohen’s |d_z| ≤ 0.34), gains too small to justify the 3.5× parameter increase. These findings show that parameter-efficient adaptation of compact open-source models can match or exceed commercial LLMs for specialised legal QA in a low-resource bilingual context. We note one scope restriction: three domains (administrative, criminal, and housing law) are represented only in Russian, so the model is not validated for Kazakh language queries in these areas. Full article

In modern livestock production, a reliable supply of high-quality forage is essential for sustaining animal productivity and product quality. Although nitrogen (N) fertilization can promote forage growth, excessive N inputs often result in low agronomic nitrogen use efficiency (NAUE) and increased environmental risks. Biochar, owing to its porous structure, high specific surface area, and physicochemical stability, can improve soil physical properties, enhance water and nutrient retention, and regulate soil N availability. However, the mechanisms by which biochar combined with reduced N rate fertilization affects NAUE in artificial grasslands remain insufficiently quantified. A two-year field experiment was conducted at the Grassland Science Experimental Station of Xinjiang Agricultural University on the northern slope of the Tianshan Mountains, Xinjiang, China. Eight treatments were established using a factorial design with two biochar rates (0 and 20 t·ha⁻¹; B0 and B20) and four N application rates (0, 75, 150, and 225 kg·ha⁻¹; N0, N75, N150, and N225). Results showed that biochar application significantly decreased soil bulk density and increased soil water content and electrical conductivity. It also elevated soil total carbon, total nitrogen, total phosphorus, NH₄⁺–N, and NO₃⁻–N concentrations, with B20N150 exhibiting the highest overall nutrient status. Plant community diversity indices did not differ significantly among treatments (p > 0.05), though B20 slightly enhanced Shannon–Wiener and Simpson indices under N0 and N75. Moderate N application significantly increased hay yield, whereas the highest N rate (225 kg·ha⁻¹) did not further improve yield and reduced NAUE. Biochar combined with N75 or N150 improved NAUE, and B20N150 achieved the best balance of high hay yield and high NAUE. Structural equation modeling revealed that soil water content (path coefficient = 0.45), NH₄⁺–N (0.27), and plant community diversity (0.20) were key positive drivers of NAUE, with biochar exerting indirect effects primarily via improving soil water and available N. Collectively, applying 20 t·ha⁻¹ biochar with 150 kg·ha⁻¹ N (B20N150) is recommended as an optimal strategy for N rate reduction and NAUE enhancement in artificial grasslands of arid and semiarid regions. Full article