Search Results (821)

Surface anomalies such as dust accumulation and bird droppings on photovoltaic (PV) panels can significantly reduce their energy production and lead to inefficient maintenance decisions. This paper proposes a vision-based deep learning framework for the automatic detection of PV panel surface conditions and validates the detected anomalies using real inverter-level energy production data. Unlike conventional studies focusing solely on detection performance, the proposed approach introduces a unified and physically interpretable framework that directly links image-based anomaly detection with inverter-level energy performance and decision-oriented PV maintenance. An EfficientNetB3-based model is trained using a two-stage transfer learning strategy on a publicly available Kaggle dataset and evaluated using standard classification metrics. The trained model is then deployed and validated at a 1 MW solar power plant located at Karaman, Türkiye. Classification results obtained from field images are systematically linked with inverter-associated hourly energy production measurements. Following panel cleaning and natural rainfall, an approximately 12.5% increase in inverter-level hourly energy production is observed for the analyzed PV group (120 panels, ~270 Wp), corresponding to an increase from 23.2 to 26.1 kWh. In addition, the study introduces an energy–water–sustainability-aware cleaning decision framework tailored for arid and semi-arid regions where water scarcity and deep groundwater extraction present critical constraints. The framework defines a quantitative decision rule in which panel cleaning is performed only when the expected recoverable energy exceeds the energy cost of water extraction and cleaning. Overall, the proposed approach enables accurate surface anomaly detection while supporting sustainability-aware, resource-efficient and data-driven maintenance decisions for PV power plant operation. Full article

Soiling in photovoltaic systems is a recurring problem that reduces energy generation and demands efficient operation and maintenance (O&M) strategies. In this context, this paper proposes a machine learning-based approach to identify dirt levels and generate cleaning alerts using operational and weather data. Initially, the models were evaluated with a decision threshold ranging from 0.5 to 0.7, using only operational features. Subsequently, the inclusion of weather features was tested, which improved the models’ performance and enabled the selection of the best models for the exhaustive features search step. The models analyzed in this step were Extra Trees, Histogram-based Gradient Boosting, Extreme Gradient Boosting, and Random Forest. Exhaustive analysis further improved model performance, as indicated by global metrics and ROC curves. The Extra Trees model with a threshold of 0.5 showed the best performance and was selected as the final configuration, achieving an accuracy of 0.9884 and an AUC-ROC of 0.9957. Finally, the selected model was applied to determine daily soiling levels and trigger alerts based on temporal persistence, indicating its potential to support predictive O&M decisions and cleaning actions in PV systems. Full article

Waterlogging and secondary salinization are major drivers of land degradation in irrigated dryland regions, undermining soil productivity and long-term sustainability. Pakistan’s Indus Basin Irrigation System (IBIS), one of the world’s largest irrigation networks, supports national food security over approximately 16.7 million hectares (Mha). However, large-scale canal irrigation, combined with flat topography, monsoonal recharge, and inefficient water management, has disrupted groundwater balance, leading to persistent shallow water tables and widespread land degradation. Currently, nearly one-third of the irrigated area is affected by groundwater depths of less than 3 m. This review synthesizes six decades of waterlogging development and management in the IBIS, analyzing the evolution of drainage infrastructure, salinity control strategies, groundwater exploitation, and institutional reforms within a land sustainability perspective. Although large-scale interventions—including 61 Salinity Control and Reclamation Projects (SCARPs) and major outfall systems—initially reclaimed substantial areas, long-term performance has been constrained by governance fragmentation, inadequate operation and maintenance, and environmentally problematic effluent disposal. The Indus Basin experience underscores the need to move beyond infrastructure-centered solutions towards more integrated land–water governance and adaptive management to enhance land system resilience in irrigated regions facing growing climatic and resource pressures. Full article

Selenium (Se) is an essential trace element. The bioactivity of Se arises from its incorporation into the 21st amino acid, selenocysteine (Sec). Twenty-five human genes have been identified that encode selenoproteins, each of which contains at least one Sec residue. Selenoprotein functions include antioxidant responses, thyroid hormone synthesis, and maintenance of cellular redox homeostasis. Due to its role in critical cellular functions, Se deficiency is associated with morbidities of the cardiovascular system and connective tissue in regions of countries with low soil Se content. While these morbidities are geography-specific and have been mitigated in adults through public health interventions, preterm infants remain susceptible to Se deficiency worldwide. Infants born preterm are deprived of fetal Se accrual in the 3rd trimester of pregnancy, a deficiency compounded by higher Se needs than term infants and older infants and dependence on parenteral nutrition (PN) and fortification. In addition, the composition of selenoproteins and selenometabolites in human milk is different from that in formula and PN, yet little is known about the biological impact of these differences. The knowledge gap in optimal Se supplementation is reflected in discrepant guidelines between North American and European/Chinese nutrition societies, whose recommended Se supplementation in preterm infants differs by more than 2-fold. In this review, we describe the biosynthesis, metabolism, and maternal-fetal transfer of Se. In addition, we address how developmentally regulated aspects of metabolism may impact how preterm infants respond to supplementation with different forms of Se. Lastly, we highlight current challenges and recommendations for optimizing Se levels in neonates based on available data. Full article

Utilizing excavated waste soil to level gullies offers significant advantages in terms of engineering economy and construction efficiency. However, the stability and deformation risks of high-fill embankments in mountainous gullies under rainfall conditions have attracted significant attention, particularly when such structures are located adjacent to residential areas. This study compares two design schemes for highway high-fill embankments, Scheme 1: high-fill slope supported by stabilizing piles and prestressed anchors, and Scheme 2: ordinary waste soil as the core, foamed lightweight soil (EPS) as the edge band, and reinforcement by a micro-pile retaining wall system. Finite element analysis was used to evaluate the Factor of Safety (FOS), displacements of retaining structures, and characteristic slope points under three conditions (no rainfall, heavy rainfall, and heavy rainfall with soil strength deterioration). The results show that Scheme 2 reduces total costs by 3.5%, shortens the construction period by 14%, and cuts maintenance costs by 65%, with a minimum FOS of 1.56 under extreme rainfall. Further parametric analysis of Scheme 2 optimized key design parameters, and field monitoring data over 6 months verified the reliability of the numerical simulation. This study provides a transferable design-verification pathway for combining lightweight and conventional fills in high embankments, offering technical support for similar projects in complex mountainous areas. Full article

Atmospheric plasma spraying (APS) represents a critical solution for enhancing the durability of agricultural components, such as harrow discs, which are subjected to synergistic wear and corrosion during soil cultivation. This study presents experimental results evaluating the electrochemical corrosion behavior and thermal shock resistance of discs coated via atmospheric plasma thermal spraying. Both metallic and ceramic materials, in powder form, from established manufacturers were used to produce the coatings, and the three types of coatings (two metallic and one ceramic) have the following chemical compositions and trade names: W₂C/WC12Co (Metco71NS), Cr₂O₃-4SiO₂-3TiO (Metco136F) and Co25.5Cr10.5Ni7.5W0.5C (Metco45C-NS). The coatings were analyzed using electron microscopy to evaluate the surfaces following corrosion testing. The ceramic coating based on the Cr₂O₃-4SiO₂-3TiO demonstrated the highest protective efficiency by increasing the charge transfer resistance from 307 Ω/cm² to 2213 Ω/cm² for the ceramic coating. It provided a superior physical barrier, reducing the corrosion current density from 0.140 mA/cm² for unprotected substrate to 0.004 mA/cm², representing an improvement of nearly two orders of magnitude. These findings demonstrate that implementing Cr₂O₃-4SiO₂-3TiO ceramic systems can significantly extend the operational lifespan of soil-engaging components, providing a cost-effective strategy for reducing maintenance intervals and material loss in aggressive agricultural environments. Full article

The Western Songnen Plain, a critical yet ecologically fragile grain-producing area, is facing sustainability risks arising from rapid land use changes, which demand scientific assessment and regulation. From an ecological security standpoint, this study synthesizes multiple data sources, including GlobeLand30 data, climate, topography, and soil data. Based on the assessment of water conservation, soil conservation and biodiversity maintenance, combined with minimum cumulative resistance model (MCR) and the CLUMondo model, this study comprehensively reveals the dynamic evolutionary patterns of land use in the Western Songnen Plain over the past two decades, concurrently analyzed the spatial heterogeneity pattern of ecosystem services, and further simulated land use changes under natural growth, farmland protection, and ecological security scenarios. According to the results, the grassland area decreased significantly, while cropland and construction land continued to expand. Water conservation, soil conservation, and habitat quality displayed remarkable regional differences, with high values predominantly situated in wetlands, grasslands, and mountainous regions. In contrast, low values exhibited strong spatial correspondence with regions of heightened anthropogenic disturbance. Although the cropland protection scenario promoted agricultural intensification, it reduced ecological heterogeneity. In contrast, the ecological security scenario achieved a higher patch density (0.408) and landscape diversity (1.142) compared to the natural growth scenario, with moderate increases in aggregation. This study identified 27 ecological pinch points, 24 ecological barrier points, and 97 ecological corridors, which provide direct support for regional water and soil resource protection and further underpin the constructed ecological security pattern of “two belts, three zones, and multiple nodes”. These findings have important reference significance for optimizing regional land use structure and maintaining the stability of terrestrial ecosystems in the Western Songnen Plain. Full article

Structural evaluation of railway tracks in operation requires the integration of field measurements and numerical models capable of adequately representing the mechanical behavior of permanent railway pavement components. In this context, this study presents the structural analysis of a railway segment based on the combination of field instrumentation, laboratory testing, and numerical simulations grounded in the Finite Element Method, adopting linear elastic and resilient material behavior for all track components, using SysTrain software (v.1.88).The objective of this work is to assess the application of a back-analysis methodology based on field instrumentation and numerical modeling, as well as to verify the structural conditions of an in-service railway pavement. The back-analysis was conducted using the SysTrain software, with a focus on calibrating the ballast resilient modulus (RM) and analyzing its effects on the propagation of stresses, internal forces, and displacements throughout the track structure. To this end, field-measured deflections obtained from LVDT sensors installed at the sleeper ends were used, together with the geotechnical, resilient, and permanent deformation (PD) characterization of the underlying soil layers obtained in the laboratory. The results indicated that the calibration of the numerical model requires a ballast resilient modulus in the order of 1500 MPa, suggesting a condition of high layer stiffness. The simulations showed vertical stress levels below 100 kPa in the lower layers, while laboratory tests revealed the high susceptibility of the soils to PD, particularly under moisture variations. It is concluded that the applied methodology enables a consistent assessment of the structural conditions of the track and contributes to a more robust understanding of the ballast response under repeated loading, providing support for railway design, maintenance, and management criteria. Full article

Open-pit mining increasingly substitutes truck-based haulage with continuous systems—such as mobile bridges and relocatable conveyors—to mitigate operational costs and environmental impacts. This PRISMA 2020-compliant systematic review (2010–2025) maps transferable evidence in structural analysis, optimization, and maintenance for truss-type mobile assets. Following a systematic search in Scopus and Web of Science, 94 studies were selected via MMAT quality appraisal and analyzed through cluster-based synthesis. Results reveal sustained publication growth since 2018, with a corpus dominated by finite element (FE) research on steel bridges and capacity assessment, supplemented by emerging areas in AI-driven structural health monitoring (SHM). Given the scarcity of mining-specific literature, bridge engineering serves as a structural proxy for mobile applications. Critical research gaps include full-scale operational validation, soil–structure interaction, and design–maintenance co-optimization. The study concludes with an evidence-anchored agenda toward validated, predictive, and sustainable monitoring frameworks, positioning digital-twin integration as a promising future horizon rather than a current industry-wide convergence. Full article

Xinjiang, a key grain production region in arid Northwest China, faces severe water scarcity and low agricultural water use efficiency. Although subsurface drip irrigation (SDI) has been widely studied for horticultural crops, a comprehensive synthesis focusing on SDI system design, water–fertilizer management, and soil–crop responses in wheat production under arid conditions remains limited. This knowledge gap restricts the development of optimized irrigation strategies for wheat cultivation in Xinjiang, where extreme aridity, widespread oasis agriculture, soil salinization risk, and the dominance of densely planted wheat create management requirements that differ from those of humid regions and horticultural production systems. Therefore, this review summarizes the development of SDI technology, its system design parameters, and integrated water–fertilizer management strategies, while systematically integrating recent advances in soil–crop–microbial interactions and resource use efficiency under arid conditions, which have rarely been synthesized in previous SDI reviews. Synthesizing current knowledge on the impacts of SDI on soil water dynamics, soil properties, microbial communities, crop root architecture, biomass production, and resource use efficiency, this review further discusses general advances in SDI in the context of their relevance to Xinjiang, with particular emphasis on how regional soil–climate conditions and wheat production practices influence system design, fertigation management, and field applicability. Multiple studies indicate that SDI can simultaneously reduce evaporation and deep percolation, mitigate surface salt accumulation, promote deeper root development, and improve crop productivity and resource use efficiency. However, high initial investment and maintenance costs, along with risks of emitter clogging, still hinder its large-scale adoption. For Xinjiang’s wheat and other densely planted crops, future research should prioritize optimizing subsurface drip irrigation (SDI) systems, as studies have shown that SDI can increase water use efficiency (WUE) by 20–30% and enhance crop yield by 10–15%, particularly under water-scarce conditions. The study’s findings are as follows: (1) optimize SDI system parameters for local soil–climate conditions, (2) elucidate the synergistic mechanisms between water–fertilizer coupling and soil–crop systems, and (3) develop cost-effective and durable system components. Importantly, these findings are particularly relevant for Xinjiang, where extreme aridity, soil salinization, and limited water resources require region-specific optimization of SDI systems. These efforts will support efficient and sustainable wheat production in Xinjiang and other arid regions. Full article

Black Soldier Fly larvae (BSFL) bioconvert a wide variety of organic waste into value compounds including the residual frass, a by-product exploitable as compost for plant growth. The use of a non-standardized waste diet that varies in terms of properties does not ensure the maintenance of a highly fertile and healthy BSF colony able to produce viable inoculum (5–7-day-old larvae) for waste bioconversion. The Gainesville diet (GD) is a balanced formulation to ensure full larval development in fertile adults, resulting in a stable rearing colony. On a large scale, the bioconversion supply chain can produce different types of frass. Frass derived from the Gainesville diet (GDf), from fruit and vegetable waste (FVWf), and from milled fruit and vegetable waste (MWf) was composted and then compared to evaluate its fertilizing effect on lettuce growth in two pot-growing experiments. Each compost was added at concentrations of 2.5, 5, and 10%. The growth of lettuce improved significantly with the addition of composted frass in a dose-dependent manner when compared to unfertilized soil. GDf 10% gave the significantly best performance in terms of plant height (20.8 cm versus 17.9 cm) and fresh weight (113.5 g versus 87.7 g) compared to FVWf. In the experiment, the combined use of composted frass at 10% of both GDf and FVWf with a double mineral fertilizer application showed no significant differences compared to triple application. However, GDf provided significantly greater chlorophyll content than FVWf. These results highlight how, under the conditions tested in the present work, the frass of the entire productive chain of BSF is a high value by-product. Full article

Natural grasslands are among the most endangered habitats in Northern, Central and Eastern Europe due to the agricultural intensification, land abandonment and afforestation, urban expansion, and the loss of traditional low-intensity management, on which their biodiversity depends. One way to increase the number of natural grasslands is by integrating them into urban green infrastructure as a nature-based solution to enhance ecological resilience and urban livability: diverse grassland systems support pollinators, improve soil structure and stormwater infiltration, mitigate urban heat and provide restorative, experience-rich public spaces. The aim of the study is to explore opportunities and barriers to integrating different types of grasslands into the green infrastructure of Latvian cities, with a primary focus on public perceptions and institutional aspects of urban grassland implementation and management. A mixed-methods approach was applied, combining resident surveys, interviews with municipal experts—territorial development specialists, planners and maintenance managers—and comparative policy analysis. Results show that although residents acknowledge the ecological benefits of urban grasslands, they prefer them in peripheral or underused areas rather than in city centres and residential zones, as these areas are often aesthetically perceived as “untidy” or neglected, conflicting with cultural norms that favour short, intensively mown lawns and raising concerns about insects. Acceptance increases through communication and participatory practices. Municipal approaches range from structured maintenance guidelines, including delayed mowing, biomass removal, and invasive species control, to flexible experimentation. The study contributes scientifically grounded insights into governance, perception, and management interfaces critical for mainstreaming socially accepted urban grasslands. Full article

This study develops and validates a two-stage comparative framework for predicting Photovoltaic (PV) cleaning schedules by integrating high-resolution operational data with regression-based simulated datasets generated from statistical models trained on real measurements. The work directly addresses the growing need to assess whether model-based regression-based simulated data can reliably substitute real measurements in predictive PV maintenance. These models are employed to generate clean-condition power baselines and to estimate daily energy losses attributable to soiling under two distinct paradigms: (i) using real historical PV performance and environmental measurements, and (ii) using regression-derived, regression-based simulated data representing idealized clean operating conditions. Model performance is rigorously quantified using correlation coefficients (R), coefficients of determination (R²), mean absolute deviations, and binary classification metrics including accuracy, precision, recall, and F1-score. The comprehensive results demonstrate that regression-based simulated datasets exhibit high fidelity with real measurements across key electrical variables. This is evident for datasets generated using PLSR, Ridge Regression, and Robust Regression. Strong correlations are observed for DC power (R² = 0.9545) and DC current (R² = 0.9520), with mean deviations consistently below 2.2%. When a threshold-based binary decision rule (“clean” versus “do not clean”) is applied, cleaning decisions derived from simulated and real datasets show near-perfect concordance, achieving a mean F1-score of 0.9792. These results indicate that for a fixed performance-loss threshold, models using regression-based simulated data reproduce real-data-based cleaning triggers with an accuracy exceeding 97%. Furthermore, the findings confirm that regression-based simulation frameworks constitute a reliable and scalable foundation for data-driven PV maintenance optimization. By enabling efficient cleaning scheduling, these frameworks can significantly reduce operational expenditure and maximize energy yield, particularly in regions where continuous, high-quality PV monitoring data are limited or difficult to obtain. Full article

Mountain forests within arid zones function as critical regional “water towers” and biodiversity hotspots, providing essential ecosystem services (ESs) such as carbon sequestration, water retention, soil conservation, and habitat maintenance. Despite their ecological significance, the spatiotemporal characteristics of these services remain insufficiently characterized. For this study, focusing on the Altai Mountains in northwestern China, we employed the InVEST model using climate, land cover, and soil survey datasets (2000–2020) to quantify ES dynamics, then applied Spearman rank correlation to analyze their spatial interactions. Results indicated the following distinct spatiotemporal patterns: (1) Temporally, water retention capacity increased by 23.5% from 2000 to 2020, with the most rapid growth occurring between 2000 and 2010, whereas carbon storage experienced a slight decline of 1.9%. (2) Spatially, water retention followed a “high-North, low-South” distribution, while carbon storage and habitat quality were highly concentrated in the central mid-elevation zones (1400–2400 m). (3) Trade-off intensification: a significant negative correlation between water retention and carbon storage deepened over the study period, highlighting an escalating “water–carbon” conflict. The aforementioned findings suggest that future management should be focused on avoiding high-density afforestation in mid-elevation water-sensitive zones to prevent excessive evapotranspiration. Instead, spatially differentiated strategies—prioritizing water yield protection in high-altitude regions and stand structure optimization in mid-altitude forests—are essential for reconciling regional ecosystem service trade-offs. Full article

Modern agriculture has to alleviate extremes in water demand and/or water waste. In this regard, this work showcases how soil moisture instruments can be combined with low-end microcontrollers, energy-efficient communication protocols, single-board computers, flow and pressure sensors, and purpose-built actuators to form a synergistic platform able to generate and study realistic irrigation scenarios. These scenarios, potentially emulating anomalies such as clogged emitters or pipe leaks with a satisfactory time granularity of a few minutes, provide valuable data that pave the way for the creation of intelligent models intercepting water misuse events and/or irrigation failures. The proposed system utilizes widely available, well-documented, low-cost components to form a functioning whole which is optimized for outdoor, low-power, low-maintenance and long-term operation and is accessible remotely via typical end-user devices. Two drip irrigation points were set up, each having a TEROS 12 and a TEROS 10 instrument placed at different depths, while a prototype water flow/pressure control and report system was developed. All modules sent data in real time, via LoRa, to a central node implemented using a Raspberry Pi for further processing and to make them widely available via common network infrastructures, also provisioning for remote scenario invocation. The system does not claim to achieve specific irrigation water savings, but it contributes to maintaining/increasing the benefits of modern irrigation practices (such as drip irrigation). This goal is served by emulating a wide variety of irrigation events and by gathering and studying the corresponding data. These multimodal data are collected at a frequency of a few minutes, reflecting key irrigation-specific parameters with an accuracy better than or equal to 3%. The exact steps for specific hardware and software component interoperation are clearly explained, allowing other teams of researchers and/or university educators worldwide to be inspired and benefit from platform replication. Full article