Search Results (6,882)

Background/Objectives: Soybean first pod insertion height (FPIH) is a key trait established during plant development, but its genetic architecture in Eurasian germplasm remains largely unknown. Methods: We performed a GWAS and fine-mapping for FPIH using 180 Eurasian varieties (SoySNP50K array, imputed to ~4M SNPs) phenotyped in four Russian environments (2021–2022), using two models: a residual-based and a covariate-adjusted. SuSiE fine-mapping was applied to refine candidate loci. Results: The covariate-adjusted model demonstrated better control of genomic inflation (λ = 1.08 vs. 1.16) and higher SNP heritability (0.381 vs. 0.014); the lower heritability in the residual-based model was expected, as this model removes environmental main effects and the genetic variance associated with them. Thus, the models are complementary: one captures stable genetic effects, the other highlights environment-dependent signals. SuSiE refined three loci. On chromosome 13, two stable independent signals (Gm13_30553403, Gm13_30909346; PIP ≥ 0.99) were identified near MYB83 and BEN1, consistent across both models. On chromosome 4, the signal shifted to Gm04_36933515 (PIP = 0.9999) near COBL4/IRX6, although model dependency was observed, and this locus is not currently recommended for marker development. On chromosome 5, the original GWAS SNP was not causal; two tightly linked SNPs (Gm05_38427309 and Gm05_38710046, r² = 0.714, PIP ≥ 0.99) formed a haplotype block located near ARR1/ARR2 and a B-box/CCT domain gene. Crucially, this signal was absent in the residual-based model (max PIP = 0.33), suggesting that the chromosome 5 locus may modulate developmental plasticity rather than exerting a direct main effect on FPIH. However, as we did not perform formal G × E testing, we present this as a hypothesis requiring further validation. The environment-dependent behavior of this locus indicates that it should be used with caution in breeding programs and validated under specific target environments. Conclusions: Chromosome 13 SNPs provide stable genetic associations across environments, whereas the chromosome 5 block requires environment-specific validation. This work provides the first fine-mapped GWAS for FPIH in Russian soybean germplasm and highlights how model choice can uncover or obscure environment-dependent genetic loci affecting plant development. Full article

Temperature sensor systems are critical for nuclear power plant (NPP) condition monitoring, whose reliability underpins unit safety and stability. Fault localization and diagnosis are essential to sustain their stable service. Conventional Principal Component Analysis (PCA) and Graph Neural Network (GNN) methods suffer clear drawbacks: PCA is vulnerable to noise and cannot classify fault types accurately, while GNNs struggle to quantify correlations among temperature data. This paper fuses PCA’s anomaly representation capability and GNN’s structural feature extraction capacity to propose an Abnormal Feature-GCN method for joint fault localization and diagnosis. First, an Adaptive PCA (APCA) model fed with multi-dimensional sensor features computes abnormal features. These features are then transformed into edge weights to construct a weighted graph. A dual-branch GCN is finally trained via a joint loss function for parallel multi-task learning to simultaneously locate faulty sensors and identify fault types. Validated on a nuclear primary circuit temperature sensor system under constant-, rising-, and falling-temperature working conditions, the proposed method realizes accurate fault localization and classification. The mean overall accuracy of the proposed method surpasses mainstream baselines by 2.23%, 1.62%, and 1.89% for the three typical working conditions, respectively. Full article

This study addresses a large-scale location–allocation problem in a three-echelon automotive supply chain comprising 382 suppliers, candidate distribution centers, and six assembly plants. The planning task is to redesign the inbound consolidation network while minimizing transportation and distribution center operating costs, enforcing a 480 km supplier-to-center service radius, and achieving at least 90% demand-weighted coverage. We formulate a mixed discrete-continuous model with supplier-to-center assignment, center location, throughput, and flow decisions. A feasibility-oriented hybrid algorithm uses a genetic algorithm as the main search engine, ant colony construction to seed solutions near the feasible region, adaptive mutation and simulated annealing to preserve exploration and refine elite solutions, and an online neural surrogate to avoid a subset of costly exact fitness evaluations. The design differs from a simple collection of metaheuristics: all components share one variable-length encoding, the same feasibility metrics, and periodic exact reevaluation of candidate solutions. Using the competition case data, the redesigned network reduces total cost by 27.0% relative to the six-center baseline, decreases the demand-weighted average supplier-to-center distance from 461.3 km to 53.0 km, lowers the maximum distance from 2807.22 km to 441.78 km, and raises coverage from 45.0% to 100%. Across ten independent runs, the hybrid method obtains a mean cost 10.3% below that of a standard genetic algorithm, with lower run-to-run dispersion. The results show that feasibility-aware initialization, adaptive search, and selective surrogate evaluation can support practical redesign of a strongly constrained, national-scale inbound logistics network. The directly attached reproducibility package provides the MATLAB implementation and the seven supplied input workbooks used by the reported model. The evidence is limited to one deterministic competition instance, a fixed cost schedule, and fixed-topology sensitivity calculations; generalization under demand uncertainty, facility disruption, and alternative road conditions remains to be tested. Full article

Background: The genus Anchusa L. includes plants used in Uyghur medicine for their anti-inflammatory and analgesic effects. However, in China, the botanical origin of HERBA ANCHUSAE (Niushecao, a Uyghur medicinal herb) is severely confused. Traditional identification methods and standard DNA barcodes do not work well for these close relatives. Chloroplast genomes are known to contain variable regions that can help distinguish species, yet no such study has been done for Anchusa. Therefore, We compared the complete chloroplast genomes of six Anchusa species and the main adulterants of Niushecao. Methods: We analyzed genome structure, repeat sequences, codon usage bias, and nucleotide diversity (Pi), as well as conducted comparative and phylogenetic analyses. Results: All genomes shared a typical ring-shaped quadripartite structure, ranged from 150,178 to 150,844 bp in size, and contained the same set of genes. Despite this overall conservation, we identified several highly variable spots, mostly located in non-coding intergenic spacer regions. Using two complementary approaches—sliding window analysis and mVISTA-based sequence visualization—we identified three overlapping regions (rbcL-psaI, petA-psbJ, and trnC-GCA-petN) as candidate DNA barcodes for species identification. Our phylogenetic tree showed that Anchusa strigosa Banks & Sol. is most closely related to the true medicinal species Anchusa azurea Mill. (Bootstrap support (BS) = 100%), while other look-alikes formed separate branches. Conclusions: These findings provide the first chloroplast genomic resources for this genus and offer potential molecular markers for authenticating Anchusa medicinal materials, laying a foundation for future development of molecular authentication methods. Full article

Background: Transport costs in industrial waste management can account for up to 60% of total expenditures, yet existing optimization models often rely on simplified distance metrics and treat facility location and routing separately. This paper addresses these gaps. Methods: A simulation model is developed integrating real OpenStreetMap road networks, differentiated environmental risk coefficients by waste hazard class, and joint optimization of the number, location, and capacity of sorting stations and recycling plants. A nearest-available-facility heuristic is applied for routing. The model is implemented as an agent-based simulation in AnyLogic and validated on real data from the Republic of Tatarstan. Results: The optimized configuration (five sorting stations and two new recycling plants) increased the recycling rate from 29.8% to 69.1%, reduced waste sent to storage from 59.3% to 21.5%, and achieved a positive net present value. Transport costs became the dominant cost item (47% of total costs). Conclusions: The model provides a practical decision-support tool for transport planners and logisticians, enabling an assessment of infrastructure decisions on transport work, mileage, and emissions. Integrating real road networks and environmental risk coefficients significantly improves the accuracy of logistics optimization in waste management systems. Full article

Türkiye’s strategic geographical location offers an exceptional opportunity for solar energy harvesting, yet optimizing large-scale investments requires rigorous pre-assessment methodologies. This study presents a comprehensive multi-criteria techno-economic analysis and real-world validation of a 13.18 MWp solar power plant (SPP) located in Kirsehir, a region characterized by high solar irradiance (1750 kWh/m²). Utilizing PVsyst software, four distinct configurations—monofacial and bifacial modules at 21° and 25° tilt angles—were systematically simulated and evaluated across varying equity-to-loan ratios using key financial indicators (NPV, IRR, PI, and Payback Period). The simulation results identified the 21° bifacial configuration, enhanced by the innovative integration of high-albedo industrial calcite (CaCO₃) waste as ground cover, as the optimal engineering solution. Crucially, the accuracy of this optimization was evaluated against 12 months of field data. While the raw measured annual production was recorded as 22,793,323 kWh, the validation was strictly based on the production adjusted for grid outages (23,499,604 kWh). Comparing this adjusted value with the simulated annual generation (22,816,114 kWh) yielded a total annual discrepancy of only 3% and a volumetrically weighted average error of 5.07%. Furthermore, to isolate model fidelity from inter-annual meteorological variability, the validation was assessed using the Performance Ratio (PR). The adjusted volumetrically weighted PR (87.43%) demonstrated a remarkably close alignment with the simulated PR (87.48%), exhibiting a marginal deviation of merely 0.05%. These performance metrics indicate a general consistency between the simulation model and operational field records across the evaluated period. Environmentally, the maximized energy yield of the 21° bifacial system facilitates the avoidance of approximately 6507.58 tonnes of CO₂ emissions annually. This research not only establishes the viability of scalable, low-cost calcite ground covers but also provides a highly robust, de-risked decision-support framework for utility-scale PV investments in similar geographic latitudes. Full article

The green peach aphid, M. persicae (Sulzer), is one of the most important pests of horticultural crops worldwide. In Chile, M. persicae causes severe losses in peach and herbaceous crops. Understanding aphid population dynamics across its primary (peach) and secondary (weed) hosts is fundamental to developing a more effective pest control strategy. To investigate the spatial genetic connectivity of M. persicae populations between cultivated and non-cultivated hosts, we conducted a longitudinal survey in three commercial peach orchards in central Chile. Apterous aphid colonies were repeatedly sampled from peach trees and weed hosts located both within and outside orchards, and population genetic structure and clonal diversity were characterized using six polymorphic microsatellite loci. Genetic differentiation was low between weed populations inside and outside orchards, whereas greater differentiation was observed between peach trees and weeds outside orchards. Three recurrent multilocus genotypes persisted across multiple seasons, orchards, and host plants, indicating substantial spatiotemporal persistence of particular clonal lineages. Comparison with insecticide-resistance profiles further showed that neutral population structure and resistance-associated variation were not necessarily concordant. These findings support an important role for non-cultivated hosts in maintaining recurrent M. persicae lineages within peach agroecosystems and highlight the value of considering surrounding vegetation in integrated pest management. Full article

This study establishes a GCN–NSGA-III-based framework for determining gas-leak sensor locations. Candidate layouts are optimized simultaneously with respect to installation expenditure, spatial coverage, leak-identification performance, and time to alarm. In the proposed framework, the GCN extracts spatial correlations and leakage-risk features among candidate monitoring locations, whereas NSGA-III optimizes the network weights and thresholds to support the selection of improved sensor placement schemes. By combining the image-based spatial feature extraction capability of CNNs, the graph-structured feature learning capability of GCNs, and the multi-objective optimization strength of NSGA-III, the model achieves significant improvements in detection accuracy and risk assessment efficiency. The model was validated using a hybrid gas-leak dataset comprising 758 training samples, including 189 real-world monitoring samples and 569 simulated samples, and 229 test samples, including 73 real-world monitoring samples and 156 simulated samples. Its engineering applicability was further evaluated using a simulated chlorine leakage scenario at a chemical plant in Changzhou, China, with a leakage rate of 2 kg/s and an ambient easterly wind speed of 1 m/s. Experimental results confirm its reliability and practical applicability in real-world engineering contexts. Compared with the original pre-optimization sensor layout under the same leakage and environmental conditions, the proposed optimization strategy reduces deployment costs by 19%, increases monitoring coverage by 8.2%, improves detection accuracy by 14.1%, and shortens alarm response time by approximately 15%. Full article

Plant-parasitic nematodes rely on the perception of host-derived chemical cues released from plant roots to locate suitable hosts, representing a critical prerequisite for successful plant infection. In this study, we selected four representative tomato (Solanum lycopersicum L.) root-derived chemical signals and demonstrated their distinct effects on the host-seeking behavior of the southern root-knot nematode (Meloidogyne incognita). Among these compounds, the flavonoids quercetin and luteolin exhibited significant attractant effects, whereas the alkaloids dihydrocapsaicin and solasodine showed repellent effects. RNA interference (RNAi) experiments further indicated that multiple chemosensory neurons are involved in the recognition of plant-derived chemical cues. Silencing of key chemosensory genes significantly impaired the directed chemotactic response of M. incognita toward tomato roots and reduced its infectivity, demonstrating that the chemosensory system represents a crucial molecular basis regulating host localization and parasitic establishment. This study provides new insights into host recognition mechanisms in root-knot nematodes and offers a theoretical foundation for developing environmentally sustainable strategies for nematode management by targeting host perception processes. Full article

Calligonum polygonoides is a rare desert shrub that is of high ecological importance in Egypt, yet its habitat requirements and physiological responses to environmental variability remain poorly understood, particularly under increasing climatic and anthropogenic pressures. To address this knowledge gap, this study integrates GIS-based habitat suitability modeling with hyperspectral leaf spectroscopy to evaluate the environmental factors associated with species distribution and leaf optical responses across two ecologically contrasting wadis (Wadi El-Galala and Wadi El-Assiuty). Environmental layers (DEM, EC, TSS, pH, temperature, rainfall, humidity, evaporation) were integrated using a multi-criteria evaluation framework, while plant cover, density, and spectral measurements were collected from 14 field plots. The results show that C. polygonoides favors moderately elevated zones characterized by low salinity, slightly alkaline soils, and intermediate climatic conditions. Approximately 24.3% of the landscape was classified as highly suitable, primarily along channel belts and alluvial fans with improved drainage and reduced salt accumulation. Spectral signatures showed descriptive differences between the two wadis. Plants from Wadi El-Galala showed relatively higher NIR reflectance and more pronounced SWIR water-absorption features compared with Wadi El-Assiuty, which may reflect differences in leaf structure and water status under contrasting habitat conditions. These spectral patterns were broadly consistent with the spatial suitability outputs and provide complementary descriptive information on leaf optical properties. Habitat suitability modeling showed that 91.7% of the recorded field occurrence points (33 out of 36 shrubs) were located within high-suitability zones, while no occurrences were recorded in low-suitability areas. This pattern indicates spatial agreement between observed occurrences and predicted suitability classes, but it should not be interpreted as formal model validation because absence data and independent validation records were not available. Overall, the findings provide preliminary spatial and spectral information that may support future field verification and site-specific conservation planning for C. polygonoides in the investigated wadis. Full article

Disamenity and amenity facilities pull property values in opposite directions, yet prior studies have treated disamenities as a homogeneous category and have been constrained by data scattered across agencies and by mismatched spatial scales. This study integrates multi-source spatial data, comprising 3426 sub-districts (eup-myeon-dong) nationwide, a 500 m population grid of 376,668 cells, and the actual point locations of individual facilities, into a single spatial framework and applies a multi-method design that combines a spatial Durbin model (SDM), explainable machine learning (XGBoost–SHAP), an environmental-justice analysis, and a micro-siting analysis. Four findings emerge. First, not all disamenities are associated with lower prices: only pollution-emitting facilities with a clear emission signature, namely, sewage treatment plants and incinerators, capitalize robustly as cross-sectional associations, into both housing prices and officially assessed land values, with the effect extending into neighboring sub-districts rather than staying purely local. The other disamenities show no robust total effect on either price; their estimates are small or unstable across specifications. Second, capitalization of pollution-emitting facilities is pronounced in the urban sample and disappears in the rural one, and the coefficient structure differs overall between urban and rural areas (global Chow test, p < 10⁻²¹). Third, micro-siting analysis shows that where a facility is placed (siting) and what it leaves behind in prices (capitalization) are distinct layers, and that capitalization tracks the population a facility keeps nearby rather than its nuisance type: pollution-emitting facilities retain enough nearby housing for their burden to register, whereas funerary facilities sited far from people do not. Fourth, the environmental inequity of pollution exposure runs along income rather than age and concentrates in cities rather than in rural areas: the exposure rate among residents of low-income urban sub-districts (49%) is 2.5 times that of the urban population as a whole (19%), while the greater Seoul metropolitan area remains comparatively insulated. These layered findings were possible only once the dispersed spatial data had been integrated and standardized, and the study demonstrates that fusing and efficiently managing spatial data is a precondition for evidence-based land-use and environmental policy. Full article

The growing share of variable renewable energy sources increases the need for operational flexibility in power systems. In regions with cascaded hydropower systems, upgrading existing plants may be a more practical short-term planning option than developing new hydropower facilities. This study employed a multi-criteria decision-making (MCDM) framework based on the VIKOR method to screen and prioritize existing hydropower plants for potential infrastructure upgrading and capacity development in the Skellefte River, located in Northern Sweden. According to this prefeasibility study, six hydropower stations with installed capacities above 50 MW along that river were evaluated using six technical criteria: installed capacity, hydraulic head, efficiency, generation cost, average turbine discharge, and normal annual production; their weights were derived using the Shannon entropy method to minimize subjectivity. The ranking suggests Gallejaur, Kvistforsen, and Bastusel as the most favorable alternatives, mainly due to their strong performance in annual production and hydraulic head. Vargfors, Krångfors, and Selsforsen rank lower because of head and/or production constraints. The proposed pre-feasibility hydropower ranking workflow provides a transparent and reproducible preliminary technical screening tool. More detailed studies, including hydraulic cascade operation, environmental permitting and grid constraints, are required before practical implementation and feasibility assessment studies for future investigations. Full article

This paper presents a detailed analysis of the operating parameters of a solar active installation with seven photovoltaic-thermal (PV/T) collectors with a total area of 14 m² in a single-family house. A comparative analysis of the work parameters was carried out for the following two locations: Warsaw (Poland) and Andravida (Greece), based on the research of the solar system model created in the TRNSYS 16 program. Considering the months with the best sunshine, from May to August, the average monthly electricity yield in PV/T solar collectors was 206 kWh (Warsaw) and 248 kWh (Andravida). In July, the monthly generation-to-consumption ratio of the PV/T system under the Polish climate conditions was 82%, and under the Greek climate conditions—99%. The heat recovery from PV/T solar collectors in July in the climate of Greece was estimated at 264 kWh and is 29% higher compared to the heat recovery in a hybrid solar installation located in Poland. The generation of electricity in the PV/T solar system instead of a coal-fired power plant can contribute to the avoidance of the annual emissions of pollutants by: 14.00–19.11 kg of SO₂, 2.72–3.72 kg of NO_X, 5.44–7.43 kg of CO, 1330.86–1816.79 kg of CO₂, and 1.09–1.49 kg of particulate matter, depending on the location. Full article

This study aimed to evaluate and quantify the environmental impacts generated by anthropogenic actions on coastal dunes globally using bibliometric data, while concurrently identifying human impacts in a local context on the Brazilian semi-arid coast. A systematic review identified 897 relevant publications from a final sample of 1838 works, revealing an increasing trend in global research, peaking in 2018. Geographically, the United States (141), Italy (95), and Spain (79) lead the field, with Brazil contributing 46 publications. The global overview highlights urbanization (217 studies) and tourism (193 studies) as the most frequent impact factors, contrasting with common mitigation measures like dune stabilization with vegetation (112 studies) and beach nourishment (74 studies). Critically, 11 studies reported that management actions, particularly stabilization without considering native flora, led to problems with invasive plants. A significant research gap was identified due to the absence of literature on wind farms, typically located in transgressive dune systems. The local case study in Ceará state confirms dynamics consistent with global trends, where intense exploitation results in management failures; quantitative temporal analysis (2015–2023) demonstrated a dramatic 86.76% loss of dune area in Fortim and a 4.24 km² loss in Jijoca de Jericoacoara. Findings suggest that, in the Ceará case study, restricting access, promoting community participation, and relocating infrastructure to more appropriate areas may help mitigate human impacts. In addition to identifying potential research gaps, this integration of global and local analysis approaches can contribute to evidence-based coastal dune management adapted to local conditions. Full article

Elucidating patterns of plant biomass allocation is an important subject in ecological research. Broadly speaking, two schools of thought are applied to understand it: allometric partitioning theory (APT) and optimal partitioning theory (OPT). Which theory prevails for the highly aggressive invasive weed Cenchrus pauciflorus regarding intraspecific variability in biomass allocation across habitats has never been studied. Using the R package smatr (Standardised Major Axis Estimation and Testing Routines), we report on allometric relationships regarding biomass allocation in four habitats of the Songnen Plain. There is a large body of literature that has generally considered the allometric coefficient. This study further combined the slope, intercept and shift along the common standardized major axis (SMA) to explain biomass partitioning. We found an isometric relationship between aboveground and root biomasses in three habitats and between stem and root biomasses in two habitats, supporting allometric partitioning theory (APT). Significant differences in slopes between habitats were observed for most relationships. Those habitats whose slopes were not significantly different showed significantly different elevations or shifts in location along a common slope, even for isometric relationships. Such inter-habitat variation suggests that the adjustment of biomass allocation may be induced by environmental conditions, supporting optimal partitioning theory (OPT). It has been suggested that phenotypic integration could constrain trait plasticity; whether this is true remains a debated topic with mixed empirical support. We provide one of the few lines of evidence showing that phenotypic integration is positively associated with the phenotypic plasticity magnitude across advantageous and stressful environments. This study provides insights into the adaptive strategies of invasive Cenchrus pauciflorus in heterogeneous habitats and a scientific basis for habitat-specific management and control. Full article