Search Results (191)

In recent years, large-scale linear infrastructure developments have been developed across hundreds of kilometers of permafrost regions on the Qinghai–Tibet Plateau. The implementation of major engineering projects, including the Qinghai–Tibet Highway, oil pipelines, communication cables, and the Qinghai–Tibet Railway, has spurred intensified research into permafrost dynamics. Climate warming has accelerated permafrost degradation, leading to a range of geological hazards, most notably widespread thermokarst landslides. This study investigates the spatiotemporal distribution patterns and influencing factors of thermokarst landslides in Qinghai Province through an integrated approach combining field surveys, remote sensing interpretation, and statistical analysis. The study utilized multi-source datasets, including Landsat-8 imagery, Google Earth, GF-1, and ZY-3 satellite data, supplemented by meteorological records and geospatial information. The remote sensing interpretation identified 1208 cryogenic hazards in Qinghai’s permafrost regions, comprising 273 coarse-grained soil landslides, 346 fine-grained soil landslides, 146 thermokarst slope failures, 440 gelifluction flows, and 3 frost mounds. Spatial analysis revealed clusters of hazards in Zhiduo, Qilian, and Qumalai counties, with the Yangtze River Basin and Qilian Mountains showing the highest hazard density. Most hazards occur in seasonally frozen ground areas (3500–3900 m and 4300–4900 m elevation ranges), predominantly on north and northwest-facing slopes with gradients of 10–20°. Notably, hazard frequency decreases with increasing permafrost stability. These findings provide critical insights for the sustainable development of cold-region infrastructure, environmental protection, and hazard mitigation strategies in alpine engineering projects. Full article

Weeds present a pervasive challenge in agricultural fields. The integration of herbicide-resistant crops with chemical weed management offers an effective solution for sustainable weed control while reducing labor inputs, particularly in large-scale intensive farming systems. Consequently, the development of herbicide-resistant cultivars has emerged as an urgent priority. In this study, we found that the G311E mutant gene of Arabidopsis MATE (multidrug and toxic compound extrusion) family transporter DTX6, designated DTX6m, confers robust resistance to bipyridyl herbicides paraquat and diquat in rice. DTX6m-overexpression lines exhibited marked resistance to these two herbicides, tolerating diquat concentrations up to 5 g/L, which is five-fold higher than the recommended field application dosage. Agronomic assessments demonstrated that grain yields of DTX6m-overexpressing plants were statistically equivalent to those of wild-type plants. Moreover, the plants displayed beneficial phenotypic changes, such as accelerated flowering and a slight reduction in height. Seed morphometric analysis indicated that in comparison with the wild-type control, DTX6m-transgenic lines exhibited altered grain dimensions while maintaining consistent 1000-grain weight. Nutritional assays further demonstrated that DTX6m increased the levels of free amino acids in seeds, while normal protein and starch contents were retained. Collectively, these results establish that DTX6m effectively boosts rice resistance to paraquat and diquat, validating DTX6m as a candidate gene for engineering plant herbicide resistance and also implying a potential role for DTX6m in amino acid homeostasis in plants. Full article

Accurate trajectory prediction of surrounding vehicles is a fundamental challenge in autonomous driving, requiring sophisticated modeling of complex vehicle interactions, traffic dynamics, and contextual dependencies. This paper introduces Adaptive Speed-Aware Prediction (ADSAP), a novel trajectory prediction framework that advances the state of the art through innovative mechanisms for adaptive attention modulation and knowledge transfer. At its core, ADSAP employs an adaptive deformable speed-aware pooling mechanism that dynamically adjusts the model’s attention distribution and receptive field based on instantaneous vehicle states and interaction patterns. This adaptive architecture enables fine-grained modeling of diverse traffic scenarios, from sparse highway conditions to dense urban environments. The framework incorporates a sophisticated speed-aware multi-scale feature aggregation module that systematically combines spatial and temporal information across multiple scales, facilitating comprehensive scene understanding and robust trajectory prediction. To bridge the gap between model complexity and computational efficiency, we propose an adversarial knowledge distillation approach that effectively transfers learned representations and decision-making strategies from a high-capacity teacher model to a lightweight student model. This novel distillation mechanism preserves prediction accuracy while significantly reducing computational overhead, making the framework suitable for real-world deployment. Extensive empirical evaluation on the large-scale NGSIM and highD naturalistic driving datasets demonstrates ADSAP’s superior performance. The ADSAP framework achieves an 18.7% reduction in average displacement error and a 22.4% improvement in final displacement error compared to state-of-the-art methods while maintaining consistent performance across varying traffic densities (0.05–0.85 vehicles/meter) and speed ranges (0–35 m/s). Moreover, ADSAP exhibits robust generalization capabilities across different driving scenarios and weather conditions, with the lightweight student model achieving 95% of the teacher model’s accuracy while offering a 3.2× reduction in inference time. Comprehensive experimental results supported by detailed ablation studies and statistical analyses validate ADSAP’s effectiveness in addressing the trajectory prediction challenge. Our framework provides a novel perspective on integrating adaptive attention mechanisms with efficient knowledge transfer, contributing to the development of more reliable and intelligent autonomous driving systems. Significant improvements in prediction accuracy, computational efficiency, and generalization capability demonstrate ADSAP’s potential ability to advance autonomous driving technology. Full article

Maize is one of the top five field crops worldwide and is indispensable as animal feed, serves as a raw material in many industries, and is a staple for human food. However, its production is under increasing pressure mainly due to abiotic stress. Drought and excessive precipitation, air temperature fluctuations, and reduced soil fertility due to inadequate soil pH reactions are among the biggest challenges that must be overcome. Therefore, the goal of this study was to determine the effects of these combined stressful abiotic conditions on maize grain yield and quality and to determine the genetic-specific response of maize genotypes in such conditions. The experiment was set up in eastern Croatia according to the randomized complete block design in four replications. A total of 10 maize hybrids of different FAO maturity groups were evaluated across four diverse environments, each subjected to one or two abiotic stresses (extreme precipitation, drought, high air temperatures, and acidic soil). Analysis of variance revealed that all treatment effects were statistically significant, except for the effect of hybrids on grain yield. Depending on the effect of abiotic stress, the variations among environments were up to 51.4% for yield and up to 12.1%, 18.9%, and 0.81% for protein, oil, and starch content, respectively. Differences among hybrids were less pronounced for yield (7.9%), while for protein (13.5%), oil (17.3%), and starch content (1.5%) were similar. However, the largest variation was found for the interaction effect. In the conducted research, ENV2 recorded the highest grain yield, along with the highest oil and starch content, as well as the second-highest protein content, while the hybrid effect remained unclear. Generally, ENV4 had the greatest negative impact due to the combined effects of extreme abiotic stresses, including soil acidity, drought, and high air temperatures. Full article

This review presents a comprehensive synthesis of existing studies on the correlations between soil electrical resistivity and fundamental geotechnical properties, with the aim of improving the applicability of resistivity testing in geotechnical engineering. Based on both laboratory and field research, the analysis highlights moisture content as the most influential factor inversely affecting resistivity, followed by plasticity index, clay content, and dry unit weight. Statistical approaches—including regression analysis and Spearman’s rank correlation—and artificial neural networks (ANNs) were evaluated for their effectiveness in modeling these nonlinear relationships. ANNs demonstrated superior predictive performance, particularly in heterogeneous and fine-grained soils. The review also examines the influence of temperature and salinity, emphasizing the need for multivariable models for robust interpretation. While resistivity testing shows great potential for non-destructive soil classification and compaction assessment, limitations related to soil type specificity, environmental variability, and model generalizability are noted. This work provides an important foundation for the development of data-driven, non-invasive techniques for subsurface characterization and outlines directions for future research, including field validation and hybrid modeling. Full article

Understanding the impact of vegetation on organic matter content in sediments is essential for sustainable reservoir management and water quality protection. This study examined the relationship between land cover, erosion processes, and organic matter accumulation in the sediments of four small water reservoirs in the Republic of Serbia. Organic matter content was quantified and analyzed in relation to basin characteristics, including land-use composition, absolute and mean flow gradients, and sediment grain size distribution. Field sampling was conducted across the catchments of four small water reservoirs—Duboki potok, Resnik, Ljukovo, and Sot—with sediment samples collected from main tributaries and accumulation basins. A multi-method approach was employed, combining remote sensing for vegetation-cover assessment, granulometric analysis, organic matter evaluation via loss-on-ignition at 350 °C, and statistical correlation analysis to assess the influence of land use and hydrological gradients on sediment composition. The results revealed a strong correlation (R = 0.892) between forest cover and sedimentary organic matter content, confirming the significant role of vegetation in stabilizing sediments and promoting organic matter deposition. Reservoirs with higher forest and shrub cover (e.g., Sot and Duboki potok) exhibited greater organic matter accumulation (5.79–5.98%), while the agriculture-dominated Ljukovo catchment (76.85% agricultural land) recorded the lowest organic matter content (3.89%) due to increased sediment displacement and reduced erosion resistance. These findings underscore the critical role of vegetation in regulating sediment dynamics and enhancing organic matter retention in small water reservoirs. To mitigate excessive organic matter deposition and improve water quality, sustainable watershed management strategies—such as vegetation buffer strips, afforestation, and erosion control measures—are recommended. Full article

Vitamin C industrial residue after evaporation (RAE) acts as both a rapid-release carbon source and a microbial activity promoter. A two-year maize field experiment assessed the effectiveness of RAE in improving soil quality in degraded semi-arid regions. The RAE formulation was applied via drip irrigation during the sixth true leaf unfolded (BBCH 24), fourteenth true leaf unfolded (BBCH 38), and middle of grain filling (BBCH 66) stages, which consisted of three treatments: (1) untreated control (CK), (2) low RAE rate (LR: 150 L/ha), and (3) high RAE rate (HR: 300 L/ha). Soil physicochemical properties, enzyme activities, maize nutrient accumulations, and yields were comprehensively analyzed at the maize maturity stage. RAE application significantly improved the following soil nutrients: dissolved organic carbon (10.40–25.92%), ammonium nitrogen (14.04–70.67%), nitrate nitrogen (14.80–78.63%), and available phosphorus (11.79–42.55%). Soil enzyme activities also increased: sucrase (12.38–30.25%), amidase (1.95–25.69%), peptidase (0.56–48.79%), β-1,4-N-acetylglucosaminidase (3.11–9.48%), protease (17.41–226.29%), and acid phosphatase (8.73–60.04%). These changes enhanced maize nitrogen (17.63–40.73%) and phosphorus (20.09–42.11%) uptake, increasing yield by 7.12–13.46%. Statistical analysis showed strong correlations between yields and nutrient accumulations (r = 0.82, p < 0.01), particularly phosphorus (r = 0.91, p < 0.001). RAE enhances crop productivity in degraded agricultural systems by improving soil nutrient availability and plant assimilation, making it a viable alternative to conventional fertilizers. Full article

Bitter vetch (Vicia ervilia L. Willd.) is an ancient Mediterranean legume, well adapted to dry climates, that has recently gained attention for its potential in organic farming and as a suitable source of bioactive compounds. This study analyzed the agrobiological variability of 12 bitter vetch accessions from the IPGR-Sadovo genebank in two-year field trials. Yield-related traits were recorded, and grains were assessed for protein, sugar, starch, free amino acids, phenols, and antitrypsin content. Statistical analyses included variance, correlation, cluster, principal component, and path-coefficient methods. Significant variation was observed in plant branching, pod and grain numbers, and grain weight per plant. Grain yield correlated strongly with pod number (r = 0.910**), grains per pod (r = 0.867**) and per plant (r = 0.965**), and pod size. Positive direct effects on grain yield had the traits germination−50% flowering, number of seeds per plant, height to first pod, and harvest index. An indirect impact was found for the number of pods per plant, number of seeds per pod, and seed starch content. Accessions formed four main clusters. BGR6207, B9E0168, and C3000003 showed high yield potential. C3000001, C3000003, C3000007, and C3000006 exhibited early maturity. C3E0118, C3000007, and C3000003 seeds had lower amounts of phenols. BGR13526 presented lower protein and antitrypsin but higher carbohydrate and phenol levels. Tolerance to moderate osmotic stress (150 mM NaCl or 10% Polyethylene glycol 6000) varied. BGR3052, BGR13526, and A3BM0178 were found to be resistant to both stressors, while accessions C3000001 and C3000007 were identified as sensitive to both adversities. C3000006 was determined as sensitive to salinity but resistant to drought, and BGR3051and C3000003 were relatively sensitive to drought but resistant to salinity. Root elongation and thinning were observed in half of the accessions as adaptive responses to stress. These findings highlight some of the advantages of the evaluated bitter vetch accessions for breeding and reintroduction into sustainable agricultural practices. Full article

In dredging operations, the efficient transportation of dredged materials presents a significant and intricate challenge. This study focuses on the motion and resistance characteristics of coarse-grained dredged materials during pipeline conveyance. A specialized simulation experiment platform was developed to investigate the horizontal pipeline transport of coarse-grained materials. The experimental design encompassed varying particle diameters, material volume concentrations, and mixed average flow rates to analyze the motion and resistance characteristics of these materials in horizontal pipelines. Three distinct particle beds were identified based on different coarse particle motion states. This study statistically analyzed the impact of the particle diameter and material volume concentration on the transport efficiency of coarse particle populations. The key findings indicate that the mixed mean flow rate significantly influences the transportation efficiency of coarse particle groups, whereas the particle diameter and material volume concentration have a minimal effect. Specifically, coarse particles with a diameter of 0.9 mm demonstrated optimal water flow following, and higher mixed mean flow rates correlated with increased transportation efficiency of the coarse particle group. The transition speed of the coarse particle group flow type was notably affected by the material volume concentration and particle diameter, exhibiting a linear relationship. Therefore, when the particle size of the dredged material increases or the concentration increases, the average flow rate of the mixture is appropriately increased to ensure that the flow pattern of the dredged material in the pipeline remains in a non-homogeneous suspended flow pattern, thereby improving the efficiency and stability of the transportation system. By optimizing the conveying characteristics of coarse-grained materials, the pipeline conveying efficiency can be improved and the risk of pipeline wear and clogging can be reduced, thus lowering engineering costs and energy consumption and promoting technological innovation in related industries. In addition, this research can enhance engineering safety, reduce resource waste and environmental pollution, promote sustainable development, and provide important theoretical support and practical guidance for emerging fields such as deep-sea mining and environmental engineering. Full article

In this work, we aimed to study the austenite grain growth behavior of an ultra-high-strength stainless steel within the temperature range of 900–1150 °C and holding time range of 0–120 min, using a metallographic microscope and metallographic image analysis software to perform a statistical analysis of grain size variation. The undissolved phases of the steel were investigated using a field emission scanning electron microscope (SEM) and transmission electron microscope (TEM). Within the temperature range of 900–950 °C, the grain growth rate of the steel was slow, while within the range of 1000–1150 °C, the grain growth rate was relatively fast. This is attributed to the precipitation of a large number of M₆C-type carbides during the forging and annealing processes. In the temperature range of 900–950 °C, the solid solubility of the M₆C phase was low and the pinning effect was significant, which hindered the growth of austenite grains. Above 950 °C, the carbides were dissolved extensively, weakening the pinning effect on the grain boundaries and accelerating the grain growth rate. A predictive mathematical model for the growth of the original austenite grains was established based on the Arrhenius equation, elucidating the effects of heating temperature, holding time, initial grain size, and number of carbides on the growth of austenite grains, providing a theoretical basis for heat treatment process design in actual production. Full article

This investigation examines the effects of straw-based nitrogen fertilization on soil hydrological properties and biomass partitioning in maize under arid zone conditions. A biennial field investigation was conducted during the 2016–2017 cropping seasons, with an equal nitrogen content of 225 kg ha⁻¹, and a total of 5 treatments, 100% fertilizer nitrogen (CK), 25% straw nitrogen + 75% fertilizer nitrogen (S25), 50% straw nitrogen + 50% fertilizer nitrogen (S50), 75% straw nitrogen + 25% fertilizer nitrogen (S75), 100% straw nitrogen (S100). The data demonstrated that in 2017, in comparison with CK, the soil water storage in the 0–60 cm soil layer of S25 and S50 in the large trumpet stage (V12) increased significantly by 23.32% and 25.14% (p < 0.05), respectively. In the two-year experiment, stratified moisture reserves (0–200 cm) in different treatment groups exhibited a fluctuating pattern characterized by successive increase-decrease-increase transitions along the soil profile, and overall S25 and S50 were larger than CK. In 2016, the biomass accumulation of the S50 treatment at the maturity stage (R6) was the highest, which increased by 18.11% and 19.49% compared with the CK and S75 (p < 0.05), respectively. There was statistical parity in water use efficiency between treatments. Soil moisture retention capacity of 180–200 cm soil was positively correlated with yield at the jointing (V6) and maturity (R6) stages, and soil water storage of 160–180 cm soil was positively correlated with yield at the tasselling stage (VT). Water consumption during the presowing–to–jointing phase demonstrated the strongest correlation with final grain yield. In summary, the S25 treatment in this experiment significantly enhanced the optimization of soil hydrological properties, increasing soil moisture storage, fully utilizing soil moisture, increasing dry matter accumulation in each growth period of maize, and replacing chemical nitrogen fertilizer with 25% of straw equivalent N fertilizers was beneficial to soil moisture storage. Full article

Lupin is an Andean legume that has gained importance in Ecuador due to the protein content in its grain. Nonetheless, in recent times the production of lupin has been affected by inadequate nutritional management. In order to avoid such circumstances, the current study spectrally analyzed lupin cultivation under the application of nanofertilizers and Fe and Zn chelates, within two controlled trials, using a radiometer spectrum, an active crop sensor and a multispectral sensor mounted on a UAV. Vegetation indices were generated and subsequently statistically analyzed using ANOVA and Tukey tests. In the field trial, the treatments lacked an indication of significant improvements, while in the greenhouse trial, the nanofertilizer treatments indicated better results compared to the control treatments. However, it was also determined that the application of nanofertilizers at a concentration of 540 ppm demonstrated significant efficiency in greenhouse conditions, which could not be achieved in the field. Furthermore, the chelate treatment presented a certain degree of toxicity for the plant. Full article

Low vegetable consumption in sub-Saharan Africa partly arises from limited availability across cereal-based zones. A field experiment in southern Benin (April to September 2023) evaluated four maize–chili and five maize–mungbean relay intercropping. Growth and yield data and farmers’ perceptions were analyzed using analysis of variance with the least significant difference test, land equivalent ratio (LER) and monetary indexes. Maize grain yield was statistically similar across patterns, whereas chili and mungbean yields differed significantly. All sowing patterns achieved LER > 1. Pattern (1:1) maize–chili had a modest LER (1.15), while treatment (1:3) had a high LER (1.60) for mungbean–maize. Both patterns showed high actual yield gain and intercropping advantage. Pattern (2:2) for maize–chili and pattern (1:3) for maize–mungbean yielded the greatest gross return (7796.6 USD/ha and 1301.2 USD/ha, respectively). Sole mungbean and all intercropping sowing patterns significantly increased mineralizable carbon. Pattern (1:3) maize-mungbean slightly increased total nitrogen and potassium. Farmers ranked the highest pattern (2:2) for maize–chili and (1:3) for maize–mungbean due to sup erior weed, water, and soil management and increased yields. These findings suggest that diversified maize systems incorporating chili pepper and mungbean offer economic benefits and better soil health in southern Benin. Full article

The soil used for the field experiment was PLb-g4 Endohipogleyic Eutric Planasol. The research aimed to investigate the effects of different nitrogen fertilisation rates and biological preparations on yield structure elements and partial factor productivity of nitrogen in maize (Zea mays L.) grown for grain production. The factors studied were Factor A—nitrogen (N) fertiliser rates: (1) 100 kg ha⁻¹, (2) 140 kg ha⁻¹, and (3) 180 kg ha⁻¹, and Factor B—use of biofertilisers: (1) no biological preparations (BP) used, (2) biological preparation (AB)—nitrogen bacteria Paenibacillus polymyxa (1.0 L ha⁻¹), (3) biological preparations (AB + C)—nitrogen bacteria Paenibacillus polymyxa (1.0 L ha⁻¹) and cytokinin, and (4) biological preparations (AB + H)—nitrogen bacteria Paenibacillus polymyxa (1.0 L ha⁻¹) and humic acids. The research showed that the yield of maize grain was significantly increased not only by increasing the rates of nitrogen fertilisation but also by using biological preparations. The highest maize grain yield (11.5 t ha⁻¹) was obtained in 2020 using N₁₈₀ fertilisation, in combination with biological preparations AB + H. In all cases, the biological preparations and their combinations significantly increased the maize grain yield compared to the control field (no use of BP). The biological preparations in combination with N significantly increased the weight of 1000 grains and thus the grain yield per plant. The highest maize grain yield per plant (154.6 g) was obtained in 2020 using N₁₈₀ fertilisation, in combination with biological preparations AB + H. In most cases, positive, strong, very strong, and statistically significant correlations were observed between the different rates of nitrogen fertilisation and the indicators studied: r = 0.76–0.94 (p < 0.01, p < 0.05). No statistically significant correlation was found between nitrogen fertilisation rates and the number of grains per cob (p > 0.05). The highest partial factor productivity of nitrogen fertiliser (92.0 kg of maize kg⁻¹ of N) was obtained in 2020 using N₁₀₀ fertilisation, in combination with AB + H. Increasing the nitrogen fertiliser rates and not using biological preparations resulted in a decrease in the partial factor productivity of nitrogen fertiliser. Full article

This paper addresses the author’s current understanding of the physics of interactions in polymers under a voltage field excitation. The effect of a voltage field coupled with temperature to induce space charges and dipolar activity in dielectric materials can be measured by very sensitive electrometers. The resulting characterization methods, thermally stimulated depolarization (TSD) and thermal-windowing deconvolution (TWD), provide a powerful way to study local and cooperative relaxations in the amorphous state of matter that are, arguably, essential to understanding the glass transition, molecular motions in the rubbery and molten states and even the processes leading to crystallization. Specifically, this paper describes and tries to explain ‘interactive coupling’ between molecular motions in polymers by their dielectric relaxation characteristics when polymeric samples have been submitted to thermally induced polarization by a voltage field followed by depolarization at a constant heating rate. Interactive coupling results from the modulation of the local interactions by the collective aspect of those interactions, a recursive process pursuant to the dynamics of the interplay between the free volume and the conformation of dual-conformers, two fundamental basic units of the macromolecules introduced by this author in the “dual-phase” model of interactions. This model reconsiders the fundamentals of the TSD and TWD results in a different way: the origin of the dipoles formation, induced or permanent dipoles; the origin of the Wagner space charges and the T_g,ρ transition; the origin of the T_LL manifestation; the origin of the Debye elementary relaxations’ compensation or parallelism in a relaxation map; and finally, the dual-phase origin of their super-compensations. In other words, this paper is an attempt to link the fundamentals of TSD and TWD activation and deactivation of dipoles that produce a current signal with the statistical parameters of the “dual-phase” model of interactions underlying the Grain-Field Statistics. Full article