Search Results (4,626)

The penetration rate of variable energy sources in the current power grid is increasing, with the aim being to expand the use of these energy sources and to replace the traditional black start power supply. This study investigates the black start of a photovoltaic storage joint system based on the system’s short-circuit ratio threshold. Firstly, the principles and control modes of the photovoltaic (PV) system, energy storage system (ESS), and high-voltage direct current (DC) transmission system are studied separately to build an overall model; secondly, computational determinations of the short-circuit ratio under different scenarios are introduced to analyze the strength of the system, and the virtual inertia and virtual damping of the PV system are configured based on this; finally, the change trend of the storage system’s state of charge (SOC) is computed and observed, and the limits of what the system can support in each stage are determined. An electromagnetic transient simulation model of a black start system is constructed in PSCAD/EMTDC, and according to the proposed recovery strategy, the system frequency is maintained in the range of 49.4~50.6 Hz during the entire black start process; the fluctuation in maximum frequency after the recovery of the DC transmission system is no more than 0.1%; and the fluctuation in photovoltaic power at each stage is less than 3%. In addition, all the key indexes meet the requirements for black start technology, which verifies the validity of the strategy and provides theoretical support and a practical reference for the black start of a grid with variable energy sources. Full article

Soil organic carbon (SOC) pool plays an extremely important role in regulating the global carbon (C) cycle and climate change. Atmospheric nitrogen (N) and phosphorus (P) deposition caused by human activities has significant impacts on soil C sequestration potential of terrestrial ecosystem. To investigate the effects of N and P deposition on soil C sequestration and C-N coupling relationship in broad-leaved evergreen forests, a 6-year field nutrient regulation experiment was implemented in subtropical Castanopsis sclerophylla forests with four different N and P additions: N addition (100 kg N·hm⁻²·year⁻¹), N + P (100 kg N·hm⁻²·year⁻¹ + 50 kg P·hm⁻²·year⁻¹), P addition (50 kg P·hm⁻²·year⁻¹), and CK (0 kg N·hm⁻²·year⁻¹). The changes in the C and N contents and stable isotope distributions (δ¹³C and δ¹⁵N) of different soil organic fractions were examined. The results showed that the SOC and total nitrogen (STN) (p > 0.05) increased with N addition, while SOC significantly decreased with P addition (p < 0.05), and N + P treatment has low effect on SOC, STN (p > 0.05). By density grouping, it was found that N addition significantly increased light fraction C and N (LFOC, LFN), significantly decreased the light fraction C to N ratio (LFOC/N) (p < 0.05), and increased heavy fraction C and N (HFOC, HFN) accumulation and light fraction to total organic C ratio (LFOC/SOC, p > 0.05). Contrary to N addition, P addition was detrimental to the accumulation of LFOC, LFN and reduced LFOC/SOC. It was found that different reactive oxidized carbon (ROC) increased under N addition but ROC/SOC did not change, while N + P and P treatments increased ROC/SOC, resulting in a decrease in SOC chemical stability. Stable isotope analysis showed that N addition promoted the accumulation of new soil organic matter, whereas P addition enhanced the transformation and utilization of C and N from pre-existing organic matter. Additionally, N addition indirectly increased LFOC by significantly decreasing pH; significantly contributed to LFOC and ROC by increasing STN accumulation promoted by NO₃⁻-N and NH₄⁺-N; and decreased light fraction δ¹³C by significantly increasing dissolved organic C (p < 0.05). P addition had directly significant negative effect on LFOC and SOC (p < 0.05). In conclusion, six-year N deposition enhances soil C and N sequestration while the P enrichment reduces the content of soil C, N fractions and stability in Castanopsis sclerophylla forests. The results provide a scientific basis for predicting the soil C sink function of evergreen broad-leaved forest ecosystem under the background of future climate change. Full article

The accelerating integration of electric vehicles (EVs) into contemporary transportation infrastructure has underscored significant limitations in traditional charging paradigms, particularly in accommodating heterogeneous user requirements within dynamic operational environments. This study presents a differentiated optimization framework for EV charging strategies through the systematic classification of user types. A multidimensional decision-making environment is established for three representative user categories—residential, commercial, and industrial—by synthesizing time-variant electricity pricing models with dynamic carbon emission pricing mechanisms. A bi-level optimization architecture is subsequently formulated, leveraging deep reinforcement learning (DRL) to capture user-specific demand characteristics through customized reward functions and adaptive constraint structures. Validation is conducted within a high-fidelity simulation environment featuring 90 autonomous EV charging agents operating in a metropolitan parking facility. Empirical results indicate that the proposed typology-driven approach yields a 32.6% average cost reduction across user groups relative to baseline charging protocols, with statistically significant improvements in expenditure optimization (p < 0.01). Further interpretability analysis employing gradient-weighted class activation mapping (Grad-CAM) demonstrates that the model’s attention mechanisms are well aligned with theoretically anticipated demand prioritization patterns across the distinct user types, thereby confirming the decision-theoretic soundness of the framework. Full article

In response to the challenges posed by soil degradation in the arid regions of Xinjiang, China, green and organic management practices have emerged as effective alternatives to conventional agricultural management methods, helping to mitigate soil degradation by promoting natural soil recovery and ecological balance. However, most of the existing studies focus on a single management practice or indicator and lack a systematic assessment of the effects of integrated orchard management in arid zones. This study aims to investigate how different agricultural management practices influence soil physicochemical properties and inter-root microbial communities in apple orchards in Xinjiang and to identify the main physicochemical factors affecting the composition of inter-root microbial communities. Inter-root soil samples were collected from apple orchards under green management (GM), organic management (OM), and conventional management (CM) in major apple-producing regions of Xinjiang. Microbial diversity and community composition of the samples were analyzed using high-throughput amplicon sequencing. The results revealed significant differences (p < 0.05) in soil physicochemical properties across different management practices. Specifically, GM significantly reduced soil pH and C:N compared with OM. Both OM and GM significantly decreased soil available nutrient content compared with CM. Moreover, GM and OM significantly increased bacterial diversity and changed the community composition of bacteria and fungi. Proteobacteria and Ascomycota were identified as the dominant bacteria and fungi, respectively, in all management practices. Linear discriminant analysis (LEfSe) showed that biomarkers were more abundant under OM, suggesting that OM may contribute to ecological functions through specific microbial taxa. Co-occurrence network analysis (building a network of microbial interactions) demonstrated that the topologies of bacteria and fungi varied across different management practices and that OM increased the complexity of microbial co-occurrence networks. Mantel test analysis (analyzing soil factors and microbial community correlations) showed that C:N and available potassium (AK) were significantly and positively correlated with the community composition of bacteria and fungi, and that C:N, soil organic carbon (SOC), and alkaline hydrolyzable nitrogen (AN) were significantly and positively correlated with the diversity of fungi. Redundancy analysis (RDA) further indicated that SOC, C:N, and AK were the primary soil physicochemical factors influencing the composition of microbial communities. This study provides theoretical guidance for the sustainable management of orchards in arid zones. Full article

Soil organic carbon (SOC) is an important part of the global C pool and is sensitive to climate change. The SOC content and fractions of rice paddies along four elevations (250, 1150, 1600 and 1800 m) on the same slope in four seasons (spring, summer, autumn and winter) at Yuanyang Terrace in southwest China were investigated, and their relationship with environmental factors was analyzed. The contents of SOC, unprotected SOC (uPOM), physically protected SOC (pPOM) and biochemically protected SOC (bcPOM) in rice paddies at a low elevation (250 m), were significantly lower by 49–51% than those at relatively high elevations (1600 m and 1800 m). Among the SOC fractions, the highest proportion (33–50%) was uPOM, followed by pPOM and bcPOM (accounting for 17–40%), and the lowest proportion was chemically protected SOC (cPOM). In addition, there were interseasonal differences among the contents of SOC fractions, with a significantly higher content of SOC, uPOM and pPOM at an elevation of 1600 m in summer than in the other three seasons, whereas the cPOM content at an elevation of 250 m in spring was significantly higher than in the other three higher elevations. According to the redundancy analysis (RDA), total nitrogen was the key environmental factor, with an explanatory degree of 56% affecting the contents of SOC and its fractions. Thus, the SOC content increased with increasing elevation, and physical and biochemical protection were potential stabilization mechanisms responsible for their stability in the rice paddy of Yuanyang Terrace. These results provides empirical evidence for the elevational distribution patterns and seasonal dynamics of SOC fractions in rice paddies across Yuanyang Terrace. These findings highlight the importance of physical and biochemical protection mechanisms in stabilizing SOC in rice paddies, which could enhance long-term C sequestration and contribute to climate change mitigation in terraced agroecosystems. Full article

Fibromyalgia is a complex disorder characterized by chronic widespread pain and a variety of related symptoms. Growing evidence suggests that the central and peripheral nervous systems are involved, with small fiber neuropathy playing a key role in its development. We retrospectively reviewed the medical records of 100 patients diagnosed with primary fibromyalgia. Those showing symptoms indicative of small fiber dysfunction who were treated with L-Acetyl Carnitine (LAC) and Palmitoylethanolamide (PEA) alongside standard care (SOC) were compared to matched controls who received only SOC. To ensure comparable groups, propensity score matching was used. Changes in Fibromyalgia Impact Questionnaire Revised (FIQR) scores over 12 weeks were analyzed using non-parametric tests due to the data’s non-normal distribution. After matching, 86 patients (43 in each group) were included. The group receiving LAC and PEA as add-on therapy experienced a significant median reduction in FIQR scores (−19.0 points, p < 0.001), while the SOC-only group showed no significant change. Comparisons between groups confirmed that the improvement was significantly greater in the LAC+PEA group (p < 0.001). These results suggest that adding LAC and PEA to standard care may provide meaningful symptom relief for fibromyalgia patients with suspected small fiber involvement. This supports the hypothesis that peripheral nervous system dysfunction contributes to the disease burden in this subgroup. However, further prospective controlled studies are needed to confirm these promising findings. Full article

This paper presents a novel data-driven framework for real-time State of Charge (SOC) estimation in lithium-ion battery systems using a data-driven Prognostics and Health Management (PHM) approach. The method leverages an optimized bidirectional Long Short-Term Memory (Bi-LSTM) network, trained with enhanced datasets filtered via exponentially weighted moving averages (EWMAs) and refined through SHAP-based feature attribution. Compared against a Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) across ten diverse drive cycles, the proposed model consistently achieved superior performance, with mean absolute errors (MAEs) as low as 0.40%, outperforming EKF (0.66%) and UKF (1.36%). The Bi-LSTM model also demonstrated higher R² values (up to 0.9999) and narrower 95% confidence intervals, confirming its precision and robustness. Real-time implementation on embedded platforms yielded inference times of 1.3–2.2 s, validating its deployability for edge applications. The framework’s model-free nature makes it adaptable to other nonlinear, time-dependent systems beyond battery SOC estimation. Full article

The midgut of Antheraea pernyi plays a critical role in antiviral defense. However, its transcriptional complexity remains poorly understood. Here, a full-length (FL) transcriptome atlas of A. pernyi midgut was developed by integrating PacBio Iso-Seq and RNA-seq techniques. The transcriptome sequences included 1850 novel protein-coding genes, 17,736 novel alternative isoforms, 1664 novel long non-coding RNAs (lncRNAs), and 858 transcription factors (TFs). In addition, 2471 alternative splicing (AS) events and 3070 alternative polyadenylation (APA) sites were identified. Moreover, 3426 and 4796 differentially expressed genes (DEGs) and isoforms were identified after ApNPV infection, respectively, besides the differentially expressed lncRNAs (164), TFs (171), and novel isoforms of ApRelish (1) and ApSOCS2 (4). Enrichment analyses showed that KEGG pathways related to metabolism were suppressed, whereas GO terms related to DNA synthesis and replication were induced. Furthermore, the autophagy and apoptosis pathways were significantly enriched among the upregulated genes. Protein–protein interaction network (PPI) analysis revealed the coordinated downregulation of genes involved in mitochondrial ribosomes, V-type and F-type ATPases, and oxidative phosphorylation, indicating the disruption of host energy metabolism and organelle acidification. Moreover, coordinated upregulation of genes associated with cytoplasmic ribosomes was observed, suggesting that the infection by ApNPV interferes with host translational machinery. These results show that ApNPV infection reprograms energy metabolism, biosynthetic processes, and immune response in A. pernyi midgut. Our study provides a foundation for elucidating the mechanisms of A. pernyi–virus interactions, particularly how the viruses affect host defense strategies. Full article

The energy generated from renewable sources has an intermittent nature since solar irradiation and wind speed vary continuously. Hence, their energy should be stored to be utilized throughout their shortage. There are various forms of energy storage systems while the most widespread technique is the battery storage system since its cost is low compared to other techniques. Therefore, batteries are employed in several applications like power systems, electric vehicles, and smart grids. Due to the merits of the lithium-ion (Li-ion) battery, it is preferred over other kinds of batteries. However, the accuracy of the Li-ion battery model is essential for estimating the state of charge (SOC). Additionally, it is essential for consistent simulation and operation throughout various loading and charging conditions. Consequently, the determination of real battery model parameters is vital. An innovative application of the red-billed blue magpie optimizer (RBMO) for determining the model parameters and the SOC of the Li-ion battery is presented in this article. The Shepherd model parameters are determined using the suggested optimization algorithm. The RBMO-based modeling approach offers excellent execution in determining the parameters of the battery model. The suggested approach is compared to other programmed algorithms, namely dandelion optimizer, spider wasp optimizer, barnacles mating optimizer, and interior search algorithm. Moreover, the suggested RBMO is statistically evaluated using Kruskal–Wallis, ANOVA tables, Friedman rank, and Wilcoxon rank tests. Additionally, the Li-ion battery model estimated via the RBMO is validated under variable loading conditions. The fetched results revealed that the suggested approach achieved the least errors between the measured and estimated voltages compared to other approaches in two studied cases with values of 1.4951 × 10⁻⁴ and 2.66176 × 10⁻⁴. Full article

Soil organic carbon (SOC) mineralization plays the critical role of regulating carbon sequestration potential. This process is strongly influenced by agricultural practices, particularly tillage regimes and straw management. However, the complex interactions between tillage methods, straw types, and application rates in terms of SOC dynamics, especially in semi-arid agroecosystems like eastern Inner Mongolia, remain poorly understood. In this study, we assessed the combined effects of no tillage (NT) vs. rotary tillage (RT), three straw types (maize/MS, wheat/WS, and oilseed rape/OS), and three application rates (0.4%/low, 0.8%/medium, and 1.2%/high) on SOC concentration and mineralization using controlled laboratory incubation with soils from long-term plots. The key findings revealed that NT significantly increased the SOC concentration in the topsoil (0–20 cm) by an average of 14.5% compared to that in the RT. Notably, combining NT with medium-rate wheat straw (0.8%) resulted in the achievement of the highest SOC accumulation (28.70 g/kg). SOC mineralization increased with straw inputs, exhibiting significant straw type × rate interactions. Oilseed rape straw showed the highest specific mineralization rate (33.9%) at low input, while maize straw mineralized fastest under high input with RT. Therefore, our results demonstrate that combining NT with either 0.8% wheat straw or 1.2% maize straw represents an optimal application strategy, as the SOC concentration is enhanced by 12–18% for effective carbon sequestration in this water-limited semi-arid region. Therefore, optimizing SOC sequestration requires the integration of appropriate crop residue application rates and tillage methods tailored to different cropping systems. Full article

Grazing affects soil organic carbon (SOC) through plant removal, livestock trampling, and manure deposition. However, the impact of grazing on SOC is also influenced by multiple factors such as climate, soil properties, and management approaches. Despite extensive research, the mechanisms by which grazing intensity influences SOC density in grasslands remain incompletely understood. This study examines the effects of varying grazing intensities on SOC density (0–30 cm) dynamics in temperate grasslands of northern China using field surveys and experimental analyses in a typical steppe ecosystem of Inner Mongolia. Results show that moderate grazing (3.8 sheep units/ha/yr) led to substantial consumption of aboveground plant biomass. Relative to the ungrazed control (0 sheep units/ha/yr), aboveground plant biomass was reduced by 40.5%, 36.2%, and 50.6% in the years 2016, 2019, and 2020, respectively. Compensatory growth failed to fully offset biomass loss, and there were significant reductions in vegetation carbon storage and cover (p < 0.05). Reduced vegetation cover increased bare soil exposure and accelerated topsoil drying and erosion. This degradation promoted the downward migration of SOC from surface layers. Quantitative analysis revealed that moderate grazing significantly reduced surface soil (0–10 cm) organic carbon density by 13.4% compared to the ungrazed control while significantly increasing SOC density in the subsurface layer (10–30 cm). Increased precipitation could mitigate the SOC transfer and enhance overall SOC accumulation. However, it might negatively affect certain labile SOC fractions. Elucidating the mechanisms of SOC variation under different grazing intensities and precipitation regimes in semi-arid grasslands could improve our understanding of carbon dynamics in response to environmental stressors. These insights will aid in predicting how grazing systems influence grassland carbon cycling under global climate change. Full article

Based on multi-source data, including synthetic aperture radar (Sentinel-1, S1) and optical satellite images (Sentinel-2, S2), topographic data, and climate data, this study explored the performance and feasibility of different variable combinations in predicting SOC using three machine learning models. We designed the three models based on 244 samples from the study area, using 70% of the samples for the training set and 30% for the testing set. Nine experiments were conducted under three variable scenarios to select the optimal model. We used this optimal model to achieve high-precision predictions of SOC content. Our results indicated that both S1 and S2 data are significant for SOC prediction, and the use of multi-sensor data yielded more accurate results than single-sensor data. The RF model based on the integration of S1, S2, topography, and climate data achieved the highest prediction accuracy. In terms of variable importance, the S2 data exhibited the highest contribution to SOC prediction (31.03%). The SOC contents within the study region varied between 4.16 g/kg and 29.19 g/kg, showing a clear spatial trend of higher concentrations in the east than in the west. Overall, the proposed model showed strong performance in estimating grassland SOC and offered valuable scientific guidance for grassland conservation in the western Songnen Plain. Full article

This study combined field survey data with Gaofen 5 (GF-5) satellite hyperspectral images of the Yuncheng Basin (China), considering 15 environmental variables. Random forest (RF) was used to select the optimal satellite hyperspectral model, sequentially introducing natural and farmland management factors into the model to analyze the spatial distribution of farmland soil organic carbon (SOC). Furthermore, RF factorial experiments determined the contributions of farmland management, climate, vegetation, soil, and topography to the SOC. Structural equation modeling (SEM) elucidated the driving mechanisms of SOC variations. Integrating satellite hyperspectral data and environmental variables improved the prediction accuracy and SOC-mapping precision of the model. The integration of natural variables significantly improved the RF model performance (R² = 0.78). The prediction accuracy enhanced with the introduction of crop phenology (R² = 0.81) and farmland management factors (R² = 0.87). The model that incorporated all 15 variables demonstrated the highest prediction accuracy (R² = 0.89) and greatest spatial SOC variability, with minimal uncertainty. Farmland management activities exerted the strongest influence on SOC (0.38). The proposed method can support future investigations on soil carbon sequestration processes in river basins worldwide. Full article

Eco-enzymatic stoichiometry provides a basis for understanding soil ecosystem functions, with implications for land management and ecological protection. Long-term climatic factors and human interferences have caused significant land-use transformations in the Qinghai–Tibet Plateau region, affecting various ecological functions, such as soil nutrient cycling and chemical element balance. It is currently unclear how large-scale land-use conversion affects soil ecological stoichiometry. In this study, 763 soil samples were collected across three land-use types: farmland, grassland, and forest land. In addition, changes in soil physicochemical properties and enzyme activity and stoichiometry were determined. The soil available phosphorus (SAP) and total phosphorus (TP) concentrations were the highest in farmland soil. Bulk density, pH, SAP, TP, and NO₃⁻-N were lower in forest soil, whereas NH₄⁺-N, available nitrogen, soil organic carbon (SOC), available potassium, and the soil nutrient ratio increased. Land-use conversion promoted soil β-1,4-glucosidase, N-acetyl-β-glucosaminidase, and alkaline phosphatase activities, mostly in forest soil. The eco-enzymatic C:N ratio was higher in farmland soils but grassland soils had a higher enzymatic C:P and N:P. Soil microorganisms were limited by P nutrients in all land-use patterns. C limitation was the highest in farmland soil. The redundancy analysis indicated that the ecological stoichiometry in farmland was influenced by TN, whereas grass and forest soils were influenced by SOC. Overall, the conversion of cropland or grassland to complex land-use types can effectively enhance soil nutrients, enzyme activities, and ecosystem functions, providing valuable insights for ecological restoration and sustainable land management in alpine regions. Full article

To enhance agricultural soil health and soil organic carbon (SOC) sequestration, it is important to accurately measure SOC. The aim of this study was to compare common methods for measuring SOC in soils in order to determine the most effective approach among different agricultural land types. The measurement methods of loss-on-ignition (LOI), automated dry combustion (Dumas), and real-time near-infrared spectroscopy (NIRS) were compared. A total of 95 soil core samples, ranging in clay and calcareous content, were collected across a range of agricultural land types from forty-eight fields across five farms in the Southwest of England. There were similar and positive correlations between all three methods for measuring SOC (ranging from r = 0.549 to 0.579; all p < 0.001). On average, permanent grass fields had higher SOC content (6.6%) than arable and temporary ley fields (4.6% and 4.5%, respectively), with the difference of 2% indicating a higher carbon storage potential in permanent grassland fields. Newly predicted conversion equations of linear regression were developed among the three measurement methods according to all the fields and land types. The correlation of the conversation equations among the three methods in permanent grass fields was strong and significant compared to those in both arable and temporary ley fields. The analysed results could help understand soil carbon management and maximise sequestration. Moreover, the approach of using real-time NIRS analysis with a rechargeable portable NIRS soil device can offer a convenient and cost-saving alternative for monitoring preliminary SOC changes timely on or offsite without personnel risks from the high-temperature furnace and chemical reagent adopted in the LOI and Dumas processes, respectively, at the laboratory. Therefore, the study suggests that faster, lower-cost, and safer methods like NIRS for analysing initial SOC measurements are now available to provide similar SOC results as traditional soil analysis methods of the LOI and Dumas. Further studies on assessing SOC levels in different farm locations, land, and soil types across seasons using NIRS will improve benchmarked SOC data for farm stakeholders in making evidence-informed agricultural practices. Full article