Search Results (171)

State-of-charge (SOC) estimation for lithium-ion batteries in smartphones is complicated by nonlinear load variation, electro-thermal coupling, aging effects, and heterogeneous user behaviors. This study proposes a multi-physics coupled SOC estimation framework, termed the Multi-Physics Thermo-Electrochemical Coupled SOC Model (MTEC-SOC), to characterize battery behavior under representative user-load conditions within controlled ambient thermal boundaries. The model combines system-level power profiling, thermal evolution, voltage dynamics, and aging-related capacity correction within a unified framework. To support model development and validation, a dual-source dataset is established using laboratory battery characterization data and real-world smartphone behavioral data, from which users are classified into light, heavy, and mixed usage patterns. Comparative results against four benchmark models (M1–M4) show that MTEC-SOC achieves the highest overall accuracy, with average MAE, RMSE, and TTE error values of 0.0091, 0.0118, and 0.08 h, respectively. The results suggest distinct degradation tendencies across user types: calendar aging dominates under prolonged high-voltage dwell in light-use scenarios, whereas, within the tested thermal range, heavy-use scenarios exhibit stronger voltage sag, relative temperature rise, and polarization-related stress; mixed-use scenarios are characterized by transient responses induced by abrupt load switching. Sensitivity analysis further indicates that the predictive behavior of the model is strongly scenario-dependent, with higher-load operation within the calibrated range amplifying parameter perturbations. Overall, the proposed MTEC-SOC framework provides accurate SOC estimation and physically interpretable insight within the evaluated dataset and operating conditions, offering potential guidance for battery management and energy optimization in intelligent mobile terminals. Full article

Accurate estimation of forest soil organic carbon (SOC) is considered critical for understanding terrestrial carbon cycling and supporting climate change mitigation strategies. However, the canopy block, intricate vertical structure of forests, and the constraints of single-source remote sensing data have presented considerable obstacles for estimating forest SOC. This study proposes a forest SOC estimation and uncertainty analysis (ForSOC-UA) framework to enhance forest SOC estimation and quantify its uncertainty in the natural secondary forests of northern China by integrating hyperspectral imagery (ZY-1F), synthetic aperture radar data (Sentinel-1), and environmental covariates (such as topography, vegetation, and soil indices). The performance of traditional machine learning models (RF, SVM, and CNN), geographically weighted regression (GWR), and a geographically weighted random forest (GWRF) model was compared across three different soil depths (0–5 cm, 5–10 cm, and 10–30 cm). The results showed that GWRF consistently outperformed all other models across all soil depth layers, with the highest accuracy achieved using multi-source data (R² = 0.58, RMSE = 27.49 g/kg, rRMSE = 0.31). Analysis of feature importance revealed that soil moisture, terrain characteristics, and Sentinel-1 polarization attributes were the primary predictors, while spectral derivatives in the red and near-infrared bands from ZY-1F also played a significant role for forest SOC estimation. The uncertainty analysis indicated a forest SOC estimation uncertainty of 37.2 g/kg in the 0–5 cm soil layer, with a decreasing trend as depth increased. This pattern is associated with the vertical spatial distribution of the measured forest SOC. This integrated approach effectively captures spatial heterogeneity and nonlinear relationships between feature and forest SOC, while also assessing estimation uncertainty, so providing a robust methodology for predicting forest SOC. The ForSOC-UA framework addresses the uncertainty quantification of SOC estimation at different vertical depths based on machine learning, providing methodological enhancements for the assessment of large-scale forest SOC and the monitoring of carbon sinks within forest ecosystems. Full article

Soil organic carbon (SOC) stocks in cacao agroecosystems are characterized by accumulating large amounts. They depend on the balance between organic matter inputs (plant residues, roots) and losses (decomposition, erosion), being closely related to climatic conditions, soil nature, vegetation type, topography, and land management practices. The objective of this study was to quantify SOC stocks (0–30 cm) and assess key soil fertility indicators across 107 georeferenced sampling locations in cacao production systems of Guamal (Meta, Colombian Llanos Piedmont). Soil pH varies between extremely acidic and moderately acidic (3.8–6.0; mean 4.57), while available P (Bray II) and exchangeable bases showed low concentrations. Organic carbon concentration averaged 1.18% and bulk density averaged 1.17 g cm⁻³. SOC stocks averaged 41.10 Mg C ha⁻¹, ranging from 7.49 to 81.55 Mg C ha⁻¹, evidencing marked spatial contrasts in carbon storage. Spearman correlations highlighted coupled soil chemical controls, including positive associations of pH with Ca²⁺ and P availability and strong negative associations of pH and P with exchangeable Al³⁺, consistent with acidity-driven fertility constraints. Principal component analysis (PCA) further identified a dominant fertility gradient structured by pH, P availability, and Ca²⁺, and a second axis related to organic carbon and cation retention. Spatial modeling using inverse distance weighting (IDW) in ArcGIS supported the visualization of SOC stock variability across the study area. Overall, the results indicate that SOC stocks in these predominantly sandy soils are strongly influenced by acidity-related constraints and heterogeneous nutrient status, underscoring the need for site-specific management to jointly enhance soil fertility and climate-mitigation potential in cacao systems. Therefore, it would be advisable in the future to address the study of differential variations in soil C storage related to chemical fertilizer application rates, especially in the long term. Full article

Soil organic carbon (SOC) is essential for ecosystem stability and long-term carbon storage in alpine grasslands, yet the relative importance and interactions of hydrothermal and biotic controls remain poorly understood at regional scales. In this study, we quantified surface SOC (0–20 cm) across the Yellow River Source Region (YRSR) on the northeastern Tibetan Plateau, a climate-sensitive alpine headwater system characterized by strong hydrothermal gradients and freeze–thaw dynamics. Field-based SOC measurements were integrated with multi-source remote sensing and reanalysis data that describe thermal conditions, moisture processes, vegetation productivity, soil properties, topography, and human influence. A two-step screening approach was applied using Boruta and variance inflation factor filtering, followed by modeling with random forest. The model outputs were interpreted using Shapley Additive Explanations (SHAP). SOC displayed significant spatial heterogeneity across the region. Vegetation productivity, moisture availability, and thermal conditions were identified as the dominant nonlinear drivers of SOC variation. Moisture availability emerged as a central regulator of SOC, affecting it both directly and indirectly through vegetation productivity and thermal conditions. These findings underscore the importance of hydrothermal stability in sustaining soil carbon stocks and provide a quantitative basis for adaptive grassland management under a warming climate. Full article

Soil carbon (C), nitrogen (N), and phosphorus (P) stoichiometry is essential for maintaining fertility and ecosystem functioning, yet its spatial patterns and drivers in large-scale agricultural regions remain unclear. We collected 225 topsoil samples across the Songnen Plain, Northeast China, and used geostatistical methods to map the spatial distributions of soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), and their ratios (C:N, C:P, N:P). Feature importance and correlation analyses were employed to assess the relative influence of environmental factors. Results revealed significant spatial heterogeneity, with a consistent north-high, south-low pattern for all elements and ratios. Mean C:N, C:P, and N:P ratios were 11.6, 32.8, and 2.8, respectively. SOC was the dominant controlling factor (importance: 0.5–0.6), showing strong positive correlations with all ratios. Mean annual temperature exerted significant negative effects, while precipitation had limited influence, primarily on C:N. Soil type also mattered, with black soils exhibiting the highest C:N and C:P ratios (11.8 and 36.7). We conclude that soil C:N:P stoichiometry in the Songnen Plain is governed by hierarchical interactions of SOC, climate, and soil type. These findings provide a mechanistic framework for understanding regional nutrient patterns and support the development of spatially targeted management strategies for sustainable soil health. Full article

To address the current issues in soil organic carbon (SOC) content prediction where data preprocessing relies on expert experience to formulate fixed rules, resulting in a lack of uniform standards and insufficient consideration of regional soil heterogeneity; while hyperparameter tuning faces problems of high computational costs and excessively long runtimes, this study proposes an intelligent modeling workflow driven by Large Language Models (LLM). This workflow focuses on optimizing two key aspects of SOC Random Forest modeling: data preprocessing and hyperparameter tuning. Results: The LLM-defined rules achieved sample retention rates of 55.33% and 61.90% in the two regions, respectively, showing more significant differences compared to traditional hard-coded rules (56.2% and 59.3%), and the mean soil organic carbon content deviations (30.27% and 20.05%) were both lower than those of traditional hard-coding. At the same time, the mean soil organic carbon content values in both regions closely matched the effectiveness of other methods, indicating that the large language model has effectively captured regional soil differences. With only a single evaluation of hyperparameter optimization, the adaptive model achieved test set R² values of 0.394 and 0.694 in the black soil region and the aeolian sandy soil region, respectively, with root mean square error values of 8.76 g/kg and 6.07 g/kg—its performance is comparable to that of Grid Search and Random Search, while computational efficiency improved by over 95%. Performance comparisons with eXtreme Gradient Boosting (XGBoost) and Partial Least Squares Regression (PLSR) show that the LLM-optimized Random Forest achieved R² = 0.394 and RMSE = 8.76 g/kg in the black soil region, and R² = 0.694 and RMSE = 6.07 g/kg in the windblown sandy soil region, demonstrating practical application value. Full article

Tirzepatide, a dual glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide 1 (GLP-1) receptor agonist, introduces a mechanistically distinct approach within incretin-based therapies. While its efficacy is established, real-world data comparing post-marketing safety with established GLP-1 receptor agonists remain limited. This study assessed System Organ Class (SOC)-level reporting patterns for tirzepatide versus semaglutide and liraglutide using EudraVigilance data. Aggregated individual case safety reports (ICSRs) were analyzed using pairwise disproportionality analyses based on a case/non-case approach. Reporting odds ratios (RORs) with 95% confidence intervals were calculated. False discovery rate (FDR) correction using the Benjamini–Hochberg procedure and sensitivity analyses restricted to serious and healthcare professional–reported cases were performed to assess robustness. After FDR adjustment, 20 SOCs were significant in tirzepatide–semaglutide and 23 in tirzepatide–liraglutide comparisons; eight SOCs remained significant across all analytical conditions. Compared with semaglutide, tirzepatide showed higher reporting for immune (ROR 1.97, 95% CI 1.75–2.21) and hepatobiliary disorders (ROR 1.71, 95% CI 1.61–1.82). Versus liraglutide, higher odds occurred for musculoskeletal (ROR 2.02, 95% CI 1.85–2.21) and psychiatric disorders (ROR 2.14, 95% CI 1.99–2.30), and lower odds for neoplasms (ROR 0.28, 95% CI 0.26–0.31). Tirzepatide shows heterogeneous reporting patterns compared with GLP-1 receptor agonists, with consistent excess reporting for hepatobiliary, immune, and musculoskeletal disorders. These findings are hypothesis-generating and warrant confirmation in exposure-adjusted studies. Full article

Establishing the soil geochemical baseline and background values is critical for agricultural soil environmental management. This study collected 5207 topsoil (0–20 cm) and 1311 subsoil (150–180 cm) samples from an intensive agricultural area in Eastern China to quantify the element enrichment and depletion patterns, evaluate the integrated soil fertility, and assess the potential ecological risks, with a focus on disentangling the links between human activities and soil environmental changes. The results showed that most elements had higher baseline/background values than national averages, except for CaO, Mo, MgO, Sr, Na₂O, and Br, reflecting the control of homogeneous parent material. Topsoil elements largely inherited subsoil characteristics, while anthropogenic disturbances such as fertilization and industrial activities caused the enrichment of Cd, Se, TN, TP, S, and SOC, and the depletion of I, V, and Mn. Soil fertility presented an obvious vertical heterogeneity, in which the topsoil had moderate-to-rich nutrients with a mean SOC of 10.05 g kg⁻¹ and mean TN of 1.10 g kg⁻¹, whereas the subsoil was severely deficient with a mean SOC of 1.96 g kg⁻¹ and TN of 0.66 g kg⁻¹. The integrated fertility index (IFI) indicated that the topsoil and subsoil in Changfeng and western Feixi exhibited higher fertility levels, while Feidong and Hefei had lower fertility levels. An ecological risk assessment identified western Feidong as a high-risk hotpot, with Cd as the primary contributor to potential ecological risk. The source analysis confirmed Ni, As, and Cr as geogenic, Cd as anthropogenic, and Pb and Cu as mixed natural–industrial–agricultural sources. Our findings highlight the necessity of adopting zoned precision fertilization to improve the nutrient efficiency and applying organic amendments to immobilize Cd and reduce the ecological risk. This study provides targeted strategies for soil fertility improvement, precision fertilization, and Cd risk control, supporting sustainable agricultural development. Full article

The rapid increase in point density and acquisition rate of UAV laser scanning (ULS) systems has shifted the primary bottleneck of LiDAR workflows from data acquisition to post-processing, particularly during direct georeferencing of massive LiDAR measurements. This study presents a systematic evaluation of parallel computing strategies for accelerating ULS direct georeferencing while preserving geodetic accuracy. Two georeferencing models are investigated: (1) a rigorous model that strictly follows the full geodetic transformation chain from sensor owned coordinates system (SOCS) to projected map coordinates, and (2) an approximate model that incorporates meridian convergence angle compensation and preprocessing of platform trajectories to reduce per-point computational complexity. For each model, a shared-memory multicore CPU implementation based on OpenMP and a heterogeneous GPU implementation based on CUDA are designed. Experiments were conducted on seven real-world ULS datasets, ranging from 2.9 × 10⁷ to 7.0 × 10⁸ points and covering diverse terrain types. Accuracy analysis shows that, in typical urban, plain, and industrial scenarios, the approximate model achieves millimeter-level mean errors and centimeter-level RMSEs relative to the rigorous model, satisfying the requirements of most engineering surveying applications. Performance evaluation demonstrates that parallelization yields substantial speedups: OpenMP-based method achieves 7–9 times acceleration, while GPU computing attains up to 24.6 times acceleration for the rigorous model and up to 16.7 times for the approximate model. The results highlight the complementary strengths of the two models and provide practical guidance for selecting accuracy-efficiency trade-offs in large-scale ULS production workflows. Full article

Background: Glioblastoma is the most lethal primary brain tumor, being characterized not only by marked intratumoral heterogeneity but also by strong systemic immunosuppression. Circulating extracellular vesicles (EVs) have gained growing recognition during the past decade as important mediators of intercellular communication, particularly through their microRNA (miRNA) cargo. However, the global EV miRNA landscape of circulating EV-associated miRNAs in glioblastoma patients and their relation with gene expression patterns in peripheral immune cells remain incompletely defined. Methods: To investigate these systemic associations, we profiled EV-associated miRNA expression in plasma samples from glioblastoma patients and matched healthy controls using the small RNA sequencing method, followed by differential expression and pathway analyses. Based on these findings and literature evidence, identified changes in selected EV miRNA levels were validated by qPCR in an extended cohort. In parallel, expression of their predicted immune-related mRNA targets was analyzed in peripheral blood mononuclear cells (PBMCs) obtained from the same individuals, allowing for the assessment of EV miRNA–PBMC mRNA correlation patterns. Results: Small RNA sequencing revealed a distinct circulating EV-associated miRNA profile in glioblastoma patients compared to controls. The validation analysis of relative expression of the identified DEmiRNAs has shown a statistically significant upregulation of hsa-miR-142-3p, hsa-miR-19b-3p, and hsa-miR-98-5p in circulating EVs of glioblastoma patients compared to controls. PBMCs from glioblastoma patients exhibited increased expression of the regulatory genes SOCS1, SOCS3, and PTEN, while CCND1 was downregulated. Correlation analyses suggested that certain EV miRNA changes parallel with alterations in PBMC gene expression in glioblastoma patients, suggesting early immune priming in the circulation. Conclusions: Our findings indicate that circulating EV miRNAs in glioblastoma patients are associated with specific gene expression patterns in peripheral immune cells, suggesting a complex regulatory balance between pro-inflammatory and anti-inflammatory cues, potentially preceding full tumor-associated macrophage polarization. These molecular interactions may offer opportunities for developing early biomarkers or new therapeutic approaches. Full article

Low Earth orbit (LEO) satellite networks require real-time routing to cope with dynamic topology variations caused by continuous orbital motion. As an alternative to conventional routing approaches, deep reinforcement learning (DRL) has recently gained attention as an effective means for optimizing routing paths. To solve routing problems modeled as a grid-based Markov decision process (grid-based MDP), DRL methods such as CNN-based Dueling DQN have been proposed. However, these approaches are difficult to implement in practice. In particular, the substantial floating-point computation and memory traffic of CNN inference make real-time onboard inference challenging under the stringent power and resource constraints of satellite platforms. To address these constraints, this paper proposes an INT8 quantization and hardware–software co-design framework using heterogeneous SoC FPGA acceleration. We offload compute-intensive CNN inference to the programmable logic (PL), while the processing system (PS) orchestrates overall control and data movement, forming a collaborative PS–PL architecture. Furthermore, we integrate the NITI-style two-pass scaling with PS–PL exponent propagation to preserve end-to-end integer consistency without floating-point conversion. To demonstrate its practical onboard feasibility, we employ standard accelerator implementation choices—such as output-stationary scheduling and on-chip prefetching—and conduct an ablation study over independently tunable axes (PE array size and PS-side buffer reuse) to quantify their incremental contributions. Experimental results show that the proposed PS–PL cooperative scheme dramatically reduces computation time compared to a PS-only reference implementation on the same platform. Full article

Alpine wetlands are highly sensitive to climate warming and anthropogenic disturbances such as grazing, highlighting the urgent need to identify operational indicators for monitoring soil functional changes. In this study, the Zequ National Wetland Park on the Qinghai–Tibet Plateau was selected as the study area. At the plot scale (n = 66), vegetation structure (aboveground biomass, vegetation height, and coverage), soil nutrient properties (soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), ammonium nitrogen (NH₄⁺–N), nitrate nitrogen (NO₃⁻–N), available phosphorus (AP)), soil enzyme activities (β-glucosidase (BG), N-acetylglucosaminidase (NAG), and acid phosphatase (ACP)) were measured simultaneously. Spearman correlation analysis and redundancy analysis (RDA) were applied to examine their statistical relationships. Descriptive statistics revealed pronounced variability among plots, with aboveground biomass ranging from 115.43 to 1505.27 g·m⁻², AP from 0.75 to 70.23 mg·kg⁻¹, and BG activity from 0.25 to 14.71 μmol·g⁻¹·h⁻¹, indicating strong spatial heterogeneity in alpine wetlands. Both correlation and RDA results consistently showed that nutrient availability—particularly inorganic nitrogen and AP—was more closely associated with soil enzyme activities, whereas total nutrient contents exhibited a relatively limited ability to explain short-term variations in soil functional processes. These findings suggest that a combined indicator framework integrating nutrient availability and soil enzyme activities has strong potential for the early detection of soil quality changes and degradation in alpine wetlands, thereby providing quantitative support for sustainable wetland management and restoration assessment. Full article

Different forest ecosystems affect the acquisition and loss of SOC by changing the niche differentiation of above-ground and under-ground, resulting in changes in the utilization efficiency of water and nutrient elements. The impact of different types of forests on carbon storage in forest soils has received significant attention in recent decades, as these ecosystems are critical for mitigating the effects of global climate change. There are significant differences in environmental factors among different types of forests, such as carbon source type, topographic characteristics, soil texture, microbial community status, climate and hydrological conditions. At present, the research on the effects of environmental factors such as climate, hydrological conditions or soil quality on SOC has been well carried out. Nevertheless, the distribution pattern of microbial carbon and particulate organic carbon in subtropical forest ecosystems and their contribution to SOC still need much of scientific research. Forest types have a significant impact on the content and distribution characteristics of MNC and particulate organic carbon fractions, but there is heterogeneity in different forests. Importantly, the random forest analysis showed that MNC and MAOC were the main factors affecting SOC compared with other variables, which indicated MNC and MAOC have higher relative importance to SOC (p < 0.05). Specifically, our research found that the total MNC and BNC content in natural forests and broad-leaved forests were significantly higher than that in coniferous forests (p < 0.05), while the FNC content and FNC/BNC in coniferous forests were significantly higher than that in the other two forests (p < 0.05). In addition, the MAOC content of natural forests was higher than others, which indicated the stability of natural forest is higher than other forests. However, CPOC, FPOC content, and POC/MAOC in coniferous forests were significantly higher than in broad-leaf forests and natural forests. Biotic and abiotic factors profoundly affect the dynamic changes in SOC accumulation and stability. Different environmental factors lead to more MNC and MAOC in forest types with faster decomposition rates. These findings have instructive implications for understanding the contributions of different forest types on SOC stability and accumulation mechanisms in forest soils. Full article

Module- and pack-level mechanical design of lithium-ion batteries in electric vehicles is a primary driver of swelling-induced stack pressure and spatially varying ageing. Current practice remains largely empirical or data-driven and configuration-specific, limiting the ability to predict how design changes translate into local pressure heterogeneity and state-of-health (SOH) loss. This motivates a compact chemo-mechanical model that maps packaging boundary conditions to pressure, swelling, and SOH evolution with few interpretable parameters. This study introduces finite-element-ready constitutive laws that couple reversible and irreversible swelling to SOH and through-thickness pressure, covering three boundary cases reported in literature: constant pressure, thickness clamp after an initial preload, and flexible support. Parameters are identified from different published datasets, and the model is validated against independent constraint scenarios. Good quantitative agreement is shown with averaged RMSE of 1.16% for SOH and 0.16 [MPa] for pressure evolution. Variance-based sensitivity analysis shows SOH uncertainty dominated by the damage-law parameters of the proposed constitutive relationship, whereas pressure evolution is primarily controlled by irreversible swelling and the non-linear through-thickness stiffness, indicating calibration priorities for engineering design studies. The framework is intended for fast comparative analyses of individual cells under a controlled environment. Further extensions, including SOC-dependent mechanics, refined hysteresis, temperature, and C-rate variations require dedicated datasets and are left for future work. Full article

Backgrounds: Single-arm studies evaluating reduced intensity (de-escalated) therapy for low-risk Human Papillomavirus-related oropharyngeal cancer (HPV+OPC) patients demonstrated high cure rates and reduced toxicity compared with historical results of standard of care (SOC). However, randomized studies demonstrated that the outcomes of de-escalated therapies were inferior to standard therapy, suggesting that a minority of patients may not benefit from de-escalation. Objectives: to review strategies and prognostic biomarkers before or early during therapy to identify low-risk HPV+OPC patients who may require SOC and who should be excluded from de-escalation trials to avoid compromising outcomes. Methods: A comprehensive narrative literature review between January 2000 and August 2025 was performed to identify prognostic biomarkers in HPV+OPC, as well as studies reporting early-response indicators with prognostic potential in clinically defined good-prognosis HPV+OPC treated with chemo-irradiation. Preclinical studies were excluded unless their findings had implications for clinical outcomes. Data were synthesized qualitatively in this narrative report due to the substantial heterogeneity of the clinical and methodological aspects of the reviewed studies. The risk of bias in non-randomized studies was assessed using the Newcastle–Ottawa Scale (NOS) for cohort studies. Results: Multiple candidate prognostic biomarkers were identified, including molecular, histopathological, imaging, and clinical factors. Almost all studies were retrospective, included small cohorts and lacked internal or external validation, and had poor NOS scores, mostly due to lack of sufficient follow-up and lack of information about loss to follow-up, thereby precluding most biomarkers from current clinical utilization. Response-based selection based on induction chemotherapy is effective but limited by its added toxicity. Early tumor responses assessed by hypoxia, metabolic imaging, and circulating HPV DNA kinetics show encouraging preliminary results that need to be validated. Conclusions: Current evidence indicates major methodological limitations in most studies of prognostic biomarkers in clinically defined good-prognosis HPV+OPC. Early tumor response-based selection strategies are promising and warrant comparison with SOC in multi-center randomized trials. Full article