Search Results (374)

Magnetic anomaly detection is vulnerable to environmental noise and insufficient prior target information, making non-periodic anomaly signals difficult to detect at low-signal-to-noise-ratio (SNR) conditions. This paper proposes a detection method based on a multi-parameter-constrained mirror dual-branch biased monostable stochastic resonance (SR) system. Nonlinear odd-order bias terms are introduced into the conventional biased monostable potential function to build a multi-parameter-controllable SR model. This improves regulation of potential-well width, depth, and wall morphology, enhancing noise-energy utilization and responses to non-periodic features. Considering peak-type, valley-type, and bipolar anomaly morphologies, a mirror dual-branch SR structure is developed to cooperatively detect features with different polarities. To preserve temporal waveforms and time–frequency structures during parameter optimization, a composite metric combining the correlation coefficient and wavelet-domain image structural similarity index is constructed. Multi-fidelity robust Bayesian optimization is used to obtain a unified robust parameter set for the magnetic anomaly signal family. Experiments with simulated colored noise and measured geomagnetic noise show that the proposed method effectively recovers magnetic anomaly features under strong noise. At −19 dB SNR, its detection probability remains above 80%. Compared with orthogonal basis function decomposition, empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise, the method achieves better noise suppression, feature preservation, and detection performance under low-SNR conditions. Full article

Background: Whether the fatal and non-fatal composition of aggregate pancreatitis burden has changed over time remains unclear. We assessed long-term changes in the fatal-to-non-fatal composition of aggregate pancreatitis burden using Global Burden of Disease (GBD) 2023 estimates. Methods: We conducted a systematic descriptive and trend analysis using publicly available estimates from the GBD 2023 Results Tool for incidence, prevalence, deaths, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) across 204 countries and territories from 1990 to 2023. Because GBD reports pancreatitis as an aggregate cause category, the analysis could not distinguish acute pancreatitis, recurrent acute pancreatitis, chronic pancreatitis, or acute exacerbations of chronic pancreatitis. Primary analyses used age-standardised rates per 100,000 population. Four burden–composition metrics were derived within each location–year stratum: the YLL:YLD ratio, YLD:DALY proportion, deaths-to-incidence ratio, and prevalence-to-incidence ratio. Temporal trends were modelled in R version 4.5, using segmented regression, with up to three joinpoints selected by a Bayesian information criterion. Results: Globally, all six age-standardised native GBD measures declined between 1990 and 2023. The age-standardised incidence rate decreased from 37.62 (95% UI 32.20–43.11) to 32.91 (28.84–37.17) per 100,000, prevalence from 93.78 (69.26–126.25) to 68.92 (52.53–90.32), deaths from 1.76 (1.49–2.16) to 1.40 (1.21–1.66), YLDs from 5.70 (2.75–9.45) to 4.34 (2.18–7.04), YLLs from 55.96 (46.50–69.72) to 43.60 (36.89–53.53), and DALYs from 61.66 (50.62–75.61) to 47.94 (40.57–58.16). However, the fatal-to-non-fatal composition changed little: the global YLL:YLD ratio was 9.82 in 1990 and 10.04 in 2023, while the YLD share of DALYs was 0.092 and 0.091, respectively. Joinpoint modelling showed fluctuation rather than a sustained shift toward disability-dominant burden: the global YLL:YLD ratio was stable until 1998, increased from 1998 to 2002 (annual percent change [APC] 1.38%, 95% CI 0.42 to 2.36), and then declined modestly thereafter (APC −0.13%, −0.20 to −0.06). Burden remained higher in males, whereas females had a greater non-fatal share of total burden (YLD:DALY in 2023: 0.134 vs. 0.073). All sociodemographic index strata remained mortality-dominant in both 1990 and 2023; low-SDI settings had the greatest fatal dominance (YLL:YLD 34.94 in 1990; 24.72 in 2023). Using a descriptive YLD:DALY ≥ 0.50 benchmark, 203 of 204 countries and territories remained below the disability-dominant threshold in both years, no country crossed from below to above this benchmark, and only Georgia moved from above to below the benchmark. Conclusions: Despite declines in global incidence, mortality, and DALY rates, the aggregate GBD pancreatitis burden remained overwhelmingly mortality-dominant from 1990 to 2023. Because GBD pancreatitis combines acute and chronic pancreatitis, this finding should be interpreted as describing the modelled aggregate pancreatitis cause category rather than proving subtype-specific mortality dominance. The intensity of fatal dominance varied by sex, SDI, region, age, and country, but a structural shift toward disability-dominant aggregate burden was not observed. Full article

Single-index greening trends can misrepresent post-mining recovery because they do not show whether disturbed surfaces are converging toward the spectral conditions of nearby stable vegetation. Here, we present a 22-year (2003–2024) Landsat-based assessment of the Nannihu molybdenum mine (Henan, China) by combining LandTrendr-based disturbance and recovery timing from annual NBR series with a benchmark-relative spectral recovery index (RSRI) and five-epoch random forest land-surface classification used as contextual support. The classifier was trained on 2024 samples and transferred to earlier epochs without independent validation at each epoch. Historical class labels should therefore be treated as approximate contextual support. A five-type recovery pathway typology showed that only 41.8% of mine-affected pixels followed vegetated recovery pathways, while 28.2% stabilized as non-vegetated surfaces and 25.0% remained under persistent disturbance. Even the combined vegetation recovery type had a mean RSRI of only 0.309 (SD = 0.143), suggesting that greening alone does not imply close benchmark-relative spectral proximity to the local stable-vegetation reference. Disturbance magnitude was the feature most strongly associated with RSRI variation (XGBoost SHAP mean, |SHAP| = 0.075). The RSRI quantifies benchmark-relative spectral proximity using local stable-vegetation benchmarks, and it does not measure species composition, biomass, or ecosystem function. This site-specific case study indicates that benchmark-relative spectral assessment can complement conventional greening metrics in retrospective mine monitoring using open-access Landsat archives, with field validation the natural next step toward linking these spectral findings to ecological or functional recovery. Full article

The intestinal microbiome is increasingly recognized as an important determinant of canine gastrointestinal health. However, interpreting microbiome sequencing data remains challenging because most analytical approaches rely on taxonomic descriptions, alpha diversity indices, or dysbiosis indices derived generally from a limited number of microbial ecological interpretation targets. While shotgun metagenomic approaches increasingly allow the identification of microbial communities, such analyses remain costly and are not yet widely accessible in routine veterinary settings. The objective of this study was to develop an integrative interpretation framework based on widely accessible biomarkers combining fecal calprotectin and 16S rRNA gene sequencing data. These data enabled the generation of complementary ecological dimensions of gut microbiome organization: biological inflammation assessed through fecal calprotectin, microbiological inflammatory pressure estimated through a Microbiological Inflammatory Score (MIS), and microbiome stability measured by a Microbiome Resilience Score (MRS) derived from alpha diversity, functional balance, and dominance structure. Fecal microbiome profiles obtained by 16S rRNA gene sequencing were analyzed in a real-life cohort of privately owned dogs. Alpha diversity, taxonomic weighting, abundance-dependent dominance rules, beta diversity based on Bray–Curtis dissimilarity, distance to a reference microbiome core, and a 16S-derived dysbiosis score were integrated into a multidimensional interpretation model. Strong ecological associations were observed between resilience, microbial diversity, and dysbiosis-related metrics. Microbiome resilience strongly correlated with Shannon diversity (Spearman ρ = 0.98, p < 0.001), while the reconstructed 16S-derived dysbiosis score showed a more moderate positive correlation with MIS (Spearman ρ = 0.41, p = 0.004), supporting the partially independent ecological dimensions captured by the framework. The results revealed a continuum ranging from stable microbiomes to inflammatory dysbiosis. Most dogs clustered near a reference microbiome core characterized by low microbiological inflammatory pressure and high resilience, whereas a subset of microbiomes showed elevated MIS values, reduced resilience, increased compositional distance from the reference core, and higher dysbiosis index values. These findings support the value of a multidimensional experimental framework integrating inflammation, dysbiosis, and resilience to improve interpretation of canine microbiome profiles under real-life conditions. Full article

This study examines pedestrian perceptions of streetscapes in Isfahan’s cultural heritage site by integrating deep learning–based image segmentation with urban morphological analysis. It addresses the opportunity to develop a scalable and context-sensitive method for assessing pedestrian-oriented heritage streetscapes, particularly where conventional street-view datasets are unavailable. Using a U-Net model applied to First-Person Pedestrian View (FPPV) images, five perceptual indices, imageability, enclosure, human scale, greenness, and walking index, were quantified to examine their associations with pedestrian experience. Street width was incorporated as a morphological variable to explore its relationship with perceptual qualities using Spearman correlation and visual trend analysis. The results indicate exploratory associations between visual composition and perceptual outcomes within the analysed heritage streetscape context, particularly between imageability, enclosure, and vegetation structure. In contrast, variables such as human scale and walking index showed weak or negligible associations with street width, indicating that pedestrian activity patterns within the analysed heritage streetscape may be influenced by additional spatial, landscape, and socio-functional factors beyond dimensional characteristics alone. Segmentation-based analysis achieved an accuracy of 83% in classifying dominant streetscape elements, offering a reproducible alternative to traditional survey-based methods. This study contributes a data-driven framework for assessing pedestrian streetscapes, emphasising morphological continuity, human-scale design, and green infrastructure as important dimensions of walkability assessment. It also identifies key challenges, including fragmented spatial morphology and inconsistent urban furniture placement, which may affect pedestrian comfort and use of space. These findings offer evidence-informed design considerations for historic streetscape assessment, with implications for balancing heritage conservation and contemporary pedestrian needs. Future research may refine perceptual metrics, incorporate behavioural or longitudinal validation, and extend the approach across diverse urban contexts. Full article

Background: Optimizing physical performance in youth football requires a comprehensive understanding of the interplay among behavioural factors, structural body composition, and functional cardiorespiratory capacity. While sports nutrition knowledge is hypothesized to influence athletic development, its concurrent relationships with regional body compartments and objective cardiopulmonary exercise testing (CPET) metrics remain poorly characterized in adolescent athletes. Methods: A cross-sectional study approach analysed body composition via bioelectrical impedance analysis (BIA), maximal cardiorespiratory testing, and sports nutrition knowledge evaluation using the Nutrition for Sport Knowledge Questionnaire (NSKQ). Structural associations and functional predictive capacities were analysed. Results: The cohort demonstrated an average VO₂max of 51.18 ± 16.67 mL/kg/min and a mean total nutrition knowledge score of 43.56 ± 18.06 out of 81 (53.8%). Total and domain-specific nutrition knowledge scores were not associated with body mass index (BMI), fat-free mass (FFM), or fat-free mass percentage (FFM%). Higher nutrition knowledge scores were independently associated with superior VO₂max and anaerobic threshold (AT) metrics. Exploratory geographic analyses revealed that rural-residing participants possessed significantly higher cardiorespiratory performance values and greater baseline nutrition knowledge profiles than their urban peers. Conclusions: In adolescent male football players, sports nutrition knowledge was not associated with static body composition measures but showed exploratory positive associations with selected cardiorespiratory fitness markers. These findings should be interpreted as cross-sectional and hypothesis-generating, as some potential confounding mediators were not assessed. These findings suggest that higher sports nutrition literacy may serve as a starting point for performance-supportive behaviours and metabolic conditioning, to some degree, warranting future interventional studies. Full article

Gully erosion is a major driver of irreversible soil loss in Northeast China’s Mollisol belt, a region that supplies roughly one-quarter of the national grain output. Existing susceptibility assessments in this region have rarely combined multi-model comparison with spatially explicit cross-validation, and the predictive contribution of composite anthropogenic indicators such as the Human Footprint Index (HFI) has not been quantitatively benchmarked against conventional topographic variables. This study addresses these gaps for the Heihe region by combining an inventory of 4020 gully polygons supported by field checks in Xunke County, 16 VIF-screened environmental factors, three tree-based ensemble models and a logistic regression baseline. Under stratified random splitting, XGBoost achieved the highest discrimination (AUC = 0.95, $\kappa$ = 0.74); under leave-one-district-out spatial cross-validation all tree-based models retained AUC above 0.83, confirming that random-split metrics overestimate discrimination by approximately 0.11 AUC units due to spatial autocorrelation and inter-district covariate shift. SHAP analysis identified LULC and HFI as the dominant predictors, exceeding all topographic variables, while slope gradient contributed least—consistent with the low-relief, intensively cultivated character of the study area. Susceptibility was highest in the southwestern agricultural lowlands. A one-factor sensitivity test in which only NDVI was increased by 20% suggested a reduction in modelled high-susceptibility area of approximately 12%, although co-occurring land-cover and hydrological changes were not simulated. The multi-model framework, integrating spatial cross-validation and post hoc interpretability, provides an explicit estimate of conventional evaluation optimism and supports spatially differentiated erosion management. Full article

Background/Objectives: To evaluate the prognostic significance of automated, volumetric body composition analysis (BCA) derived from pretreatment thoracic computed tomography (CT) scans in patients with head and neck cancer (HNC). Methods: We retrospectively assessed 160 patients (median age: 63 years; 26.9% women) undergoing primary treatment. BCA quantified various tissue volumes, including bone (B), skeletal muscle (SM), and subcutaneous adipose tissue (SAT). Optimal sex-specific cutoffs for BCA metrics were established via maximally selected log-rank tests. Internal validation of BCA cutoffs was conducted via bootstrap resampling. Kaplan–Meier survival analysis and Cox proportional hazards modeling were used to investigate progression-free survival (PFS). Results: The median PFS for all patients was 51.7 months (95% confidence interval (CI): 31.4–68.8). Among the continuous BCA parameters, only SM/B was significant across the total cohort (hazard ratio (HR): 0.23; 95%CI: 0.12–0.46; p < 0.0001, males (p = 0.0009), females (p = 0.004)). Internal validation of gender-specific cutoffs demonstrated strong-to-intermediate stability for SM/B across both sexes and for SAT/B in males. In contrast, SAT/B exhibited only weak stability among female participants. In univariate PFS analysis, dichotomized SM/B, SAT/B, Union for International Cancer Control (UICC) stage, Eastern Cooperative Oncology Group (ECOG) status, higher body mass index (BMI), normal albumin, and Charlson Comorbidity Index were identified as significant predictors of PFS. Multivariable analysis identified high SM/B (HR: 0.53; 95% CI: 0.3–0.93; p = 0.026) and high SAT/B (HR: 0.58; 95% CI: 0.35–0.95; p = 0.029) as independent prognostic factors, alongside lower UICC stage (p = 0.045) and lower Charlson Comorbidity Index (p = 0.038). Patients with high SM/B and SAT/B ratios had the longest median PFS (65.9 months, 95%CI: 51.7–.), compared to 36.4 months (95%CI: 19.4–.) for high SM/B or SAT/B and 12.6 months (95%CI: 4.2–25.1) for low SM/B and SAT/B (p < 0.0001). Conclusions: Although the BCA parameters SM/B and, to a lesser extent, SAT/B appear to be promising biomarkers, external validation and investigation within well-defined patient subgroups are warranted to establish their generalizability in clinical practice. Full article

Useful quantum neural networks should not merely explore large Hilbert spaces but should organise their expressive capacity according to the symmetries of the learning problem. We introduce symmetry-organised complexity as an ansatz-level, representation-theoretic trajectory diagnostic for quantum neural networks. The diagnostic combines symmetry-sector organisation, cross-irreducible representation organised complexity, and symmetry metastability into a composite index, which is then multiplied by a compliance factor that penalises apparent complexity arising from symmetry violation. This compliance factor is defined at the level of the implemented trainable generators rather than as a representation-independent channel metric. The representation-theoretic basis of the construction is that, for an exactly equivariant network, the effective trainable operators lie in the commutant of the group action and are controlled by multiplicity dimensions rather than by the full Hilbert-space dimension. We show that joint sector collapse and state freezing force the index to vanish under an explicit multiplicity–purity condition and that networks with identical qubit and parameter counts can have different values of the index. Two analytically tractable four-qubit examples with excitation number and total spin symmetry illustrate how the diagnostic separates sector-collapsed, symmetry-organised, and symmetry-breaking behaviour. A controlled $U\left(1\right)$-compatible teacher–student classification task further shows that, in this validation setting, the ordering of the composite index across equivariant, hybrid, and non-equivariant ansatze agrees with the ordering of generalisation accuracy. The framework is most informative when the relevant symmetry of the learning problem is known. Full article

Operational Technology (OT) and Industrial Control Systems (ICSs) along the NATO-EU eastern flank face escalating hybrid threats, yet existing cyber-resilience metrics remain IT-centric, lacking OT-specific constraints and geopolitical exposure dimensions. This paper presents a Design Science Research contribution: the development and simulation-based feasibility demonstration of two interconnected artefacts. The first is the AI-enabled Cyber-Resilience Index (ACRI)—a composite 0–100 metric operationalized through 16 indicators across four domains (detection performance, operational continuity, governance maturity, supply-chain risk), aggregated as a three-term convex combination of capability domains with a linear subtractive supply-chain exposure penalty, weighted via AHP-based illustrative sector-reference profiles. The second is the Unified Compliance Framework (UCF), a structured R → C → E → SLO mapping linking 47 atomic regulatory requirements (NIS2, DORA, CER, AI Act, CRA) to standards (IEC 62443, ISO/IEC 27001) and auditable evidence artifacts, with a Continuous Assurance Loop operationalizing continuous control monitoring. Feasibility is demonstrated through digital twin simulation under three OT-representative threat scenarios (energy SCADA APT, railway supply-chain compromise, manufacturing ransomware). Results in simulated environments show ACRI improvement from Moderate-Risk baselines (45–61) to Adequate-Resilience thresholds (65–73); the proposed federated autoencoder–LSTM detector attains a composite $D_{perf}$ of 0.883 versus 0.510 for a static ±3$\sigma$ threshold baseline (a 73% relative improvement at the domain level). Sensitivity analysis confirms classification robustness ($\pm 7.3\%$ weight perturbation; coefficient of variation below 9.1% across 10,000 Monte Carlo iterations). Critical limitations are explicit: simulation-only evidence ($n=12$ scenario instances), illustrative (non-empirical) AHP weights, no operational field validation, and limited inferential statistical power. instances), illustrative (non-empirical) AHP weights, no operational field validation, and limited inferential statistical power. The contribution is positioned as a proof-of-concept design artifact establishing methodological foundations for OT-centric resilience assessment and compliance-to-engineering traceability, not as a field-validated operational system. Full article

The effects of substituting soybean meal with black soldier fly larvae (BSFL) meal on the growth performance, immunological response, intestinal morphology, and gut microbiota of Pekin ducks were assessed. A total of 150 one-day-old Pekin ducks were randomly allocated to three isonitrogenous and isocaloric diets: a control diet (0% BSFL), and diets including 10% or 30% BSFL meal (T10 and T30, respectively). Over 42 days, growth metrics, organ indices, serum and jejunal immune parameters, intestinal morphology, and caecal microbiota composition were evaluated. Ducks fed BSFL-containing diets exhibited a significant increase in spleen index compared to the control group (0.06% ± 0.02% for T10 and T30 vs. 0.05% ± 0.01% for control, p < 0.05), while other organ indices and growth performance remained unaffected. Serum levels of immunoglobulins (IgG, IgM, sIgA), cytokines (IFN-γ, IL-2), and complements (C3, C4) were significantly elevated in both BSFL groups (p < 0.05). Jejunal immune indices, including IgG, IgM, IL-2, and C3, were also significantly higher in the T10 group (p < 0.05). Intestinal morphology showed a significantly increased villus-to-crypt ratio in the duodenum and jejunum (p < 0.05). Analysis of gut microbiota indicated that BSFL supplementation was associated with changes in caecal microbial composition, including a lower relative abundance of Proteobacteria. However, alpha diversity indices did not differ significantly among groups. These findings suggest that BSFL meal can replace soybean meal at inclusion levels up to 30% in Pekin duck diets without adversely affecting growth performance, while enhancing select immune parameters and modulating gut microbiota composition. Full article

Heat stress (HS) is among the most economically consequential environmental challenges to global dairy production, causing progressive declines in milk yield, compositional quality, and mammary cellular integrity. The temperature–humidity index (THI) is the primary thermal load metric, with performance-impairment thresholds typically beginning at THI 68 in Holstein cattle, with severe impacts manifesting beyond THI 72; breed-specific thresholds for Jersey, Brown Swiss, and Simmental cows differ owing to their lower metabolic heat load and greater inherent thermotolerance. At the molecular level, HS activates heat shock protein networks—notably HSPA1A, HSP90B1, and HSPH1—through HSF1/HSF4 transcriptional activation, while simultaneously suppressing casein genes (CSN1S1, CSN2, CSN3), lipogenic genes (FASN, SCD, CD36), amino acid transporters (SLC7A5, SLC38A2), and mTOR-AKT-STAT5 translational machinery, collectively impairing milk biosynthetic capacity. Pro-apoptotic signaling (BAX, CASP3 upregulation; BCL2 downregulation) and mitochondrial dysfunction further compromise mammary epithelial viability. Post-transcriptional regulation through miRNA, circRNA, and lncRNA competing endogenous RNA networks, alongside epitranscriptomic m6A modifications, adds further regulatory complexity. Genome-wide association studies have identified SNPs in HSP70A1A, HSPA4, TLR4, and PRLR as thermotolerance candidates compatible with sustained milk production. Nutritional supplementation with methionine, arginine, and taurine partially restores cellular synthetic capacity. Integrating multi-trait genomic selection with Bos indicus introgression, precision cooling, and targeted nutrition offers the most viable path toward climate-resilient, high-producing dairy cattle. Full article

Vehicular ad hoc networks (VANETs) enable direct wireless communication between moving vehicles for safety and cooperative driving. Routing in VANETs is challenging due to high mobility, frequent topology changes, and variable node density. The Greedy Perimeter Stateless Routing (GPSR) protocol maintains only a one-hop neighbor position table through periodic beacon exchanges, making it highly scalable. Each node forwards packets to the neighbor geographically closest to the destination. However, this distance-only criterion leads to a low packet delivery ratio (PDR). Existing improvements, such as Weight-Based Path-Aware GPSR (W-PAGPSR) combining distance progress, velocity direction, neighbor density, and link duration, incorporate multiple factors but complicate parameter tuning and lack a unified neighbor quality metric. This paper proposes Directional Propagation Capacity Index–GPSR (DPCI-GPSR), integrating neighbor information into a single directional metric capturing propagation capacity. Two enhancements are introduced: (1) an eight-direction DPCI computing a composite propagation capacity index per sector, exchanged via Hello packets, and (2) a trapezoidal link quality function treating 30–200 m as optimal while penalizing edge-zone neighbors. Implemented in NS-3 with SUMO-generated mobility, results across four node densities (30–120 vehicles), five concurrent sender–receiver pairs, and 15 random seeds show DPCI-GPSR achieves 63.08–98.39% PDR, outperforming both W-PAGPSR (52.38–80.14%) and standard GPSR (50.23–66.31%). Full article

Traditional urban landscape evaluations have primarily relied on either objective spatial metrics, such as the Green View Index (GVI), or subjective human surveys, often failing to capture the complex mechanisms of human environmental perception. This study proposes a novel Explainable Artificial Intelligence (XAI) framework that integrates objective physical configuration with subjective cognitive assessment to predict human landscape preference. Utilizing 159 urban landscape images, we extracted physical features via semantic segmentation (SegFormer) and psychological perceptions via a zero-shot vision-language model (CLIP). Our hybrid Random Forest model successfully bridged these dimensions, achieving moderate yet promising predictive performance (Rsquare = 0.442). SHAP (Shapley Additive exPlanations) analysis revealed that psychological perceptions—specifically Safety (0.104), Fascination (0.096), and Tranquility (0.080)—outperformed traditional objective metrics like GVI (0.067) in determining overall preference, while sub-model interpretation linked these psychological responses to specific physical elements such as buildings, sky openness, low vegetation, and water bodies. The findings suggest that urban green space design should move beyond maximizing greenery quantity and instead prioritize spatial compositions that induce psychological security, visual interest, and restoration. The proposed framework offers a scalable and interpretable tool for human-centered landscape assessment, while acknowledging limitations related to sample size, cultural generalizability, pretrained model bias, and reliance on static two-dimensional imagery. Full article

As a typical mountain ecosystem in the western Tianshan Mountains, the Ili River Valley possesses abundant vegetation resources. Soil nematodes are effective biological indicators for evaluating soil micro-food webs. Nevertheless, the response mechanisms of nematode community structure to distinct vegetation types, especially native trees and forest-edge shrubs, remain poorly understood in this region. In this study, two dominant tree species (Picea schrenkiana and Malus sieversii) and two forest-edge shrub species (Berberis heteropoda and Berberis sibirica) were investigated. We analyzed the composition, diversity, and energy structure of rhizosphere soil nematodes and further compared their differences among plant species. The results indicated that tree rhizospheres had significantly higher amounts of nitrate nitrogen ($\mathrm{N}{\mathrm{O}}_3^{-}$-N and microbial biomass carbon (MBC), along with a lower amount of extractable organic carbon/extractable total nitrogen (EOC:ETN) than shrub rhizospheres (p < 0.05). Picea schrenkiana (PS) exhibited greater root carbon storage, higher root biomass, and a higher root carbon-to-nitrogen ratio (RC:RN) than Berberis heteropoda (BH) and Berberis sibirica (BS) (p < 0.05). The genus Chiloplacus dominated the nematode community across all four woody plants. The relative abundance of omnivore-predatory nematodes was markedly higher in shrubs (BH and BS) than in trees (PS and MS). The soil food webs of PS and MS were degraded, whereas shrub food webs were in a transitional state between structured and degraded habitats. Shrubs presented a higher maturity index, structural metabolic footprint, and energy flux of omnivore-predatory nematodes, but a lower energy flux of bacterivorous nematodes. Additionally, PS had the highest nematode carbon use efficiency (NCUE) and the lowest energy flux uniformity (U). $\mathrm{N}{\mathrm{O}}_3^{-}$-N extractable total nitrogen (ETN), soil organic carbon (SOC), and root traits were the primary factors driving variations in nematode communities and carbon indicators. Therefore, nematode carbon indicators closely associated with soil carbon and nitrogen cycling have the potential to serve as sensitive auxiliary biological metrics for evaluating material cycling and energy flow in pure forests and shrub ecosystems. This study provides empirical support for the assessment of regional ecosystem stability. Full article