Search Results (6,968)

k-entanglement, including entanglement relative to full separability and genuinely multipartite entanglement, is a fundamental quantum resource in multipartite quantum systems. Its identification and quantification play essential roles in quantum information processing, quantum simulation, and quantum metrology. However, the practical computation of rigorous k-entanglement measures remains highly challenging due to the need for high-dimensional optimization. In this work, we propose a machine-learning-based surrogate framework for approximating the witness-based k-entanglement measure $E_w^{(k,n)}$. The numerical evaluation of the computationally realized quantity ${\tilde{E}}_w^{(k,n)}\left(\rho \right)$ is reformulated as a supervised regression problem, where the input is the density matrix $\rho$ and the labels are obtained from finite witness databases. The framework combines multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and light gradient boosting machine (LightGBM) through a stacking ensemble. Numerical experiments are performed for 3- and 4-qubit systems as representative demonstrations of the proposed workflow. The results show that the learned models achieve high predictive accuracy in terms of MAE, MSE, and $R^2$, while providing millisecond-level inference for single-state evaluation. Werner state tests serve as symmetric benchmark checks, and an additional four-qubit noisy circuit-generated state family, obtained from finite-depth circuit preparation followed by local amplitude-damping noise, is used as a structured physical test beyond random density matrices. Compared with the optimization-based evaluation, the trained surrogate model significantly reduces the computational time while maintaining accuracy within the tested system sizes and data distributions. These results show that the proposed framework provides an efficient numerical surrogate for rapid approximation of witness-based k-entanglement measures, while extensions to larger systems and experimental data require further validation. Full article

A monocrystal of the natural mineral hambergite, Be${}_2$BO${}_3$OH, is studied by ${}^{10}$B-NMR spectroscopy. The nuclide ${}^{10}$B possesses spin $I=3$, and thus for a single boron site in a periodic solid, the ${}^{10}$B spectrum is composed of three doublets. For the four inversion-related pairs of boron sites existing in the crystal structure of hambergite, orientation-dependent ${}^{10}$B spectra are recorded and both the chemical shift and electrical field gradient (EFG) tensor are extracted. The resulting numerical values are in very good agreement with a previous ${}^{11}$B-NMR study of the same sample. Comparing the line widths of the NMR resonances of ${}^{11}$B against ${}^{10}$B, the concept of inherently better resolution being available from ${}^{10}$B due to the scaling down of dipolar interactions is confirmed. Full article

Understanding microclimatic variability at the land–atmosphere interface is essential for improving knowledge of atmospheric moisture dynamics in heterogeneous mountainous ecosystems. This study analyzes atmospheric relative humidity and soil moisture variability in an experimental Juglans neotropica Diels plantation located in the Ecuadorian Andes under real field conditions. An autonomous photovoltaic-powered monitoring system equipped with low-cost environmental sensors was deployed continuously for 60 days, generating more than 86,000 environmental measurements of atmospheric relative humidity above and below the canopy, together with soil moisture observations. The results revealed persistent vertical humidity stratification associated with canopy structure, characterized by systematically higher atmospheric humidity beneath the canopy compared to the upper atmospheric layer (ΔRH ≈ −36%). Strong intersensor coherence was observed between canopy levels (r = 0.8535), indicating stable temporal consistency in atmospheric variability patterns throughout the monitoring period. Soil moisture exhibited comparatively more stable temporal dynamics than atmospheric humidity, suggesting partial microclimatic decoupling between atmospheric and edaphic layers. The observed humidity gradients remained temporally stable during both daytime and nighttime conditions, supporting the interpretation of canopy-mediated atmospheric buffering processes within the plantation environment. From an ecological perspective, the results indicate that vegetation structure contributes to localized moisture retention, attenuation of short-term atmospheric fluctuations, and regulation of near-surface microclimatic conditions under heterogeneous Andean environmental conditions. Rather than focusing on instrumentation performance, the study provides empirical evidence of persistent canopy-related atmospheric regulation and moisture stratification in a native Andean forest species under continuous field monitoring conditions. These findings contribute to the understanding of land–atmosphere interactions, ecohydrological dynamics, and vegetation-mediated microclimatic regulation in mountainous ecosystems. Full article

Natural inland saline–alkaline wetlands offer opportunities for evaluating microbial responses to long-term salinity stress. This study examined surface soils from non-saline, moderately saline, and hypersaline sites in the Luyang Lake wetland, measuring comprehensive edaphic variables (including SAR, ESP, carbonate/bicarbonate chemistry, moisture, DOC, and inorganic N) alongside bacterial and fungal communities via 16S rRNA and ITS sequencing. A coupled salinity–ion and nutrient gradient was identified, with hypersaline soils characterized by high Na⁺, Cl⁻, SAR, and ESP alongside depleted organic carbon and nitrogen. Bacterial α-diversity exhibited a significant unimodal response along the salinity gradient (quadratic regression: p < 0.001), peaking at moderate salinity. Fungal Shannon diversity declined with increasing salinity, but fungal Chao1 richness showed a U-shaped response, highlighting domain-specific and metric-dependent patterns along the gradient. Community assembly analyses revealed contrasting dynamics: deterministic processes were more prevalent in bacterial assembly in hypersaline soils, while fungal assembly remained predominantly stochastic. Co-occurrence networks showed sparser topological structure in high-salinity soils. These patterns are consistent with domain-specific microbial variation along the gradient to coupled edaphic stressors in inland saline–alkaline wetlands. Full article

Addressing the critical challenge of signal degradation in biosensor cardiac monitoring, this paper introduces an efficient hybrid dilated–long short-term memory (LSTM) with residual learning (HDLR), which is a novel architecture engineered for a high-fidelity electrocardiogram (ECG) denoising signal. The proposed HDLR model synergistically integrates with dilated convolutions to expand the receptive field for multi-scale feature extraction. This is an LSTM-based backbone used to resolve long term temporal dependencies with residual learning paths to stabilize gradient flow and accelerate convergence. The proposed HDLR architecture integrates three core functional components with dilated convolutional layers utilized for local temporal feature extraction, where varying dilation rates expand the receptive field to capture both local waveform patterns and broader morphological structures without increasing computational complexity. Experimental results demonstrate a significant leap in performance, with the HDLR model achieving a mean squared error (MSE) of 0.002176, a signal-to-noise ratio (SNR) of 14.4420 dB, and a Matthews correlation coefficient (MCC) of 0.9822. Beyond quantitative metrics, the proposed HDLR architecture exhibits exceptional robustness in preserving cardiac morphology, specifically the P-wave and QRS complex of the ECG signal under stochastic noise conditions. These findings underscore the HDLR model’s potential as a backbone for next generation, real time diagnostic systems in intelligent healthcare. Full article

With the continuous increase in coal mining depth in China, concealed geological structures—such as collapsed columns and faults—pose a severe threat to mine safety by triggering water inrush accidents. Mine DC resistivity methods exhibit high sensitivity to water-bearing characteristics but suffer from limited resolution, while mine seismic exploration offers superior resolution but weak sensitivity to water-rich bodies. Single-method inversion is inevitably plagued by solution non-uniqueness. This study aims to enhance the detection accuracy of concealed structures by implementing a joint seismic–electric inversion that exploits the complementary strengths of both methods. For the DC resistivity component, a forward model was established using the finite element method with unstructured meshes, and inversion was performed via Occam regularization. For seismic exploration, forward modeling employed curved-ray tracing, and inversion was conducted via the LSQR algorithm. Cross-gradient constraints were incorporated into the joint inversion to establish a structurally coupled framework. The novelty of this study lies in the integration of unstructured mesh discretization, curved-ray seismic tomography, and cross-gradient-constrained joint inversion for mine water detection. Numerical simulation results demonstrate that joint inversion effectively constrains the spatial extent of anomalies, accurately characterizes the morphology of multiple anomalous bodies and water-conducting fault channels, and substantially reduces solution non-uniqueness compared to single-method inversions. This research provides a reliable methodology for the refined detection of concealed hazard-inducing structures, offering considerable practical value for safeguarding coal mine safety. Full article

The use of liquid hydrogen (LH₂) as a zero-emission energy carrier is increasingly relevant for next-generation transport systems, requiring reliable cryogenic storage solutions operating at approximately −253 °C. This study presents a computational analysis of LH₂ storage tanks, focusing on the coupled thermal–structural behavior of insulated cryogenic vessels under varying design parameters. The present work expands upon our previous effort by employing the Finite Element Method (FEM) to perform a parametric investigation of these tanks. This is accomplished by examining the effect of the insulating material and the change in the storage volume of LH₂, as well as the change in the selected hydrogen boil-off rate (BOR). Results show that increasing the BOR from 0.1%/h to 1.0%/h reduces the required insulation thickness and mass by approximately 90% across all configurations, significantly altering system-level mass distribution. Substituting polyurethane foam (PUR-64) with lower-density equivalent (PUR-32) yields heat-flow reductions of up to 19.5% in two configurations, while producing an unexpected increase of approximately 10% in one case due to nonlinear thermal-gradient effects. Furthermore, increasing the hydrogen storage volume from 50 m³ to 150 m³ enhances gravimetric efficiency by 58%, 75%, and up to 104% depending on tank geometry. The results demonstrate the demand for robust numerical models, due to the nonlinear dependence of the materials involved with temperature. Full article

The Brazilian Amazon is a contested frontier, shaped by destructive and conservationist forces. Most forest clearing is driven by agro-extractivism, an agrarian pathway based on raw commodity production. The socio-biodiversity economy (SBE) has emerged in response, widely regarded as a transformative agrarian pathway capable of reconciling environmental conservation and rural livelihoods. However, recent research suggests that, as socio-biodiversity products scale up, agro-extractivist dynamics can be reproduced within the SBE. We examine this tension in the cocoa frontier of the Transamazon, where cocoa is institutionally promoted as an SBE alternative. We conduct an exploratory, mixed-methods study combining a geospatial analysis of land use and land cover (LULC) change (2020–2025, random forest classification) with a qualitative analysis drawing on participatory mapping and 87 semi-structured interviews with farmers, cooperatives, buyers, and institutional actors. Using a gradient framework, we read LULC transitions and farmers’ land use practices along an agro-extractivism–SBE continuum. The cocoa frontier emerges as a hybrid geography. The landscape is predominantly stable but internally reorganizing: anthropogenic forest declines while full-sun monoculture expands over pasture, with intensification concentrated in peri-urban areas and restoration in remote ones. Land use practices form five recurring configurations, two firmly anchored at the socio-biodiversity or agro-extractivist poles and three whose alignment with the SBE depends on access to markets, knowledge, and institutions. We argue that the frontier contestation unfolds not only between distinct economies but within the cocoa economy itself, and we identify the policy areas relevant to sustaining SBE-oriented practices in the Transamazon. More broadly, this study suggests that the classification of Amazonian forest-based economies as inherent alternatives to agro-extractivism should be treated as an empirical question rather than an assumption. Full article

Laser shock peening (LSP) is an advanced surface modification technique that significantly enhances the fatigue resistance of metallic components through the synergistic implantation of deep compressive residual stresses (CRSs) and gradient microstructural refinement. Existing investigations have shown that LSP can generate strengthening layers extending from several hundred micrometers to approximately 1 mm in depth, with affected zones reaching 5–6 times the depth typically achieved by conventional shot peening in representative titanium alloys. In specific cases, LSP has increased the fatigue limit from 483.2 MPa to 593.6 MPa, corresponding to an improvement of approximately 22.8%, while optimized treatment of Ti-17 compressor blades has extended fatigue life by more than two orders of magnitude. This review systematically elucidates the anti-fatigue strengthening mechanisms of LSP across a range of metallic systems, with emphasis on three key aspects: (i) the mechanistic retardation of fatigue crack initiation and propagation, mediated by CRS-induced reductions in the stress intensity factor and enhanced crack closure effects; (ii) the parametric sensitivity of surface integrity and stress field homogeneity to laser energy density, spot overlap ratio, and multiple-impact sequencing; and (iii) the process-specific characteristics of emerging LSP variants, including laser peening without coating, warm laser shock peening, and cryogenic laser shock peening. Furthermore, we critically evaluate the role of multiscale numerical simulations—encompassing macroscopic finite element analysis, mesoscopic crystal plasticity modeling, and molecular dynamics—in optimizing process parameters and predicting fatigue life. By integrating experimental, computational, and theoretical perspectives, this review establishes a coherent process–structure–property framework to guide the rational design of LSP protocols for targeted fatigue performance enhancement. Full article

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder worldwide, affecting approximately 56.9 million people in 2021 and projected to reach 152 million by 2050. Its defining pathological features, amyloid-beta plaques and neurofibrillary tangles, accumulate for up to two decades before cognitive symptoms emerge, placing the preclinical and mild cognitive impairment (MCI) stages at the centre of the early detection problem. Despite this, current diagnostic practice in routine clinical settings remains unreliable, with post-mortem studies placing the specificity of clinical AD diagnosis between 44.3 and 70.8% even in specialist memory clinics. Machine learning (ML) and deep learning (DL) applied to neuroimaging and electrophysiological data have emerged as candidate tools for closing this diagnostic gap, yet whether the accuracy figures reported in published studies translate into clinically useful performance on independent data remains unresolved. This study presents a structured comparative review of machine learning and deep learning methods reported across four publicly available Alzheimer’s disease datasets, namely the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Open Access Series of Imaging Studies (OASIS), the OpenNeuro ds004504 electroencephalography (EEG) dataset, and the Kaggle Alzheimer’s magnetic resonance imaging (MRI) dataset. Thirteen model families are examined through the published literature rather than through new experiments, and for each model and dataset combination, the best accuracy reported in the source study is recorded alongside the model’s mathematical formulation. All performance figures reported in this abstract and throughout the paper are taken from the published studies reviewed, not from new experiments conducted by the authors. Across the reviewed studies, deep learning architectures pre-trained on ImageNet and fine-tuned on neuroimaging data are reported to produce the highest accuracy on MRI classification tasks. Residual Network (ResNet)-101 is reported at 98.21 percent on ADNI and 97.45 percent on OASIS, while the IncepRes fusion architecture reaches 98.35% on OASIS by combining multi-scale feature extraction from InceptionV3 with residual connectivity from ResNet152V2. Traditional machine learning classifiers remain competitive on tabular clinical and biomarker data, with Extreme Gradient Boosting (XGBoost) reaching 91% on ADNI multiclass features. Logistic Regression achieves 82 to 85% on binary ADNI classification and is the only classifier in this review that provides explicit per-feature prediction contributions without post hoc tooling. Gaussian Naïve Bayes achieves 80 to 83% on the same task. On the OpenNeuro EEG dataset, K-nearest neighbours (KNN) with singular value decomposition (SVD) entropy features achieves 91% binary accuracy, with feature engineering quality determining performance more reliably than classifier architecture. Eight principal findings emerge from the cross-dataset analysis. Binary classification consistently outperforms multiclass by 10 to 30% across all datasets, reflecting the genuine biological ambiguity of the mild cognitive impairment category. Dataset size and augmentation predict reported accuracy more reliably than model architecture. Ensemble methods outperform individual classifiers by 5 to 8% in nearly every imaging study. Deeper architectures can overfit small clinical cohorts. EEG models trail MRI models by approximately 10 to 15% on comparable binary tasks. Cross-dataset generalisation has not been systematically evaluated in most studies, and the few that have tested it report accuracy drops of 5 to 10% or more when models encounter data from different scanners or cohorts. Eight recurring limitations constrain the clinical utility of these findings. Small sample sizes and limited demographic diversity, severe class imbalance inflating raw accuracy metrics, poor cross-dataset generalisation driven by scanner heterogeneity, limited deep learning interpretability, the dominance of binary over multiclass tasks, the absence of longitudinal modelling despite available datasets, inadequate standardisation of preprocessing and evaluation protocols, and the signal-to-noise ratio constraints specific to EEG recordings of elderly patients collectively define the gap between benchmark performance and clinical readiness. Future work must prioritise multi-centre training cohorts, multimodal fusion architectures, longitudinal progression modelling, and standardised interpretability evaluation as non-optional requirements for any system intended for clinical deployment. Full article

Intrusion detection systems for 5G networks are typically reported at a single operating point, obscuring the trade-off between missed attacks and false alarms that governs real deployments. Building on a lightweight conditional counter-inspection pipeline, in which a global classifier is selectively validated by curriculum-biased experts under a unanimous dissent rule, we remove the constraint that all components share one learning algorithm, assigning decision trees, random forests, extremely randomized trees, and histogram-based gradient boosting independently to the global (G), malicious-biased (EM), and benign-biased (EB) roles. Across two datasets of contrasting difficulty, 5G-NIDD and UNSW-NB15, all 14 evaluated tree-based configurations reduce missed attacks, by 36.5–79.6% on 5G-NIDD, confirming that the recovery effect is a property of the architecture rather than of decision trees. The expert assignment also selects which error the system controls: the same pipeline can be steered toward fewer false alarms, fewer missed attacks, or higher aggregate F1 without retraining the first stage. The mechanism also rescues a weak linear filter: on 5G-NIDD it cuts false positives and false negatives by 92.8% and 95.8%, and on UNSW-NB15 it raises F1 from 0.903 to 0.934 while reducing missed attacks by 35.5%. These results reframe the pipeline as a configurable validation layer matched to a deployment’s cost structure. We further show, through direct measurement on both datasets, that the conditional routing evaluates at most four of seven models per record, keeping classifier inference below 0.1 ms per record and leaving the detection stage a small contributor to overall processing cost. Full article

Coastal lagoons are crucial transitional zones connecting land and sea, and their ecological connectivity with adjacent open waters directly sustains regional biodiversity and ecosystem functions. However, how lagoon fish communities connect on a diurnal timescale remains poorly understood. In this study, we selected Xincun Lagoon on the southeastern coast of Hainan Island, China, as the study area. We established six sampling stations along an environmental gradient from the lagoon interior, through the tidal inlet, to the open sea, and conducted repeated diurnal sampling at six time points on 24 January 2024. Using eDNA metabarcoding, we analyzed the spatiotemporal dynamics of fish communities to examine spatial functional differentiation within the lagoon ecosystem and the potential corridor role of the tidal inlet. Our results showed clear spatial functional differentiation of fish communities along the environmental gradient. The lagoon inlet (XC4) exhibited the highest rate of community temporal change and the greatest amplitude of diversity fluctuation, with a peak change rate of 0.854 during the evening period (18:00–21:00). Its coefficient of variation for Shannon diversity was 6.2 times that of the mangrove creek (XC3). The mangrove creek area (XC3) had the highest Shannon diversity and the smallest fluctuations, functioning as a biodiversity hotspot and a region of relatively stable community structure. Within the lagoon interior, the temporal dynamics at the marginal zone (XC1) were substantially stronger than those in the central zone (XC2). Offshore stations indicated that the ecological influence of the lagoon extended to the 5 m isobath but weakened at the 10 m isobath. Furthermore, Clupeidae eDNA signals were strongest inside the lagoon during the day, at the tidal inlet in the evening, and in offshore waters at night. This study demonstrates the utility of eDNA metabarcoding for capturing fine-scale diel variation in fish community composition and distribution across the lagoon–inlet–offshore gradient, providing information that is difficult to obtain through conventional surveys. These patterns suggest, rather than confirm, that the tidal inlet may play an important role as a corridor linking lagoon and offshore habitats, and provide a reference case for using eDNA technology to study fine-scale connectivity in similar systems. Full article

The intricate nature of multi-class histopathological images, combined with pronounced class imbalances, complicates automated breast cancer diagnosis and demands AI models capable of generalizing well beyond often limited training data. To address these challenges, this paper explores the generative modeling capability of a symmetry-driven enhanced auxiliary classifier GAN (LSWACGAN) as an all-in-one, data-efficient framework for breast cancer histopathological image classification. LSWACGAN incorporates the Wasserstein loss with gradient penalty to promote greater training stability by mitigating overfitting and preventing vanishing gradients. Assigning smooth category labels to generated samples further helps alleviate the mode collapse problem. The proposed framework brings together three types of symmetry to improve its reliability: the inherent metric symmetry of the Wasserstein distance, the structural symmetry within the auxiliary classifier GAN, and the architectural symmetry between the generator and discriminator networks. Extensive experiments conducted on the well-known BreakHis dataset, supplemented by a thorough ablation study, demonstrate the framework’s competitive edge in a lower-data regime. For binary classification, LSWACGAN closely matches or slightly outperforms leading benchmarks on most selected evaluation metrics. Conversely, in the multi-class scenario, it emerges as a clear forerunner, consistently producing superior results and maintaining robust performance across varying magnification levels. Full article

Mangrove sediments harbor complex bacterial communities that play critical roles in ecosystem functioning. In this study, we investigated the spatial patterns and environmental drivers of sediment bacterial communities across intertidal flats and vegetated zones in five representative mangrove habitats spanning the Northern Beibu Gulf, South China. Utilizing high-throughput 16S rRNA gene amplicon sequencing, we analyzed bacterial composition and diversity from sediments collected in Qinzhou, Fangchenggang, and Beihai. Results indicated that bacterial communities were dominated by Proteobacteria (37–45%), followed by Bacteroidota and Acidobacteriota; however, a considerable proportion of taxa remained unclassified, particularly in intertidal flats. Bacterial richness and diversity were generally higher in vegetated zones, where sediments were more acidic and nutrient-rich, whereas intertidal sediments were more alkaline and contained higher chlorophyll-a (Chl-a) concentrations. Beta-diversity analysis revealed distinct differences in community structures between zones and regions, which were shaped by both deterministic environmental filtering and stochastic processes. Redundancy analysis (RDA) indicated that bacterial communities assembly driven by niche differentiation influences salinity (Sal), pH, biogenic silica (BSi), total phosphorus (TP), and total nitrogen (TN), as these factors vary across sites due to differing environmental filtering intensities. These findings highlight the distinct differences in mangrove sediment bacterial communities between intertidal flats and vegetated zones, which are shaped by local sediment physicochemical conditions and regional environmental gradients. They provide critical insights for the conservation and restoration of mangrove ecosystems. Full article

Accurate camera parameter calibration is fundamental to scene geometric reconstruction, target localization, and metric measurement. Existing roadside monocular camera calibration methods usually rely on geometric information such as lane markings. However, in real-world roadside traffic scenarios, such information is easily affected by road textures, shadows, reflections, and noise responses, resulting in unstable extracted edge directions, scattered line–intersection distributions, and reduced vanishing point localization accuracy, which significantly affects camera parameter estimation and scene scale recovery. To address these issues, this paper proposes an automatic calibration method for roadside monocular cameras based on an adaptive structure tensor and hierarchical vanishing point estimation, and further applies the calibrated parameters to vehicle speed measurement. First, an adaptive structure tensor model based on local gradient extrema is constructed to reduce directional deviations caused by noise, reflections, and local textures, thereby enabling stable extraction of edge-direction features in complex scenarios. Second, a hierarchical vanishing point estimation framework integrating spatial focusing, Mean-Shift clustering, and nonlinear optimization is proposed. By progressively filtering outlier intersections and introducing geometric consistency constraints, the proposed framework achieves robust self-calibration in cluttered traffic environments. On this basis, the geometric prior information of road markings is exploited to establish a road-marking contour optimization model constrained by the two vanishing points. Through joint optimization, scene scale recovery is achieved without the need for calibration targets, thereby completing automatic calibration of the roadside monocular camera. Experimental results on the public BrnoCompSpeed dataset demonstrate that the proposed method achieves a mean relative camera calibration error of 5.31%, indicating higher calibration accuracy and stability than existing automatic calibration methods. In addition, the calibrated camera parameters are applied to vehicle speed estimation, yielding a mean speed estimation error of 1.79 km/h. These results verify the effectiveness of the proposed calibration method in real-world traffic scenarios and provide reliable support for geometric measurement and visual perception applications in intelligent transportation systems. Full article