Search Results (390,869)

In this paper, we propose the Manifold Random Drift Particle Swarm Optimization (MRDPSO) algorithm for matrix optimization on smooth manifolds. Conventional swarm intelligence methods generally converge prematurely in constrained domains. To mitigate this issue, we introduce the swarm intelligence methods to the manifold and a Random Drift mechanism that regulates the search process. Using Riemannian geometry, our framework treats constrained problems as unconstrained ones on the manifold, which preserves the intrinsic geometric structure of the data. Particles are initialized on the manifold, while updates are performed in tangent spaces. Since geodesic calculations are computationally expensive, we use an inverse retraction as a faster alternative to standard logarithmic mapping. Numerical experiments on Grassmann, Stiefel, and Oblique manifolds show that MRDPSO achieves higher accuracy and superior convergence stability compared to recent state-of-the-art manifold-adapted heuristics, namely IISSO and MSSO. Full article

Moving beyond the limits of purely geometric or descriptive documentation, the study conceives the digital models as a structured information system capable of coherently and queryably organising both the formal-typological and the interpretative-historical dimensions of heritage. The methodology is developed within the framework of the European Horizon MSCA project Hephaestus, which investigates cross-border Cultural Heritage Routes (CHRs) and historic fortification systems in the Adriatic and Baltic basins. The paper focuses on Adriatic CHR, through the selection, organisation, and interrelation of a distributed corpus of fortified architectures, articulated according to historical phases, territorial clusters, typological classes, and multilevel relationships. The study adopts an approach centered on HBIM models and ontological frameworks, implemented through complementary top-down and bottom-up processes. The results show the possibility of structuring HBIM-derived data within an ontology-based framework capable of linking, within a single information system, architectural elements, fortified systems, and territorial entities across heterogeneous case studies. The application to differentiated contexts highlights the ability of the models to adapt to different scales and levels of complexity, supporting querying, comparison, and multi-level interpretation of heritage. The variety of sources and contexts enables the methodology to be tested across heterogeneous historical and typological scenarios, strengthening its applicability and robustness within a multiscalar information structure. Full article

In Northeast China’s degraded croplands, nitrate (NO₃⁻-N) leaching is the dominant pathway for fertilizer-nitrogen (N) loss, which presents an increasing threat to the quality of groundwater. Conservation tillage, defined as no-tillage (NT) and straw retention, is a widely adopted management strategy to maintain cropland fertility in the black soil (BS) regions. At present, however, the impact of shifting from conventional to conservation tillage on the vertical distribution and regulatory mechanisms of NO₃⁻-N derived from applied fertilizer-N (F_NO3) remains poorly understood. Based on a 12-year field experiment, we integrated ¹⁵N-tracing field monitoring with ¹⁵N-paired-labeling incubation to quantify the vertical migration of F_NO3 into deep soil profiles, and specify the dominant processes regulating N retention and supply. Across the tested BS croplands, total NO₃⁻-N production rates (4.06–6.58 mg N kg⁻¹ soil day⁻¹) were faster than their consumption rates (0.36–0.92 mg N kg⁻¹ soil day⁻¹), leading to a net accumulation of NO₃⁻-N, and implying a potential for leaching of NO₃⁻-N, from the perspective of substrate availability. The results of the field ¹⁵N micro-plot experiment also indicated that, by maize maturity in the first growing season, an average of 7.5% of F_NO3 had migrated to the 80–100 cm soil layer. During the following two growing seasons, the maximum accumulation of F_NO3 had shifted downward to 140–160 cm and 180–220 cm, respectively. Such a pattern, particularly in light of the increased extreme precipitation in the studied regions, raises clear concerns about NO₃⁻-N leaching losses. Compared with conventional management, no-tillage with full-rate straw mulching decreased net rates of NO₃⁻-N production from 6.22 to 3.14 mg N kg⁻¹ soil day⁻¹. This reduction resulted from a decline in the gross oxidation of NH₄⁺-N to NO₃⁻-N (from 6.39 to 3.70 mg N kg⁻¹ soil day⁻¹) and an increase in DNRA (from 0.35 to 0.85 mg N kg⁻¹ soil day⁻¹), which collectively delayed the downward transport of F_NO3. Conservation tillage also increased the gross rate of heterotrophic nitrification (from 0.19 to 0.36 mg N kg⁻¹ soil day⁻¹) and its proportion relative to total nitrification (from 2.8% to 8.9%). Despite this shift, autotrophic nitrification remained the dominant process for NO₃⁻-N production in the tested BS croplands, likely due to a pH constraint on heterotrophic nitrification. With the increasingly widespread promotion of conservation tillage for soil fertility improvement, heterotrophic nitrification warrants greater attention, particularly in BS regions where pH < 6.5 and C/N contents are relatively high. Collectively, our findings provide a scientific basis for tailoring tillage practices to maintain sustainable agriculture in Northeast China. Full article

Bioelectromethanogenesis, the microbial conversion of carbon dioxide (CO₂) into methane (CH₄) using a cathode, offers a promising route for biogas upgrading and renewable energy storage. The flow field is an essential factor influencing the performance of bioelectromethanogenesis, and the stability and efficiency of the biocathode play important roles in this process. This study systematically investigated the effect of different internal-circulation flow rates on the biocathode initiated without the electric field and the reactor effluent. It was found that the methane production of the biocathode initiated without the electric field was increased by around 30% at an internal-circulation flow rate of 18 mL/min, which was stronger than that of the biocathode initiated by the reactor effluent. The relative content of the extracellular polymeric substance (EPS) heme was increased by 4%, while the EPS electron accepting capacity was much higher than that initiated by reactor effluent. Furthermore, the microbial community analysis showed that the functional methanogen on the biocathode initiated without an electric field was Methanosaeta (17%) and Methanobacterium (8%). This study could provide support for the dynamic operation of biogas upgrading in microbial electrolysis cells. Full article

Interlayer bond strength is critical for ensuring the safety and durability of 3D-printed concrete (3DPC) structures. However, there remains a lack of real-time prediction methods addressing interlayer performance under the combined effects of interval time and environmental factors during the in situ printing process. To address this issue, this study conducted experiments considering various printing interval times and environmental conditions, incorporating monitoring of dielectric constant and water evaporation, alongside interlayer splitting tensile tests. By integrating the SHAP interpretability algorithm with nonlinear regression analysis, the results indicate that the printing interval time is the dominant factor inducing interlayer strength decay (with a contribution rate of 68.6%), while relative humidity emerges as the primary environmental variable (with a contribution rate of 21.3%). Mechanism analysis reveals that prolonged printing intervals intensify the hydration of the lower deposited layer, leading to reduced interfacial moisture content and loss of plasticity. Furthermore, environmental evaporation significantly regulates this process, with high-humidity environments notably mitigating the moisture loss and strength reduction caused by time delays. Based on the correlation mechanism between moisture and strength, a dimensionless general prediction model for 3DPC interlayer strength was established, incorporating printing interval time and an evaporation index (goodness of fit, R² = 0.96). Consequently, a digital twin quality inversion scheme based on companion specimen monitoring and printing timestamps was proposed. This study quantifies the intrinsic relationships among printing interval time, environmental conditions, and interlayer strength, offering a novel approach for determining the construction window and achieving non-destructive quality prediction for 3DPC in complex environments. Full article

Fractures as critical discontinuous structural planes in rock masses, directly govern their stability and serve as the core controlling factor in rock mechanics engineering. Existing deep learning models for fracture extraction face persistent challenges, including imbalanced integration of deep and shallow features, limited suppression of background noise, inadequate multi-scale feature representation, and large parameter sizes—making it difficult to strike a balance between detection accuracy and deployment efficiency. Focusing on the Wanshanshan quarry in Yunnan, this study first constructs a high-precision digital model using close-range photogrammetry and 3D real-scene reconstruction. A lightweight yet high-accuracy intelligent detection method, termed MSEM-Deeplabv3+, is then proposed for rock mass fracture extraction. The model adopts lightweight MobileNetV2 as the backbone network, incorporating inverted residual modules and depthwise separable convolutions, resulting in a parameter size of only 6.02 MB and FLOPs of 30.170 G—substantially reducing computational overhead. Furthermore, the proposed MAGF (Multi-Scale Attention Gated Fusion) and SCSA (Spatial-Channel Synergistic Attention) modules are integrated to enhance the representation of fracture details and semantic consistency while effectively suppressing multi-source and multi-scale background interference. Experimental results demonstrate that the proposed model achieves an mPA of 89.69%, mIoU of 83.71%, F1-Score of 90.41%, and Kappa coefficient of 80.81%, outperforming the classic Deeplabv3+ model by 5.81%, 6.18%, 4.53%, and 9.2%, respectively. It also significantly surpasses benchmark models such as U-Net and HRNet. The method accurately captures fine and continuous fracture details, preserves the spatial distribution of long-range continuous fractures, and maintains robust performance on the CFD cross-scene dataset, showcasing strong adaptability and generalization capability. This approach effectively mitigates the risks associated with manual high-altitude inspections and provides a lightweight, high-precision, non-contact intelligent solution for fracture detection in high-steep rock slopes. Full article

Synthetic aperture radar altimeter (SARAL) directs the radar beam toward the nadir point of the flight trajectory. It is capable of capturing elevation variations in the terrain of interest. To ensure that the nadir point remains within the beam coverage under complicated flight attitudes, a wide beamwidth is necessary. However, the wide beamwidth introduces a spatial-variant delay problem with respect to different scatters in the along-track direction, which degrades the accuracy in obtaining the terrain elevation contour. To this end, a spatial-variant Delay-Doppler (SVDD) algorithm is proposed in this paper. The core advantage of the proposed algorithm is that an analytical spectrum is obtained through rigorous mathematical derivation for the wide-beam SARAL geometry. Accordingly, all correction functions are implemented via complicated multiplications without interpolation operations. High computational efficiency is therefore ensured. To address the spatial-variant delay problem, a direct geometric relationship is first established between the Doppler frequency and the azimuthal position. Based on this relationship, the spatial-variant characteristic is mapped from the spatial domain to the Doppler domain. This mapping is then directly employed to construct the spatial-variant delay correction function. At the same time, range walk correction and range curve correction are carried out. In such cases, the variation of the undulating terrain can be recovered from the Delay-Doppler Map (DDM). Both simulated and raw data of the radar altimeter are applied to verify the effectiveness of the proposed SVDD algorithm. Comparisons with the conventional algorithm are also performed to demonstrate the superiority of the SVDD algorithm. Full article

The adjustable speed hydro-generating unit has a complex three-phase alternating current excitation structure. The existing rotor winding short circuit (RWSC) fault diagnosis methods are generally difficult to use to locate the fault location and identify the severity of the fault. Therefore, an offline diagnosis method for the internal RWSC of an adjustable speed hydro-generating unit is proposed in this paper. Firstly, after the unit is shut down, the low-voltage pulse signal is repeatedly injected into the rotor winding by the pulse generator. By comparing and analyzing the voltage response characteristics under different types of short circuit faults, an identification method of rotor winding short circuit fault type and fault phase based on detecting the reverse polarity sub-spike is proposed. Furthermore, the short circuit fault point can be accurately located by combining ensemble empirical mode decomposition (EEMD) with the Teager energy operator (TEO). Finally, the fault factor is constructed based on the area between the characteristic waveform and the zero line, and the quantitative evaluation of the severity of the short circuit fault is realized based on this. The effectiveness of the proposed fault diagnosis and location method is verified by the simulation results. Full article

Predicting acute toxicity across species is essential for early-stage drug safety evaluation. While recent efforts have primarily focused on improving predictive accuracy, they often fail to address two critical issues: the substantial divergence in toxicity mechanisms among different species, and the inherent noise present in experimental data. To bridge this gap, we introduce a Probabilistic Multitask Active Learning (PMAL) framework for multi-species acute toxicity prediction. Our framework integrates two key modules: a Probabilistic Multitask Learning (PML) component which jointly models the predictive distributions of multiple toxicity endpoints from a probabilistic viewpoint, and an Uncertainty-based Active Learning (UAL) component which strategically selects the most informative compounds for experimental annotation based on predictive uncertainty. Empirical evaluations demonstrate that PMAL surpasses state-of-the-art methods and is capable of providing well-calibrated uncertainty estimates for small molecules across diverse toxicity endpoints. Beyond advancing multi-species toxicity prediction, the core design principles of PMAL offer a generalizable paradigm for learning in noisy multi-task environments. Full article

(1) Background: Hospitalized preterm infants often require months of vital signs monitoring in the neonatal intensive care unit. Today, wired sensors are essential for survival, but are associated with numerous disadvantages including sensor dislocations, skin trauma and hygiene risks. Non-contact vital sign monitoring would therefore represent a significant improvement in the care of hospitalized neonates. (2) Objective: This study aims to lay the foundation for non-contact radar-based monitoring of the respiratory rate, which could be used in the neonatal intensive care unit. (3) Methods: We developed a radar-based vital parameter monitoring system for recording the respiratory rate of premature infants in a pediatric incubator. The novel system employs a four-channel I/Q FMCW radar with compact, application-specific antennas optimized to cover the defined area of interest on the infant’s thorax. As a proof-of-principle study, the system was tested in six anesthetized newborn piglets. (4) Results: Using the radar-based system, thorax movements were detected and the respiratory rate was calculated. We observed a high accordance between the signals of respiration detected by the novel radar sensor with the signals of the cable-bound monitor in resting piglets. (5) Conclusions: The novel radar sensor is suited for measuring respiration in the piglet model. In future, the sensor should be optimized in order to improve its robustness against disturbances body movements and in order to allow detection of heartbeat. Full article

This paper proposes the use of laser-assisted cutting technology to control the brittle–plastic transition of single-crystal CaF₂ through local thermal softening, thereby suppressing its processing anisotropy. Nano-scratch experiments show that heating significantly increases the critical plastic cutting depth of each crystal plane and reduces the inter-plane differences. Based on this, laser-assisted ultra-precision turning was used to fabricate CaF₂ optical microcavities with a surface roughness below 10 nm, achieving a maximum quality factor of ~7.79 × 10⁷, and significantly reducing the performance differences among different crystal orientations. The research indicates that this method can effectively promote uniform plastic flow on each crystal plane, providing an effective approach for the high-performance and consistent fabrication of anisotropic brittle optical components. Full article

Background: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection and remains a leading cause of mortality in intensive care units (ICUs). Although the Sequential Organ Failure Assessment (SOFA) score is widely used for prognostic stratification, organ dysfunction represents a downstream manifestation of sepsis, whereas immune and inflammatory dysregulation may precede overt organ failure. Monocyte distribution width (MDW) is a novel hematological parameter reflecting monocyte activation and is approved for the diagnosis of sepsis; however, its prognostic value and potential role within composite biomarker models in critically ill surgical patients with sepsis remain incompletely defined. Methods: We conducted a prospective, observational, single-center pilot study in two surgical intensive care units between November 2022 and December 2023. Adult patients with sepsis defined according to Sepsis-3 criteria were enrolled. Laboratory and clinical variables—including MDW, neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), procalcitonin (PCT), and SOFA score—were measured on admission and during the first five days of ICU stay. Patient-level median values across five days were used for analysis. The primary outcome was in-hospital mortality. Prognostic performance was assessed using receiver operating characteristic (ROC) curve analysis and logistic regression. A composite bioscore was constructed by combining dichotomized MDW, NLR, CRP, and PCT values. Results: Sixty patients were included; 24 (40%) died during hospitalization. Non-survivors were older and had significantly higher SOFA scores. MDW, NLR, CRP, and PCT were significantly higher in non-survivors. SOFA demonstrated the strongest discriminative ability for mortality prediction (AUC 0.839, 95% CI 0.730–0.948). Among biomarkers, NLR (AUC 0.741) and PCT (AUC 0.714) showed good discriminative performance, while MDW (AUC 0.690) and CRP (AUC 0.662) showed moderate discrimination; MDW exhibited the highest specificity (80.6%). In multivariable analysis with individual biomarkers, only SOFA remained an independent predictor of mortality. The composite bioscore demonstrated good discriminative ability (AUC 0.805) and, when evaluated alongside SOFA, remained independently associated with fatal outcome (OR 11.92, 95% CI 1.76–80.75); however, given the modest sample size and wide confidence intervals, this finding should be interpreted with caution. Repeated-measures correlation analysis revealed no strong collinearity among biomarkers. Conclusions: A composite bioscore incorporating MDW, NLR, CRP, and PCT provides prognostic information comparable to SOFA and remains independently associated with mortality. This approach may complement organ dysfunction-based assessment and support early risk stratification in sepsis. Full article

Obesity is increasingly recognized as a disease of dysregulated intercellular communication rather than merely an energy imbalance. Extracellular vesicles (EVs), membrane-bound nanoparticles (30–1000 nm) released by nearly all cell types, act as central mediators of this pathological crosstalk. In obesity, hypertrophic adipocytes, pro-inflammatory macrophages, and dysfunctional endothelial cells secrete EVs carrying altered cargo, including pro-inflammatory miRNAs (e.g., miR-34a, miR-155), bioactive lipids, and stress proteins, which propagate systemic metabolic dysfunction. Adipose tissue-derived EVs impair hepatic fatty acid oxidation, promote steatohepatitis, suppress pancreatic beta-cell insulin secretion, induce skeletal muscle insulin resistance via PPARγ repression, and contribute to endothelial dysfunction and atherosclerosis. EV-mediated adipocyte–macrophage crosstalk reinforces chronic adipose inflammation. Circulating EVs also provide biomarkers: subpopulation ratios, miRNA signatures, and tissue factor-positive EVs reflect disease severity, predict cardiovascular risk, and monitor therapeutic responses, with machine learning enhancing diagnostic precision. Therapeutically, EVs from mesenchymal stem cells, Wharton’s jelly MSCs, adipose progenitors, and M2 macrophages reverse insulin resistance, hepatic steatosis, and adipose inflammation in preclinical models. Engineering strategies improve EV potency and tissue targeting, and Phase I trials confirm safety, though manufacturing and cost remain barriers. Preclinical and early clinical studies of MSC-EVs confirm a favorable safety profile, though manufacturing scalability and cost remain barriers to widespread clinical adoption. Overall, EVs represent both diagnostic tools and therapeutic vehicles in precision obesity medicine, offering a pathway from symptom management toward true disease remission. Full article

Habitat quality is critical for spatial planning strategies and ecological conservation initiative, evaluating the health of the natural environment that supports human survival. However, current approaches pay insufficient attention to revealing the evolution and spatial heterogeneity of the habitat quality simultaneously. In this study, a comprehensive and practical framework was therefore developed for mechanistic habitat quality analysis, which incorporates an adaptable evolutionary model alongside multiple spatial statistical methods. Ningxia, located in Northwest China, was selected as a case study area due to its fragile ecosystem. The proposed framework was then applied to characterize the evolutionary process and spatial heterogeneity of habitat quality in Ningxia. Key factors driving spatial heterogeneity were also found at the same time. From 2000 to 2024, habitat quality in Ningxia is characterized by good habitat and shows significant improvement, following a progressive trajectory. The proportion of poor habitat has been significantly reduced from 29.26% to 24.63%, while that of excellent habitat has been increased from 1.68% to 2.33% over the past two decades. Variation in habitat quality is more pronounced in northern and southern regions, while remaining relatively stable in the central Yellow River ecological corridor. Both natural and socioeconomic factors have an impact on the habitat change in this region, such as the Normalized Difference Vegetation Index (NDVI), Net Primary Productivity (NPP), and Gross Domestic Product (GDP). Vegetation factors play vital roles in spatial variation in habitat quality, while the influences of socioeconomic factors are relatively small. The spatial heterogeneity is driven by nonlinear synergistic effects among numerous factors. This paper developed a feasible framework to retrieve the evolution and spatial heterogeneity pattern of habitat quality, which provides a robust methodology for further habitat assessment at the ecologically fragile regions worldwide. Full article

The Turpan–Hami Basin, a critical energy hub in northwestern China, is plagued by frequent ground collapses induced by extensive mining over karst geology, threatening ecology and safety. Current hazard assessment methods, mainly single linear or traditional machine learning models, fail to capture the complex nonlinear interactions inherent to this coupled geo-mining environment. This study addresses this gap by establishing a multi-dimensional “Geology-Mining-Hydrology-Environment” index system comprising 14 critical factors—including lithology, goaf distribution, mining intensity, and their interaction terms. A coupled gradient boosting decision tree and logistic regression (GBDT-LR) model, optimized for the multi-factor coupling characteristics of ground collapse in arid mining basins, was applied for the hazard assessment. The results reveal a distinct spatial pattern of “core agglomeration with multi-level gradient differentiation.” Extremely high-hazard areas, covering 9.21% of the area, are concentrated in the core mining areas northwest of Turpan and southwest of Hami, while high-hazard areas (4.63%) form surrounding belts. The GBDT-LR model (AUC = 0.871) demonstrated significantly superior performance over a single logistic regression model (AUC = 0.813), proving its enhanced capability to identify high-hazard areas by modeling complex factor interactions. This work provides an essential scientific foundation for implementing zonal hazard management and prioritizing disaster prevention projects in key areas of the basin. Full article