Search Results (36)

Matrix Product States (MPSs) have become an indispensable symmetry-based representation for simulating quantum systems on near-term hardware by constraining entanglement entropy through a fixed bond dimension $\mathsf{\chi }$. This study identifies a critical “rank explosion” phenomenon that destabilizes this low-rank manifold during conditional quantum diffusion processes. We empirically demonstrate that the introduction of conditional guidance—essential for semantic control—injects global correlations that drive the effective Schmidt rank to increase by $4\times$ (from $\mathsf{\chi }=4$ to $16$), saturating the simulation limits and necessitating quantum circuits with approximately $1.8\times {10}^3$ Controlled-NOT (CNOT) gates. Such circuit depths fundamentally exceed the operational coherence budgets of Noisy Intermediate-Scale Quantum (NISQ) devices. To mitigate this structural instability, we propose Rank-Aware Conditional Synthesis (RACS), a sampling framework that maintains the latent trajectory within a prescribed MPS manifold through step-wise manifold projection and time-shift error correction. Experimental results on real-world semantic data reveal that RACS reduces reconstruction error, or Mean Squared Error (MSE) by 30.8% and enhances latent trajectory smoothness by 36.8% compared to conventional post hoc truncation. At a fixed hardware-efficient rank of $\mathsf{\chi }=4$, RACS achieves a +4.8% fidelity gain and exhibits superior robustness against depolarizing noise. By resolving the tension between conditional expressivity and entanglement constraints, RACS provides a principled, hardware-aware methodology for high-fidelity quantum generative modeling. Full article

Background: Gross tumor volume (GTV) segmentation of Nasopharyngeal Carcinoma (NPC) crucially determines the precision of image-guided radiation therapy (IGRT) for NPC. Compared to other cancers, the clinical delineation of NPC is especially challenging due to its capricious infiltration of the adjacent rich tissues and bones, and it routinely requires multimodal information from CT and MRI series to identify its ambiguous tumor boundary. However, the conventional deep learning-based multimodal segmentation method suffers from limited prediction accuracy and frequently performs as well as or worse than single-modality segmentation models. The limited multimodal prediction performance indicates defective information extraction and integration from the input channels. This study aims to develop a 3D Gaussian-prompted Diffusion Model (3DG-PDM) for more clinically targeted information extraction and effective multimodal information integration, thereby facilitating more accurate and clinically interpretable GTV segmentation for NPC. Methods: We propose a 3D-Gaussian-Prompted Diffusion Model (3DGS-PDM) that operates NPC tumor contouring in multimodal clinical priors through a guided stepwise process. The proposed model contains two modules: a Gaussian Initialization Module that utilizes a 3D-Gaussian-Splatting technique to distill 3D-Gaussian representations based on clinical priors from CT, MRI-t2 and MRI-t1-contract-enhanced-fat-suppression (MRI-t1-cefs), respectively, and a Diffusion Segmentation Module that generates tumor segmentation step-by-step from the fused 3D-Gaussians prompts. We retrospectively collected data on 600 NPC patients from four hospitals through paired CT, MRI series and clinical GTV annotations, and divided that dataset into 480 training volumes and 120 testing volumes. Results: Our proposed method can achieve a mean dice similarity cofficient (DSC) of 84.29 ± 7.33, a mean average symmetric surface distance (ASSD) of 1.31 ± 0.63, and a 95th percentile of Hausdorff (HD₉₅) of 4.76 ± 1.98 on primary NPC tumor (GTVp) segmentation, and a DSC of 79.25 ± 10.01, an ASSD of 1.19 ± 0.72 and an HD₉₅ of 4.76 ± 1.71 on metastasis NPC tumor (GTVnd) segmentation. Comparative experiments further demonstrate that our method can significantly improve the multimodal segmentation performance on NPC tumors, with superior advantages over five other state-of-the-art comparative methods. Visual evaluation on the segmentation prediction process and a three-step ablation study on input channels further demonstrate the interpretability of our proposed method. Conclusions: This study proposes a performant and interpretable multimodal segmentation method for GTV of NPC, contributing greatly to precision improvement for NPC therapy treatment. Full article

Background: Exertional oxygen desaturation (SpO₂ ≤ 88%) during the six-minute walk test (6MWT) is a key prognostic marker in interstitial lung disease (ILD), yet access to the test is often limited in clinical practice. Developing simple, bedside tools to identify patients at risk may support early risk stratification and guide clinical decision-making. Methods: We conducted a retrospective, real-world cohort study in a tertiary referral center between January 2024 and July 2025, including 150 patients, of whom 67.33% (101 patients) were using supplemental oxygen. Clinical and physiological data collected within 30 days of the 6MWT were analyzed. The primary outcome was exertional hypoxemia, defined as peripheral oxygen saturation (SpO₂) ≤ 88% at the end of the test. Four predictive approaches were evaluated: multivariable logistic regression, stepwise logistic regression, and a simplified clinical score (0–3). The simplified score assigned one point for each of the following: forced vital capacity (FVC) ≤ 61% predicted, diffusing capacity for carbon monoxide (DLCO) ≤ 53% predicted, and presence of chronic cough. Model performance was assessed by receiver operating characteristic (ROC) curves, sensitivity, specificity, predictive values, and risk stratification. Results: The simplified score demonstrated robust discriminative performance, comparable to more complex statistical models, with high sensitivity and acceptable specificity. A threshold of ≥2.0 points identified patients at high risk for exertional desaturation with 100% sensitivity and 0.66 specificity. Observed desaturation risk increased progressively across score categories: 17.1% for scores 0–1 (low risk), 58.6% for score 2 (intermediate risk), and 95.1% for score 3 (high risk). Conclusions: Compared with multivariable models, the simplified 0–3 clinical score—based on widely available variables (FVC ≤ 61%, DLCO ≤ 53%, and chronic cough)—maintained similar predictive performance (AUC 0.82) with greater operational simplicity. Owing to its high sensitivity and bedside applicability, it represents a promising screening tool for identifying patients at high risk of exertional desaturation, particularly when the 6MWT is unavailable. Full article

Temporal network diffusion models play a crucial role in healthcare, information technology, and machine learning, enabling the analysis of dynamic event-based processes such as disease spread, information propagation, and behavioral diffusion. This study addresses the challenge of reconstructing temporal network diffusion events in real time under conditions of missing and evolving data. A novel non-parametric reconstruction method by simple weights differentiationis proposed to enhance source detection robustness with provable improved error bounds. The approach introduces adaptive cost adjustments, dynamically reducing high-risk source penalties and enabling bounded detours to mitigate errors introduced by missing edges. Theoretical analysis establishes enhanced upper bounds on false positives caused by detouring, while a stepwise evaluation of dynamic costs minimizes redundant solutions, resulting in robust Steiner tree reconstructions. Empirical validation on three real-world datasets demonstrates a 5% improvement in Matthews correlation coefficient (MCC), a twofold reduction in redundant sources, and a 50% decrease in source variance. These results confirm the effectiveness of the proposed method in accurately reconstructing temporal network diffusion while improving stability and reliability in both offline and online settings. Full article

The emergency industry refers to a comprehensive industrial system of products, technologies, and services aimed at preventing, responding to, and mitigating emergencies. The emergency industry is primarily oriented toward disaster prevention and mitigation, providing direct support to enhance societal resilience. Given the frequent occurrence of natural disasters and the strategic layout of global emergency technologies, it is of great practical significance to study how the science and technology systems of disaster-prone countries respond. Based on the theories of disaster economics and innovation geography, this paper constructs a mediation effect model to investigate how China improves the key technological capabilities of its emergency industry through three response pathways—demand stimulation, technological advancement, and educational enhancement—following natural disasters. The stepwise testing approach, which integrates the mediation effect model with the spatial Durbin model, consists of three stages. The first stage tests the total effect model to assess how disasters impact local key technologies and their spatial spillover on adjacent regions. The second stage examines the direct influence of disasters on the three pathways and their spatial spillover using the mediator equation. The third stage uses the outcome equation with the mediator to evaluate how the pathways affect local key technologies and neighboring regions after controlling for disaster impacts. We offer both theoretical insights and empirical evidence to support specialized research on technological diffusion induced by disasters. The result shows that although the direct negative impact of disasters is inevitable, the institutional advantages of China’s emergency rescue and innovative collaborative efforts have played a significant role in promoting key technologies. Under the new national system, China is progressively establishing a spatial framework wherein emergency products are allocated across regions, key technologies are synergistically integrated, and the development of emergency-related disciplines is promoted through regional collaboration in response to the frequent occurrence of natural disasters. This demonstrates that the advancement of key technologies in China’s emergency industry is significantly supported by inter-regional cooperation and linkage mechanisms. Full article

Accurate and robust calibration of multifocal plenoptic cameras is essential for high-precision 3D light field reconstruction. In this work, we propose a blur feature-guided cascaded calibration for the plenoptic camera. First, white images at different aperture values are used to estimate the high-confidence center point and radius of micro-images, and the defocus theory is used to estimate the initial values of the intrinsic parameters. Second, the gradient value is introduced to quantify the degree of blurring of the corner points, which are then divided into three types: clear, semi-clear, and blurred. Furthermore, a joint geometric constraint model of epipolar lines and virtual depth is constructed, and the coordinates of the semi-clear and blurred corner points are optimized in a step-by-step manner by using the clear corner point coordinates. The micro-image center ray projection equation is then devised to assist in the optimization of the microlens array core parameters and establish blur-adaptive credibility weights, thereby constructing a global nonlinear optimization. Finally, the proposed method is tested on both simulated and captured datasets, and the results exhibit superior performance when compared with the established methods described by Labussière, Nousias, and Liu. The proposed method excels in corner feature extraction, calibration accuracy of both internal and external parameters, and calibration sensitivity when applied to multifocal-length light field cameras, highlighting its advantages and robustness. Full article

Proton exchange membrane water electrolyzers (PEMWEs) are a promising technology for green hydrogen production. However, the adoption of PEMWE-based hydrogen production systems remains limited due to several challenges, including high material costs, limited performance and durability, and difficulties in scaling the technology. Computational modeling serves as a powerful tool to address these challenges by optimizing system design, improving material performance, and reducing overall costs, thereby accelerating the commercial rollout of PEMWE technology. Despite this, conventional models often oversimplify key components, such as porous transport and catalyst layers, by assuming constant porosity and neglecting the spatial heterogeneity found in real electrodes. This simplification can significantly impact the accuracy of performance predictions and the overall efficiency of electrolyzers. This study develops a mathematical framework for modeling variable porosity distributions—including constant, linearly graded, and stepwise profiles—and derives analytical expressions for permeability, effective diffusivity, and electrical conductivity. These functions are integrated into a three-dimensional multi-domain COMSOL simulation to assess their impact on electrochemical performance and transport behavior. The results reveal that although porosity variations have minimal effect on polarization at low voltages, they significantly influence internal pressure, species distribution, and gas evacuation at higher loads. A notable finding is that reversing stepwise porosity—placing high porosity near the membrane rather than the channel—can alleviate oxygen accumulation and improve current density. A multi-factor comparison highlights this reversed configuration as the most favorable among the tested strategies. The proposed modeling approach effectively connects porous media theory and system-level electrochemical analysis, offering a flexible platform for the future design of porous electrodes in PEMWE and other energy conversion systems. Full article

With the development of urbanization and industrialization worldwide, soil heavy metal pollution has become a critical and pressing environmental problem in urban areas. Soil heavy metals exhibit complex and varying spatial aggregation and diffusion processes within diverse urban landscapes, especially in different urban areas with varying urbanization levels. However, many existing experimental methods and conventional models overlook the crucial aspects of spatial autocorrelation and heterogeneity between soil heavy metals and influencing factors. This neglect poses significant environmental concerns, as rapid monitoring of soil heavy metals and accurate identification of their determinants become imperative. This study investigated four environmentally sensitive and potentially harmful soil heavy metals, arsenic (As), cadmium (Cd), copper (Cu), and lead (Pb), in two urban areas in China with varying urbanization levels. Enshi (a prefecture-level city) and Wuhan (a provincial capital city) were selected for comparison of the spatially variable relationships between soil heavy metals and their influencing factors. We employed a global stepwise linear regression (STR) model and a local spatial model-geographically weighted regression (GWR) to map the spatial distribution of soil heavy metals based on 121 auxiliary variables, including terrain, geophysical, socioeconomic factors, and remote sensing data. Our results showed that: (1) soil heavy metals exhibited strong spatial aggregation in the prefecture-level city (Enshi) but, nonetheless, have strong spatial heterogeneity in the provincial capital city (Wuhan) due to elevated anthropogenic disturbances; (2) GWR accurately mapped the spatial distributions of As (r = 0.47 and 0.66), Cd (r = 0.74 and 0.53), Cu (r = 0.60 and 0.54), and Pb (r = 0.44 and 0.50) based on auxiliary variables in different cities and also can clearly reveal the spatially variable relationships with main influence factors; (3) human activities were the primary driving factors influencing As and Pb, while natural environment variables were identified as the main potential sources of Cd and Cu. This study demonstrates a methodology to explore spatially variable characteristics of soil heavy metals and their spatial varying relationships with influence factors. The comparative analysis between two cities provides insights that can greatly enhance quantitative source apportionment and support sustainable management strategies for controlling soil heavy metal pollution across varied urban environments. Full article

As application conditions become increasingly demanding and usage becomes more aggressive, the performance of traditional insulated gate bipolar transistor (IGBT) and fast recovery diode (FRD) systems can no longer meet the required specifications. In these systems, FRDs are required to carry load current and allow current to return from the load to the IGBTs. Consequently, the reverse recovery performance of the FRDs significantly restricts the overall efficiency of the system. Therefore, how to predict the reverse recovery characteristics of the FRDs with greater precision has attracted considerable attention. In this context, this paper presents an in-depth investigation of the high-level injection carrier distribution and reverse recovery characteristics of punchthrough P-I-N (PT-PIN) FRD with a buffer layer. Specifically, the research explores the physical properties of the materials, doping concentrations, and the geometric structure of the devices. Furthermore, it takes into account the complex interactions among carrier recombination, diffusion, and drift, leading to the development of a model that delineates the spatial distribution of carriers and their influence on current conduction. Building upon the traditional step-wise analysis method, subsequently, the temporal aspects of the FRDs reverse recovery process were further segmented. Utilizing the derived carrier distribution model, a reverse recovery analytical model was constructed. The model was validated using a 1200 V, 100 A IGBT with 1200 V, 60 A FRD configured in a reverse parallel arrangement, which demonstrated a 5% improvement in prediction accuracy of V_R compared with previous models that employed the lumped charge method. Finally, a range of experiments with varying R_G, V_CC and I_F confirmed the broad applicability of this analytical model. Full article

Apple and ginger mixed pomace is a by-product that can be valorized by drying. In this study, mixed pomace was subjected to hot-air drying (HAD) at 45, 62, and 70 °C and stepwise at 45 °C followed by at 62 °C or the reverse, at 62 °C followed by at 45 °C (2.5 mm layer), and microwave drying (MWD) at 100, 180, and 300 W (2.5 mm and 1.5 mm layers) and stepwise at 100 W followed by at 300 W (2.5 mm layer). The results show that the Crank model well fitted the HAD kinetics, with a water effective diffusivity (Deff) of 2.28 ± 0.06 × 10⁻¹⁰–4.83 ± 0.16 × 10⁻¹⁰ m²/s and energy of activation of 23.9 kJ/mol. The step approach of drying at 45 °C followed by at 62 °C resulted in a higher Deff than the reverse approach (drying at 62 °C followed by at 45 °C). The Midilli et al. model presented a good fit for the MWD kinetics. The drying time was calculated using these models to achieve 12% moisture content in the pomace and found to be 125.0 ± 9.2–439.5 ± 118.2 min for HAD, and 11.1 ± 0.2–61.5 ± 6.0 min for MWD. The specific energy required was 410.78 ± 6.30–763.79 ± 205.4 kWh/kg and 1.32 ± 0.01–2.26 ± 0.05 kWh/kg, respectively. MWD at 180 W preserved the total phenolic content and the antioxidant activity (ABTS, DPPH) better than HAD at 62 °C. The former technology also preserved the pomace color well, with a low color difference, ΔE, of 7.39 ± 1.1. Therefore, MWD is more promising than HAD to dry apple and ginger pomace, reducing the environmental impact of the drying process due to its lower energy consumption, shorter drying time, and better quality. The dried product could be converted into apple and ginger pomace flour to be used as a novel food ingredient. Full article

This work is focused on developing a computational model to predict the production rate and pressure evolution of natural gas from unconventional reservoirs, particularly shale gas deposits. The model is based on the principle of conservation of mechanical energy and was developed from the transient solution of Bernoulli’s equation. This solution was obtained by computing the pressure evolution in the well resulting from the combined action of extracting the free gas and of gasification from kerogen. The transient behavior of gas production by hydraulic fracturing was calculated by numerically integrating Bernoulli’s equation. The curves representing gas flow evolution were considered as a series of stepwise steady states under a constant gas flow rate, similar to the pressure–time curves. These time steps were connected by instantaneous drops in pressure or gas flow rates. On the other hand, the delayed release of the adsorbed and dissolved gas in the kerogen was accurately calculated by introducing a semi-empirical gas pressure source term into the gas well pressure equation. The effect of this source is to gradually increase the gas pressure in the reservoir, emulating the gas release mechanisms from the organic matter. Model validation was based on production data from the unconventional reservoirs Eagle Ford, U.S.A., and Burgos basin, México. The initial measured gas production rate was used to determine a global friction factor of the gas flowing out from soil cracks and ducts. Additionally, measured production rate data were used to determine the coefficients of the source term function. Pearson correlation coefficients of 0.97 and 0.96 were obtained for Eagle Ford and Burgos basins data, respectively. In contrast, the corresponding coefficients calculated from the traditional Arps’ model were 0.89 and 0.5, respectively. The present pressure source model (PSM) represents a new approach to characterize the process of gas production from unconventional reservoirs, proving to be accurate in forecasting both the gas flow rate and pressure evolution during gas production. The postulated pressure source term was shown to mimic the desorption and diffusion kinetics, which release free gas from the kerogen. Full article

The low selectivity of materials to gases of a similar nature may limit their use as sensors. Knowledge of the adsorption kinetic characteristics of each gas on the surface of the material may enable the ability to identify them. In this work, copper-containing silicon–carbon films were formed using electrochemical deposition on the Al₂O₃ substrate with interdigitated Cr/Cu/Cr electrodes. These films showed good adsorption characteristics with several different gases. The adsorption kinetics of nitrogen dioxide, sulfur dioxide, and carbon monoxide on the film surface were investigated by the change in the resistivity of the material. Pseudo-first-order and pseudo-second-order kinetics, Elovich, Ritchie, and Webber intraparticle diffusion models were applied. It was found that the largest approximation factor and the lowest Root-Mean-Square Error and Mean Bias Error for all three gases were for the Elovich model. The advantages of silicon–carbon copper-containing films for gas sensor applications were shown. An algorithm for gas recognition was proposed based on the dependence of the change in the resistivity of the material under stepwise gas exposure. It was found that parameters such as the values of the extrema of the first and second derivatives of the R vs. t dependence during adsorption and the slope of R vs. t dependence in the Elovich coordinates are responsible for gas identification among several one-nature gases. Full article

In view of the problems of heavy work, long cycle, high cost and low efficiency in the process of indirect determination of coal seam gas content, the basic gas parameters and coal quality indexes of 24 coal samples from 5 coal mines in the Hancheng area of Shanxi Province are measured by the laboratory measurement method. The raw coal gas content–gas desorption index of drilling cuttings (W–K₁) relationship model is characterized by logarithmic function. Using SPSS data analysis software, a stepwise multiple linear regression method is used for statistical analysis. The results show that the factors that have a significant impact on the regression slope C in the W–K₁ relationship model are gas adsorption constant (a), apparent density (ARD), initial velocity of gas diffusion (Δp) and consistent coefficient (f). The factors that have a significant impact on the regression constant D are Δp and atmospheric adsorption (Q). Then, the mathematical model of rapid prediction of coal seam gas content is determined. Compared with the measured values, the average absolute error rate is 12.84%, which meets the prediction requirements and provides a simple and easy method for rapid determination of coal seam gas content in coal mines in the Hancheng area. Full article

Background: To assess the diagnostic performance of diffusion-weighted (DW) magnetic resonance imaging (MRI) in the characterization of mediastinal lymph nodes and compare them with morphological parameters. Methods: A total of 43 untreated patients with mediastinal lymphadenopathy underwent DW and T2 weighted MRI followed by pathological examination in the period from January 2015 to June 2016. The presence of diffusion restriction, apparent diffusion coefficient (ADC) value, short axis dimensions (SAD), and T2 heterogeneous signal intensity of the lymph nodes were evaluated using receiver operating characteristic curve (ROC) and forward step-wise multivariate logistic regression analysis. Results: The ADC of malignant lymphadenopathy was significantly lower (0.873 ± 0.109 × 10⁻³ mm²/s) than that of benign lymphadenopathy (1.663 ± 0.311 × 10⁻³ mm²/s) (p = 0.001). When an ADC of 1.0955 × 10⁻³ mm²/s was used as a threshold value for differentiating malignant from benign nodes, the best results were obtained with a sensitivity of 94%, a specificity of 96%, and an area under the curve (AUC) of 0.996. A model combining the other three MRI criteria showed less sensitivity (88.9%) and specificity (92%) compared to the ADC-only model. Conclusion: The ADC was the strongest independent predictor of malignancy. The addition of other parameters failed to show any increase in sensitivity and specificity. Full article

Forests on oceanic islands, such as the Azores archipelago, enable interesting dendroclimatic research, given their pronounced climatic gradients over short geographical distances, despite the less pronounced seasonality. The Lauraceae play an essential ecological role in Macaronesian natural forests. An example is Laurus azorica (Seub.) Franco, a relevant species given its high frequency and physiognomic dominance in Azorean laurel forests. This study aims to quantify climate–growth relationships in L. azorica using a dendroecological approach. We sampled four stands at São Miguel and two stands at Terceira islands, for a total of 206 trees. Following standard dendrochronological methods and rigorous sample selection procedures, we obtained relatively low rbar values and high temporal autocorrelation. Using a stepwise Random Forest analysis followed by Generalized Linear Models calculation, we found prominent effects of present and previous year temperature, but a low precipitation signal on growth rings, with some model variation between stands. Our results agreed with previous observations for broad-leaved species with diffuse porous wood, contributing to increase the baseline dendroecological knowledge about Azorean forests. Due to the high levels of within- and between-stand variation, and to refine the climatic signal analysis, complementary approaches should be explored in the future. Full article