Search Results (8,707)

The Yangtze River Economic Belt serves as a pilot region for China’s strategy of ecological priority and green development. Through its time–space compression effect, the launch of high-speed rail can significantly shape factor flows and industrial distribution. Therefore, investigating the relationship between high-speed rail opening and inclusive green development carries important practical significance. This study selects 110 cities in the Yangtze River Economic Belt as research samples and constructs an inclusive green development evaluation system covering three dimensions: economic growth, social equity, and ecological sustainability. On this basis, a multi-period difference-in-differences model is employed to identify the effect of high-speed rail opening on inclusive green development, and the mediation and moderation effect models are used to reveal its mechanism. Given the staggered rollout of high-speed rail across cities, heterogeneity-robust estimators and a series of sensitivity analyses are further employed to examine the robustness of the baseline results. The empirical results show the following: (1) Under the preferred two-way fixed-effects specification, the average effect of high-speed rail opening on inclusive green development is statistically insignificant. The dynamic and heterogeneity-robust estimates indicate a delayed effect that becomes significantly positive six to seven years after opening, suggesting an economically meaningful accumulated effect in the longer run. (2) Technological innovation and industrial structure upgrading are examined as transmission channels. The point estimates are consistent with the proposed directions, but the mediation evidence is statistically weak and is reported as suggestive rather than conclusive. (3) Environmental regulation significantly strengthens the relationship between high-speed rail and inclusive green development, whereas the moderating role of financial development is not statistically significant. (4) The estimated effects show no significant regional differentiation under the preferred specification, and a negative association appears among provincial capital cities. This study provides theoretical support and practical reference for optimizing the layout of the high-speed rail network in the Yangtze River Economic Belt and promoting the coordinated development of transportation infrastructure construction and inclusive green development. Full article

Background/Objectives: Accurate preoperative feeding artery mapping remains challenging for hepatocellular carcinoma (HCC) even with modern CT techniques. This study aimed to optimize dynamic multi-phase photon-counting CT (PCCT) hepatic artery protocols and assess dynamic-fusion PCCT against best single-phase PCCT and energy-integrating-detector dual-energy CT (EID-DECT) for feeding artery detection, vascular-map construction and digital subtraction angiography (DSA) path-planning prior to transarterial chemoembolization (TACE) in HCC. Methods: This prospective single-center study contained workflow-optimization among cirrhotic non-HCC patients and clinical validation in TACE-candidate HCC patients. Two radiologists performed blinded independent reading. The composite reference standard consisted of DSA, selective/superselective angiography, available cone-beam computed tomography (CBCT) and embolization records. Primary endpoint: patient-level paired difference in DSA-verified feeding artery coverage. Secondary endpoints included complete-atlas rate, DSA-path-prediction accuracy, missed-vessel count and extrahepatic-supply detection. A pre-specified comparison between dynamic-fusion PCCT and EID-DECT was performed. Results: Dynamic phase 1–6 fusion was the optimal PCCT scheme, while optimal single-phase varied individually. Dynamic-fusion PCCT achieved superior feeding artery coverage (97.78% ± 8.61% vs. 88.33% ± 15.37%, difference 9.44 pp, 95% CI 1.82–17.06, p = 0.016), complete-atlas rate (93.3% vs. 53.3%, p = 0.039) and path-prediction accuracy (96.67% ± 11.18% vs. 80.00% ± 24.49%), with fewer missed culprit vessels. Two extrahepatic feeders were only detected by dynamic-fusion PCCT. Compared with EID-DECT, dynamic-fusion PCCT showed favorable atlas-related metrics without statistically significant difference in feeding artery coverage. Conclusions: Dynamic-fusion PCCT outperforms single-phase PCCT in pre-TACE feeding artery mapping and DSA path-prediction and shows potential benefits over EID-DECT. It delivers comprehensive preoperative vascular data and may reduce intraoperative exploratory procedures. Full article

The Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1) is a Chinese-developed satellite that employs both active and passive remote sensing technologies for forest carbon monitoring. Equipped with a multi-beam lidar system comprising a laser altimeter and a dedicated atmospheric lidar channel, TECIS-1 supports critical applications in forest vegetation classification and the mapping of cloud and aerosol distributions. In this study, we propose a complete workflow for cloud-aerosol detection and parameter retrieval using 532 nm and 1064 nm attenuated backscatter data from TECIS-1 Level 1B products, combined with GMAO MERRA-2 reanalysis data. The color ratio, depolarization ratio and prior statistical information are incorporated to classify the cloud and aerosol layers. Then the cloud-top height (CTH) and aerosol optical depth (AOD) are derived from the geometry of cloud layers and aerosol extinction profiles. The retrieval results from the study area, encompassing northern/central China and the western North Pacific, are compared with MODIS cloud products and MODIS AOD data, Quantitative evaluations indicate that, based on 394 samples across six tracks from latitudes 10° to 75°N in 2023, TECIS-1’s CTH measurements were consistent with those from MODIS, with a root mean square difference (RMSD) of 2.13 km for single-layer opaque clouds and 2.42 km for single-layer optically thin clouds. The AOD comparison area is located in a region of northern China prone to dust storms. When compared with MODIS products, the overall root mean square difference (RMSD) of the retrieved AOD was 0.278. These results indicate that TECIS-1 can be used for cloud and aerosol detection, and provide a methodological reference for data processing and application of Chinese spaceborne atmospheric lidar systems. Full article

Unmanned aerial vehicles (UAVs) offer flexible, three-dimensional access for air pollution monitoring, but rotor-induced aerodynamic disturbance can bias onboard sensor readings, an effect not yet well characterized for multi-rotor platforms. This paper presents a physics-informed framework unifying rotor wake characterization, turbulence zone classification, pollutant sensitivity analysis, sensor placement strategy, and bias correction into one design tool. Grounded in actuator disk momentum theory and literature-constrained relationships, it predicts or bounds measurement bias across nine pollutants and multiple rotor configurations, quantitative for seven sensor classes and provisional for O₃ and VOCs. It also introduces a four-zone contamination classification and a two-stage bias correction for motion-induced and environmental factors. Hover and constant-speed co-location flight tests on a 21-inch quadrotor, comparing a UAV-mounted sensor against a fixed reference under Zone 4 conditions, support these predictions: observed bias (3–10% in hover; 6–15% at 5 m/s, across NO₂, CO, O₃, PM₁₀, and PM_2.5) was directionally consistent with the predictions, though correlation dropped as low as r = 0.44 in forward flight, below typical validation thresholds. The tests thus support the framework’s qualitative trends more than quantitative agreement, with wind-tunnel and multi-speed validation as natural next steps. The framework offers a reproducible foundation for UAV sensor integration design, uncertainty estimation, and campaign planning. Full article

Reliable randomness assessment is essential for evaluating random number generators used in cryptographic systems. This study presents the Bidirectional Convolutional Multi-head Attention Network (BCM-Net), which combines convolutional layers, a bidirectional gated recurrent unit, and multi-head attention for empirical discrimination between candidate and reference sequences. The evaluation uses Random.org reference data, linear congruential generators (LCGs) with moduli from $2^{26}$ to $2^{34}$, and an amplified spontaneous emission-based quantum random number generator under three post-processing settings. BCM-Net flags $X_{LCG-30}$ and $X_{LCG-32}$, although they pass the reported NIST SP 800-22 tests. In the baseline comparison on $X_{LCG-32}$, its absolute difference between mean output scores is 52.38 percentage points (pp), compared with 32.88 pp for LSTM, 9.66 pp for CNN, and 0.02 pp for FNN. For the QRNG data, the 11-LSB output is flagged, while the 8-LSB and Toeplitz outputs are not. Under the generator configurations, finite observation lengths, preprocessing, and evaluation protocol examined here, these findings support empirical sequence discrimination as a complementary screening method. They do not establish detection performance for untested fractions of a generator period or certify randomness or cryptographic security. Full article

Classical liquid chromatography–tandem mass spectrometry (LC-MS/MS) remains a cornerstone of proteomics, but deep profiling of complex biofluids is constrained by the extreme dynamic range of protein abundance and low sample throughput. Emerging high-multiplex platforms address these limitations through distinct high-throughput configurations, including antibody-based proximity assays, modified aptamers, nanoparticle-assisted mass spectrometry, and digital single-molecule immunoassays. Despite their discovery potential, these technologies operate within alternative bottom-up or affinity-based frameworks, meaning that they are not interchangeable. Quantitative measurements of nominally identical proteins frequently show poor to moderate cross-platform agreement, reflecting fundamental differences in chemistry, epitope accessibility, matrix susceptibility, genetic variants, and peptide-level protein inference. Crucially, because these platforms generally yield aggregate quantitative readouts, they typically average across the proteoform spectrum, limiting their ability to differentiate whether an abundance shift is driven by specific splice variants, localized post-translational modifications, or a uniform increase in the protein’s overall concentration. This review provides a balanced, decision-oriented comparison of the Olink Proximity Extension Assay, SomaLogic SomaScan, Seer Proteograph and Quanterix Simoa, delineating their operational utility alongside their respective technical limitations. We propose a practical framework to guide platform selection based on the specific biological question, cohort size, and required structural resolution, while evaluating strategies to enhance cross-platform reproducibility via standardized reference matrices, structure-informed interpretation, and multi-tiered validation workflows. Full article

Sarcopenia is an independent predictor of adverse outcomes in hospitalized patients, associated with increased mortality, prolonged length of stay, and functional decline, yet it remains underdiagnosed owing to inconsistent diagnostic criteria and the practical challenges of assessing acutely ill patients. This narrative review summarizes current evidence on sarcopenia in hospitalized patients, with emphasis on diagnostic approaches, nutritional requirements, and optimal nutritional support strategies. Sarcopenia is particularly prevalent among critically ill patients and is driven by the convergence of systemic inflammation, immobility, and anabolic resistance. Diagnosis remains challenging: no ICU-specific framework exists, and the recent Global Leadership Initiative of Sarcopenia consensus established a unified, setting-independent conceptual definition as a foundation for future operational criteria, with ICU-specific frameworks yet to be developed. Bedside tools, including handgrip strength, bioelectrical impedance analysis, and ultrasound, retain strong prognostic value despite imperfect agreement with reference imaging. Nutritional intervention is a key component of management; however, large-scale trials have demonstrated that higher energy or protein delivery does not universally improve outcomes and may be harmful in selected critically ill patients. A gradual increase in energy and protein delivery according to the phase of illness, with protein targets of ≥1.2–1.5 g/kg/day, is recommended; leucine-enriched supplementation may help counteract anabolic resistance, although direct evidence in non-ICU hospitalized patients remains limited. Early mobilization is an essential complement, as nutrient provision without a mechanical stimulus cannot reverse immobility-induced muscle loss. A practical clinical algorithm integrating screening, nutritional target-setting, route selection, and reassessment is proposed to guide decision-making in this population. Future research should focus on improving the accuracy and feasibility of muscle assessment tools in critically ill patients to enable the timely diagnosis of sarcopenia and nutritional intervention, alongside the development of validated multi-marker panels and adequately powered trials examining the combined effect of nutrition and exercise in this population. Full article

Aero-engine performance prediction provides an essential basis for condition assessment and health management during ground testing. Conventional component-level models require costly calibration, whereas purely data-driven models are prone to overfitting under data-scarce conditions and generally offer limited physical interpretability. To address these limitations, this study proposes a turbofan engine performance prediction method that integrates a component-topology prior with season-aware multi-source transfer learning. Based on the main gas-path connections and the mechanical coupling between the high- and low-pressure spools, a component-topology-informed fusion prediction model (Engine-BLT) is developed. It characterizes the dynamic coupling behavior of the entire engine through component-specific feature extraction, feature propagation along the gas-flow direction, and cross-component feature fusion. In addition, multiple source domains are constructed according to seasonal information, including ambient temperature, ambient pressure, and relative humidity. A similarity-based weighting strategy is then employed to improve model adaptability under small-sample transfer-learning conditions. Finally, a component-level GasTurb model is employed to provide an independent aerothermodynamic reference under representative steady-state operating conditions. The results show that Engine-BLT achieves R² values of 0.9650, 0.9991, and 0.9962 for corrected low-pressure-turbine outlet total temperature (T_5,corr), corrected net thrust (F_N,corr), and corrected high-pressure-compressor outlet total pressure (P_t3,corr), respectively, yielding the best overall prediction accuracy among the evaluated models. The season-aware weighted multi-source transfer learning (SWMT) strategy also provides the best overall performance among the investigated transfer-learning schemes. Under ground idle, maximum continuous thrust, and maximum takeoff thrust conditions, its mean absolute relative errors for P_t3,corr, T_5,corr, and F_N,corr are 1.17%, 0.99%, and 0.33%, respectively, which are comparable in magnitude to the steady-state aerothermodynamic reference obtained from the GasTurb model. The proposed method improves prediction accuracy under small-sample acceptance-test conditions while maintaining target-domain adaptability and physical interpretability. Full article

Mountain–hill–plain transition zones combine terrain sensitivity, agricultural land use, and construction pressure and therefore require spatially differentiated management. This study develops a pressure-oriented framework that distinguishes composite ecological vulnerability from soil-erosion pressure and translates their multi-period co-occurrence into management demand. Using multi-source raster data for 2000, 2005, 2010, 2015, and 2020 on a common 30 m reference grid, we constructed an ecological vulnerability index (EVI) from 14 ecological, environmental, and human-activity indicators using a sensitivity–resilience–pressure framework, combined analytic hierarchy process (AHP)–entropy weighting, and unified natural breaks. Soil erosion intensity was estimated with the Revised Universal Soil Loss Equation (RUSLE) using digital elevation model (DEM)-derived flow accumulation, a capped effective slope length, and a fractional vegetation cover (FVC)-based cover-management factor. The EVI and RUSLE dimensions were integrated through a two-dimensional pressure matrix, a Coupled Pressure Index (CPI), and an Integrated Management Demand Index (IMDI). High vulnerability was concentrated in northern, urban-fringe, and selected agricultural areas, whereas moderate-or-above erosion was concentrated on slopes, in gullies, and on hilly farmland; areas subject to both pressures were spatially selective. The largest EVI weights were assigned to mean annual precipitation (0.3841), mean annual temperature (0.1727), ecosystem type (0.0820), biological abundance (0.0810), and built-up land ratio (0.0746). Non-vulnerable and slightly vulnerable areas increased from 40.62% to 64.27%, whereas highly and extremely vulnerable areas decreased from 38.98% to 23.29%. Moderate-or-above soil erosion accounted for 13.56% in 2020. The five-zone comprehensive management classification comprised stable conservation (40.19%), soil and water conservation priority (13.73%), ecological vulnerability regulation (27.94%), integrated management priority (1.48%), and transition management (16.66%). By linking ecological diagnosis, erosion-pressure identification, persistence analysis, and county-level zoning, the framework provides an interpretable spatial basis for prioritizing ecological conservation, soil and water conservation, and land-use management in mountain–hill–plain transition regions. Full article

Conventional logging and elemental logging-based lithology identification for volcanic buried-hill reservoirs is hampered by overlapping logging responses, low vertical resolution of elemental measurements, and inter-sample class imbalance, which lead to unsatisfactory classification accuracy. Existing cross-plot empirical methods cannot reliably distinguish lithologies with similar geophysical signatures (e.g., volcanic breccia versus andesite), especially for complex Mesozoic volcanic successions in offshore Bohai Bay Basin. To fill this technical gap, this work proposes an improved random-forest workflow integrating multi-source log-data fusion, depth-aligned linear interpolation, mutual-information feature screening, and SMOTE oversampling. The core methodological innovations lie in: (1) depth matching between conventional continuous logs and sparsely sampled elemental logging by linear interpolation to construct complete multi-feature datasets; (2) eliminating redundant input variables via mutual-information-based feature selection; and (3) mitigating lithology-sample imbalance with SMOTE synthetic-sample generation prior to random-forest training, rather than directly applying off-the-shelf random-forest classifiers. Six dominant lithologies are recognized within the Mesozoic buried-hill of Block KL16-1: basalt, andesite, rhyolite, volcanic breccia, tuff, and tuffaceous conglomerate. The proposed improved random-forest model yields an overall lithology-identification accuracy of 87% and average recall of 85.9%, substantially outperforming traditional cross-plot approaches. Confusion-matrix error analysis demonstrates that the workflow greatly reduces misclassification between easily confused lithological pairs (volcanic breccia andesite, tuff andesite). This study not only delivers a practical tool for fine reservoir evaluation and reservoir-facies prediction in Bohai Mesozoic buried-hill plays but also provides a reproducible reference for machine-learning-driven lithology interpretation in analogous offshore volcanic-reservoir settings. Full article

Fully prefabricated bridge construction technology has gradually replaced traditional cast-in-place methods, becoming a core approach for efficient, environmental, and intelligent construction of bridges in cities. However, due to city-embedded restrictions, i.e., large width for more lanes, prefabricated cap beams in municipal bridges often demand segmental prefabrication and multi-point lifting, which hinders the overall construction efficiency. Based on a real project, this paper proposes a vertical rotation construction method for the bridge substructure, achieving integrated vertical rotation and positioning of prefabricated cap beams and piers by designing rotating steel shoes at the bottom of piers. This approach avoids segmental lifting of extraordinarily heavy cap beams while eliminating the need for prestressing and grouting operations high above the ground. Subsequently, detailed finite element models for critical rotational components, such as steel shoe, hinge pin and lug plates, are established to verify the stress distribution under various rotation conditions. The results demonstrate that the proposed layout, consisting of four 60 mm diameters 40Cr steel hinge pins with Q345 steel shoe and lug plates, effectively controls the representative stress within yield under all conditions, leaving the 10° scenario most unfavorable. Compared with the 10° rotation scenarios, increasing the initial rotation angle to 20° reduces the most critical stress inside the steel shoes by up to 39%, while further increasing to 30° only produces an additional 1% stress reduction. The design parameters of auxiliary equipment for the vertical rotation process can provide valuable references for future engineering practices. Full article

Reliable wireless connectivity in underground public transportation can vary with train position because rolling stock modifies propagation paths and local interference conditions. This study proposes a transport-state-aware radio-planning framework for a three-node distributed antenna system (DAS) in a reference underground metro station. A three-dimensional 3.5 GHz reference station model, three operating states (empty station, train at platform, and train entering), a discrete set of 69 candidate antenna positions, and multi-objective placement/power optimization are combined to evaluate received power, SNIR, joint service coverage, and serving-area balance. The optimized configuration increased worst-state joint service coverage from 66.12% to 68.26%, improved worst-state P5 received power from −71.92 to −71.49 dBm, and reduced aggregate transmit power from 753.57 to 682.49 mW. The mean serving-area coefficient of variation decreased from 0.202 to 0.114. Threshold analysis showed that the optimized layout was advantageous at moderate and high SNIR requirements but not at a relaxed 5 dB criterion. The results support treating underground DAS deployment as a transport-state-aware infrastructure-planning problem rather than a static geometric-spacing problem. Full article

Background/Objectives: Multidimensional lifestyle interventions that combine healthy diet, physical activity, and psychosocial support are central to the effective self-management of type 1 and type 2 diabetes. Although digital health tools can improve lifestyle monitoring, existing AI-based dietary assessment methods often struggle to estimate food quantity accurately because the food size and scale are difficult to determine from images. This can lead to inconsistent estimates of food mass and energy content. This paper aimed to develop and technically evaluate PC²FoodNet, an AI-based framework for food recognition and physics-constrained physical quantity estimation, and to integrate dietary assessment and exercise support within the proposed AI-assisted Diabetes Care (AIDCare) mHealth platform for multidisciplinary lifestyle support. Methods: The AIDCare platform incorporates AI-assisted nutrition and professionally guided exercise modules to support personalized lifestyle management for individuals with diabetes. For dietary assessment, PC²FoodNet uses an EfficientNetV2-S backbone with multi-task regression and food-density priors to jointly model food volume, mass, and energy. A nutrition professional-in-the-loop approach supports the development and review of individualized diet plans and the assessment of patients’ daily key nutrient intake according to individual nutritional requirements. The exercise module includes professionally designed exercises using Unreal Engine’s MetaHuman plugin. The model was evaluated using four-fold stratified cross-validation on a dataset of 22,070 images covering 40 Turkish food categories. For physical quantity modeling, volume, mass, and energy targets were derived from standardized category-level references anchored to a 100 g reference portion rather than from image-specific ground-truth measurements. Results: PC²FoodNet achieved a mean Top-1 classification accuracy of 94.39% ± 0.47% and Top-5 accuracy of 98.90% ± 0.18% across the four folds. The corresponding Macro-$F_1$ and Weighted-$F_1$ scores were 93.14% ± 0.59% and 94.16% ± 0.36%, respectively. For the standardized category-level reference targets, the physics-constrained regression framework achieved mean MAE and RMSE values of 2.14 mL and 3.01 mL for reference volume, 21.65 g and 30.17 g for reference mass, and 41.20 kcal and 57.55 kcal for reference energy across all 22,070 pooled out-of-fold predictions. The confidence-based referral mechanism identified uncertain cases for clinical review, resulting in a clinical referral rate of 23.81%. Conclusions: AIDCare combines physics-constrained AI-based dietary assessment with personalized physical activity and continuous psychosocial support. A nutrition professional remains involved when AI predictions are uncertain, reducing the risks associated with fully automated lifestyle recommendations. The proposed approach provides a practical foundation for safer, more personalized, and evidence-based diabetes lifestyle management. Full article

The production of sustainable gaseous fuels via anaerobic digestion (AD) is a promising pathway to decarbonize fossil-reliant heat and electricity systems. Despite their potential, the development and optimization of biogas and renewable natural gas (RNG) production systems remain constrained by the time-consuming nature of experiments and the limitations of extrapolating pilot-scale results. This review examines the use of computer-aided simulations as tools for the analysis, replication, and assessment of AD systems. First, a background discussion of the underlying biochemical processes and the major substrate categories is presented to establish key considerations for process modelling. Subsequently, articles published over the past five years are systematically surveyed to explore the recent application of solver-oriented (including single-equation, multi-step, and machine learning models) and flowsheet-oriented models (principally in Aspen Plus v9.0, v10.0, v12.1, and v14.0; and in SuperPro Designer v8.5 and v9.0). This review demonstrated substantial diversity in model structures, input requirements, and validation strategies. From an investigative perspective, the predictive performance of models is found to strongly depend on the quantity, quality, and representativeness of input and reference data. From an industrial standpoint, the observed simulation-derived techno-economic benchmarks illustrate the applicability of computer-assisted approaches for the preliminary comparison of project scenarios within delimited case-specific frameworks. A key knowledge gap is identified in the limited availability of studies comparing models under consistent process conditions, input datasets, and validation criteria. To enable proper comparison of model strengths and limitations, future research should prioritize the development of standardized cross-model benchmarking and explore the integration of machine learning and life cycle assessments. Full article

To enhance fine-grained recognition of adjacent rice ripening phases, this study employs a rice ripening-stage identification method based on multi-source UAV remote sensing data fusion and a modified MobileNetV3-Small network. RGB and multispectral images were acquired using a DJI Mavic 3 Multispectral UAV at Houbai Improved Seed Farm, Jurong City, Jiangsu Province, China, and were combined with vegetation indices to construct a fused 15-channel input. The original three-channel input layer of MobileNetV3-Small was modified to accommodate RGB, multispectral, and vegetation-index features. Based on the agronomic characteristics of late reproductive rice growth, the ripening process was divided into five phenological stages: filling, milky ripening, early waxy ripening, late waxy ripening, and full ripening. In the field-scale patch assessment using the HB31 dataset, the model correctly identified 4147 of 4185 valid patches, achieving an overall accuracy of 99.09%, with macro-averaged precision, recall, and F1-scores of 99.06%, 99.12%, and 99.08%, respectively. To reduce the influence of within-field spatial correlation, an independent field-holdout test was further conducted using the HB32 field, which was excluded from training, hyperparameter tuning, and model selection. Among 2046 valid HB32 patches, 1897 were correctly classified, yielding an overall accuracy of 92.72% and a macro-averaged F11-score of 92.60%. These results indicate that fusing RGB, multispectral, and vegetation-index features can characterize canopy color, spectral responses, and vegetation-index variations during rice maturation, providing reference information for rice ripening-stage identification, field-scale ripening mapping, and harvest scheduling. Full article