Search Results (7,142)

Using 1135 customer–supplier–year observations involving Chinese A-share listed firms and their major suppliers from 2011 to 2023, together with prefecture-level government work reports and patent-assignment records, we examine whether environmental policy pressure faced by downstream customers shapes upstream suppliers’ external green technology acquisition. External green technology acquisition is measured as the log-transformed annual number of green patents assigned to each supplier. We find that suppliers acquire more external green patents when their customers face stronger local environmental policy pressure, with a one-standard-deviation increase in policy intensity associated with approximately 5.7% higher external green patent acquisition. The result is robust to placebo tests, alternative measures, and PPML estimation, while instrumental-variable estimates point in the same direction. Further analyses document stronger customer green-transition urgency and supplier-perceived supply-chain uncertainty under greater downstream policy intensity, and they show that the relationship is stronger among suppliers with weaker bargaining power or lower R&D intensity. Environment-related policy intensity is more strongly associated with end-of-pipe technology acquisition, whereas energy-transition and market-incentive policy intensity are more strongly associated with source-control technology acquisition. Overall, the findings indicate that environmental policy intensity in downstream customers’ cities is associated with upstream suppliers’ external green technology acquisition within observed customer–supplier relationships. Full article

Power boiler tubes are exposed to severe operating conditions that lead to wall thinning and material degradation. Reliable assessment of tube wall thickness is therefore essential for ensuring safe and efficient boiler operation. This exploratory laboratory study investigates the applicability of active thermography combined with analytical and machine learning (ML) approaches for non-contact wall thickness estimation in power boiler tubes. Experimental investigations were performed on a single boiler tube specimen with artificially introduced wall-thickness reductions. Thermal responses were recorded using an infrared camera under both heating and cooling excitation conditions. Based on the acquired thermographic data, analytical models and machine learning algorithms were developed to estimate tube wall thickness. The machine learning approach was implemented using Random Forest and Support Vector Regression models and compared with conventional analytical modeling techniques. For separately analyzed and relatively homogeneous measurement series, the machine learning models produced lower descriptive errors than the analytical models, with the estimated three-RMSE error envelope decreasing from 0.51 mm to 0.17 mm. However, when heating and cooling datasets were aggregated, the analytical models achieved lower root mean square error values and demonstrated greater stability than the machine learning methods. These findings indicate that model performance strongly depends on the size, characteristics, and homogeneity of the available training data. Owing to the limited number of independent measurement series, the reported results should be interpreted as a small-sample feasibility assessment rather than as evidence of the general superiority of machine learning modeling. The results support the potential of active thermography for non-contact assessment of boiler tube wall thickness under controlled laboratory conditions. Further validation using additional specimens, grouped cross-validation, and physics-based synthetic data is required before the methodology can be considered for industrial implementation. Full article

Falls are a major cause of severe injury and mortality among older adults, requiring rapid state recognition and alerts in healthcare and caregiving environments. However, many existing fall-recognition approaches primarily focus on post-fall detection or assume access to cloud/GPU computing, leaving limited evidence for short-term pre-fall recognition on low-end CPU-based edge devices. This study proposes a lightweight CNN–TCN–Attention framework for recognizing fall and pre-fall states on resource-constrained edge devices. Using MediaPipe, three-dimensional coordinates and visibility scores of 33 human body landmarks are extracted from input videos. Raw RGB frames are not directly used by the classifier, thereby reducing the processing of visually identifiable information. A total of 2300 fall-related videos from the AI-Hub dataset were reorganized into three classes: normal, pre-fall, and fall. The pre-fall onset was defined as the point at which the downward vertical velocity of the nose landmark exceeded a dataset-specific threshold. The model uses a CNN to extract frame-level skeletal patterns, a temporal convolutional network (TCN) to learn temporal changes in posture, and an attention module to aggregate temporal features. It was deployed on a Raspberry Pi Zero 2 W using ONNX Runtime. The proposed model achieved 94.71% accuracy and a macro F1-score of 93.63%, with an average state-decision latency of 325.88 ms/decision. It improved accuracy by 2.83 percentage points over the lightweight CNN–LSTM–Attention baseline while maintaining comparable latency and achieved approximately 1.75× faster processing than the MobileNetV3–TCN–Attention model. Full article

To address the limited pumping-mode flexibility of fixed-speed pumped-storage units, this paper presents a simulation case study of a one-pipe–two-unit parallel ternary system developed in OpenModelica and coupled with Python 3.7. The study examines pure charging and generation, hydraulic short-circuit (HSC) charging, and the PID control-activation transient at nominal speed. A 50 MW benchmark reproduces published pump and turbine shaft powers to within approximately 0.5%, while the system-efficiency difference is 0.14 percentage points, supporting implementation consistency; no plant-measurement validation is claimed. Dual-pump operation reduces the charging time by approximately 44% relative to single-pump operation but lowers efficiency, whereas dual-turbine operation is slightly more efficient because of improved per-unit flow matching. Across the full-cycle HSC sweep, average efficiency increases from 38.6% at 40 MW to 74.1% at 90 MW as internal recirculation decreases. In the PID sensitivity screen, the proportional gain k has the largest effect on the response, a small integral time T_i amplifies sensitivity to k and can increase overshoot or hydraulic loading, and the derivative coefficient w_d has only a minor effect within the tested range. The numerical bounds are specific to the selected characteristic maps, reservoir, and waterway; the results are intended as retrofit-screening guidance rather than universal design limits. Full article

The mechanical behavior of S135 and V150 (Mo-V-Nb microalloyed) drill pipe steels was systematically investigated at room temperature (RT) and elevated temperatures (100–300 °C), alongside the microstructural evolution after long-term thermal exposure at 310 °C (200–500 h). V150 steel exhibits a superior RT yield strength (1099 vs. 1012 MPa) relative to S135, attributed to grain refinement and precipitation strengthening from nanoscale MC precipitates. However, at 200–300 °C, S135 steel displays strength recovery due to dynamic strain aging (DSA) facilitated by the formation of Cottrell atmospheres. Conversely, in V150 steel, V and Nb pin free interstitial atoms, suppressing Cottrell atmosphere formation and DSA. Consequently, V150 cannot gain DSA-induced strengthening, resulting in a steeper yield strength decline (a 17.3% drop at 300 °C versus 11.5% for S135). Long-term thermal exposure further reveals divergent microstructural evolution: S135 steel achieves synchronous improvements in strength and ductility via the transformation of coarse M₃C into stable alloy carbides and the precipitation of nanoscale Mo-enriched carbides. In contrast, V150 steel undergoes Ostwald ripening and coherency loss of high-volume-fraction nano-MC precipitates, weakening dislocation pinning and accelerating dislocation annihilation, ultimately leading to the simultaneous degradation of strength and ductility. This study elucidates that while Mo-V-Nb microalloying enhances RT strength, it compromises high-temperature mechanical stability. Full article

External safety-related piping in nuclear power plants may be exposed to tornado missile impact, resulting in local indentation, global bending deformation, and transient support responses, which may threaten the structural integrity and safety functions of nuclear power plants. Existing pipe impact studies have mainly focused on failure behavior under idealized or prescribed boundary conditions, while the application of viscous damper supports remains limited. In this study, a three-dimensional nonlinear finite element model of a pipe-support system is established in Abaqus. The model is used to investigate the effects of support type, damper parameters, support stiffness ratio, pipe span, and wall thickness on impact response and energy dissipation. The results show that support flexibility accounts for the major reduction in global bending relative to rigid supports, while viscous dampers further reduce support displacement and velocity and provide additional energy dissipation. In the benchmark case, the viscous damper support reduces peak global bending by 78.9% compared with the rigid support, and reduces peak support displacement and velocity by 55.6% and 54.5%, respectively, compared with the elastic support. Its influence on local indentation remains limited. These findings indicate that viscous damper supports mitigate impact responses primarily by controlling support motion and altering the energy dissipation behavior of the pipe–support systems, rather than by directly suppressing local indentation. This study provides a reference for support layout and parameter selection of external safety-related piping subjected to tornado missile impact. Full article

This study demonstrates that supercritical carbon dioxide phase-change fracturing technology can reduce acute pressure-related injury potential compared to conventional explosives in underwater reef clearance operations along the Yangtze River. Employing a stepwise “pipe test, numerical simulation and engineering application” framework, a novel rock–water–fish coupled HJC–Gruneisen elastoplastic model was established to simulate cross-medium shock wave attenuation processes. The supercritical CO₂ shock wave exhibits characteristics of “low peak overpressure (13% of equivalent explosives) and long duration (6–7 times longer than conventional explosives),” attenuating in water with a power-law index α = 0.717. A dual-parameter “resistance line (intact rock buffer between the fracturing tube and the rock–water interface)–water depth” correction model indicates that the resistance line reduces peak overpressure by 18.6%, while each 10 m increase in water depth enhances attenuation by 60.6%. The preliminary engineering critical safety threshold for 30 cm silver carp (indicated by swim bladder rupture) is 0.20 MPa. Based on the representative engineering scale of the Chaofu Waterway Regulation Project, characterized by water depths of approximately 6–16 m and a resistance-line-controlled buffer condition, a theoretical safety distance model R_safe was also derived. Under these engineering constraints, the application results indicate that the lethal zone was confined to 7.5–9.9 m, enabling precise lethal-injury-safe zoning. This work establishes a fish injury threshold and safety assessment system for supercritical CO₂ subaquatic fracturing, providing direct green guidelines for Yangtze River navigation projects. Full article

This study evaluated the potential use of waste mineral wool recovered from heating pipe insulation as a functional additive in cement concrete. The research aimed to determine the effect of this fibrous recycled material on the mechanical, physical, durability, thermal, and microstructural properties of concrete. One reference mix and three modified mixes were prepared, incorporating waste mineral wool at 12%, 16%, and 20% by cement mass. All mixes were prepared using CEM I 32.5R cement, 0/2 mm basalt aggregate, 2/8 mm granite aggregate, and a superplasticizer. After 28 days of sample curing, compressive strength, splitting tensile strength, density, water absorption, frost resistance, and thermal parameters were determined. Microstructural observations were also performed on concrete fracture surfaces. The results showed that the addition of mineral wool reduced the compressive strength from 84.9 MPa for the reference concrete to 63.8–53.3 MPa for the modified concretes. At the same time, moderate dosing improved the splitting tensile strength, reaching a maximum of 3.93 MPa with a 16% addition. The most favorable thermal effect was achieved with a 12% addition, for which the thermal conductivity coefficient decreased from 1.3461 to 1.1988 W/(m·K). The results indicate that waste mineral wool can be used in concretes with limited structural function; however, its dosage requires optimization due to increased water absorption and decreased compressive strength. Full article

Complex water conveyance systems often involve mixed flow conditions comprising pressurized pipe networks and free-surface open channels. Every single model has its limitations in long-distance and complex water transfer projects; coupling different models can leverage their respective advantages. For this reason, this study proposes an integrated modeling framework that couples the Storm Water Management Model (SWMM) and Environmental Protection Agency Network Evaluation Tool (EPANET) models to simulate water quality in pressurized–unpressurized coupled systems. Taking the typical Yin Chao Ji Liao water diversion project as a case study, total nitrogen (TN) and total dissolved solids (TDS) were selected as representative pollutants. A total of 32 simulation scenarios were systematically evaluated across four concentration levels at four distinct monitoring points. During this synthesis, key emergency response indicators, specifically T0 (optimal gate-opening time) and T2 (drainage operation duration), were quantified. The results indicate that pollutant dispersion in the unpressurised tunnel section is primarily governed by pollutant loading, whereas in the pressurised pipeline section, it is controlled by flow velocity and pressure differentials. High-concentration pollution in the tunnel section allows for a relatively longer emergency response time (T0 = 453 min). In contrast, due to rapid transport in the pipeline section, emergency operations must be completed swiftly (≤30 min). This coupled approach effectively addresses the challenge of water quality simulation in cross-regime conveyance systems. Future research will focus on integrating real-time sensor monitoring data with this coupled model to further optimize automated early warning protocols for long-distance diversion projects. Full article

Reproducible high-performance computing pipelines for transcriptomic analysis are essential for population-scale precision oncology, yet most published workflows address only a single sequencing modality or lack benchmarking on underrepresented populations. We present KazRNA-Pipe, an open-source Nextflow v26.04.6 workflow processing both bulk and single-cell RNA sequencing (RNA-seq) within a unified Singularity-containerized environment with graphics processing unit (GPU) acceleration through the RAPIDS ecosystem. The pipeline was validated on 22 esophageal squamous cell carcinoma (ESCC) bulk RNA-seq samples from a Kazakhstan cohort (PRJNA608223) and on the largest public ESCC single-cell atlas (GSE160269). Quantification concordance across STAR, HISAT2, and Salmon reached Spearman ρ ≥ 0.90. BayesPrism deconvolution resolved cell-type proportions across all 22 samples. Full article

The results of the experiments on the transport process of fluid flow through a pipeline under temperature gradient conditions between the internal and external environments, and on continuous gas generation at the contact boundary of the transported media, are presented in this paper. The test results showed that under non-isothermal flow conditions, a slippage effect will impact flow velocity and pressure, as well as the temperature distributions in variable cross-section pipes. Laboratory experiments were conducted in order to study the effects of the gas nucleus at the pipe walls on the hydrodynamic characteristics of the fluid flow. It is shown that the throughput capacity of the pipe is affected by the temperature difference between the oil and the pipe walls. The test results also demonstrated that at certain temperature gradients on the border layer, the pipe’s capacity reaches its maximum value. Quantitatively, the hydroconductivity of Q/ΔP increased from about 1.45 × 10⁻⁵ m³/(s·MPa) under relatively isothermal conditions to a maximum value of approximately 2.04 × 10⁻⁵ m³/(s·MPa) with a temperature difference in the oil–pipe-wall zone of about 3–5 K, which corresponds to an increase of about 41%. With a further increase in the temperature difference, the hydroconductivity decreased to about 1.64 × 10⁻⁵ m³/(s·MPa) at 10 K and then stabilized in the range of (1.60–1.64) × 10⁻⁵ m³/(s·MPa). This non-monotonic behavior is explained by the temperature-induced release of gas and the formation of a gas-saturated wall zone, which initially reduces the effective resistance of the wall and creates an apparent sliding effect. At high temperature differences, gas accumulation, thermal insulation of the wall area and two-phase flow disturbances limit this effect, which leads to the decrease and subsequent stabilization of the pipe capacity. Full article

Remote-controlled UAS-based soil sampling offers great potential for scientific research and practical applications in various fields, including agriculture, environmental monitoring, hazardous waste, radiation measurements, chemical plants, snow sampling and glaciology. The added value of automated sampling using a UAS (unmanned aircraft system) compared to manual sampling lies primarily in improved accessibility to hazardous or remote locations, increased operational safety, and the potential to improve efficiency in applications requiring repeated or difficult-to-access sampling. This article introduces two different 3D-printed constructions (grab arm and screw pipe) for UAS-based, remote-controlled sample collection of different soil types. The grab arm construction is based on an adapted version of an open-source CAD from GrabCAD. The screw pipe construction is a new design. During an expedition to Greenland in August 2025, the two constructions were manually evaluated during several days of field work near the Danish and Austrian research stations at the Sermilik Fjord to assess their mechanical sampling performance on challenging Arctic surface materials, including dry and wet sand, gravel, glacial sediment, snow, and glacier surfaces. Because flight testing was not possible during the expedition due to unavailable UAS batteries, these experiments were limited to manual ground evaluation of the constructions. Independent flight tests were subsequently conducted in Austria using a DJI Matrice 300 to evaluate the integration of both the grab arm and the screw pipe with the UAS platform and their operational handling during flight. In Greenland the performance of manual sampling was documented precisely. Further the coordinates of each sampling point were recorded by using GPS, sample images were taken on site, and the collected material was weighed. The results show that UAS in combination with 3D printed constructions is a flexible and location-independent solution for obtaining soil samples of varying composition. In addition to the design, materials, and electronics of the systems, the article also describes the connection to the UAS and the field work results. Full article

The oilfield gathering and transportation system is an important component of oilfield energy use and therefore provides practical opportunities for supporting the dual-carbon goals through operating-parameter optimization. This study combined field cooling trials on high-water-cut well pipelines, thermal-hydraulic calculations of the downstream gathering network, regression-based surrogate models of the dehydration-station equipment, and coordinated system-level energy accounting. A constraint-based direct-search procedure initialized from the actual field operating condition was used to identify the best feasible operating point within the examined ranges. The search was terminated when a complete update cycle produced no further reduction in energy consumption while all engineering constraints remained satisfied. The field trials showed that the investigated well pipelines could be operated below the corresponding crude-oil pour points under the tested high-water-cut conditions. For the transfer-station-to-central-station stage, the original three-pipe heat-tracing process was adjusted to electric heating and hot-water blending according to the pipeline conditions. Within the central processing station, the equipment operating temperatures were coordinated with the upstream pipeline scheme. For the investigated operating condition, the resulting best feasible scheme produced a deterministic 3.18% reduction in total standard-coal-equivalent energy consumption. The results demonstrate the engineering value of coordinating low-temperature operating boundaries, pipeline heating processes, and station operating parameters in an existing high-water-cut gathering system. Full article

Background/Objectives: Motor abnormalities are frequently observed in schizophrenia, but accessible methods for objective gait quantification remain limited. This exploratory controlled-setting study examined whether markerless smartphone-based video analysis combined with interpretable machine learning could quantify gait-related motor phenotypes in individuals with schizophrenia. Methods: Gait videos were collected from 100 individuals with schizophrenia and 35 healthy controls using a single-site, single-device, standardized recording setup. MediaPipe Pose was used to extract skeletal landmarks and derive 12 image-plane spatiotemporal and estimated two-dimensional knee-kinematic gait features. After temporal segmentation and quality control, 404 usable gait segments derived from 135 participants were analyzed as repeated segment-level observations. Decision Tree and Support Vector Machine models were applied for exploratory segment-level group-separation analysis using segment-wise 15-fold cross-validation after the full post-quality-control dataset had been balanced before fold allocation. Results: Several extracted gait features differed between groups, particularly image-plane ankle displacement, mean step displacement, displacement velocity, step characteristics, and knee-joint motion. In the Decision Tree model, image-plane ankle displacement served as the primary root node, indicating its central role in internal segment-level group separation. However, the healthy control group was substantially younger and not age-matched. In addition, the segment-level statistical comparisons did not account for within-participant clustering. Accordingly, the reported p values and confidence intervals may overstate statistical precision. Separately, segment-wise cross-validation allowed segments from the same participant to occur across folds and resampling was performed before fold partitioning. Because oversampling was performed with replacement, duplicated segment instances could also occur across training and validation folds. Consequently, the statistical findings should be interpreted as exploratory segment-level patterns rather than participant-level inference, and the machine-learning performance estimates should be regarded only as potentially optimistic apparent internal segment-level results and should not be regarded as evidence of participant-level generalization, diagnostic validity, screening accuracy, or clinical applicability. Conclusions: Markerless video-based gait analysis with interpretable machine learning may provide a feasible research-support approach for quantifying gait-related motor phenotypes in individuals with schizophrenia. These findings should not be interpreted as evidence for participant-level clinical classification, diagnostic or screening validity, clinical utility, or readiness for deployment, or as proof of cross-device or cross-environment reproducibility. Future studies require matched controls, psychiatric comparison groups, subject-wise validation, external datasets, calibrated gait measures, systematic cross-configuration reproducibility testing, and privacy-preserving data governance. Full article

In saline strata, lubricating slurry for pipe jacking deteriorates due to salt ion intrusion, causing stratum instability and increased frictional resistance, which pose major risks in underground construction in coastal and inland saline areas. This study investigates the evolution of filter cake impermeability under salt intrusion and establishes a three-stage evaluation method covering forward grouting, post-damage recovery, and reverse water sealing. Using a self-designed permeation apparatus, comparative tests were conducted on ordinary bentonite slurry (4% bentonite) and salt-resistant composite slurry (5% attapulgite + 5% bentonite + 0.8% HV-CMC + 0.35% NaOH) in sand layers of 0.25–2.0 mm particle sizes. Results show that the salt-resistant slurry achieves a forward pressure attenuation rate of 97% (formation pressure of 3–7 kPa at 250 kPa grouting pressure), versus only 10% for the ordinary slurry (formation pressure of 45–51 kPa)—a difference of approximately one order of magnitude. After mechanical filter cake damage, the salt-resistant slurry restores impermeability within 15–180 s through re-grouting, while the ordinary slurry cannot recover. The salt-resistant slurry resists approximately 20 kPa of reverse groundwater pressure, whereas the ordinary slurry fails at 10 kPa. Optimal performance occurs in 0.5–1.0 mm medium sand, where the filter cake and seepage zone exhibit the strongest synergy, with post-damage recovery of only 15 s. Solid particles retained in the seepage zone provide a substrate for filter cake reconstruction—the core mechanism for impermeability recovery under repeated damage during pipe jacking advancement. A three-stage evaluation framework integrating pressure attenuation rate, recovery time, and critical breakthrough pressure is proposed to guide slurry selection and grouting parameter optimization in saline strata. Full article