Search Results (3,331)

In recent years, the production of tight reservoirs with waterflooding in China has entered a progressively declining phase with unstable oil rate and higher water cut, rising challenges to any further enhancement of oil recovery. Targeting the high water cut and complex pore structure characteristics typical of these reservoirs, this work evaluates the reservoir compatibility of a microspheres-alternating-nanoemulsion flooding process and optimizes its injection strategy. Representative reservoir scenarios were first established; laser-particle-size analyzers and other laboratory instruments were then employed to quantify formulation-reservoir compatibility. A multiscale numerical study has been performed with CMG-STARS v.2022. The core-scale simulations systematically examined the influence of key factors on displacement efficiency improvement and water cut reduction, matched with the experimental results of core flooding tests. The combined experimental/numerical workflow furnishes a theoretical framework for optimizing the injection scheme. Beyond assessing formulation compatibility, the study delivers optimized injection parameters and strategies for microspheres-alternating-nanoemulsion flooding, providing both theoretical analysis and practical technology reference for improving oil recovery in tight reservoirs with higher water cut. Specifically, when the microsphere concentration increased from 0.1% to 0.3%, the minimum water cut was reduced by approximately 5%, while further concentration increases showed no significant additional impact on water content. Compared with water flooding, the relative permeability curve of the microspheres-alternating-nanoemulsion flooding system shifted entirely to the right. Numerical simulation of representative well groups revealed that a slug design with a microsphere-to-nanoemulsion ratio of 1:3 yielded the optimal enhanced oil recovery effect, and after ten years of production, the recovery factor increased by 0.46%. Full article

While urban historic areas are most vulnerable to disasters, they offer insights into leveraging their features to mitigate risk. This study analyzes scientific approaches to evacuation simulations to assess the tolerance of historic areas. Using a heritage-led disaster risk reduction approach, this study uses a heritage site as a case study for evacuation. This study uses a GIS-based methodology to define various blockage risks, categorizing them as no-obstruction, rubble-obstruction, on-street vehicle obstruction, and combined obstruction. The input parameters were transferred from a GIS to a simulation application, with combined obstruction representing the worst-case scenario. No-obstruction served as a baseline for measuring historic area vulnerability. Statistical analysis evaluated time usage and the number of evacuees, while GIS identified vulnerable places and street congestion. Obstructions significantly increase evacuation risks, with combined obstructions posing a 3.8 times higher risk than the no obstruction scenario (2638 s compared to 683 s). Vehicle obstruction causes a vulnerability of 1404 s, while building collapse-related rubble obstruction causes a vulnerability of 1073.1 s, despite creating dead-end streets. The strategy of reinventing heritage sites as temporary evacuation sites appears viable. This approach can support evacuees during and after disaster responses and expand options for ensuring urban heritage resilience. Full article

Traffic signs exposed to long-term outdoor conditions frequently exhibit deformation, inclination, or other forms of physical damage, highlighting the need for timely and reliable anomaly assessment to support road safety management. While point-cloud data provide accurate three-dimensional geometric information, their sparse distribution and lack of appearance cues make traffic sign extraction challenging in complex environments. High-resolution sequential video-log images captured from multiple viewpoints offer complementary advantages by providing rich color and texture information. In this study, we propose an integrated traffic sign detection and assessment framework that combines video-log images and mobile-mapping point clouds to enhance both accuracy and robustness. A dedicated YOLO-SIGN network is developed to perform precise detection and multi-view association of traffic signs across sequential images. Guided by these detections, a frustum-based point-cloud extraction strategy with seed-point density growing is introduced to efficiently isolate traffic sign panels and supporting poles. The extracted structures are then used for geometric parameterization and damage assessment, including inclination, deformation, and rotation. Experiments on 35 simulated scenes and nine real-world road scenarios demonstrate that the proposed method can reliably extract and evaluate traffic sign conditions in diverse environments. Furthermore, the YOLO-SIGN network achieves a localization precision of 91.16% and a classification mAP of 84.64%, outperforming YOLOv10s by 1.7% and 8.7%, respectively, while maintaining a reduced number of parameters. These results confirm the effectiveness and practical value of the proposed framework for large-scale traffic sign monitoring. Full article

Optimizing window insulation is crucial for reducing heat loss and energy use in industrial buildings in Northeast China’s severe cold regions. Based on six typical building prototypes identified via cluster analysis of field survey data, this study used DesignBuilder (Version 6.1.0.006) to simulate the influence of key parameters for insulation materials (type, thickness, emissivity) and installation methods (position, air cavity, operation). Simulations reveal that the energy-saving potential is inversely proportional to a building’s existing thermal performance, reaching a maximum of 10.3%. Regarding material selection, results indicate that reducing surface emissivity from 0.92 to 0.05 effectively substitutes for approximately 20 mm of physical insulation thickness. Transparent films prioritize daytime comfort, raising nighttime temperatures by 1.5 °C, whereas opaque panels excel at nighttime insulation with a 2.28 °C increase. Techno-economic analysis identifies low-emissivity foil combined with EPS or XPS as the most cost-effective strategy, achieving rapid payback periods of 0.6–3.2 years. Regarding installation, an external configuration with a 20 mm air cavity and vertical operation was identified as optimal, yielding 1.5–2.0% greater energy savings than an internal setup. This study provides tailored retrofitting strategies for industrial building windows in these regions. Full article

Many engineering design problems are formulated as constrained optimization tasks that are nonlinear and nonconvex, and often treated as black boxes. In such cases, metaheuristic algorithms are attractive because they can search complex design spaces without requiring gradient information. In this work, we propose an Enhanced Henry Gas Solubility Optimization (eHGSO) algorithm, which is an improved version of the physics-inspired HGSO method. The enhanced variant introduces six main contributions: (i) a more diverse, population-wide initialization strategy to cover the design space more thoroughly; (ii) adaptive temperature/pressure control parameters that automatically shift the search from global exploration to local refinement; (iii) an elitist archive with differential perturbation that accelerates exploitation around high-quality candidate designs; (iv) a simple combination of the global HGSO search moves with a lightweight gradient-free local search to refine promising solutions; (v) a constraint-handling mechanism that explicitly prioritizes feasible solutions while still allowing exploration near the constraint boundaries; and (vi) a complexity and ablation analysis that quantifies the impact of each mechanism and confirms that they introduce only modest computational overhead. We evaluate eHGSO on four classical constrained engineering design problems: the stepped cantilever beam, the tension/compression spring, the welded beam, and the three-bar truss. Its performance is compared with seventeen recent metaheuristic optimizers over multiple independent runs. eHGSO achieves the best average objective value on the cantilever, spring, and welded-beam problems and shares the best average result on the three-bar truss. Compared to the second-best method, the mean objective is improved by about $0.84\%$ for the cantilever beam and $0.35\%$ for the welded beam, while the spring and truss results are essentially equivalent at four significant figures. Convergence and robustness analyses show that eHGSO reaches high-quality solutions quickly and consistently. Overall, the proposed eHGSO algorithm appears to be a competitive and practical tool for constrained engineering design problems. Full article

Freeform surfaces play a critical role in complex light-field modulation. However, traditional geometric mapping and standard optimization methods are limited by computational cost and convergence instability in large-scale ray tracing and complex surface modeling. This paper introduces DiffRayFlow, which integrates discrete optimal transport (OT), end-to-end differentiable ray tracing (DRT), and an adaptive multi-scale strategy. OT provides a global, energy-conserving geometric map. Differentiable tracing parameterizes the surface using the finite difference method (FDM) and constructs a differentiable link from height parameters to target landing points. The multi-scale approach, combined with early stopping, enhances efficiency and stability. For typical tasks involving over a million rays, the core heightmap optimization is usually completed within 20 s. The method can output standard Computer-Aided Design (CAD) data for rapid prototyping and physical validation. Ablation studies show that the multi-scale strategy is key to achieving high-precision convergence, while the early stopping mechanism can reduce optimization time by about 40% without sacrificing reconstruction quality. DiffRayFlow provides an efficient engineering path for interactive design and large-scale customization. Full article

Blockchain technology has rapidly evolved as a cornerstone of decentralized computing, transforming how trust, data integrity, and transparency are achieved in digital ecosystems. However, despite extensive adoption, significant gaps remain in understanding how key blockchain variables, such as block size, consensus mechanisms, and network latency, affect system vulnerabilities and susceptibility to cyberattacks. This survey addresses this gap by combining qualitative and quantitative analyses across multiple blockchain environments. Using simulation tools such as Ganache and Bitcoin Core, and reviewing peer-reviewed studies from 2016 to 2024, the research systematically maps blockchain parameters to cyberattack vectors including 51% attacks, Sybil attacks, and double-spending. Findings indicate that design choices like block size, block interval, and consensus type substantially influence resilience against attacks. The Blockchain Variable Quantitative Risk Framework (BVQRF) introduced here integrates NIST’s cybersecurity principles with quantitative scoring to assess risks. This framework represents a novel contribution by operationalizing theoretical security constructs into actionable evaluation metrics, enabling predictive modeling and adaptive risk mitigation strategies for blockchain systems. Full article

Machining of medium-carbon steels, such as AISI 1050, poses a significant challenge in terms of achieving stable cutting conditions, controlled chip evacuation and high surface integrity, in particular when full-face milling is performed under elevated material removal rates. The tool surface engineering approach, particularly laser-induced micro-texturing, comprises a promising route toward modifying the tribological conditions at the tool–chip interface, thus affecting friction, heat generation, chip formation and the resultant surface finish. This study investigates the combined effects of cutting speed, axial depth of cut and tool micro-texture orientation (parallel versus orthogonal to the chip flow direction) on machining performance under wet conditions. In addition to the experimental analysis of cutting forces, chip morphology and surface roughness, this work integrates a full factorial Design of Experiments, regression modeling, and ANOVA to quantify the statistical significance of each factor and to identify dominant interactions. The regression models show strong predictive capability across all measured responses, while the ANOVA confirms the axial depth of cut and tool texture orientation as the most influential parameters. Multi-objective optimization by Pareto analysis further underlines the superiority of orthogonal micro-texturing, which consistently reduces the cutting forces and improves surface quality while promoting controlled chip segmentation. The results provide quantitative and statistically validated evidence of the enhancement of lubrication effectiveness, reduction in interface friction, and stabilization in chip formation provided by the micro-textured tools. Overall, the findings contribute to the development of data-driven machining strategies and surface-engineered cutting tools in view of improved productivity, energy efficiency and surface integrity in advanced manufacturing applications. Full article

Limited phosphorus (P) availability and declining soil biological health are major constraints in intensive rice (Oryza sativa L.)—wheat (Triticum aestivum L.) systems. Rock phosphate–enriched compost (REC), combined with microbial inoculants, offers a sustainable strategy for improving soil biological functioning. A field experiment was conducted under a randomized block design with seven treatments involving different combinations of REC, chemical fertilizers, phosphate-solubilizing bacteria (PSB), and arbuscular mycorrhizal fungi (AMF). Post-harvest soil samples from rice and wheat were analyzed for microbial biomass carbon (MBC), microbial biomass phosphorus (MBP), enzymatic activities, microbial populations, root colonization, yield, and P uptake. The combined application of REC with PSB and AMF significantly enhanced soil biological parameters compared with recommended fertilizer doses. Under the REC + PSB + AMF treatment, dehydrogenase, acid phosphatase, and alkaline phosphatase activities increased by 77.4%, 24.8%, and 18.1%, respectively, while MBC and MBP improved by 51.6% and 106.6%. Bacteria, fungi, and actinomycete population increased by 55.0%, 76.7%, and 82.8%, respectively, as well as mycorrhizal root colonization increased by 18.7%. Grain yield of rice and wheat increased by 16% and 6%, respectively, along with higher P uptake. The integrated use of REC with PSB and AMF improved soil enzymatic activity, microbial biomass, and nutrient acquisition, leading to higher crop productivity. These results indicate that REC combined with PSB and AMF is an effective nutrient management strategy for improving soil biological health, P utilization, and crop productivity in rice–wheat systems. Full article

Background/Objectives: Hemiplegic shoulder pain (HSP) is a frequent post-stroke complication affecting 30–65% of survivors, contributing to motor dysfunction and reduced quality of life. Balance impairment is another major concern that increases fall risk. This study aimed to examine the associations between HSP, shoulder range-of-motion (ROM) limitations and balance performance using both clinical and sensor-based evaluations. Methods: In this cross-sectional study, 108 stroke survivors (54 with HSP, 54 without) were assessed. Pain intensity was evaluated using the Visual Analog Scale (VAS), balance with the Berg Balance Scale (BBS), and shoulder mobility and postural sway with the validated Euleria Lab IMU-based system integrated with a force platform. Between-group differences were analyzed using the Mann–Whitney U test, and correlations between pain, ROM, balance, and fall-risk indices were determined via Spearman coefficients. Results: Participants with HSP had significantly lower BBS scores (20.96 ± 8.71) than those without HSP (34.58 ± 11.71; p < 0.001). VAS activity scores were negatively correlated with BBS (r = −0.196, p = 0.043) and positively correlated with postural sway and fall-risk parameters, particularly under eyes-closed conditions. Shoulder ROM limitations were linked to poorer balance, and both static and dynamic fall-risk indices were strongly correlated with pain severity (r = 0.676 and r = 0.657; p < 0.001). Conclusions: HSP was associated with impaired balance and elevated fall risk in stroke survivors. The combination of clinical scales and wearable sensor-based measurements provides a comprehensive understanding of postural control deficits. These findings emphasize the need for rehabilitation strategies targeting pain reduction, shoulder mobility, and balance to support functional recovery. Full article

Limited annotated data often constrain accurate yield prediction in underrepresented crops. To address this challenge, we developed a cross-crop deep transfer learning (TL) framework that leverages potato (Solanum tuberosum L.) as the source domain to predict sweet potato (Ipomoea batatas L.) yield using multi-temporal uncrewed aerial vehicle (UAV)-based multispectral imagery. A hybrid convolutional–recurrent neural network (CNN–RNN–Attention) architecture was implemented with a robust parameter-based transfer strategy to ensure temporal alignment and feature-space consistency across crops. Cross-crop feature migration analysis showed that predictors capturing canopy vigor, structure, and soil–vegetation contrast exhibited the highest distributional similarity between potato and sweet potato. In comparison, pigment-sensitive and agronomic predictors were less transferable. These robustness patterns were reflected in model performance, as all architectures showed substantial improvement when moving from the minimal 3 predictor subset to the 5–7 predictor subsets, where the most transferable indices were introduced. The hybrid CNN–RNN–Attention model achieved peak accuracy ($R^2\approx 0.64$ and RMSE ≈ 18%) using time-series data up to the tuberization stage with only 7 predictors. In contrast, convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and bidirectional long short-term memory (BiLSTM) baseline models required 11–13 predictors to achieve comparable performance and often showed reduced or unstable accuracy at higher dimensionality due to redundancy and domain-shift amplification. Two-way ANOVA further revealed that cover crop type significantly influenced yield, whereas nitrogen rate and the interaction term were not significant. Overall, this study demonstrates that combining robustness-aware feature design with hybrid deep TL model enables accurate, data-efficient, and physiologically interpretable yield prediction in sweet potato, offering a scalable pathway for applying TL in other underrepresented root and tuber crops. Full article

The dead-zone input and hydraulic cylinder friction of the pump-controlled automatic gauge control (AGC) system introduce significant challenges to the high-precision rolling of lithium battery pole pieces. To address these nonlinearities, this paper establishes the friction and dead-zone model of the pump-controlled AGC system, and a slide-mode observer is designed to estimate the friction state z in the LuGre model. Furthermore, an adaptive compensation method is adopted to identify the unknown parameters of the input dead-zone and friction models. Meanwhile, combined with the framework of backstepping control design, both matched and mismatched disturbances are effectively compensated. Stability analysis guarantees the convergence of the estimation errors and closed-loop signal boundedness. Finally, experimental results validate the effectiveness and robustness of the proposed control strategy. Full article

In urban underground space construction using shield tunnelling, the geological conditions ahead of the tunnel face are often uncertain. Without timely and accurate classification of the ground type, mismatches in operational parameters, uncontrolled costs, and schedule risks are likely to occur. Using observations from an earth pressure balance (EPB) project on an urban railway, a data-driven classification framework is developed that integrates shield tunnelling operating measurements with physically derived quantities to discriminate among soft soil, hard rock, and mixed strata. Principal component analysis (PCA) is performed on the training set, followed by a systematic comparison of tree-based classifiers and hyperparameter optimization strategies to explore the attainable performance. Under unified evaluation criteria, a categorical bosting (CatBoost) model optimized by a Nevergrad combination strategy (NGOpt) attains the highest test accuracy of 0.9625, with macro-averaged precision and macro-averaged recall of 0.9715 and 0.9716, respectively. To mitigate optimism from single-point estimates, stratified bootstrap intervals are reported for the test set. A Monte Carlo experiment applies independent perturbations to the PCA-transformed features, producing low label-flip rates across the three classes, with only minor changes in probability calibration metrics, which suggests consistent decisions under sensor noise and sampling bias. Overall, within the scope of the considered EPB project, the study delivers a compact workflow that demonstrates the feasibility of uncertainty-aware ground-type classification and provides a methodological reference for developing decision-support tools in underground tunnel construction. Full article

As global efforts to combat climate change and promote sustainable development intensify, PODEn, as an innovative type of clean, sustainable fuel, has gained growing attention for its potential to support eco-friendly energy transitions, especially concerning the spray characteristics of its blended fuels. Environmental conditions are crucial in the fuel spraying process, which is essential for optimizing combustion efficiency and reducing emissions—key elements of sustainable energy use and climate action. In this study, the parameters of spray morphology, droplet size distribution, and velocity were accurately measured using a constant-volume combustor and high-speed photography. The results demonstrate that as ambient pressure increases, both the spray cone angle and boundary gas entrainment volume increase, while the spray penetration distance and spray volume decrease. These changes, driven by pressure differences and variations in gas density that influence droplet movement and fragmentation, are critical for optimizing fuel injection strategies to enhance combustion efficiency and reduce environmental impact. This aligns closely with the Sustainable Development Goals focused on clean energy, responsible consumption, and climate mitigation. Conversely, as ambient temperature rises, the penetration distance and spray volume increase, whereas the entrainment volume decreases and the spray cone angle narrows. This phenomenon results from the combined effects of temperature on gas density, viscosity, evaporation rate, and convective flow, underscoring the need for adaptive engine designs that leverage these characteristics to improve fuel efficiency and reduce carbon emissions—an essential step toward sustainable development in the energy and automotive sectors. Full article

Decarbonizing heavy-duty freight transport is essential for achieving climate neutrality targets. Although internal combustion engine (ICE) trucks currently dominate logistics, they contribute substantially to greenhouse gas emissions. Zero-emission alternatives, such as battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (H2), provide different decarbonization pathways; however, their relative roles remain contested, particularly in small economies. While BEVs benefit from technological maturity and declining costs, hydrogen offers advantages for high-payload, long-haul operations, especially within energy-intensive cold supply chains. The aim of this paper is to examine the gradual transition from ICE trucks to hydrogen-powered vehicles with a specific focus on cold-chain logistics, where reliability and energy intensity are critical. The hypothesis is that applying a system dynamics forecasting approach, incorporating investment costs, infrastructure coverage, government support, and technological progress, can more effectively guide transition planning than traditional linear methods. To address this, the study develops a system dynamics economic model tailored to the structural characteristics of a small economy, using a European case context. Small markets face distinct constraints: limited fleet sizes reduce economies of scale, infrastructure deployment is disproportionately costly, and fiscal capacity to support subsidies is restricted. These conditions increase the risk of technology lock-in and emphasize the need for coordinated, adaptive policy design. The model integrates acquisition and maintenance costs, fuel consumption, infrastructure rollout, subsidy schemes, industrial hydrogen demand, and technology learning rates. It incorporates subsystems for fleet renewal, hydrogen refueling network expansion, operating costs, industrial demand linkages, and attractiveness functions weighted by operator decision preferences. Reinforcing and balancing feedback loops capture the dynamic interactions between fleet adoption and infrastructure availability. Inputs combine fixed baseline parameters with variable policy levers such as subsidies, elasticity values, and hydrogen cost reduction rates. Results indicate that BEVs are structurally more favorable in small economies due to lower entry costs and simpler infrastructure requirements. Hydrogen adoption becomes viable only under scenarios with strong, sustained subsidies, accelerated station deployment, and sufficient cross-sectoral demand. Under favorable conditions, hydrogen can approach cost and attractiveness parity with BEVs. Overall, market forces alone are insufficient to ensure a balanced zero-emission transition in small markets; proactive and continuous government intervention is required for hydrogen to complement rather than remain secondary to BEV uptake. The novelty of this study lies in the development of a system dynamics model specifically designed for small-economy conditions, integrating industrial hydrogen demand, policy elasticity, and infrastructure coverage limitations, factors largely absent from the existing literature. Unlike models focused on large markets or single-sector applications, this approach captures cross-sector synergies, small-scale cost dynamics, and subsidy-driven points, offering a more realistic framework for hydrogen truck deployment in small-country environments. The model highlights key leverage points for policymakers and provides a transferable tool for guiding freight decarbonization strategies in comparable small-market contexts. Full article