Search Results (2,223)

This study established a stepwise procedure for calibrating the contact parameters and Tavares UFRJ breakage-model parameters required for discrete element method (DEM) simulations of Denghai 605 maize kernels. Kernel–kernel and kernel–carbon-steel contact parameters were calibrated using a cylinder-lifting angle-of-repose test, partitioned-tray restitution tests, and a Box–Behnken design. Single-kernel quasi-static compression tests characterised the distribution and size dependence of specific breakage energy, while unsupported electromagnetic impact tests provided estimates of the damage accumulation coefficient γ and fragment-size distribution parameter b. The estimation of b assumed A = 50%. The calibrated restitution, static-friction, and rolling-friction coefficients were 0.72, 0.18, and 0.06 for kernel–kernel contact and 0.75, 0.28, and 0.08 for kernel–carbon-steel contact, respectively. The simulation produced an angle of repose of 24.91°, with a relative error of 4.27% from the experimental value of 23.89°. Within the observed range, an upper-truncated lognormal distribution described the specific breakage energy (E₅₀ = 386.83 J/kg, σ = 0.4267, and R² = 0.9897). The size-effect parameters E_∞, d₀, and φ were 175.25 J/kg, 9.21 mm, and 1.55, respectively (R² = 0.9381). The calibrated values of γ and b were 5.2 and 0.0253, respectively. The relative error between the simulated and experimental mean breakage forces was 3.17%, and the root-mean-square error of the cumulative particle-size distribution was 4.04 percentage points. Under the tested material and loading conditions, these results provide a parameter set for evaluating subsequent DEM and coupled CFD–DEM simulations. Full article

Deep and ultra-deep reservoirs are increasingly important, while gas kicks remain a major well-control risk. Accurate prediction of gas migration velocity across different wellbore inclinations is essential for locating the influx front, enabling timely kick detection, and optimizing well-killing procedures. Owing to pressure and temperature variations along the wellbore, the same influx gas may change from a highly compressed, relatively high-density state near the bottomhole to a lower-density, conventional gas-like state as it migrates upward. Accordingly, air bubbles and kerosene droplets were used to represent conventional and highly compressed gas, respectively. Adjustable-inclination annular experiments investigated CMC-controlled viscosity in bubble tests and HCOOK-induced coupled changes in density and viscosity in droplet tests. With increasing inclination, both dispersed phases became increasingly deformed. Bubble migration velocity decreased monotonically with viscosity and inclination, whereas droplet velocity first increased and then decreased, peaking at 15–30°. Along the tested HCOOK formulation path, droplet velocity varied non-monotonically. Based on 155 bubble observations and 75 droplet observations, separate correlations were integrated into a combined gas-migration model. Validation against three field wells yielded absolute relative errors of 8.75–11.20%, demonstrating its practical value for estimating field gas migration velocities. Full article

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

Three-dimensional optical ranging has become an effective tool for railway infrastructure monitoring, enabling accurate geometric characterization of tracks and the detection of structural anomalies. Beyond track inspection, this work investigates the potential of a low-cost optical ranging approach for indirect monitoring of railway suspension components. Two low-cost 1D laser ranging sensors arranged in quadrature were used to observe the lateral profile of a helical bogie spring under loading conditions. Owing to the 18° field of view of each sensor, the measured responses exhibited a quasi-sinusoidal dependence on spring height. By applying phase unwrapping to the quadrature signals, the spring height was reconstructed in real time. Two independent vertical displacement tests were performed, achieving mean errors of 0.66 mm and −1.47 mm, with standard deviations of 2.35 mm and 2.16 mm, respectively, demonstrating consistent accuracy and repeatability of the method. The proposed quadrature sensing approach provides a simple, non-contact, and cost-effective solution for indirect estimation of the bogie spring height. It is particularly suitable for installations where direct vertical measurements are impractical and offers a promising method for monitoring spring compression and suspension loading in railway condition monitoring systems. Full article

Running a dense neuro-fuzzy sliding mode controller (SMC) on a resource-constrained UAV edge platform forces a three-way trade-off between compute, formal stability guarantees, and robustness to uncertainty. We recast the extended Kalman filter (EKF) from state estimation into weight-space prediction, which lets the controller drop inactive fuzzy rules on the fly while retaining stability proofs built on martingale theory. The framework couples four ideas. EKF weight-space compression treats the innovation sequence as an approximate martingale difference sequence. A graph attention network (GAT) learns time-varying inter-channel coupling and feeds it straight into the SMC equivalent-control term. Reinforcement learning with Lyapunov post-regularization, wrapped in a Wasserstein distributional-robustness constraint, is shown to form a supermartingale. Finally, INT8 quantization-aware training prepares the whole stack for edge deployment. These pieces rest on a common martingale foundation that yields finite-sample pruning-error bounds (Azuma–Hoeffding), worst-case tracking bounds (Doob), stochastic stability via supermartingale Lyapunov functions, and a minimum dwell-time theorem for switching rule sets. Across 10,000 high-fidelity PyBullet simulation runs, the framework cuts active fuzzy rules by 73% on average at the cost of 0.3–0.5 cm of simulated tracking error, and the INT8-compiled inference stack attains 4.2 ms mean latency and 0.93 W measured on a Jetson Orin Nano in a bench-top (non-flight) benchmark. The hardware experiments reported here are the edge-inference benchmarks; closed-loop control results are simulation-based, and flight validation on the physical platform is left to future work. Full article

This article presents the results of a research study on the development and evaluation of a mineral modifying additive for concrete produced from mineral processing tailings, shale waste from shungite production, and silica fume. Compositions of both the modifying additive and concretes based on it were developed. The properties of the mineral modifying additive were studied; mechanochemical activation was applied to increase and modify its activity, and its pozzolanic activity was determined. Depending on the component ratio, CaO absorption ranged from 7.9% to 94%. In the technology of high-strength and functional concretes, high-carbon slag with a fraction of 10–20 mm was used as a partial replacement for natural coarse aggregate. The introduction of 3–15% mineral modifying additive into B40 concrete increased the 28-day compressive strength by 0.19–16%. The greatest increase was observed for concrete containing 8% mineral modifying additive by cement mass. Because compressive strength is not directly correlated with freeze–thaw resistance and water impermeability, capillary suction tests were performed. This non-destructive durability indicator makes it possible to assess concrete permeability, characterize the pore structure, indirectly evaluate freeze–thaw resistance through water-absorption behavior, and estimate the potential resistance of concrete to repeated freezing and thawing. After 24 h, the maximum increase in capillary suction ranged from 4.13% to 5.17%. Direct freeze–thaw testing confirmed a freeze–thaw resistance grade of F400. Three main criteria were used to assign the freeze–thaw resistance grade: average mass loss not exceeding 2%; compliance with the compressive strength ratio XminII >= 0.9 XminI; and the absence of cracks, spalling, and edge-scaling on the specimens. Full article

Comprehensive evaluation of open-pit blasting is challenging because performance indicators originate from different sensing sources and are predicted with different levels of model performance. This study proposes a multi-source sensing and interpretable ensemble learning framework for model-performance-weighted blast-performance evaluation. Data from 70 production blasts were organized into 12 input variables and 10 indicators spanning fragmentation quality, muckpile morphology, safety and adverse effects, and operational efficiency. Random forest, XGBoost, LightGBM, and CatBoost models were developed separately for each indicator and compared using five-fold cross-validation, while SHAP was applied to interpret the selected models. The indicator-specific models achieved cross-validated R² values of 0.7812–0.9012; these are selection-conditioned cross-validated estimates obtained under the same five-fold partition that was used to select the model for each indicator, and they are not unbiased estimates of generalization performance. Powder factor and uniaxial compressive strength were influential for fragmentation, whereas maximum charge per delay strongly affected peak particle velocity and muckpile displacement. Cross-validated R² was then incorporated into AHP–entropy weighting as a relative model-performance coefficient that is not a confidence level, a probability of correctness, or an uncertainty estimate. In field application, engineering adjustment based on model feedback increased the comprehensive score from 65.4 to 87.5, improved the grade from III to II, and reduced D80 and PPV by 25.4% and 27.8%, respectively. The models are specific to the monitored mine rather than transferable, and the field implementation is an application case rather than an independent external validation. The framework provides an interpretable and model-performance-aware basis for site-specific blast-scheme comparison and iterative improvement. Full article

The use of β-hemihydrate phosphogypsum (β-HPG) in building materials is limited by insufficient strength and water sensitivity. This study evaluated calcium sulfate whiskers (CSW) as a modifier at 0–4 wt% relative to β-HPG mass under a fixed water-to-gypsum ratio of 0.58. Workability, dry flexural and compressive strengths, wet compressive strength, and water resistance were assessed alongside isothermal calorimetry, X-ray diffraction, mercury intrusion porosimetry, and scanning electron microscopy. The 2 wt% mixture showed the highest mean dry flexural and compressive strengths among the tested formulations, reaching 5.95 and 22.0 MPa, respectively, approximately 49% and 23% higher than the control. Wet compressive strength after 24 h immersion increased from 7.4 to approximately 8.5 MPa at this dosage. The corresponding paste spread diameter was 155 mm. Higher dosages further reduced mercury-accessible porosity and water absorption without exceeding these mean strengths. The higher wet strengths were accompanied by lower softening-coefficient estimates. CSW advanced the hydration heat-flow peak, while dihydrate gypsum remained the dominant crystalline phase. This integrated assessment distinguishes pore-volume reduction from mechanical improvement and absolute wet strength from relative strength retention. The findings provide an experimental basis for selecting CSW-modified phosphogypsum formulations according to strength, workability, and moisture-exposure requirements. Full article

A companion study documented that the resource efficiency adoption gap between medium-sized and microenterprises narrowed by over 80% across the EU27 between 2017 and 2024, but its EU-aggregate design could not establish whether this was a Europe-wide transition or a few national trajectories, nor which mechanism drove it. This article decomposes that finding into 27 country-specific convergence parameters using a two-step estimation and meta-analysis strategy, and tests whether the 2021–2022 energy price shock, renewable energy endowment, or environmental policy stringency explain the cross-country heterogeneity. Heterogeneity is robust to estimator choice (I² = 53.1%, p < 0.001), ranging from intense compression in Romania, Austria, Germany, and Spain to null or reversed dynamics in Estonia, Slovenia, and Slovakia, and is driven disproportionately by the largest national SME populations. The average convergence effect is more fragile, excluding unity under a conventional interval (random-effects IRR 0.905, 95% CI 0.820–0.999) but not under the more conservative Hartung–Knapp–Sidik–Jonkman correction appropriate for k = 27 (95% CI 0.818–1.001); we treat it as indicative rather than confirmed. None of the theory-driven moderators explains the heterogeneity (all coefficients below |0.029| SD, all p > 0.57), a null substantiated by a formal power analysis and, for an internal initial-gap moderator, by a split-sample design removing a regression-to-the-mean artefact present in a naive test; this rejects national-policy-mediated coercive pressure specifically, not supranational or network-transmitted channels. Two exploratory, purely descriptive extensions accompany the analysis: a small non-EU contrast group shows convergence not visibly different from the EU27, though underpowered to adjudicate between mechanisms; and services-sector SME share shows a nominally significant, non-multiple-testing-robust, association with stronger convergence. Published as the EU’s Omnibus I reform narrows CSRD-obligated reporters and caps value-chain information requests to smaller partners, this country-level baseline provides the reference point for assessing the durability of SME sustainability convergence as new data become available, with direct implications for EU-level SME greening policy. Full article

Polymer concretes with fiber reinforcement have many desirable properties when compared to cementitious concretes, including rapid development of mechanical properties, excellent bond to concrete and other substrates, high tensile strength, and resistance to abrasion and aggressive chemical environments. However, their use as a structural material has been limited, in part, by temperature-dependent mechanical properties, affecting bond and development of steel reinforcement, deformation of structural elements, and section capacity of structural members. This research investigated the development of mechanical properties for a commercially available polymer concrete with fiber reinforcement to determine the effects of temperature on compressive strength, elastic modulus, modulus of rupture, and pull-out bond strength. The compressive, flexural, and bond strengths of the tested polymer concrete with fiber reinforcement were over 70% of their 7 d values within 4 h after mixing when cured at laboratory temperature, demonstrating the rapid development of mechanical properties that is possible with polymer binders. The average 7 d compressive strength across the experimental program was 62.2 MPa at 25 °C, with the measured elastic modulus and modulus of rupture roughly half and three times that of estimated values using established code relationships and the measured compressive strength, respectively. The pull-out bond strength at 25 °C was found to be similar to non-proprietary ultra-high performance and polymethyl methacrylate concretes, and the variation in mechanical properties with temperature was roughly linear and independent of the mechanical property tested when normalized by the value at laboratory temperature. This limited test series supports the structural use of polymer concrete with fiber reinforcement, when in-service temperature is expressly considered in the design process, although further testing is needed to develop rational design procedures. Full article

Remote photoplethysmography (rPPG) estimates pulse signals from facial videos, but motion, illumination variation, exposure control, and compression can distort the waveform used for heart-rate (HR) estimation. The Plane-Orthogonal-to-Skin (POS) waveform provides an observed physiological source that often preserves recording-specific periodic structure. Existing methods commonly regress the complete waveform directly or reconstruct it from a stochastic state without using this source as the starting point. We propose PulsePath, a two-stage framework that transports POS toward paired-contact photoplethysmography (PPG) and models only the residual remaining after deterministic inference. Stage A performs conditional Flow Matching (FM) from POS using a six-channel multiscale spatiotemporal map and a 25-channel recording-level frequency representation. Stage B fixes the deterministic FM estimate and reconstructs its remaining error with variance-preserving residual diffusion and velocity prediction. In intra-dataset testing, PulsePath achieves mean absolute error (MAE), root mean square error (RMSE), and Pearson correlation values of $0.46/0.94/0.99$ on PURE and $0.29/0.80/0.99$ on UBFC-rPPG. In cross-dataset testing, PulsePath achieves an MAE/RMSE of $0.23/0.63$ when trained on UBFC-rPPG and tested on PURE, and $1.13/2.49$ when trained on PURE and tested on UBFC-rPPG. The additional analyses show protocol-dependent component effects and training-run variability; the reported HR results do not establish waveform fidelity. Full article

Reactive autoscalers compress workload state into a scalar, but bounded ceiling partitions jointly induced by signal and target determine replica recommendations. We formalize five signals and distinguish the positive target, bounded desired recommendation, and deployed workers. We prove exact recommendation equivalence, a tight charge-estimation error bound, and an expected pre-bound quantization premium for i.i.d. mean-matched charges. A 100-seed executable check verified theorem implementation, and 27,900 simulations mapped threshold–capacity trade-offs. Four paired Windows process-level faster-whisper studies tested near-equivalence, partition crossing, and target relocation. E4 provided a local adjacent-frontier comparison in 60 fresh three-arm blocks; count targets 1.00 and 0.90 and a mean-matched work target replayed the same 48-request manifest in each block. Count 0.90 versus 1.00 improved deadline attainment by +2.92 pp (95% CI +0.97 pp to +4.90 pp) while increasing capacity by +0.0322 worker-s/audio-s. Work differed from count 1.00 by +2.57 pp and from count 0.90 by −0.35 pp. An independent audit found 134/137 held-out bundles deadline-feasible in isolated service; conditioning preserved both the E2 work-minus-count effect (+2.56 pp) and the E4 target effect (+2.96 pp). Within the tested partitions, these results separate threshold relocation from signal information and motivate testing adjacent targets before adopting a richer estimator; they do not establish the global count-target frontier. Full article

With the tightening of global methane regulations (EU Methane Regulation 2024/1787, U.S. EPA GHG rules, OGMP 2.0) and China’s dual-carbon commitments, precise and traceable carbon accounting for natural gas gathering systems has become an urgent engineering imperative. To tackle manual workflows, data isolation, low precision, and poor traceability in carbon accounting for natural gas field surface systems, this study develops a four-tier digital delivery framework. The main goal is to establish an end-to-end engineering paradigm that transforms carbon management from passive post-hoc statistics into an active, traceable decision-support tool. By embedding carbon attributes into seed files and adopting a unified one-code-through equipment coding scheme, static design data, dynamic SCADA measurements, and emission factor databases are interoperably consolidated within a central data hub. The hub implements a three-stage progressive accounting workflow and closed-loop emission mitigation. Methodologically, a three-tier progressive accounting model (Tier 1: system-level, Tier 2: process-level, Tier 3: equipment-level) is constructed, with Tier 3 supported by OGMP 2.0 source-level emission factors locally calibrated against 386 field LDAR measurements. Deployed at Gas Field A, the framework realizes full diagram-model consistency and narrows systematic error from ±30% to ±3.5%. Tier-2 accounting quantifies gathering, compression, and dehydration emissions at 322,000, 504,300, and 162,500 tCO₂e, while Tier-3 analysis pinpoints reciprocating compressors and heaters as the primary contributors (51.70% of total emissions). Optimizations including compressor energy conservation, enhanced LDAR, and waste heat recovery provide an estimated annual mitigation potential of 97,400 tCO₂e (10.10%). In conclusion, this framework mitigates data fragmentation and accuracy defects, delivering a transferable technical route for oil and gas operators to advance methane abatement and dual-carbon targets. Full article

This work presents a data-driven approach for state-of-charge estimation of rechargeable zinc-air batteries based on electrochemical impedance spectroscopy. Due to the nonlinear electrochemical behavior and flat discharge voltage profile of zinc-air batteries, accurate state-of-charge estimation remains challenging. The investigated cells employ a three-electrode configuration with a dedicated gas diffusion electrode for discharge and a separate electrode for charging. This work focuses exclusively on discharge operation, as the two current paths involve physically distinct electrodes with fundamentally different impedance characteristics. High-dimensional impedance spectra are combined with physically interpretable features derived from a simplified equivalent circuit model and compressed via principal component analysis. A long short-term memory network models the relationship between the resulting feature representation and state-of-charge, with Bayesian hyperparameter tuning applied to optimize architecture and training configuration. Performance is compared against baseline models including multilayer perceptrons. The model is trained on multiple battery cells and evaluated on a completely held-out cell to assess cross-cell generalization. The results show that principal component analysis compression of the combined impedance spectrum and equivalent circuit feature vector is the decisive optimization step, achieving a mean absolute error of 1.04% on an unseen test cell. In contrast, the choice of model architecture has a smaller impact on performance. Full article

This study investigated the performance of concrete incorporating fly ash (FA) and bentonite (B) as partial replacements for ordinary Portland cement (OPC). Ten mixtures were evaluated: one OPC control, five binary mixtures containing 10–30% FA, and four ternary mixtures with a fixed total replacement of 30%, comprising FA/B replacement percentages of 20/10, 15/15, 10/20, and 5/25 by total binder mass. The total binder content and nominal water-to-binder ratio were maintained at 430 kg/m³ and approximately 0.55, respectively. Fresh-state and binder-paste properties, compressive and splitting tensile strengths, hardened density, water absorption, hydrochloric-acid resistance, sorptivity, and ultrasonic pulse velocity were assessed. FA20B10 provided a favorable balance of mechanical and durability-related performance among the tested ternary mixtures, whereas FA30 achieved the highest overall 90-day compressive strength of 33.1 MPa. Water absorption decreased from approximately 7.6% for the control to 5.1% for FA20B10. The ternary mixtures also exhibited lower sorptivity and smaller strength losses following hydrochloric-acid exposure than the control. A provisional partial material-cost comparison was restricted to the control and binary mixtures and did not establish the cost-effectiveness of the ternary formulations. Environmental reporting was limited to a mass-based, OPC-only screening estimate using a fixed emission factor. Emissions associated with FA, B, other constituents, and the remaining life-cycle processes were excluded; therefore, the calculation does not establish the total concrete carbon footprint or a life-cycle CO₂ reduction. The findings support further development of fly-ash–bentonite ternary concrete, subject to source-specific characterization and field-scale validation. Full article