Search Results (130,102)

Sodium sulfate-activated slag cement is considered a highly promising low-carbon cementitious material; however, its application is limited by low early-age activation efficiency and slow strength development. This study aims to systematically elucidate the coupled regulatory mechanism of alkalinity (2% and 4% Na₂O equivalent) and sodium sulfate dosage on the performance of alkali-activated slag (AAS). Under standard curing conditions (20 ± 2 °C, relative humidity ≥ 95%), the macroscopic properties of the samples (workability, setting time, and compressive strength) and the evolution of their microstructure (analyzed by XRD, FTIR, and SEM-EDS) were evaluated. The results indicate that the effect of sodium sulfate on alkali-activated slag (AAS) strongly depends on the alkalinity. Under low-alkalinity conditions (2% Na₂O), sodium sulfate exhibits a synergistic activation effect by increasing the ionic concentration, promoting slag depolymerization and the nucleation of ettringite (AFt). Specifically, compared with the control, incorporating 6 wt% sodium sulfate (N2S6 mix) increased compressive strength by approximately 82% at 3 days and 21% at 28 days. In contrast, under high-alkalinity conditions (4% Na₂O), excessive sodium sulfate (≥2 wt%) shows an inhibitory effect. This is likely because an excess of sodium sulfate interferes with the normal polymerization pathways of the aluminosilicate network, suppressing the formation of the primary C-(A)-S-H gel and thus significantly reducing later-age strength. Microstructural analysis revealed that the hydration products in the composite-activated system mainly consist of C-(A)-S-H gel, ettringite (AFt), monosulfate (AFm), and hydrotalcite. This study investigates the observed kinetic trends of anion-competitive hydration under different alkalinity conditions, providing a theoretical basis for the mix design of low-carbon alkali-activated materials and the valorization of coal chemical industrial salts. Full article

The selective oxidative transformation of 5-hydroxymethylfurfural (HMF) is a key route toward producing a wide variety of chemicals in the biorefinery industry. Herein, we report a NiAl layered double hydroxide (NiAl-LDH) catalyst as a highly effective electrocatalytic oxidation catalyst for the transformation of HMF into 2,5-diformylfuran (DFF), a valuable furan-based chemical, with about 75.53% DFF selectivity under neutral conditions. It demonstrated good stability without deactivation after 9 cycles of repeated electrolysis. The NiAl-LDH electrocatalyst was deposited on a nickel foam support via a hydrothermal method, and its structural properties and surface morphology were extensively investigated. Systematic studies of reaction temperature, current intensity, and electrolyte concentration revealed that the neutral electrolyte plays a critical role in achieving high DFF selectivity by suppressing aldehyde over-oxidation. Mechanistic investigations with electrochemically active surface area (ECSA), electrochemical impedance spectroscopy (EIS), Tafel slope and density functional theory (DFT) calculations revealed that the reversible transformation between Ni(OH)₂ and active NiOOH species in the NiAl-LDH electrocatalyst was the main reason for the oxidation of HMF, while the incorporation of Al provided structural support to the electrode, enabling the catalyst to exhibit excellent stability during electrolysis. Overall, this work demonstrates an active, earth-abundant metal electrocatalyst for the valorization of biomass-derived 5-HMF to DFF. Full article

Digital inclusive finance has grown rapidly in China in recent years, yet its effect on agricultural economic resilience remains debated. This study investigates the effect of digital inclusive finance on agricultural economic resilience, focusing on the mediating role of rural industry integration. Using annual panel data covering 29 Chinese provinces from 2011 to 2021, we employ two-way fixed-effect panel regressions, mediation analysis, threshold analysis, instrumental variable estimation, and spatial econometric models. The results show that digital inclusive finance has a significant negative effect on agricultural economic resilience, and this finding is robust across alternative specifications and instrumental variable estimations. Rural industry integration serves as an important transmission channel, with the indirect effect accounting for approximately one-third of the total effect. The two stages of this mediation pathway are moderated by distinct threshold variables: rural digital infrastructure positively moderates the effect of digital inclusive finance on rural industry integration, while government fiscal support negatively moderates the effect of rural industry integration on agricultural economic resilience. The spatial analysis further reveals that digital inclusive finance generates negative spatial spillovers onto neighboring provinces. Based on these findings, we suggest that the government continue to invest in rural digital infrastructure, guide digital finance toward rural industry integration in underdeveloped regions, and maintain fiscal support at an appropriate level to preserve the vitality of integrated industries. Full article

The application of digital twin (DT) technology to intelligent machining shows promise, but its effectiveness in predicting vibration and assessing surface quality has not been thoroughly validated for widespread industrial use. This study presents a physics-informed predictive digital twin framework operating in an offline or near-real-time predictive configuration for vibration prediction and surface roughness modeling in milling processes. Impact hammer testing was conducted to extract the dominant modal properties of the spindle–tool assembly, which were embedded into a Simulink-based dynamic framework to predict tool vibration under varying cutting conditions. Full-immersion slot milling experiments on AL6061 were performed for validation. Within all datasets, including training phase and validation phase, the predicted vibration amplitudes exhibit a coefficient of determination $R^2=0.94$ with measured values. The overall MAPE and RMSE are about 10.39% and 0.234, respectively. Power-law regression-based surface roughness prediction models were subsequently established using cutting parameters and both measured and DT-predicted vibration features through logarithmic transformation and least-squares fitting. The results show that the roughness prediction model using vibration features predicted by the digital twin model achieved a correlation coefficient of approximately $R^2=0.84$, with MAPE = 9.57% and RMSE = 0.16 μm, which is comparable to the predictive model based on experimentally measured vibration. These results indicate that, within the investigated machining conditions, the digital twin can provide vibration features suitable for surface roughness prediction, demonstrating its potential as a virtual sensing approach. This work advances digital twin applications from process monitoring toward predictive, quality-oriented machining systems and provides a foundation for adaptive parameter updating in intelligent manufacturing environments. Full article

Aiming at the challenges of class imbalance, tiny defect scales, and complex brushed background interference in the surface defect detection of electrical commutators, this paper proposes a high-precision and lightweight improved instance segmentation algorithm named WG-YOLOv11. Firstly, to overcome the barrier of highly imbalanced positive and negative samples in actual industrial data collection, a Balanced Defect Synthesis (BDS) data augmentation strategy is introduced to effectively enrich the morphological diversity of tiny defects. Secondly, a Wavelet Transform Convolution (WTConv) module is collaboratively integrated into the feature extraction network to expand the receptive field while preserving the high-frequency edge details of hairline cracks. Thirdly, a Group CBAM Enhancer (GCE) module is introduced to filter out high-reflection and brushed background noise through grouped attention and weight re-calibration mechanisms. Finally, addressing the difficulty of pixel-level alignment for tiny defects, an $\alpha$-IoU loss function is utilized to improve the high-precision segmentation and localization capabilities by dynamically adjusting the gradient distribution. Comprehensive evaluations are conducted on two real-world electrical commutator surface defect datasets: KolektorSDD2 and KolektorSDD. Experimental results show that on the KolektorSDD2 dataset, compared to the YOLOv11 baseline, the Mask mAP@50 of WG-YOLOv11 increases from 85.2% to 89.2%, and the stringent metric Mask mAP@50:95 improves from 52.7% to 56.9%. Additional computational analysis on the same dataset validates that the proposed method maintains high efficiency, matching the baseline computational cost without compromising real-time inference speed. Furthermore, evaluations on the public MSD dataset confirm the model’s cross-domain generalization capabilities. The proposed framework effectively achieves a balance between detection accuracy, anti-interference robustness, and a lightweight architecture. Full article

Modular construction advances sustainability and is reshaping designer competencies, making workforce development critical to industry transition. Existing competency models rely mainly on expert interviews and Delphi methods, offering limited quantitative evidence on role-specific labor-market demands, causal relationships among competencies, or experience-based perceptual differences. This study presents a preliminary, data-driven competency-mapping study for modular construction designers by integrating BERTopic, Ward clustering, CVR, Bayesian BWM, and Fuzzy DEMATEL. Applied to 243 job postings from six countries, the text-mining stage identifies a candidate competency structure of 3 domains, 9 categories, and 36 performance statements. This candidate structure was then examined through an exploratory survey of 30 Korean respondents. The results suggest that Codes and Compliance represents the most clearly recognized high-consensus competency area within this local validation sample, whereas Modular Construction shows an indicative experience-related divergence in perceived causal position. Given the small and uneven subgroup sample and the formative state of Korea’s modular construction industry, the findings should be interpreted as preliminary evidence rather than as a validated competency framework or a confirmed expert–novice model. The study contributes a reproducible mixed-method workflow, a candidate competency map, and an illustrative maturity prototype for future validation and refinement. Full article

In response to the dual pressures of energy consumption and environmental pollution faced by the global shipping industry, this paper proposes an optimization method for the energy storage capacity of a ship’s hybrid energy system based on a two-layer optimization model, aiming to enhance the energy utilization efficiency and operational stability of the system. A DNN-IPSO optimization framework integrating deep neural networks (DNN) and the improved particle swarm optimization algorithm (IPSO) was constructed, and combined with robust control strategies, it optimized the energy storage capacity configuration problem under complex dynamic conditions. The results show that the proposed method exhibits superior performance in terms of energy utilization efficiency, system dynamic response, and stability. The energy utilization efficiency of the system has been increased to 91.3%, the bus voltage fluctuation has been reduced to 3.98%, the load tracking error has been decreased to 17.6 kW, and the average convergence iteration times have been reduced to 71 times. The 17.6 kW load tracking error accounts for only 1.76% of the rated propulsion power of the 1 MW-level experimental platform, which is approximately 38% lower than that of the GA-PSO method. The experimental results on the real ship show that after using the DNN-IPSO optimization, the unit voyage energy consumption has been reduced to 41.7 kWh/km, the propulsion power stability coefficient has been increased to 0.956, the system transient recovery time has been shortened to 3.2 s, and the power reserve margin has been increased to 18.4%. The proposed method can effectively enhance the energy management capability, dynamic response performance, and operational stability of the ship’s hybrid energy system in the actual operating environment, providing reliable technical support for the engineering application of the integrated energy system of ships. Full article

Stabilizing weak soils is a well-known pavement and geotechnical engineering technique. This technique involves introducing minimal cementitious materials to improve the soil’s geotechnical characteristics. This paper investigates the use of recycled industrial polymer waste (IPW) as a reinforcement material in the presence of cementitious binders to stabilize weak silty sand soil (SM), supporting sustainable engineering practices. The randomly distributed IPW were added as percentages of 0%, 5%, and 10% to a mixture of lime soil and cement soil, with varying amounts of 0% to 6% of lime (L) and 0% to 6% of ordinary Portland cement (OPC), respectively. The laboratory experiments were conducted on natural and stabilized samples in wet (unsoaked) and submerged (soaked) conditions. The experimental program included Proctor compaction, California bearing ratio (CBR), unconfined compressive strength (UCS), durability tests, scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction analyses. The resilient modulus (Mr) was estimated using an empirical equation. The outcomes of this experimental study show that adding a combination of IPW shreds with a small amount of L and/or OPC to the SM soil provides a significant increase in the UCS, CBR, durability and Mr values compared with case of SM with only L, which allows for superior characteristics and increases strength and stiffness parameters throughout any phase of earthwork construction design, resulting in stronger and stiffer subgrades. These results were reinforced by microstructural observations from SEM, EDS, and DRX, confirming the formation of cementitious gels and chemical compounds, consistent with the macro-scale mechanical improvements. The expected practical outcomes include potential reductions in pavement thickness, which can help lower pavement stabilization costs and extend its service life. Additionally, the use of waste materials to replace raw materials contributes to decreased energy consumption and emissions, although detailed assessments are needed to quantify these effects. Full article

The modernization of global energy infrastructure within the Industry 5.0 framework requires the use of high-strength steels and reliable joining technologies to ensure safe, sustainable pipeline transport. This study focuses on the analysis of heterogeneous welded joints formed between high-strength alloy steel (34KhN2MA/EN 34CrNiMo6) and an austenitic welded seam (ER 307). While austenitic welds mitigate the risk of cold cracking, they introduce significant structural and mechanical heterogeneity. To address this, the research proposes and validates a material homogeneity criterion (MHC) derived from the LM-hardness methodology. By analyzing the statistical dispersion of macrohardness (HRC) through indicators such as the Weibull homogeneity coefficient (m) and the coefficient of variation (ν), the study establishes a quantitative approach to assess material degradation and structural uniformity across key weld zones. Results demonstrate that macrohardness profiling effectively distinguishes between structurally heterogeneous regions near the weld axis characterized by low homogeneity coefficients (m = 4.04 < 10, A_m = 0.742 < 0.878), elevated variability (ν = 29.68% > 11.6%), and high technological damageability (D = 0.92 > 0.81, j_D = 11.87 > 4.38) with pronounced step-like variation in macrohardness (HRC ∈ [12.6; 47]), on the one hand, and stabilized homogeneous zones in the base material, where m = 24.89 > 10, A_m = 0.947 > 0.878, ν = 4.39% < 11.6%, D = 0.52 ⟶ 0, j_D = 1.09 ⟶ 0, and characteristic range of HRC = 47–55, on the other hand. This methodology provides a robust, quasi-non-destructive tool for enhancing predictive maintenance, digital twins, and the overall integrity management of “smart” pipeline systems. Full article

In the analysis of complex engineering systems, managing uncertainty and optimizing information processing structures are critical for reliable state prediction. The Belief Rule Base (BRB) provides a powerful machine learning approach for integrating expert knowledge with uncertain information. However, mitigating the combinatorial complexity of BRBs through conventional structure simplification often causes unintended information loss, introducing systematic prediction biases that undermine reliability. To address the trade-off between system complexity and modeling accuracy, this study proposes an adaptive belief rule base framework integrating sensitivity analysis with posterior consistency calibration (BRB-ARR). First, an information-driven rule screening mechanism is developed to dynamically determine the pruning threshold based on optimized Mean Square Error (MSE) fluctuations. This method effectively filters redundant rules while avoiding the cognitive biases associated with fixed empirical values. Second, a low-dimensional optimization process is employed to readjust the parameter vector, significantly enhancing computational efficiency. Finally, a posterior calibration module is introduced to compensate for the systematic biases caused by dimensionality reduction, strictly preserving the interpretability of the core inference architecture. To validate the effectiveness of the proposed framework, experimental evaluations are conducted on petroleum pipeline networks and liquid propellant launch vehicles. In the petroleum pipeline scenario, the rule base scale is reduced by over 60 percent from 56 to approximately 20 rules, while the parameter dimensionality decreases from 338 to 122. Compared to the conventional model, the mean squared error is reduced from 0.5291 to 0.3619. Furthermore, in the liquid propellant launch vehicle case, the model achieves a prediction accuracy of 98.57 percent with a mean squared error of 0.00029 while reducing the rule scale from 441 to 109. These results demonstrate that the BRB-ARR model effectively balances structural compactness with high precision prediction, offering a novel approach to uncertainty modeling in intelligent systems. Full article

This study investigates the corrosion behaviour of a WC–6Co cemented carbide (94 wt% WC, 6 wt% Co) in acidic (pH 2) and alkaline (pH 13) electrolytes used for industrial PVD coating removal. The removal of the coating was not investigated, since no coatings were applied or analysed in this study. The objective was exclusively to simulate the corrosion response of the exposed substrate after the coating had been removed during electrochemical stripping. Potentiodynamic polarisation measurements were performed from OCP −0.2 V to +3 V at a scan rate of 1 mV·s⁻¹, followed by surface characterisation using SEM/EDS and laser profilometry to identify corrosion mechanisms and quantify material degradation. In an acidic solution, corrosion was dominated by cobalt dissolution, followed by the formation of a W–O-rich corrosion-product layer, as indicated by increased tungsten and oxygen contents in SEM/EDS analyses. The layer became increasingly porous and mechanically unstable at higher potentials. Progressive thickening of the corrosion-product layer and subsequent breakdown resulted in significant material loss, including surface abrasion up to ~8 µm. In alkaline electrolytes, SEM/EDS analyses revealed a Co–O-rich surface layer, suggesting cobalt-containing hydroxide/oxide corrosion products. These results suggest that surface-layer formation on WC–Co does not necessarily provide reliable corrosion protection, as stability and morphology strongly depend on pH. These findings provide valuable guidance for the use of cemented carbides in electrochemical stripping processes for PVD coating removal. Full article

Iris sanguinea features upright, stiff leaves, making it an excellent cut-foliage material, with its tall leaf architecture greatly enhancing ornamental value in landscaping. However, during the leaf expansion phase, plants frequently exhibit loose foliage arrangement, excessive spreading, and compromised mechanical strength, culminating in lodging and a concomitant decline in ornamental quality. Plant height in I. sanguinea is strongly regulated by phytohormones. This study showed that exogenous GA at concentrations of 50 mg·L⁻¹, 100 mg·L⁻¹, and 200 mg·L⁻¹ increased seedling height by 5.7%, 8.8%, and 12.7%, respectively, through foliar spraying on I. sanguinea seedlings grown ex vitro in a greenhouse; conversely, PAC treatment at equivalent concentrations suppressed growth by 19.3%, 21.0%, and 22.2%, respectively. Two pivotal GA signaling components, GAI and GID1a, were isolated from I. sanguinea. Subcellular localization confirmed that both IsGAI and IsGID1a proteins localize to the nucleus. Overexpression vectors pCAMBIA1300-IsGAI-GFP and pCAMBIA1300-IsGID1a-GFP were constructed and expressed in Arabidopsis thaliana. Transgenic lines overexpressing IsGAI showed significantly reduced plant height, hypocotyl elongation, and bolting, whereas IsGID1a overexpression promoted these traits. Exogenous GA application partially reversed the dwarf phenotype induced by IsGAI overexpression and further potentiated the height enhancement observed in IsGID1a-overexpressing lines. This study identifies two key genes controlling plant height and provides a theoretical basis and genetic resources for precisely engineering plant architecture in I. sanguinea. This is especially important for developing dwarf varieties with enhanced ornamental and agronomic traits, offering significant potential in the landscaping and cut flower industries. Full article

Reliable solar energy harvesting demands advanced control strategies capable of maintaining thermal precision despite inherent environmental unpredictability. This research addresses the critical challenge of temperature regulation in the solar furnace system, which is hindered by severe non-linearities and stochastic environmental uncertainties. The study aims to transition Fractional-Order PID (FOPID) control from theoretical design to reliable industrial application by accounting for the Uncertain Design Vector (UDV) during the tuning phase. A Reliability-Based Design Optimization (RBDO) framework is proposed, utilizing a hybrid Multi-Objective Imperialist Competitive Algorithm (MOICA) integrated with Monte Carlo Analysis (MCAR). This approach simultaneously optimizes the Maximum Sensitivity (Ms), the integral of Time-weighted Absolute Error (ITAE) and their sensitivities, while ensuring physical realizability through the FOPID structure. Crucially, the simulation results demonstrate that the RBDO-tuned FOPID design achieves optimal performance levels comparable to deterministic methods while significantly reducing the overall system sensitivity by 35% to 55% compared to both deterministic and literature-based methods (GA-FOPID and PSO-FOPID). The study concludes that integrating probabilistic reliability into multi-objective metaheuristics provides a robust control strategy for high-temperature solar facilities, effectively mitigating the performance degradation caused by real-world parameter fluctuations and ensuring consistent operational stability. Full article

This study develops a bottom-up cost optimization model (DC-DECOM) to evaluate decarbonization pathways for Japan’s data center industry, targeting carbon neutrality of the information and communications technology (ICT) sector by 2040. The model represents Power Usage Effectiveness (PUE) as a dynamic function of ambient temperature and cooling technology, and integrates technology selection, regional energy supply, and carbon pricing within a single cost-minimization framework. Three scenarios are compared: a reference case (REF), a centralized carbon-neutral scenario (C-CN) that restricts new capacity to metropolitan areas, and a regional decentralization scenario (R-CN) that allows for nationwide siting. Input parameters are calibrated against data from the International Energy Agency (IEA), the Uptime Institute, Japan’s Ministry of Internal Affairs and Communications (MIC) White Papers, and the Japan Science and Technology Agency (JST). The R-CN scenario achieves the 2040 net-zero target at 18–23% lower total system cost than C-CN. The cost gap decomposes into four channels (cooling-energy reduction ∼35%, lower regional renewable procurement cost ∼30%, lower carbon cost ∼25%, and lower siting-related cost ∼10%). Sensitivity analysis identifies the carbon-price trajectory and the hardware-efficiency improvement rate as the most influential parameters; the R-CN advantage remains positive across all $\pm 1\sigma$ parameter variations and across two combined-scenario stress tests. Full article

The Industrial Internet of Things (IIoT) is central to modern industrial automation, yet its growing connectivity exposes critical systems to evolving cyber threats. Traditional intrusion detection methods struggle in IIoT environments due to class imbalance and limited adaptability to zero-day attacks. This systematic review evaluates generative AI techniques for IIoT intrusion detection and identifies deployment requirements for industrial environments. We searched five databases (IEEE Xplore, ACM Digital Library, Springer, ScienceDirect, and arXiv) for studies published between January 2019 and December 2025, applying predefined inclusion criteria. Following a systematic selection process (identification plus three progressive screening stages) across 342 records, 42 primary studies were included for systematic synthesis. We examined four GenAI paradigms—Generative Adversarial Networks, Transformers, Diffusion Models, and Variational Autoencoders—analyzing nine state-of-the-art frameworks through comparative performance analysis. Hybrid Transformer architectures (e.g., Transformer-GAN-AE) achieve the most consistent detection performance, while diffusion-based models (e.g., Diff-IDS) provide computational advantages for edge deployments. However, substantial variability in evaluation methodologies and limited reporting of statistical rigor indicate important gaps in current research practices. These findings inform the development of GenAI-driven strategies tailored to industrial infrastructure constraints and highlight key directions for advancing IIoT cybersecurity. Full article