Search Results (140,913)

The incomplete mechanism of grain-scale hydraulic fracture (HF) propagation induced by gradient pore pressure limited the understanding of multi-scale HF propagation. This paper studied the grain-scale evolution of cracks under the disturbance of gradient pore water pressure. A sandstone meso-structure construction method based on the K-means clustering algorithm was proposed. A fluid–solid force transfer principle of fluid–solid coupling was improved to achieve the loading of pore water pressure in the form of body forces. The results show that the continuous intensified disturbance of gradient pore water pressure serves as the dominant factor controlling the initiation and coalescence of grain-scale microcracks within the tensile force-chain region, the formation of microscopic HFs, and final opening into macroscopic HFs. Influenced by the grain-scale meso-heterogeneity in permeability, the originally isolated pore clusters become seepage channels non-collinear with HFs when partially connected with HFs, inducing the bifurcation of the pore water pressure field. Influenced by the meso-heterogeneity in tensile strength, discrete micro-cracks initiate in the bifurcated region of the pore pressure field, further inducing more complex HF propagation modes such as grain-scale deflection and discontinuous propagation. Grain-scale evolved cracks interconnect isolated pore clusters in rock to improve connectivity. Full article

This paper addresses the set stabilization problem of probabilistic Boolean control networks (PBCNs) via a self-triggered control strategy. Firstly, the algebraic representation of the considered PBCNs is established by employing the semi-tensor product (STP) of matrices. Secondly, Lyapunov functions (LFs) are introduced for the set stabilization analysis, and a constructive algorithm for deriving such LFs is provided. On this basis, a necessary and sufficient condition in terms of the LF is obtained to determine whether a PBCN can achieve stabilization to a prescribed target set with probability one. Furthermore, a design method for self-triggered controls (STCs) is developed. Finally, the Escherichia coli lactose operon is presented as an example to validate the theoretical results of this paper. Full article

The LWD acoustic transmitting system acts as a capacitive load with a narrow effective operating bandwidth and significant reactive power. In this paper, a KLM equivalent circuit model of the transmitting system, incorporating the mechanical coupling of the drill collar, is established based on its structural characteristics. This model is coupled with a dual-frequency impedance matching network to formulate an impedance function, whose parameters are solved using an adaptive Gauss–Newton algorithm. Subsequently, systematic electrical and acoustic tests were conducted on an anechoic water tank platform. Experimental results demonstrate that the active power at the transducer terminals is significantly enhanced within the target frequency bands after impedance matching. Taking 14 kHz and 4.5 kHz as examples, the peak-to-peak transmitting voltage at the transducer increases to approximately 1.75 times the pre-matching level at both frequencies. Meanwhile, the peak-to-peak receiving voltage of the hydrophone increases to about 1.7 and 3.5 times the pre-matching values at 14 kHz and 4.5 kHz, respectively. Furthermore, the transmitting voltage responses (TVR) of the quadrupole and monopole modes within their target bands improve by approximately 5–17 dB and 10–18 dB, respectively. Directivity measurements reveal that the horizontal main lobe of the quadrupole mode is highly pronounced, while the monopole mode maintains a near-circular radiation pattern with an overall increase in the sound pressure level. Full article

Low-cost optical particle counters (OPCs) are increasingly deployed to resolve spatial variability in indoor particulate matter levels that sparse reference networks cannot capture, but their output requires field calibration. We evaluated three co-located low-cost sensors against the means of reference nephelometers over seven discrete co-location runs of 20–33 h each, conducted between June and September 2022. We used 1-min and hourly aggregations and six calibration models (multiple linear regression, support vector regression, random forest, gradient boosting, XGBoost, and LightGBM). Reference PM₁ averaged 8.4–9.2 µg m⁻³. Factory-calibrated sensor output underestimated reference values by 5.3–6.1 µg m⁻³, with an RMSE of 6.5–7.6 µg m⁻³, representing 74–82% of the reference mean. Under a conventional random 80/20 hold-out, tree ensembles reached R² = 0.86–0.89 at 1-min resolution. When progressively stricter designs (day-blocked, leave-one-run-out, forward chaining and chronological hold-out) were applied to the same data and models, R² fell sharply from random to day-blocked to run-blocked validation and remained near or below zero under forward chaining and chronological hold-out. Calibration remained worthwhile, reducing prospective RMSE by 22–48%, but the calibrated slope fell to 0.18–0.44, so real variation was substantially compressed. Validation design accounted for 63–91% of the spread in R², while algorithm choice accounted for 3.6–10.6%. We conclude that reported machine learning calibration performance is governed more by validation design than by algorithm choice. Full article

Due to inter-frequency phase bias (IFB), GLONASS is ignored when discussing real-time kinematic (RTK) positioning. Although this problem has been effectively solved, defects still exist when the number of tracked satellites is small or when single-frequency observations are processed. This contribution proposes an enhanced method that can effectively overcome these existing shortcomings by combining GLONASS and GPS observations. By using an enhanced algorithm, the performance of estimated IFB rate is more stable and reliable when there are few tracked satellites, and estimating the IFB rate with a single frequency is available. In this contribution, the GLONASS dual-frequency RTK ambiguity fixed rate and positioning accuracy are significantly improved by adding GPS observations, and the GPS/GLONASS single-frequency RTK using the enhanced algorithm has a similar performance to GPS dual-frequency RTK, according to long-term statistics. And, another code-division multiple access (CDMA) system, BDS, is introduced, which shows that the IFB rate estimated by BDS+GLONASS observations is as good as that by GPS+GLONASS. The experiment with the muti-GNSS single-epoch RTK shows that the precision and reliability are enhanced by adding another system or frequency observation. Thus, single-/dual-frequency GLONASS observations are available and reliable for single-epoch RTK and can improve the precision and reliability of GPS RTK by using the enhanced algorithm. Full article

Digital twin (DT) technology is increasingly mobilized to improve the efficiency and operational performance of renewable energy (RE) systems. This study maps the efficiency-oriented segment of DT research in RE through a bibliometric analysis of 383 documents indexed in Web of Science and Scopus (2018–September 2026), obtained after screening out records in which the acronym DT denotes another concept, and compares the results with two earlier versions of the corpus. Research gaps are derived through a three-level triangulation of keyword prevalence, co-occurrence cluster composition, and position on the strategic diagram; they are tested across keyword thresholds, clustering algorithms, corpus subsets, and 246 runs of the strategic diagram, and are interpreted as gaps in salience within the analyzed corpus rather than as proof of absence from the wider literature. Annual output roughly doubled each year from 2021 to 2025 under every growth estimator, with China being the leading contributor. Machine learning and energy management form the most developed themes, whereas economic appraisal remains marginal: no economic term reaches the keyword core, no theme is organized around an economic construct in any run, and the two economic magnitudes reported by the most cited documents never set the cost of the twin against its benefit. Three gaps are retained—the absence of standardized appraisal frameworks for the twin itself, the weak consolidation of interoperability research, and the scarcity of work at the integrated, multi-energy scale—together with the peripheral coverage of hydropower and retrofit. Four stakeholder-specific recommendations follow, each linked to its evidence and to an existing practical precedent. Full article

This study proposes and validates a data-driven statistical process control (SPC) framework for monitoring and reducing particle contamination in automotive aluminium die-casting, addressing a documented gap in the structured application of inferential statistics to technical cleanliness management in automotive SMEs. An action research strategy based on DMAIC and CRISP-DM was conducted over eleven months in an automotive die-casting SME. The study encompassed a VDA 19.1/19.2-aligned audit, descriptive analysis of 64 pre-intervention samples, negative binomial regression with Wald hypothesis testing, Individual Moving Range (I-MR) control charts for before-and-after comparison, and microscopic characterisation of out-of-specification particles using Microsoft Excel, Minitab, and RStudio. The negative binomial model consistently outperformed Poisson regression across all granulometric classes (ΔAIC up to 2958.77). Mould cavity, injection machine condition, and injection operator were the dominant contamination predictors. Statistically significant variability reductions were achieved for total particles in the 150–400 µm range and for metallic particles in the 150–200 µm and 200–400 µm classes; the metallic 200–400 µm class also showed a significant mean reduction. Particles exceeding 400 µm remained dominated by sporadic special-cause events. The study is restricted to one component and site; the post-intervention sample (n = 17) limits statistical power, and extension to additional components, processes, and larger datasets is recommended. The reported findings should accordingly be read as case-study evidence rather than as generalisable, definitive conclusions. The framework offers a replicable, cost-effective approach to data-driven quality management for automotive SMEs, aligned with Industry 5.0 human-centric manufacturing principles. This paper bridges a literature gap by integrating negative binomial regression, Wald testing, and I-MR control charts into a unified SPC framework for technical cleanliness in automotive die-casting, connecting quality intelligence with the transversalities of artificial intelligence, innovation, and sustainability. The methodology itself relies on classical inferential statistics and SPC rather than on artificial intelligence algorithms; its Industry 5.0 relevance lies in providing the structured, human-centric, data-driven decision-making foundation on which future AI-assisted particle classification and real-time monitoring can be built. Full article

Steel–concrete composite tubular columns have become increasingly attractive for sustainable structural applications due to their high load-carrying capacity and efficient material utilization. However, optimizing their structural performance while simultaneously minimizing embodied carbon emissions and material cost remains a challenging multi-objective problem. This study presents a comprehensive optimization framework for circular, rectangular, and square composite tubular columns composed of high-strength materials, and using the Particle Swarm Optimization (PSO) and Multi-Objective Particle Swarm Optimization (MOPSO) algorithms. The multi-objective optimization simultaneously maximizes axial load capacity while minimizing embodied CO₂ emissions, considering both sections with (WR) and without (WoR) additional longitudinal reinforcement, enabling the identification of optimal trade-offs between structural performance and environmental impact. Design variables include the cross-sectional dimensions, concrete compressive strength, steel yield strength, and reinforcement configuration. The resulting Pareto-optimal solutions are further evaluated using a multi-criteria decision-making approach based on Minkowski Metrics combined with Entropy Theory to identify the best overall compromise solution. The numerical results demonstrate that the use of high-strength materials can reduce embodied CO₂ emissions by up to 16% in the analyzed cases. The implemented load-bearing capacity formulation was also validated against experimental results, yielding a mean N_exp/N_NBR16239 ratio of 1.01 for the six specimens analyzed. The multi-objective formulation associated with Minkowski metrics produces a robust tool for determining the best solutions in general, as well as the best solutions related to the maximum load that the columns can support. Finally, for both the single-objective and multi-objective problem analyses, increasing the slenderness of the columns resulted in more costly solutions, both economically and environmentally. Full article

Real-time environmental monitoring is increasingly supported by Internet of Things (IoT)-based sensing infrastructures and machine learning techniques capable of processing high-frequency urban environmental data. However, many existing solutions remain focused primarily on data acquisition, visualization, or isolated prediction tasks, with limited integration of predictive analytics and human expertise within a unified decision-support framework. This limitation highlights the need for intelligent monitoring approaches that combine continuous sensing, short-term prediction, and Human-in-the-Loop interpretation. Hybrid Human–AI Collaborative Networks (HCNs) are increasingly relevant for supporting environmental monitoring and decision-making in smart cities. This paper proposes an Artificial Intelligence of Things (AIoT)-enabled collaborative framework that integrates distributed uRADMonitor sensors, cloud-based data management, machine learning models, and human stakeholders into a unified monitoring ecosystem. A distributed uRADMonitor sensing network was deployed across multiple locations in Sibiu for continuous environmental monitoring, while the machine learning experiments presented in this study were conducted independently using three sensing-node datasets collected from different neighbourhoods in Sibiu. The same overall modelling and validation methodology was applied to each dataset, while the predictor set reflected the environmental variables available at each sensing node. Several machine learning algorithms were evaluated for Air Quality Index (AQI) prediction, including Linear Regression, Random Forest, and Gradient Boosting. Under a random train–test split, Random Forest was the best-performing contemporaneous model across all three sensing nodes, although predictive performance varied substantially between locations. In contrast, the short-term forecasting experiment provided the operationally relevant predictive component, with Ridge Regression achieving positive skill relative to persistence across forecasting horizons from 5 to 120 min. Full article

Nasopharyngeal carcinoma (NPC) is highly prevalent in Southeast Asia, including Indonesia, with most patients presenting at advanced clinical stages. Reliable, minimally invasive circulating biomarkers for monitoring treatment response are lacking. This study aimed to identify candidate blood-based transcriptomic biomarkers for advanced-stage NPC through integrative bioinformatic analysis and to evaluate the expression dynamics of a prioritised candidate gene, TSPAN32, in peripheral blood of patients undergoing chemoradiation. Differentially expressed genes (DEGs) were identified from the GEO microarray dataset GSE53819 (18 advanced-stage NPC tissues vs. 18 non-cancerous nasopharyngeal tissues) using GEO2R. Candidate DEGs were prioritised through protein–protein interaction (PPI) network analysis (STRING confidence ≥ 0.9), functional enrichment analysis (GO via Enrichr; KEGG pathway via ShinyGO v0.82), and survival analysis (Kaplan–Meier). Overlapping DEGs were identified against a previously published blood-based NPC transcriptomic dataset with 11 early-stage NPC patients, 11 advanced-stage NPC patients, and 11 healthy donors. Analysis of GSE53819 identified 943 upregulated and 1503 downregulated DEGs (|log₂FC| ≥ 1; Padj < 0.05). Functional enrichment of upregulated DEGs was dominated by inflammatory response and cytokine activity pathways, while downregulated DEGs were enriched in B-cell activation and immunoglobulin receptor binding. Protein–protein interaction network construction (STRING confidence ≥ 0.9) and hub gene identification using the MCC algorithm identified six candidate genes among the downregulated DEGs: ADRA2A, SELP, TSPAN32, PEAR1, P2RX1, and DGKG. Kaplan-Meier survival analysis demonstrated that lower expression of SELP (HR = 0.57; p = 0.00053), TSPAN32 (HR = 0.56; p = 0.00025), and P2RX1 (HR = 0.48; p = 1.2 × 10⁻⁵) was significantly associated with shorter overall survival in head and neck carcinoma, supporting TSPAN32 as the primary candidate for clinical validation. RT-qPCR examination demonstrated that TSPAN32 transcript abundance in peripheral blood was significantly upregulated following chemoradiation in nine of ten patients (mean Log₂FC = 2.08 ± SD 1.09, 95% CI 1.29–2.86; one-sample t-test t(9) = 6.01, p = 0.0002; Wilcoxon signed-rank test p = 0.002). Primer specificity for TSPAN32 and ACTB was confirmed by melt-curve analysis and in silico validation (Primer-BLAST, OligoAnalyzer, UCSC In-Silico PCR). RBC transfusion status did not significantly modulate the magnitude of TSPAN32 expression change (independent-samples t-test, t = 1.86, p = 0.101). This integrative study identifies TSPAN32 as a candidate circulating biomarker responsive to chemoradiation in advanced-stage NPC. These exploratory findings require confirmation in larger, prospective, longitudinal cohorts before clinical translation. Full article

In multi-mission air operations, area-patrol missions and long-range missions typically share a limited tanker fleet while imposing different constraint structures: patrol refueling is bounded by hard time windows, whereas long-range missions are governed by restricted-zone avoidance, detour tolerances, and multi-segment fuel verification. Conventional scenario-wise independent planning splits resources and wastes cross-region ferry mileage. This paper adapts the established paradigms of the location–routing problem (LRP) and the vehicle routing problem with time windows (VRPTW) to this joint refueling scenario: a joint planning model prioritizes the number of tanker sorties over total system flight distance, and a decoder-coupled adaptive large neighborhood search (ALNS) integrates airspace selection, task assignment, tanker routing, and dual-timeline rendezvous decoding, with all mission hard constraints embedded in a deterministic, reproducible evaluator that adjudicates feasibility at every search iteration. Experiments at three scales (17, 42, and 100 tasks) show 100% mission coverage and 100% patrol time-window satisfaction: relative to scenario-wise independent planning, tanker sorties decrease by 16.2–19.7% and tanker flight distance by 14.6–15.5% (significant after Bonferroni correction on 90 paired replicates per scale); against genetic algorithm (GA) and ant colony optimization (ACO) baselines—and against a route-encoding GA under an equal solution-space representation—the method is superior in solution quality and runtime ($p<0.001$), and the separation persists when the baselines receive a 25-fold evaluation budget. Monte Carlo simulations characterize how plan feasibility degrades under execution-time disturbances. Within the studied instance families, the framework yields executable joint refueling plans within operational runtimes. Full article

Data centers consumed 415 TWh of electricity in 2024, about 1.5% of global demand, and foundation model training and inference are a growing part of this load. As training and inference continue to grow, energy-efficient foundation models are becoming essential. An efficiency gain may come from the model, the accelerator, or the facility, and existing reviews and primary studies usually address one of these aspects. However, energy is obtained by a different method at each of these stages, so unified and systematic optimization is more difficult than results at any single stage may suggest. Reported metrics range from floating-point operations (FLOPs) and tera-operations per second per watt (TOPS/W) to throughput, power usage effectiveness (PUE), carbon, and water. This review follows energy through each stage and treats each reported value together with its measurement boundary and evidence class. This review covers the chain from how models are designed, compressed, and served through how accelerators execute them at reduced precision to how facilities cool and power them. This review identifies open research problems in wall-plug measurement, cross-layer co-design, lifecycle accounting, and the deployment maturity of emerging accelerators. It aims to serve as a reference for researchers and practitioners seeking a unified view of energy, carbon, and water across foundation model systems. Full article

Ecuadorian agri-food supply chains are characterized by strong territorial heterogeneity, crop perishability, and unequal production concentration, creating significant socio-economic and logistical challenges. To address these territorial disparities, this study employs an AI-driven clustering approach to classify Ecuador’s 23 continental provinces by their agri-food production and supply chain integration profiles. Using official 2024 provincial tabulations from the National Institute of Statistics and Censuses of Ecuador, seven variables were analyzed: harvested banana area, banana production, cocoa production, rice production, corn production, potato production, and banana yield. The methodology combined descriptive statistics and Pearson correlations with unsupervised machine learning techniques, specifically hierarchical agglomerative clustering and k-means algorithms after z-score standardization. The descriptive results showed extreme dispersion across provinces, with coefficients of variation exceeding 100% for all variables and marked right skew. The final four-cluster solution (k = 4, BSS/TSS = 78.65%, average silhouette = 0.43) differentiated: (cluster 1) a potato-specialized Andean profile represented by Carchi; (cluster 2) a broad group of low-intensity provinces requiring inclusive logistical integration; (cluster 3) medium-scale banana provinces with high yields; and (cluster 4) the dominant tropical production core formed by Guayas and Los Rios. The typology explains 78.65% of the standardized variability and provides the territorial evidence base required for designing differentiated supply-chain planning, resilience strategies, and public policies tailored to each provincial typology. Full article

The development of complex equipment faces prominent challenges, including unequal status among participating agents, multi-objective full confrontation, strong super-conflict among multi-indicators, dynamic risk evolution, delayed on-site perception, and the absence of collaborative negotiation. Traditional risk management and control methods, based on the ideal assumptions of equal subjects and independent indicators, struggle to characterize and resolve super-conflict games dominated by super decision-makers. Furthermore, they lack dynamic early warning and closed-loop execution mechanisms linked to real-time perception, commonly suffering from drawbacks such as low early warning accuracy, high decision-making conflict, delayed response, and inefficient collaboration. To address these issues, this paper integrates multi-agent conflict negotiation with intelligent perception and learning technologies to propose an intelligent risk early warning and closed-loop control method for complex equipment development. The three-dimensional risk evolution dynamics model and Virtual Risk Center (VRC) are employed to decouple super-conflict indicators, while the industrial inspection unmanned aerial vehicle (UAV) perception relative motion model enables the unified mapping of physical risks and decision-making games. A super-conflict gray target negotiation (SCGTN) model is constructed to achieve stable consensus decisions among multiple parties under conflicting indicators. Based on Markov Decision Processes and the PPO algorithm, the optimal virtual risk trajectory is generated, which is then combined with the Archimedean spiral convergence trajectory to synthesize executable control trajectories. This forms an integrated system of UAV real-time perception → super-conflict resolution → intelligent decision-making → closed-loop regulation. Validated through a case study of large-scale complex aviation equipment development, the proposed method achieves field-validated risk early warning accuracy of 94.7% evaluated against real-world on-site ground truth labels. The numerical simulation results, whose parameters are fully calibrated against real-world engineering datasets, indicate that under simulated test conditions, our method yields simulation-predicted performance: it reduces decision-making conflict intensity by 49.3%, controls risk deviation error within 1.38%, and shortens closed-loop response time to 158 ms. Note that conflict reduction level, risk deviation error, and closed-loop response time are pure simulation outputs and have not been directly measured from physical on-site closed-loop experiments. Under the same simulation setup, the end-to-end response speed is 7.6 times faster than the peer dynamic closed-loop Digital Twin-Proximal Policy Optimization (DT-PPO) benchmark algorithm with identical online sensing and reinforcement learning architecture and roughly 4700 times faster than simulated counterparts of traditional static offline evaluation modes that rely on periodic manual statistics and offline meetings. It is adaptable to complex equipment development scenarios characterized by strong super-conflict, high dynamics, and unequal subjects, providing a theoretical framework and technical support for intelligent risk prevention and control throughout the full lifecycle of complex equipment. Full article

In this study, a machine learning model for the anisotropic mechanical behavior of AA7075-T6 was successfully developed by combining classical plasticity theory with neural network algorithms. First, a yield criterion incorporating strength asymmetry and orientation dependence was constructed based on a fourth-order linear transformation of invariants, and a stress update algorithm was implemented using the return mapping and implicit integration schemes. Subsequently, a dataset comprising experimental results from tension, compression, and shear tests at multiple orientations, as well as theoretically generated data from additional strain paths, was established to train a genetic algorithm-optimized two-hidden-layer neural network. Plastic-stage assessments indicated that, for the equal-biaxial path, the RMSE values of the stress–strain curves predicted by the machine learning model relative to the constitutive implementation were 31.87 and 39.24 MPa. It should be noted that the stress–strain response under equal-biaxial loading represents an additional prediction case. The trained model exhibited satisfactory predictive performance when compared against experimental data for tension, compression, and shear and was capable of reproducing the theoretical stress paths derived from the classical constitutive model. This approach leverages the physical interpretability of conventional constitutive modeling and the high efficiency of data-driven methods, providing a viable solution for efficiently predicting the anisotropic mechanical response of metallic materials under complex stress states. Full article