Search Results (575)

This paper aims to present efficient alternative mixed-integer non-linear programming models and a customized hybrid metaheuristic, the Genetic-Based Algorithm (GBA), for a resource-constrained project-scheduling problem with a flexible network structure (RCPSP-FNS). We also consider the cost–time trade-off in the problem with a flexible network by using activity-duration compression. We present three approaches to solve the problem, including a mixed-integer non-linear program (MINLP) using binary variables representing activity completion times (MINLP1), an alternative mixed-integer non-linear program using integer variables representing activity-completion times (MINLP2) that has not presented before in RCPSP-FNS modeling, and the GBA. A total of 35 different problems are solved to examine the computational efficiency of the solution approaches. The MINLP1 and MINLP2 models are both solved by the GEKKO solver. The results indicate that solving the MINLP2 model can reach the optimal objective value obtained by solving the MINLP1 model in significantly less time. In addition, the proposed genetic-based algorithm can solve some large problems in a more efficient way in comparison to solving MINLP1 by using GEKKO. However, solving the MINLP2 model using GEKKO is the most efficient solution approach in comparison to both MINLP1 and the proposed genetic-based algorithm. MINLP2 can be solved to proven optimality (in much less time) for problems in which the MINLP1 model can, at most, reach near-optimal solutions. Full article

Aiming at frequency fluctuations caused by high-penetration renewable energy, a coordinated secondary frequency regulation strategy is proposed for hybrid energy storage stations in regional power grid. Hybrid energy storage stations contain electrochemical energy storage stations (EESSs) and compressed air energy storage stations (CAESSs). Complete mathematical models of concerned energy storage stations are constructed, and a multi-objective exponential distribution optimization (MOEDO) algorithm is proposed to optimize regulation cost, automatic generating control (AGC) command tracking and constrain the state of charge (SoC) of energy storage equipment. The proposed strategy rationally distributes secondary regulation instructions for hybrid energy storage stations and thermal power plants. In the end, simulation results on a two-area interconnected power grid are given to verify the effectiveness of proposed strategy. Results reveal that the presented method can effectively suppress frequency deviation and tie-line power oscillation and maintain SoC within a safe operating range. Full article

Construction schedule compression requires balancing project-duration reduction against direct, indirect, and risk-related cost increases under uncertain activity performance. Many construction time–cost trade-off models still rely on deterministic durations or predefined execution modes, which limits their ability to represent practical activity-level crashing decisions under uncertainty. This study develops a stochastic risk-aware time–cost optimization framework for construction scheduling using bounded integer crash-day decision variables. Activity durations are represented using triangular distributions based on optimistic, most-likely, and pessimistic estimates, while Monte Carlo simulation is used to propagate uncertainty through the precedence network. Expected and Conditional Value-at-Risk-oriented indicators are integrated into risk-adjusted duration and cost measures, which are then combined through a nonlinear normalized objective function. A Hybrid Genetic Algorithm–Gray Wolf Optimizer is implemented to solve the resulting discrete stochastic optimization problem and is benchmarked against seven metaheuristic algorithms under identical evaluation conditions. The framework is demonstrated using a 30-activity construction project reconstructed from Microsoft Project data. The proposed Hybrid GA–GWO reduced the deterministic project duration from 895 to 699 working days and achieved the best descriptive objective performance across 30 independent runs. However, after Bonferroni correction, its differences from the Genetic Algorithm and MPGWO-DLL were not statistically significant, indicating that these methods remain competitive alternatives. Additional Monte Carlo convergence, tornado sensitivity, correlated-duration sensitivity, and computational-time analyses were added to evaluate the stability, parameter dependence, and practical applicability of the framework. The findings show that the proposed framework can support risk-aware construction schedule-crashing decisions by identifying activity-level acceleration plans while explicitly accounting for downside schedule and cost risk. Broader validation in larger and more diverse real-world projects remains necessary. Full article

A solar cooling facility with two forms of energy storage, electrochemical (batteries) and thermal (tanks containing the heat transfer fluid, HTF), is simulated. The technical specifications used in the simulation are taken from a recently installed system in an institutional building at the University of Almería (Spain). Electricity generated by photovoltaic (PV) panels is either stored in a battery bank, or supplied directly to the chiller, which is also connected to the grid as a backup. The HTF circulates through a storage tank and is driven to a heat exchanger to cover the refrigeration demand. The whole system is modeled in TRNSYS with the corresponding meteorological data: the PV array has a peak power of 23.4 kW, the compression chiller power is 70 kWt, and the building demands of refrigeration from high and low season amount to 413.5 kWh/day and 62.8 kWh/day, respectively. The battery bank capacity is 40.8 kWh and the tank has a volume of 4000 L. Different configurations of energy storage (only electrical, only thermal, and hybrid) are tested for both demands. The results show that the refrigeration needs can be covered with solar energy and storage in the low season, with a surplus that can be driven to the building. In the high demand season, an extra input from the grid is needed as the current facility covers 89.3% of the total electricity requirements per day (54.5% if no storage is used). Finally, the system performance is evaluated over an intermediate-demand month. Overall, it is found that electrical consumption from the grid is minimal when batteries are used, either alone or in conjunction with thermal storage. Full article

Precision agriculture and sustainable farming practices require autonomous environmental monitoring systems capable of operating in remote areas with limited energy and connectivity. However, the high cost of existing professional technology remains a significant barrier to widespread adoption. This study presents the development of a solar-powered smart sensing node designed for autonomous operation that integrates TinyML and dual-mode wireless connectivity via LoRaWAN and Wi-Fi for intelligent monitoring. The system features a custom-designed cup anemometer and multispectral sensing capabilities integrated into a compact single-tower architecture. All structural components, including radiation shields and a modular PVC frame, were designed for low-cost manufacturing and mass production. A single hermetic housing protects the core control electronics and is designed to improve durability in harsh outdoor environments. A Multi-Layer Perceptron model was implemented on the edge to enable intelligent data fusion and compensation, while a dynamic sampling strategy optimized power consumption. Experimental results demonstrate the feasibility of the proposed architecture through adaptive spectral acquisition over a daily illumination cycle, embedded MLP-based sensor fusion, and telemetry-oriented data compression that substantially reduces the number of transmitted samples. The main contribution of this work is a system-level architecture that integrates sensing, embedded intelligence, solar-energy harvesting, hybrid wireless communication, and telemetry optimization into a compact, low-cost, and field-deployable prototype IoT platform for sustainable precision agriculture. Full article

Heat pumps are core equipment for efficient low-grade thermal energy utilization and low-carbon transformation of the energy structure, offering significant energy-saving potential in building heating and industrial waste heat recovery. This paper reviews the research progress and technical challenges of compression, absorption, and adsorption heat pumps as well as nanofluid-enhanced heat transfer technology and elastocaloric heat pump systems. Air source heat pumps can delay frosting through variable frequency, heat storage, and waste heat recovery. However, accurate prediction models for performance degradation under extreme cold conditions are lacking. Although ground source and water source heat pumps exhibit significant energy efficiency advantages, ground source systems may suffer from performance degradation due to underground thermal imbalance. The application of water source systems is strictly constrained by water resource conditions. Driven by low-grade waste heat, absorption heat pumps employing traditional working pairs suffer from crystallization, corrosion, or high rectification energy consumption. The COP of a single-effect cycle under 80~100 °C waste heat is only 1.2~1.9, while hybrid cycles can reach approximately 3.2 at 120~150 °C. Although adsorption heat pumps achieve significantly improved performance under continuous heat recovery cycles, the full-scale power density of novel adsorbents such as metal–organic frameworks is inferior to the power density of traditional silica gel. Moreover, under off-design conditions, the performance drops by 23~48% compared to theoretical values. Nanofluids can enhance heat transfer, but the long-term effects of particle agglomeration at high temperatures on pump power consumption and system compatibility remain to be systematically evaluated. Elastocaloric heat pump systems can achieve refrigerant-free cooling, but current prototypes still cannot compete with traditional vapor compression systems in long-cycle fatigue reliability and power density. Current heat pump technologies generally face challenges such as insufficient adaptability to extreme conditions, bottlenecks in working fluids and materials, and a lack of long-term validation. Future research must construct a multi-source coupling optimization system, address common problems in working fluids and materials, promote long-term validation and kilowatt-level prototype demonstrations, and drive the large-scale deployment and engineering application of heat pump technology toward high efficiency, intelligence, and high reliability. Full article

Accurate and scalable estimation of rock shear strength parameters is essential for remote-sensing-supported geological hazard assessment, slope stability evaluation, and engineering geological mapping. However, determining cohesion and internal friction angle requires multiple triaxial tests under different confining pressures, which are time-consuming, costly, and difficult to apply widely. To support remote-sensing-oriented geoscience and civil engineering applications, this study develops a hybrid machine learning framework for estimating cohesion and internal friction angle from geophysical and mechanical indicators. A cross-source database was compiled from published rock records collected from the Jinchuan mining area in China and the Luhri area in India. After completeness screening and unit harmonization, 213 mixed-lithology cases were retained for modeling, with P-wave velocity, density, uniaxial compressive strength, and tensile strength used as input variables. An Equilibrium Optimizer was coupled with a multilayer perceptron to optimize the network weights and biases, and model performance was evaluated using five-fold cross-validation, independent testing, repeated runs, and comparisons with conventional MLP and several typical machine learning models. The proposed EO–MLP model achieved high prediction accuracy, with test-set coefficient of determination values of 0.946 for internal friction angle and 0.983 for cohesion and corresponding RMSE values of 1.072 and 0.671, respectively. Robust scaler normalization produced the best performance among the three tested normalization strategies. SHapley Additive exPlanations analysis indicated that density was the dominant predictor of cohesion, whereas uniaxial compressive strength and P-wave velocity made the largest contributions to internal friction angle prediction. The proposed framework provides an indirect data-driven tool for estimating shear strength parameters and can complement engineering-geological investigation and rock engineering design. Full article

High penetrations of renewable generation are increasing the need for long-duration energy storage capable of intertemporal balancing and reserve provision. However, the market value of heterogeneous storage portfolios under shared grid-connection constraints remains insufficiently quantified. This study develops a risk-averse day-ahead co-bidding model for a hybrid pumped-hydro and compressed-air energy storage (CAES) portfolio participating jointly in energy and spinning-reserve markets. Monte Carlo sampling and scenario reduction are used to represent price uncertainty, while conditional value-at-risk (CVaR) captures downside-profit risk. Shared point-of-common-coupling (PCC) constraints explicitly couple electricity sales, purchases, and reserve offers. Compared with homogeneous pumped-hydro expansion, replacing the equivalent incremental pumped-hydro capacity with CAES increases the cumulative reserve bid by 65.71%, while expected profit decreases by 1.17% and raw-scenario back-test CVaR remains nearly unchanged, decreasing by only 0.05%. Relative to the unconstrained hybrid-storage case, the shared PCC constraints reduce expected profit, raw-scenario back-test CVaR, and reserve bids by 1.01%, 1.34%, and 18.62%, respectively. Scenario-reduction sensitivity and synthetic price–spread analyses indicate that the main operating mechanisms remain stable within the assumed scenario-generation framework, while sensitivity analyses reveal diminishing returns from CAES expansion and saturation of PCC-related profit gains near 5000 MW. Because all price scenarios are synthetic and neither historical nor independent out-of-sample market data are used, these analyses constitute model-based robustness tests rather than seasonal or real-market validation. The findings support the coordinated configuration of heterogeneous storage, grid-interface capacity, and risk preferences, but should be interpreted as market-bidding-level comparative evidence under the adopted equivalent CAES representation rather than as market-specific profitability forecasts. Full article

Hydrogen-rich compounds under high pressure are promising for high-temperature superconductivity, but many high-$T_{\mathrm{c}}$ hydrides rely on rare-earth or alkaline-earth elements and remain difficult to tune chemically. Transition-metal hydrides offer an alternative platform because partially filled d states can modify the electronic density of states, metal–hydrogen hybridization, and electron–phonon coupling. Here, ${\mathrm{VH}}_3$ is used as a parent high-pressure transition-metal hydride framework, and Ti substitution is introduced as a chemically compatible way to tune the d-derived states near the Fermi level. Two ternary hydrides, ${\mathrm{Ti}}_3{\mathrm{VH}}_{12}$ and ${\mathrm{TiV}}_3{\mathrm{H}}_{12}$, are therefore constructed from the ${\mathrm{VH}}_3$ lattice and investigated by first-principles calculations at 200 GPa. Both compounds are thermodynamically and dynamically stable under this pressure condition, as indicated by formation energies, the Ti–V–H convex hull, and phonon spectra. Within the same ultrasoft-pseudopotential computational framework, ${\mathrm{Ti}}_3{\mathrm{VH}}_{12}$ and ${\mathrm{TiV}}_3{\mathrm{H}}_{12}$ yield Allen–Dynes $T_{\mathrm{c}}$ values of 42.1 K and 36.8 K, respectively, higher than the corresponding ${\mathrm{VH}}_3$ value. A norm-conserving cross-check for ${\mathrm{VH}}_3$ gives a different absolute value, indicating that the $T_{\mathrm{c}}$ estimates are method-dependent. Electronic structure analysis indicates that Ti incorporation shifts pronounced van Hove singularities close to the Fermi level, enhances the density of states, and changes the Fermi surface topology. These results suggest that Ti–V–H hydrides are a useful model system for examining how transition-metal substitution can couple structural stability with electronic tuning in compressed hydride superconductors. Full article

The rapid growth of Intelligent Transportation Systems (ITSs) and Internet of Things (IoT) technologies has generated massive volumes of distributed traffic data, creating significant challenges related to privacy, scalability, communication overhead, and heterogeneous traffic patterns. To address these challenges, this paper proposes FedTraffic, a hierarchical federated learning framework for traffic flow forecasting that integrates Edge–Fog–Cloud computing, hybrid deep learning, adaptive federated optimization, and Explainable Artificial Intelligence (XAI). The proposed framework combines a Temporal Convolutional Network–Conditional Variational Autoencoder (TCN–CVAE) with traffic-behavior clustering, adaptive client selection, and hierarchical model aggregation to enable accurate, privacy-preserving, and interpretable traffic prediction under heterogeneous non-IID environments. Extensive experiments demonstrate that FedTraffic achieves a best Mean Absolute Error (MAE) of 2.12, a Root Mean Square Error (RMSE) of 4.28, a Mean Absolute Percentage Error (MAPE) of 5.47%, and an $R^2$ score of 0.966. Compared with the strongest federated baseline, it improves MAE by up to 18.77%, RMSE by 16.41%, and MAPE by more than 22%, while reducing communication overhead through an 8:1 latent representation compression ratio. These results demonstrate the effectiveness of FedTraffic as a scalable, privacy-preserving, and interpretable solution for next-generation intelligent transportation systems. Full article

The increasing demand for predictive maintenance in industrial environments requires edge-intelligent solutions in which latency, energy consumption, memory footprint, and data movement are strictly constrained. This paper presents a two-stage Edge AI architecture for rolling-bearing prognostics, designed for heterogeneous embedded deployment across two distinct processing levels: a smart programmable sensing unit for local low-complexity signal preprocessing and a low-power embedded multiprocessor for higher-level temporal prognostic reasoning. In the first stage, a tiny neural preprocessor processes high-frequency vibration measurements directly at the sensing level and produces a compact low-dimensional degradation representation, complemented by lightweight health-related physical features. In the second stage, an embedded temporal reasoning model analyzes sequences of these compressed representations to estimate bearing degradation and remaining useful life. In addition to numerical remaining useful life regression, the second stage includes diagnostic reasoning heads that produce maintenance-oriented categories from a restricted vocabulary, enabling interpretable diagnostic summaries without relying on cloud-based language models. The complete pipeline is designed for fully edge-resident operation and exported in deployment-compatible formats, with the objective of supporting practical integration into heterogeneous industrial edge platforms. The proposed framework is trained and evaluated on the PRONOSTIA bearing degradation dataset and positioned against representative recurrent and hybrid prognostic baselines from the literature. From the deployment viewpoint, the sensor-side stage requires 68.62 ms inference time with 2.07 KiB RAM and 1.35 KiB flash/weights, whereas the edge temporal stage runs in 49.2 ms with 90.68 MiB RAM and 67.71 MiB flash/weights. In terms of prognostic performance, the proposed model achieves an average normalized RMSE of 0.1616 and an average normalized MAE of 0.1311 on three held-out bearings, while the weakly supervised diagnostic heads reach accuracies of 0.9066 for degradation trend and 0.8872 for vibration evidence. Experimental results show that the proposed architecture provides an effective trade-off between compact sensor-side processing, temporal prognostic accuracy, monotonic degradation consistency, hardware deployability, and interpretable maintenance-oriented outputs, supporting the feasibility of fully edge-based predictive maintenance systems for rolling-bearing health monitoring. Full article

Cementitious sand and gravel (CSG) is commonly used in construction engineering; however, its mix proportion design is complex, and traditional physical experiments face limitations such as long cycles, high costs, and susceptibility to external factors when obtaining high-quality sample data. In this study, a foundational dataset was first acquired through physical experiments: 100 sets of CSG specimens with different mix proportions (cement content 40, 50, 60, 70 kg/m³; water-to-binder ratio 1.0, 1.2, 1.4; sand ratio 0.1, 0.2, 0.3, 0.4; fly ash content 20, 30, 40, 50 kg/m³) were prepared. After 28 days of standard curing, compressive strength and splitting tensile strength tests were conducted using a WAW-1000 electro-hydraulic servo universal testing machine, yielding 100 sets of real mechanical property data. The coefficients of variation for all test groups were below 10%, confirming the reliability and repeatability of the experimental data. On this basis, a data augmentation method based on Conditional Tabular Generative Adversarial Networks (CTGAN) is proposed. Through adversarial training between the generator and the discriminator, the model learns the multi-dimensional distribution characteristics of the original CSG data and generates 100 synthetic samples, which are then merged with the original data to expand the dataset to 200 samples. The quality of the synthetic data is evaluated using Wasserstein distance and correlation matrix heatmaps. Furthermore, a hybrid XGBoost–LSTM prediction model is proposed—XGBoost is used for feature construction to capture nonlinear interactions among mix proportion variables, and the constructed features are then fed into an LSTM network for sequential learning and regression prediction. The results show that the CTGAN-generated data are highly consistent with the original data in terms of kernel density distributions and variable correlations, with Wasserstein distance significantly superior to four comparative methods: Bootstrap, SMOTE, GaussianCopula, and TVAE. After augmentation, the XGBoost–LSTM model achieves a coefficient of determination (R²) of 0.9897 for compressive strength prediction (vs. 0.9793 before augmentation) and 0.9801 for splitting tensile strength (vs. 0.9882 before augmentation, a slight decrease). The mean absolute percentage errors (MAPE) are 4.49% and 4.11%, and the root mean square errors (RMSE) are 0.201 and 0.049, respectively; both error metrics are reduced compared with those before augmentation. Compared with baseline models including XGBoost, LSTM, Random Forest (RF), and Support Vector Regression (SVR), the XGBoost–LSTM model exhibits the best performance across all evaluation metrics, and Wilcoxon signed-rank tests confirm that the performance differences are statistically significant (p < 0.05). The proposed method of CTGAN-based data augmentation combined with the XGBoost-LSTM hybrid model provides an effective solution to the problem of insufficient CSG sample data and offers a reference for data enhancement and performance prediction of other small-sample materials. Full article

Multivariate structural-response classification supports bridge monitoring by integrating synchronized measurements from multiple sensors and assigning an observation window to a predefined structural or loading condition. The main technical challenge is to compress temporally and spatially distributed sensor information into a fixed-length representation without losing transient or localized condition-sensitive features. However, previous studies have mainly focused on backbone architecture design, whereas the influence of sequence-level aggregation has rarely been isolated under a controlled backbone and training configuration. In this study, xLSTM-m is proposed as a modified variant of the extended Long Short-Term Memory (xLSTM) network for multivariate bridge-response classification. The conventional last-hidden-state readout is replaced with masked mean pooling over all valid hidden states, while the hybrid sLSTM/mLSTM backbone and the remaining training configuration are kept unchanged. This controlled design isolates the sequence-level aggregation strategy as the only architectural variable. A comparative evaluation is conducted using eight neural models. These models include xLSTM-m, the baseline xLSTM, and six Transformer-based architectures. Three bridge-monitoring datasets are considered: an FBG strain-response dataset and a PCB Piezotronics acceleration-response dataset acquired from a laboratory-scale cable-stayed bridge model, together with the field-scale Z24-9Setup acceleration benchmark. A strict five-fold cross-validation protocol is adopted. The proposed xLSTM-m ranks first on all three datasets. It achieves a mean accuracy of 95.29%, a mean F1-score of 95.57%, and the lowest inter-fold standard deviation of 0.84. Relative to the strongest Transformer baseline for each dataset, the corresponding accuracy gains are 6.30 percentage points for FBG, 3.66 percentage points for PCB, and 31.76 percentage points for Z24-9Setup. These results indicate that, for the evaluated bridge strain and acceleration datasets, sequence-level aggregation substantially affects both classification accuracy and inter-fold stability. Further validation using quasi-static responses, environmental variables, other structural systems, and unseen operating conditions is required before broader applicability across the SHM domain can be established. Full article

High-Early-Strength Concrete (HESC) is increasingly required in accelerated construction, yet most existing studies focus on single nano-additives rather than hybrid waste-derived systems. This study investigates the individual and combined effects of nanoclay (NC), nanosilica (NS), and cellulose nanofibers (NCel)—each produced from industrial or agricultural waste—on the mechanical and microstructural properties of HESC. A Box–Behnken response surface methodology (RSM) design was employed to optimize nanomaterial dosages with respect to early-age compressive strength, while microstructural evaluation (SEM, EDS, elemental mapping) clarified the mechanisms of enhancement. The results demonstrate that NC, NS, and NCel play complementary roles in hydration acceleration, particle packing, pore refinement, and crack-bridging. The optimized hybrid system (1.64% NC, 0.115% NS, 0.027% NCel) achieved a 3-day compressive strength of 59.7 MPa, 7-day strength of 71.2 MPa, and 28-day strength of 94.6 MPa, representing increases of 42.14%, 36.92%, and 21.59%, respectively, over the control mixture. Microstructural observations confirmed matrix densification, reduced Ca/Si ratio (from 2.05 to 1.68), refined pore structure (<0.4 μm vs. 0.9–1.2 μm in control), and enhanced ITZ in the optimized mixtures. Statistical analysis yielded robust predictive models (R² = 0.977–0.996) with significant interaction terms confirming synergistic effects among the three nanomaterials. This work demonstrates that waste-derived hybrid nano-systems offer a sustainable and effective strategy for producing high-performance HESC, with the RSM-derived optimum providing balanced early- and later-age strength while maintaining practical feasibility for field implementation. Full article

Fiber-reinforced self-compacting concrete (FR-SCC) incorporating granite powder (GP), an expansive agent (EA), steel fibers (SFs), and polypropylene fibers (PPFs) was investigated for potential pre-cast tunnel-segment applications. Sixteen mixtures, covering GP replacement ratios of 0–18%, EA dosages of 0–8% by binder mass, and SF and PPF volume fractions of 0–0.75% and 0–0.15%, were tested in uniaxial compression on 100 mm × 100 mm × 300 mm prisms with acoustic emission (AE) monitoring. Within the tested range, 12% GP and 8% EA gave the most favorable binder composition. XRD and SEM analyses indicated that GP acted predominantly as an inert filler with no detectable portlandite consumption, while the expansive agent was associated with additional ettringite formation. At this composition, hybrid SF/PPFs increased the post-peak energy by a factor of 7.66 relative to the fiber-free mixture, mainly improving the post-peak rather than the pre-peak behavior. Among the Carreira–Chu, GB 50010, and modified Weibull formulations, the GB 50010 piecewise model best reproduced the full stress–strain curves and was used as the primary constitutive model. Two-variable regressions were established to separate the apparent effects of the SF and PPF volume fractions on the ascending- and descending-branch shape parameters, and a ductility-calibrated expression was developed for the descending-branch parameter. The Pearson coefficient between the descending-branch parameter and the AE characteristic strain was −0.904, while that between the AE characteristic strain and the macroscopic residual strain was +0.983. These results link constitutive modeling, AE damage evolution, and macroscopic post-peak ductility for FR-SCC within the tested range of mix proportions. Full article