Search Results (10,692)

This study presents a novel technological framework that integrates web-based 3D modeling and digital prototyping into interdisciplinary design education. Addressing the gap between traditional theoretical assessment and modern industry demands, the research investigates the implementation of interactive micro-websites and high-fidelity 3D product models as standard deliverables. Using a quasi-experimental design, the proposed digital workflow was tested on 53 final-year graphic design students at the University of Zagreb, divided into three groups based on the end users of their digital prototypes: real industry clients, peers, or academic mentors. The systemic reliability of the technological framework was measured through the technical quality of the final output (grades) analyzed via ANOVA, while user engagement with the digital process was tracked longitudinally. Results indicate that the implemented technological pipeline produced consistently high-quality outputs across all cohorts, with the client-facing group achieving the highest technical scores (M = 4.37; SD = 0.57). The lack of statistically significant variance between groups highlights a “ceiling effect,” demonstrating that the structured digital workflow itself is operationally stable and ensuring top-tier technical performance and prepress accuracy regardless of the evaluator. The study concludes that integrating advanced 3D web technologies and interactive public deliverables into the curriculum provides a scalable, industry-aligned technological model that successfully prepares design engineers for complex professional environments. Full article

Commonly occurring polychlorinated biphenyls (PCBs) in the environment have been linked to a broad range of adverse toxicological effects in both animals and humans. In this study, in vitro, in silico, and in vivo models were used to investigate the surface interactions of PCBs with green-engineered clays (GECs). Earlier studies showed that these GECs significantly reduced the toxicities of important planar aromatic chemicals such as benzene and aflatoxin B₁ along with ochratoxin A, a chlorinated aromatic chemical. The overall objective for this study was to show that GECs could tightly adsorb PCBs, resulting in a decrease in toxicity of a commercial PCB mixture (Aroclor 1260). Gastrointestinal pH and temperature were simulated in vitro, and the clay surface binding interactions of six PCBs were characterized using isothermal analyses. Molecular dynamics (MD) simulations were employed to provide atomistic understanding into PCB congener interactions with parent and chlorophyll-amended clays. To confirm the ability of GECs to protect a living organism, Aroclor 1260 was investigated using a well-established hydra bioassay. According to simulations, coplanar PCBs had an increased probability of binding to parent clay compared to non-coplanar ones, in line with experiments, due to their ability to lay flat on the clay surface. Chlorophyll amendments enhanced binding of all PCBs according to both experiments and computations. Within the simulations, chlorophyll amendments facilitated both coplanar as well as non-coplanar PCBs to directly bind to the clay and additionally interact with chlorophyll amendments, as well as to bind to chlorophyll amendments without necessarily interacting with the clay. Aroclor 1260 caused irreversible damage to hydra. At 0.05% inclusion, parent clay offered limited protection (20%) while GECs offered 55% to 65% protection, showing the advantage of GECs over parent clays. The findings of this study indicate that edible GECs adsorb PCBs, with the highest sorption associated with the coplanar congeners. Further studies are warranted to determine the application of GECs as potential disaster-response supplements in the diet to reduce the bioavailability of PCBs from contaminated food and water, especially following floods and other emergencies. Full article

This study quantifies coastal mangrove carbon stocks and their interannual variability along the Pakistan coastline by developing a multi-sensor fusion framework integrated with a process-based light use efficiency (LUE) modeling approach. To ensure high-cadence monitoring and overcome persistent cloud cover over the Indus Delta, data from multiple satellite sensors including Landsat 8/9 and Sentinel-2 within Google Earth Engine were utilized. Sentinel-2-derived Normalized Difference Vegetation Index (NDVI) data composited for the January–March period was processed to estimate vegetation productivity. Field-based validation of biomass estimates was conducted using 57 georeferenced sampling points, cross-compared with Sentinel-2 data. Mangrove extent was delineated through land use and land cover (LULC) classification into water bodies, mangroves, mudflats, land parcels, and sand surfaces. The LUE model incorporated environmental stress scalars, including temperature, vapor pressure deficit (VPD), salinity, and photosynthetically active radiation (PAR) to estimate gross primary productivity and derive total biomass, which was subsequently converted into carbon stocks. Results indicate a mean carbon stock of 31.95 Mg C ha⁻¹ (equivalent to 117.3 Mg CO₂ ha⁻¹), with significant interannual variation (coefficient of variation = 19.8%). A significant decline in carbon stocks was observed in 2021 (−11.11%; 3.56 Mg C ha⁻¹), corresponding to a reduction in NDVI value (0.55 compared to 0.58 in other years). Spatial analysis revealed substantial heterogeneity in carbon distribution (20.51 to 55.93 Mg C ha⁻¹), primarily influenced by localized salinity gradients and water stress conditions. This study mapped mangrove extent, quantified environmental stress, and estimated carbon stocks across Pakistan’s coast from 2020 to 2024, delivering a spatially resolved, multi-year baseline for coastal carbon assessment and ecosystem monitoring in arid tidal environments. Full article

As substation equipment continues to evolve toward higher voltage levels, larger capacities, and more complex operating conditions, voiceprint signals exhibit greater sensitivity and observability during the early stages of faults. However, traditional modeling approaches still suffer from limitations in capturing long-range temporal dependencies, suppressing noise interference, and adapting to unlabeled data. To address these issues, a state space model-based Mamba self-supervised voiceprint framework, termed MSANet, is proposed. A bidirectional state space scanning mechanism is introduced into the network architecture to avoid the high computational complexity of attention mechanisms while simultaneously preserving both global contextual correlations and local detail representations of voiceprint signals. In addition, a spectrum block masking-based self-supervised learning strategy is incorporated, enabling the model to extract stable time–frequency structural features even under unlabeled or limited labeled samples. Experimental results demonstrate that MSANet achieves high accuracy in voiceprint-related tasks. Furthermore, the lightweight version of the model maintains competitive performance while significantly reducing computational and storage overhead, indicating its feasibility for deployment on edge devices in resource-constrained scenarios such as substation environments. The proposed method provides a potential methodological basis for enhancing fault-related voiceprint feature extraction, representation learning, and future practical engineering deployment. Full article

In this work, the structural, electronic, charge-transfer, thermal, and magnetic properties of pristine and Fe-doped silicon carbide nanosheets (SiCNShs) were systematically investigated using spin-polarized density functional theory (DFT) within the Local Spin Density Approximation including Hubbard correction (LSDA + U). A 4 × 4 SiCNSh supercell containing 80 atoms was considered, where Fe atoms were substitutionally introduced at carbon sites to evaluate dopant-induced modifications in the nanosheet. Structural optimization, energy convergence, force minimization, and stress evolution analyses confirm that Fe incorporation preserves the structural integrity of the SiCNSh and leads to energetically stable configurations. The calculated defect formation energy (−7.44 eV/atom) demonstrates the thermodynamic feasibility of Fe substitution, while ab initio molecular dynamics (AIMD) simulations at 300 K verify the thermal stability of the energetically favorable Fe-doped configuration. Electronic-structure calculations reveal that pristine SiCNSh exhibits a nonmagnetic semiconducting nature with a band gap of approximately 2.4 eV, whereas Fe incorporation significantly modifies the electronic structure through pronounced Fe–3d/C–2p/Si–3p orbital hybridization. The band gap is reduced to approximately 1.1 eV for the single-Fe-doped system and further decreases to 0.53/0.51 eV (spin-up/spin-down) in the double-Fe configuration, while preserving semiconducting behavior. Spin-polarized band structure and density of states analyses demonstrate clear spin asymmetry near the Fermi level, indicating strong dopant-induced spin polarization and exchange interactions. Charge-density difference and Bader charge analyses reveal substantial dopant-induced charge redistribution characterized by electron depletion around Fe atoms, enhanced electron accumulation on neighboring carbon atoms, and partial charge neutralization of nearby Si atoms, resulting in a more localized covalent Si–C–Fe bonding environment. Mulliken spin population analysis further demonstrates robust ferromagnetic ordering, where the Fe dopant acts as the dominant magnetic center with strong induced spin polarization extending into neighboring Si and C atoms. Comparison between ferromagnetic (FM) and antiferromagnetic (AFM) configurations confirms that the 2Fe@C-doped SiCNSh stabilizes in a ferromagnetic ground state, exhibiting a favorable FM–AFM energy difference of 0.216 eV. Based on the mean-field approximation, the Curie temperature was estimated to be approximately 837 K, indicating strong magnetic stability significantly above room temperature. The present findings collectively demonstrate that Fe incorporation effectively tailors the electronic and magnetic properties of SiCNSh through band-gap engineering, spin-symmetry breaking, and stabilization of high-temperature ferromagnetism. These combined characteristics establish Fe-doped SiCNShs as promising candidates for spintronic devices, magnetic semiconductors, spin injectors, spin filters, and non-volatile magnetic memory applications. Full article

Electrocatalytic nitric oxide reduction (NORR) shows great potential for mitigating NO_x emissions from motor vehicles and other internal combustion engine exhausts, enabling the resource utilization of pollutant NO and the synthesis of NH₃ under mild conditions. The overall performance of NORR largely depends on the development of efficient electrocatalysts. Based on a coordination-engineering strategy, this study constructs a series of Sc-based single-atom catalyst systems coordinated with nonmetal heteroatoms (X = B, C, O, Si, P, S, As, Se, Te), denoted as Sc@XN₃, and systematically investigates their NORR reaction pathways, limiting potentials (U_L, the minimum applied potential required to make all elementary steps downhill in free energy), and selectivity using density functional theory (DFT) calculations. The results indicate that Sc@CN₃, Sc@PN₃, and Sc@SN₃ possess relatively low U_L, with values of −0.17, −0.31, and −0.07 V, respectively, among which Sc@SN₃ is thermodynamically the most favorable. Moreover, Sc@CN₃ and Sc@SN₃ can suppress the hydrogen evolution reaction (HER) and the formation of N₂O/N₂ by-products, thereby affording higher selectivity toward NH₃ formation. Considering the characteristics of NO_x emissions from engine exhaust, these coordination-engineered Sc centers show promising potential for future electrified aftertreatment systems that couple NO_x control with ammonia-based energy utilization in vehicles. This study clarifies at the atomic scale how the coordination environment modulates the electronic structure and catalytic behavior of Sc single-atom centers and provides theoretical guidance for the rational design of high-performance NORR electrocatalysts targeted at automotive exhaust NO_x control. Full article

Integrating solar power into electricity grids requires accurate short-term forecasting of the global horizontal irradiance to accurately predict the expected solar power generation. This paper compares five tree-based machine learning models against a Persistence baseline for multi-resolution forecasting in arid climates. A 13-year dataset from Basra, Iraq, has been employed in this study for verification purposes, and the models are tested across various very-short- to short-term forecasting horizons of 5, 10, 15, 30, and 60 min. Unlike most existing studies that focus on single forecasting horizons or mixed climatic conditions, this work systematically benchmarks multi-resolution irradiance forecasting under distinct sky conditions in a hot arid environment using a strict anti-data-leakage framework. To avoid data leakage in these models, feature engineering has used only lagged inputs. The dataset has been split into three groups for training, validation, and testing (respectively 70, 15, and 15% of the entire available dataset). The models were then tested separately under clear, partly cloudy, and cloudy skies. Numerical studies prove that picking the best model depends heavily on the forecast horizon. For very-short-term predictions, the Persistence model was competitive (RMSE = 21.32 W/m²), while the Gradient Boosting model proved slightly more accurate (RMSE = 17.65 W/m²). For the 60 min horizon, the boosting models took a clear lead. The HistGradientBoosting model resulted in a 67% reduction in the RMSE compared to the Persistence baseline. Also, the top-performing model changed depending on the weather and the time scale. Gradient Boosting was the clear winner for short-term clear sky forecasts, while XGBoost handled the longer horizons. Partly cloudy skies showed a rotating mix of different boosting algorithms taking the lead. However, studies show that when skies were fully overcast, complex machine learning models fail to capture chaotic patterns, making the simple Persistence baseline a necessary reliability safeguard. The results reveal that no single model consistently dominates all forecasting horizons and weather conditions, highlighting the necessity of adaptive model selection for operational solar forecasting. These findings highlight the importance of horizon- and weather-adaptive model selection for operational solar forecasting. Rather than relying on a single universal algorithm, grid operators in arid regions can improve forecasting reliability by dynamically selecting models based on prevailing sky conditions and forecast horizons. Full article

Discrete production planning under integer constrained resource framework is a challenging issue which requires simultaneous consideration of output maximization, resource efficiency, and balanced resource. This research focuses on simulation of an integer-driven production planning model for an automotive production system. It combines the genetic algorithm-based stochastic data generator with a precise Diophantine feasibility enumeration. The genetic algorithm is used as a constraint-aware stochastic specification generator to generate feasible production parameter sets within certain operational constraints. Its main purpose is to create representative production environments for feasibility analysis and not to optimize production. A normalized multi-objective scoring function is presented to address the imbalance in the scales of economic and operational measures. A total of 64,518 feasible automotive production plans were enumerated under engine, tire, labor and budget constraints using the proposed framework. The Pareto-efficient solutions to the cost–output space that were identified, formed a discrete, piecewise Pareto frontier. The best production plan had a total of 83 units with 99% of the labor and tire resources exploited, whereas the budget and engine capacities were not binding. The optimal strategy implies full saturation of the labor capacity (>99%) due to the binding nature of labor as an objective. In practice, a safety buffer can be imposed through the introduction of an upper-bound utilization policy (e.g., 95%), which moves the optimal solution marginally inwards along the Pareto frontier. The analysis of sensitivity to changes in resources of $\pm 10\%$ showed the preservation of the Pareto structure and resilient adaptability in the output, which validated the usefulness of the suggested strategy in discrete manufacturing decision support. Full article

Three-year atmospheric field-exposure tests were conducted on 304 austenitic stainless steel at the Great Wall and Zhongshan Stations in Antarctica to evaluate its corrosion behavior under severe polar conditions. The exposed specimens were dominated by localized corrosion with pronounced pitting characteristics at both sites. Corrosion was more severe at Zhongshan Station, and the mean corrosion rates at Great Wall and Zhongshan Stations were 1.428 and 1.643 μm y⁻¹, respectively. The mean/maximum pit depths were 4.16/5.51 μm at Great Wall Station and 5.85/8.24 μm at Zhongshan Station. Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), grazing-incidence X-ray diffraction (GIXRD), and focused ion beam-transmission electron microscopy (FIB-TEM) showed that the corrosion products consisted mainly of β-FeOOH, α-FeOOH, and γ-Fe₂O₃, and the Antarctic exposure substantially altered the thickness, structure, and electrochemical response of the passive film. Compared with the unexposed specimen, the exposed specimens exhibited markedly lower charge-transfer resistance and higher donor density, indicating degradation of the protective passive film. Combined with the site-specific environmental features, the lower temperature, more intense freeze–thaw cycling, freezing-induced concentration of electrolytes, and stronger irradiation at Zhongshan Station are inferred to promote Cl⁻ enrichment in localized surface liquid films and destabilization of the passive film, thereby accelerating pit initiation and growth. These findings provide a mechanistic basis for material selection and corrosion-protection design for 304 stainless steel in polar engineering environments. Full article

Aluminum alloys are attractive for oil production, refining, and hydrocarbon-processing equipment because of their low density, high specific strength, and heat-transfer properties; however, their use is limited by localized corrosion in chloride-, sulfur-, and water-containing environments. This review analyzes combined anodic oxide/polymer and anodic oxide/fluoropolymer coating systems as surface-engineering approaches for improving corrosion resistance, adhesion, and durability of aluminum alloys under such conditions. The reviewed data show that coating performance is governed by anodic oxide morphology, pore sealing or polymer impregnation, and oxide/polymer interfacial stability. Quantitative results indicate that anodizing and pore widening can increase aluminum/polyamide lap-shear strength from 5.0 to 17.4 MPa, while optimized interfacial treatment can provide 22.5 ± 0.5 MPa before aging and 18.1 ± 0.2 MPa after humid aging. Corrosion data show that anodizing can increase the polarization resistance of aluminum alloy 6061 in seawater from 17.2 kΩ·cm² to 2.24 MΩ·cm². For wear-related durability, optimized anodizing can increase the critical scratch load from 37.3 to 118.9 N. These values provide practical benchmarks for designing anodic oxide/polymer systems for complex oilfield and hydrocarbon-processing environments. Full article

GNSS/INS integrated navigation systems play a critical role in marine navigation, providing high-precision position and attitude information for moving platforms. However, in complex marine environments—such as occlusions caused by offshore engineering platforms—GNSS signal attenuation frequently leads to a rapid degradation of positioning accuracy. To address this issue in post-processing applications, this paper proposes an Adaptive Rauch-Tung-Striebel Smoother (ARTSS)-based GNSS/INS integrated navigation method. The proposed method first performs forward filtering using an Error-State Extended Kalman Filter (ESKF). Subsequently, an adaptive equivalent weight is dynamically constructed using the Huber M-estimation cost function based on the forward filtering innovations. This adaptive factor is utilized to dynamically modulate the smoothing gain in the backward pass, thereby effectively suppressing the interference of measurement outliers. To verify the effectiveness of the algorithm, comparative experiments are conducted using real-world land vehicle and shipborne kinematic datasets. Three methods are evaluated: the standard ESKF, the fixed-interval backward smoothing (RTSS), and the proposed ARTSS approach. The loosely coupled solutions from the Inertial Explorer (IE) software serve as the reference truth. Experimental results demonstrate that the proposed algorithm achieves significant improvements in positioning and attitude accuracy during GNSS signal outages. Specifically, compared with the conventional ESKF and RTSS methods, the 3D position accuracy of the shipborne experiment is improved by 31.07% and 6.97%, respectively, while that of the land vehicle experiment is increased by 48.05% and 8.67%. Therefore, the method presented in this paper effectively mitigates the accumulation of forward filtering errors and significantly enhances the accuracy, stability, and reliability of the integrated navigation system in complex environments. Full article

High-plasticity clays (CH) are widely recognized in geotechnical engineering for their poor engineering behavior, including low shear strength, high compressibility, and swelling potential, yet their presence in infrastructure projects is often unavoidable. This study investigates a sustainable alternative to ordinary Portland cement (OPC) by evaluating the durability of soil–geopolymer mixtures (SGMs) incorporating quarry dust (QD), an industrial by-product from sand and gravel operations in the Philippines. Durability assessment was emphasized due to the country’s tropical climate, marked by alternating wet and dry seasons that may accelerate deterioration of stabilized soils. QD was activated using sodium silicate (SS) and sodium hydroxide (SH) and blended with CH to form SGMs. Index property tests were conducted to characterize raw materials and identify optimal mix proportions. After 28 days of curing, specimens were subjected to wetting–drying (WD) cycles consisting of 5 h of water submersion and 42 h of oven-drying at 70 °C. Mass loss and surface degradation were evaluated by brushing in accordance with ASTM procedures. The SGMs exhibited an average mass loss of 6.83% after 12 WD cycles, satisfying the Portland Cement Association (PCA) criterion of less than 7.00% for stabilized clays. These results demonstrate that QD-based geopolymers are a viable and sustainable stabilizer for CH soils in tropical environments. Full article

Monitoring dam deformation is critical for ensuring structural integrity and identifying long-term settlement trends. However, traditional InSAR techniques often face limitations in tropical environments due to severe temporal decorrelation. This study addresses these challenges at Jatiluhur Dam, Indonesia, by implementing an integrated framework using Sentinel-1 InSAR, in situ leveling, GNSS, and reservoir water-level data from 2019 to 2024. To overcome the observation bottlenecks, Tracy–Widom-guided PSI (TW-PSI) was employed and compared against SBAS and conventional PSI. The TW-PSI approach successfully increased on-structure measurement point density by approximately 40%, supporting a first-order ascending–descending decomposition into east–west and quasi-vertical components. The analysis reveals a persistent settlement bowl at the central crest (C7–C12), consistent with long-term leveling observations and supported by regional GNSS trend checking. While the 2022 Mw 5.6 Cianjur earthquake showed no statistically significant co-seismic crest deformation, a strong correlation (r = −0.709) was identified between crest deformation and reservoir water-level variations, suggesting an observational association between reservoir level and crest settlement tendency. Furthermore, the application of the Annual Structural Deformation Tolerance Ratio (ASDTR) identified specific priority monitoring zones. These findings demonstrate that the proposed integrated framework can support operational dam deformation monitoring by linking satellite-derived measurements with in situ observations and engineering-oriented interpretation. Full article

Titanium alloys are irreplaceable in aerospace, biomedical and marine industries due to their low density, high specific strength and excellent biocompatibility. Conventional manufacturing methods suffer from low material utilization and difficulty in fabricating complex components, while additive manufacturing (AM) realizes near-net-shape forming of customized structures but introduces unique non-equilibrium microstructures and defects, which significantly alter the corrosion behavior and limit the long-term service reliability of additively manufactured (AMed) titanium alloys. This work systematically analyzes the corrosion behavior of titanium alloys fabricated by four mainstream AM processes: LPBF (laser powder bed fusion)/SLM (selective laser melting), EBM (electron beam melting), DED (directed energy deposition) and WAAM (wire arc additive manufacturing). It quantitatively summarizes the key electrochemical parameters and discusses the regulatory effects of matrix composition, post-treatment and service environment on their corrosion behaviors. The universal corrosion mechanisms—namely, passive film breakdown, micro-galvanic corrosion, and defect-induced localized corrosion—as well as process-specific corrosion mechanisms inherent to AMed titanium alloys are systematically elucidated. This study offers theoretical foundations for optimizing corrosion resistance and ensuring the reliable engineering implementation of AMed titanium alloys. Full article

Wildfires pose serious risks to ecosystems, air quality, and human health. Effective wildfire monitoring requires accurate detection and timely validation, but current approaches are often constrained by fragmented data sources, false alarms, and delays in field verification. This study presents WildfireGO, a multi-source wildfire detection and validation system that integrates crowdsourced observations, satellite hotspot data, and image-based classification in a geospatial monitoring environment. The system combines user-submitted images, Sentinel-2 imagery, and Moderate Resolution Imaging Spectroradiometer (MODIS) hotspot data processed through Google Earth Engine (GEE) to support wildfire detection and verification. Four classification models, namely Convolutional Neural Network (CNN), Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting (GB), were evaluated using 10-fold cross-validation and an independent test dataset of 800 wildfire-related images. The CNN model produced the best result, with an accuracy of 97.5% on the independent test dataset. By combining image-based classification with crowdsourced reporting, the system helps screen user-submitted wildfire information and reduce false detections. Satellite-derived hotspot data provide spatial evidence for cross-checking reported events and improving spatial situational awareness for wildfire monitoring and response planning. WildfireGO supports near real-time data submission, automated processing, and interactive map-based visualization through a web-based interface. The findings indicate that combining crowdsourced reports, satellite observations, and image classification in a single geospatial system has the potential to support more reliable wildfire detection and provide practical support for environmental monitoring, disaster response, and spatial decision-making. Full article