Search Results (29,533)

Against the backdrop of increasing penetration of renewable energy sources such as wind and solar power, coupled with intermittent regional power restrictions, ensuring the quality of power transmission has become increasingly critical. The volatility and uncertainty of wind and photovoltaic output exacerbate dynamic fluctuations in net load on the grid side, necessitating hydroelectric units to undertake more frequent Automatic Generation Control (AGC) regulation tasks in complementary hydro–wind–solar operations. However, frequent regulation processes significantly intensify the operational stress on actuating mechanisms within the governor system, thereby accelerating wear and degradation of equipment such as hydraulic turbine servomotors. This study employs modeling and simulation to investigate the influence and mechanistic role of key control parameters in the AGC process on the wear of hydraulic turbine servomotors. Utilizing pulse count and pulse width metrics, a reasonable quantification of this impact is established. A multi-objective optimization framework for AGC parameters is constructed, and frontier solutions are selected based on quantified equipment wear values. Simulation results indicate that the optimized parameters achieve a balanced performance in terms of settling time, steady-state performance, and comprehensive dynamic metrics during power closed-loop transition processes. This approach effectively mitigates the actuation intensity of servomotors while satisfying regulation quality requirements, thereby enhancing the overall performance of the power closed-loop adjustment process. Full article

With the development of autonomous driving and intelligent connected vehicle technologies, the vehicle cabin is shifting from a simple transportation space to an intelligent mobile space integrating infotainment, interaction, and rest, and passenger comfort has gradually become an important factor affecting user experience, system trust, and perceived safety. Focusing on three categories of cabin environmental factors, namely the acoustic, optical, and thermal environments, this study develops an experimental design and comprehensive modeling method for passenger comfort evaluation. First, controlled single-factor experiments were conducted to establish quantitative mapping relationships between physical environmental parameters and subjective comfort ratings. The analytic hierarchy process (AHP) was then used to determine the weights of each indicator, and a penalty-based aggregation mechanism was introduced to construct a comprehensive comfort evaluation model. Finally, external validation was performed on an independent vehicle platform to examine the model’s applicability and consistency. The results show that acoustic comfort decreases as the sound pressure level increases, whereas optical and thermal comfort exhibit nonlinear behavior with optimal intervals. AHP weight results show that the thermal environment has the highest weight (0.4280), followed by the acoustic environment (0.3305) and the optical environment (0.2415). The external validation results indicate that the proposed model exhibits good predictive consistency across three steady-state operating conditions, with a mean absolute error of 0.122, a root-mean-square error of 0.150, and a Pearson correlation coefficient of 0.960. The findings show that the penalty-based aggregation model can effectively characterize the limiting-factor effect under the joint action of multiple environmental factors, providing a computable and interpretable evaluation framework for intelligent cockpit environmental control and automotive engineering experimental teaching. The conclusions of this study are mainly applicable to the current experimental platform and steady-state operating conditions, and further validation is still required with more vehicle models, dynamic road scenarios, and complex multi-environment factor disturbances. Full article

In recent decades, the introduction of e-procurement has profoundly transformed the methods of procuring goods, services, and works, redefining traditional procurement processes and significantly impacting global economic, operational, and regulatory dynamics. The construction sector has also been affected by this transition, which has altered the operating models of public procurement and favoured the adoption of digital tools aimed at more efficient, transparent, and automated process management. This study proposes a systematic literature review based on the analysis of 95 scientific contributions, with the aim of outlining the evolution of the e-procurement paradigm in the construction sector and identifying the main directions for research development. Despite the widespread dissemination of studies on the topic, it emerges that the actual maturity of e-procurement systems is still limited, often resulting in a logic of document dematerialization rather than full process digitalization. In this context, the review critically analyses the role of Building Information Modelling as an enabling factor for the evolution of e-procurement, exploring the potential of its integration into procurement flows. Particular attention is paid to the contribution of the Digital Building Logbook, an information tool capable of extending the value of data generated during the tender phase throughout the building’s entire life cycle, supporting advanced management and maintenance strategies. The results highlight how, despite the significant potential of integrating e-procurement and BIM, significant technological, regulatory, and cultural issues persist that limit its large-scale adoption. This underscores the need to develop shared and interoperable methodological approaches capable of transforming procurement from a document-based process to an integrated information system, oriented toward value creation throughout the entire life cycle of projects. Full article

This paper studies subset construction in NP-complete problems from the perspective of structured exploration of combinatorial search spaces. Classical approaches rely on exhaustive enumeration of subsets, which leads to exponential growth in time and memory requirements. To address this limitation, we introduce an indexed framework based on the correspondence between a finite set and its associated index set. Within this framework, subsets are represented as ordered index sequences, allowing subset construction to be reformulated as a constraint-guided search process over index space. Candidate subsets are characterized by numerical descriptors derived from their indices (referred to as index certificates), which guide and filter the construction process. Subset generation is further organized through admissible index intervals that restrict feasible transitions and reduce the effective search space. The framework is based on an index-based representation and structured traversal of pairwise index combinations. Computational experiments on representative instances illustrate the behavior of the indexed construction procedure and indicate its efficiency relative to classical enumeration-based methods for small and medium-sized instances. The proposed approach provides a structured perspective on combinatorial search and offers a basis for further development of algorithms based on constrained exploration of subset structures. Full article

Source-free domain adaptation (SFDA) for cross-modality prostate MRI segmentation is challenging because source data are unavailable and pseudo-labels on target ADC images are often noisy. To address this problem, we propose Curriculum Anatomy-guided Learning Method with Population Template Priors (CALM), a source-free adaptation framework for this task. CALM constructs a population template prior from target predictions using top-k consensus aggregation and cross-round exponential moving average, then combines this prior with instance-level predictions through Soft-AND fusion. A high-confidence background constraint is further introduced to provide reliable negative supervision, and a coverage-driven curriculum is used to expand training from easy to hard cases based on pseudo-label/template agreement. This design forms an iterative process in which prior refinement and sample-reliability refinement reinforce each other during adaptation. Experiments on the PI-CAI dataset under the T2W-to-ADC setting show that CALM achieves an average Dice score of 73.63% and outperforms representative SFDA baselines in both segmentation accuracy and boundary quality. Ablation and model analyses support the contribution of each component. These results suggest that population-level anatomical priors can provide practical structural guidance for source-free cross-modality adaptation. Full article

Transient analyses of liquid-fueled molten salt reactors involve strong coupling among neutronics, delayed neutron precursor transport, thermal–hydraulics, and solid heat transfer, leading to high computational costs for repeated high-fidelity simulations. To enable fast multi-physics prediction at unseen operating conditions, a parametric non-intrusive reduced-order model (ROM) combining proper orthogonal decomposition (POD) and higher-order dynamic mode decomposition (HODMD) is developed. Coupled full-order snapshots generated from an OpenFOAM-based one-eighth symmetric core model based on a simplified MSRE benchmark configuration are used to construct reduced representations for 11 physical fields. The POD truncation rank, HODMD delay dimension, and interpolation model are selected using leave-one-out cross-validation, with polynomial, radial basis function, and Gaussian process regression models considered as interpolation candidates. For unseen parameter points, the model maintains high accuracy in both the interpolation stage and the temporal extrapolation stage. In the temporal extrapolation stage, the highest mean relative L₂ error for the inlet-temperature-step case is 2.112%, whereas all mean relative L₂ errors for the inlet-velocity-step case remain below 0.177%. The results indicate that, under the present cases and parameter settings, the proposed framework provides an accurate and rapid surrogate for multi-physics transient prediction. Full article

The elevation datum is a critical element in surveying and mapping, as variations in elevation systems can lead to discrepancies between Digital Surface Model (DSM) products generated from satellite imagery. To eliminate these differences and ensure high-precision data consistency, this study constructs an elevation datum conversion scheme for multi-source DSM products using the SGG-UGM-2 (2190 degree) global gravity field model to calculate elevation anomalies. While traditional serial algorithms suffer from significantly decreased efficiency as the volume of DSM image files increases, this paper proposes a novel HDC-MPI elevation datum conversion algorithm based on Message Passing Interface (MPI) parallel technology. By leveraging distributed memory parallel computing, the processing task is partitioned into multiple sub-tasks, substantially enhancing overall throughput. Experimental results demonstrate that: (1) the HDC-MPI algorithm improves conversion efficiency by approximately 8 times compared to the serial approach when processing 12 image scenes; (2) the algorithm’s efficiency is primarily governed by image memory usage rather than terrain complexity; and (3) the conversion accuracy of the HDC-MPI algorithm remains fully consistent with serial results, ensuring the reliability of the elevation datum transformation. Full article

Series arc faults on the DC side of photovoltaic (PV) systems are a critical hazard that can trigger system fires. Conventional contact-based detection methods suffer from cumbersome installation and high retrofit cost, whereas existing non-contact approaches mostly rely on megahertz-level high-frequency sampling and therefore require expensive radio-frequency instrumentation or high-performance computing platforms. As a result, it remains difficult to simultaneously achieve strong interference immunity and real-time performance on low-cost embedded devices with limited resources. To address this engineering paradox between high-frequency sampling and constrained computational capability, this paper proposes a fully embedded, non-contact arc fault detection system based on a 12–80 kHz low-frequency sub-band selection strategy. By exploiting the physical characteristic of broadband energy elevation induced by arc faults, the proposed strategy avoids dependence on high-bandwidth hardware. Guided by this strategy, a Moebius-topology coaxial shielded loop antenna is employed as the near-field sensor, while an ultra-simplified passive analog front end is constructed directly by using the on-chip programmable gain amplifier and analog-to-digital converter of the microcontroller unit, enabling efficient signal acquisition and fast Fourier transform processing within the target sub-band. To cope with complex background noise in the low-frequency range, an environment-adaptive baseline mechanism based on exponential moving average and exponential absolute deviation is developed for dynamic decoupling. In addition, a lightweight INT8-quantized multilayer perceptron is introduced as a nonlinear auxiliary module, thereby forming a robust hybrid decision architecture with complementary rule-based and artificial intelligence components. Experimental results show that, under the tested household, laboratory, and PV-site conditions, the proposed system achieved an overall detection rate of 97%, while the remaining 3% mainly corresponded to failed ignition or non-sustained arc attempts rather than persistent false triggering during normal monitoring. Full article

Lunar optical and synthetic aperture radar (SAR) imagery provide complementary information for characterizing the lunar surface. However, their joint use remains challenging because of substantial cross-modality differences and severe illumination constraints, particularly in polar regions. To address this challenge, we propose LS2ODiff (Lunar SAR-to-Optical Diffusion), a diffusion-based framework designed for SAR-to-optical image translation in lunar environments. LS2ODiff uses SAR observations as conditional guidance in the diffusion process and incorporates a partial-convolution strategy into the U-Net backbone to handle irregular invalid regions. In addition, self-attention modules are incorporated into the downsampling stages of the U-Net to model long-range spatial dependencies and enhance global structural consistency in complex lunar terrain. We further construct a dedicated paired dataset of the lunar south polar region by registering Chandrayaan-II DFSAR data with Lunar Reconnaissance Orbiter (LRO) Narrow-Angle Camera (NAC) imagery. Comparative experiments against Pix2Pix, CycleGAN, SynDiff, and ConDiff demonstrate that LS2ODiff achieves better visual fidelity and quantitative performance in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), Fréchet inception distance (FID), and learned perceptual image patch similarity (LPIPS). These results demonstrate the potential of diffusion models for high-fidelity lunar image translation, offering new opportunities for polar terrain interpretation and future exploration missions. Full article

Molecular reconstitution of petroleum is a computer simulation based on petroleum characterization data and applying various computational techniques to generate individual molecules, predict their properties, and construct a composition model. The accuracy of molecule physical property calculation affects the exactness of the whole process of building the molecular composition model of petroleum. To the best of our knowledge, no report has appeared yet that deals with the accuracy of the calculation of the properties of the predefined molecules used in the process of molecular reconstitution of oil. To bridge this gap we used three of the most employed group contribution methods of Joback and Reid (1987) (J&R), Abdulelah–Gani (2022) (A&G) and Constantinou–Gani (1994) (C&G) to calculate the properties of 110 molecules from gasoline range and 139 molecules from diesel range, whose measured properties were found in the Design Institute for Physical Properties (DIPPR) and API Technical Databook databases, and which were used by Xie et al. (2026) to reconstruct the molecular composition of 22 crude oils. It was found that no single group contribution method was universally superior. The J&R method performed better for most heteroatomic compounds, while the A&G method outperformed it for certain hydrocarbon classes. Only the A&G method can be used to calculate specific gravity. In general, the error of physical property calculations was variable, reaching as high as 20% (absolute) depending on the molecular weight of the molecules and their chemical class. The implementation limitations of these methods in software libraries must be carefully considered during the process of molecular reconstitution of petroleum. Full article

Biological invasions and environmental pollution are the two primary threats facing contemporary agricultural ecosystems, and their interaction exacerbates agroecological risks and undermines agricultural sustainability. This study was conducted to systematically elucidate how heavy metals (HMs) and microplastics (MPs) alter the relative advantages of invasive plants in ecosystems, clarify the ecological processes involved, and propose recommendations for the protection of farmland ecosystems. The main conclusions are as follows: (1) Pollution acts as an environmental filter that negatively affects native species, including crops, while creating relative advantages for invasive plants with high tolerance and adaptive physiological mechanisms. (2) Pollution stress enables invasive plants to gain a competitive advantage over native plants through highly plastic resource allocation strategies, prioritization of growth, and more powerful allelopathic effects. (3) Pollution systematically amplifies the advantage of invasive plants by altering the strength of plant–soil feedback (PSF) and driving the restructuring of rhizosphere microbial communities. (4) Invasive plants can be used to produce biochar, which can then be applied in farmland ecosystems for the control of invasive plants and remediation of soil pollution. The framework constructed in this study indicates that heavy metal and microplastic pollution may enhance the invasion of alien plants, posing a serious threat to agroecosystem health and food security. However, using invasive plants as feedstock to produce biochar may offer a solution to the intertwined challenges of plant invasion and environmental pollution. Full article

Traditional Peruvian cuisine has become a globally recognized experience, but its impact on visitors to the Caral Supe archaeological site—one of the oldest centers of civilization in South America and a UNESCO World Heritage Site—has not been studied. The main objective was to explain the constructs of the perceived authenticity of traditional food, loyalty to traditional food, service quality at traditional restaurants, and tourist satisfaction with visits to archaeological sites, based on the experience economy theory. An explanatory study was conducted using a structural equation modeling approach (PLS-SEM), applied to a sample of 381 tourists who visited the archaeological site and consumed local cuisine at restaurants in the destination of Barranca. The findings confirmed significant relationships among the model’s constructs (p < 0.01). It is suggested that the perception of authenticity of traditional food is a determining factor for loyalty (R² = 0.743) and a driver of satisfaction with the visit to the archaeological site (R² = 0.617), which constitutes the study’s contribution. However, the R² value for the construction of the tourist experience at the destination (R² = 0.301), the model does not fully capture the complexity of experiential processes at this particular heritage destination, which may depend on emotional, cultural, or contextual variables not included in this study. Satisfaction with the visit to the archaeological site is primarily related to staff attentiveness, the quality of guide explanations, and safety. It is concluded that the interplay between satisfaction with the visit to the archaeological site, the perceived authenticity of traditional food, and the quality of service in restaurants is fundamental to enhancing the experience at the heritage destination, thereby positioning traditional food and archaeotourism. It is recommended that the public and private sectors design strategies aimed at generating authentic and sustainable experiences for visitors, strengthening factors such as the destination’s reputation, the positive image of the site, satisfaction with the trip at the destination, and the positive experience. Full article

This study investigates the tide-dominated hydrodynamic response of Pulandian Bay to shoreline changes by comparing numerical simulations under shoreline conditions in 2004 and 2020 using the FVCOM. The results indicate that shoreline changes exert significant spatially heterogeneous effects on tidal dynamics. Channel narrowing caused by aquaculture enclosures and saltpan construction increased flow velocity near Boji Island. Meanwhile, tidal prism decreased during both spring and neap tides due to the loss of intertidal areas from northern reclamation, thereby weakening water exchange capacity. The outer bay, directly connected to the open sea, exhibits stronger water exchange than the relatively enclosed inner bay. However, the removal of seawalls in the inner bay enhanced flow in the central deep trough, resulting in improved water exchange capacity in 2020 compared to 2004. Shoreline changes also intensified tidal residual currents, with high-value Eulerian residuals mainly distributed in the northern and central parts of the bay. In addition, the restoration of tidal channels in the inner bay slightly increased residual current velocity. Overall, shoreline modification plays a critical role in regulating tidal hydrodynamic processes, providing important implications for coastal engineering and aquaculture management. Full article

During freeway reconstruction and expansion, median crossover sections where traffic is maintained during construction are vulnerable to changes in lane configuration, abrupt geometric changes, and construction interference. These factors may lead to safety risks and operational efficiency losses. Existing studies have mainly relied on microscopic traffic simulation to evaluate speed limit schemes, while engineering costs, environmental impacts, driver responses, and policy constraints have rarely been considered in an integrated manner. This study proposes a two-stage evaluation framework that integrates VISSIM microscopic traffic simulation, the Entropy Weight Method–Technique for Order Preference by Similarity to an Ideal Solution (EWM–TOPSIS), and the Fuzzy Analytic Hierarchy Process (FAHP). A four to eight-lane freeway expansion project in a plain area of northern China is used as the case study. Field speed data from a representative median crossover section are used for model calibration and speed-pattern analysis. A total of 27 simulation scenarios is then constructed by combining three bottleneck types, three traffic saturation levels, and three speed limit schemes. The EWM–TOPSIS results show that the 80→70 km/h scheme achieves the highest relative closeness in all scenarios. The FAHP evaluation, based on six criteria and 21 indicators, also ranks this scheme first. Its ranking remains unchanged under ±10% criteria weight perturbations. Field speed comparison indicates that vehicles exhibit a deceleration–recovery pattern when passing through the crossover opening. Overall, the 80→70 km/h gradual speed reduction scheme can be regarded as a candidate scheme for work zones with similar median crossover configurations. Under localized calibration conditions, it can provide decision-making support for reducing operational disturbances, fuel consumption, and external impacts associated with crash risk. Full article

Mining activities can generate effluent contamination with potentially toxic elements such as iron (Fe) and manganese (Mn), posing environmental and technological challenges, particularly during mine closure and the decommissioning of mining structures. Constructed wetlands have been proposed as a nature-based, passive, and low-cost alternative for treating mining effluents; however, the mechanisms, controlling factors, and performance patterns governing Fe and Mn removal remain insufficiently synthesized across different wetland configurations and effluent types. This study performs a systematic review combined with a meta-analysis to synthesize Fe and Mn removal mechanisms, quantify removal performance, and identify the operational, hydraulic, physicochemical, and biological factors influencing system performance. A total of 55 primary studies were analyzed, comprising 155 observations for Fe and 96 for Mn. The results indicate that Fe removal is generally high (medianln(RR)ln(RR) = −1.89), whereas Mn removal is more variable and less efficient (medianln(RR)ln(RR) = −0.59), highlighting the greater complexity of Mn removal processes. Fe removal was mainly associated with hydraulic retention time and pH, while Mn removal was more strongly influenced by redox conditions and the type of support material, particularly mineral substrates. Overall, wetland performance is governed by the interaction among hydraulic retention time, pH buffering, redox conditions, support media reactivity, vegetation-mediated rhizosphere processes, and influent geochemistry. A significant research gap remains regarding neutral mine drainage (NMD), since this effluent category was not explicitly reported in the primary studies and could not be robustly isolated as an independent subgroup, especially in relation to Mn removal efficiency. Full article