Search Results (1,588)

The uncertainties arising from high renewable energy penetration on both the generation and demand sides pose significant challenges to distribution network security. Smart microgrids are considered an effective way to solve this problem. Existing studies exhibit limitations in prediction accuracy, Alternating Current (AC) power flow modeling, and integration with optimization frameworks. This paper proposes a closed-loop technical framework combining high-confidence interval prediction, second-order cone convex relaxation, and robust optimization to facilitate renewable energy integration in distribution networks via smart microgrid technology. First, a hybrid prediction model integrating Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), and Quantile Regression (QR) is designed to extract multi-frequency characteristics of time-series data, generating adaptive prediction intervals that accommodate individualized decision-making preferences. Second, a second-order cone relaxation method transforms the AC power flow optimization problem into a mixed-integer second-order cone programming (MISOCP) model. Finally, a robust optimization method considering source–load uncertainties is developed. Case studies demonstrate that the proposed approach reduces prediction errors by 21.15%, decreases node voltage fluctuations by 16.71%, and reduces voltage deviation at maximum offset nodes by 17.36%. This framework significantly mitigates voltage violation risks in distribution networks with large-scale grid-connected photovoltaic systems. Full article

Rural roads are critical for connecting isolated communities to essential services such as education and health and administrative services, as well as production and market opportunities in low-income countries. More than 70% of movements of people and goods in Sub-Saharan Africa are heavily reliant on rural transport systems, using both motorised but mainly alternative means of transport. However, rural roads often suffer from poor construction due to the use of low-strength, in situ soils and limited financial resources, leading to premature failures and subsequent traffic disruptions with significant economic losses. This study investigates the use of rice husk ash (RHA), a waste byproduct from rice production, as a sustainable supplement to Ordinary Portland Cement (OPC) for soil stabilisation in order to increase durability and sustainability of rural roads, hence limit recurrent maintenance needs and associated transport costs and challenges. To conduct this study, soil samples collected from Mulungushi, Zambia, were treated with combinations of 6–10% OPC and 10–15% RHA by weight. Laboratory tests measured maximum dry density (MDD), optimum moisture content (OMC), and California Bearing Ratio (CBR) values; the main parameters assessed to ensure the quality of road construction soils. Results showed that while the MDD did not change significantly and varied between 1505 kg/m³ and 1519 kg/m³, the OMC increased hugely from 19.6% to as high as 26.2% after treatment with RHA. The CBR value improved significantly, with the 8% OPC + 10% RHA mixture achieving the highest resistance to deformation. These results suggest that RHA can enhance the durability and sustainability of rural roads and hence improve transport systems and subsequently improve socioeconomic factors in rural areas. Full article

Large-scale wind power grid-connected systems can trigger the risk of power system instability. In order to enhance the stability margin of grid-connected systems, this paper accurately characterizes the topology of the global boundary of stability domain (BSD) of the grid-connected system based on BSD theory, using the method of combining the manifold topologies and singularities at infinity. On this basis, the effect of large-scale doubly fed induction generators (DFIGs) replacing synchronous units on the BSD of the system is analyzed. Simulation results based on the IEEE 39-bus system indicate that the negative impedance characteristics and low inertia of DFIGs lead to a contraction of the stability domain. The principle of singularity invariance (PSI) proposed in this paper can effectively expand the BSD by adjusting the inertia and damping, thereby increasing the critical clearing time by about 5.16% and decreasing the dynamic response time by about 6.22% (inertia increases by about 5.56%). PSI is superior and applicable compared to traditional energy functions, and can be used to study the power angle stability of power systems with a high proportion of renewable energy. Full article

Thermal management is an area of study in electronics focused on managing temperature to improve reliability and efficiency. When temperatures are too high, cooling systems are activated to prevent overheating, which can lead to reliability issues. To monitor the temperatures, sensors are often placed on-chip near hotspot locations. These sensors should be very small to allow them to be placed among compact, high-activity circuits. Often, they are connected to a central control circuit located far away from the hot spot locations where more area is available. This paper proposes sensing units for a novel temperature sensing architecture in the TSMC 180 nm process. This architecture functions by approximating the current through the sensing unit at a reference voltage, which is used to approximate the temperature in the digital back end using fitting functions. Sensing units are selected based on how well its temperature–current relationship can be modeled, sensing unit area, and power consumption. Many sensing units will be experimented with at different reference voltages. These temperature–current curves will be modeled with various fitting functions. The sensing unit selected is a diode-connected p-type MOSFET (Metal Oxide Semiconductor Field Effect Transistor) with a size of W = 400 nm, L = 180 nm. This sensing unit is exceptionally small compared to existing work because it does not rely on multiple devices at the sensing unit location to generate a PTAT or IPTAT signal like most work in this area. The temperature–current relationship of this device can also be modeled using a 2nd order polynomial, requiring a minimal number of trim temperatures. Its temperature error is small, and the power consumption is low. The range of currents for this sensing unit could be reasonably made on an IDAC. Full article

The aim of this study was to determine the connection between oscillations in operating pressure values and the appearance of various irregularities on machined surfaces. Such oscillations are a consequence of the high water pressure generated during abrasive water jet machining. Oscillations in the operating pressure values are periodic, namely due to the cyclic operation of the intensifier and the physical characteristics of water. One of the most common means of reducing this phenomenon is installing an attenuator in the hydraulic system or a phased intensifier system. The main hypothesis of this study was that the topography of a machined surface is directly influenced by the inability of the pressure accumulator to fully absorb water pressure oscillations. In this study, we monitored changes in hydraulic oil pressure values at the intensifier entrance and their connection with irregularities on the machined surface—such as waviness—when cutting aluminum AlMg3 of different thicknesses. Experimental research was conducted in order to establish this connection. Aluminum AlMg3 of different thicknesses—from 6 mm to 12 mm—was cut with different traverse speeds while hydraulic oil pressure values were monitored. The pressure signals thus obtained were analyzed by applying the fast Fourier transform (FFT) algorithm. We identified a single-sided pressure signal amplitude spectrum. The frequency axis can be transformed by multiplying inverse frequency data with traverse speed; in this way, a single-sided amplitude spectrum can be obtained, examined against the period in which striations are expected to appear (in millimeters). In the lower zone of the analyzed samples, striations are observed at intervals determined by the dominant hydraulic oil pressure harmonics, which are transferred to the operating pressure. In other words, we demonstrate how the machined surface topography is directly induced by water jet pressure frequency characteristics. Full article

Computational Fluid Dynamics (CFD) is regarded as an important tool for analyzing the flow field, thermal environment, and air quality around the built environment. However, for built environment applications, the high computational cost of CFD hinders large-scale scenario simulation and efficient design optimization. In the field of built environment research, surrogate modeling has become a key technology to connect the needs of high-fidelity CFD simulation and rapid prediction, whereas the low-dimensional nature of traditional surrogate models is unable to match the physical complexity and prediction needs of built flow fields. Therefore, combining machine learning (ML) with CFD to predict flow fields in built environments offers a promising way to increase simulation speed while maintaining reasonable accuracy. This review briefly reviews traditional surrogate models and focuses on ML-based surrogate models, especially the specific application of neural network architectures in rapidly predicting flow fields in the built environment. The review indicates that ML accelerates the three core aspects of CFD, namely mesh preprocessing, numerical solving, and post-processing visualization, in order to achieve efficient coupled CFD simulation. Although ML surrogate models still face challenges such as data availability, multi-physics field coupling, and generalization capability, the emergence of physical information-driven data enhancement techniques effectively alleviates the above problems. Meanwhile, the integration of traditional methods with ML can further enhance the comprehensive performance of surrogate models. Notably, the online ministry of trained ML models using transfer learning strategies deserves further research. These advances will provide an important basis for advancing efficient and accurate operational solutions in sustainable building design and operation. Full article

In mining areas with high groundwater levels, intensive coal mining has led to the accumulation of substantial surface water and significant alterations in regional landscape patterns. Reconstructing the ecological security pattern (ESP) has emerged as a critical focus for ecological restoration in coal mining subsidence areas with high groundwater levels. This study employed the patch-generating land use simulation (PLUS) model to predict the landscape evolution trend of the study area in 2032 under three scenarios, combining environmental characteristics and disturbance features of coal mining subsidence areas with high groundwater levels. In order to determine the differences in ecological network changes within the study area under various development scenarios, morphological spatial pattern analysis (MSPA) and landscape connectivity analysis were employed to identify ecological source areas and establish ecological corridors using circuit theory. Based on the simulation results of the optimal development scenario, potential ecological pinch points and ecological barrier points were further identified. The findings indicate that: (1) land use changes predominantly occur in urban fringe areas and coal mining subsidence areas. In the land reclamation (LR) scenario, the reduction in cultivated land area is minimal, whereas in the economic development (ED) scenario, construction land exhibits a marked increasing trend. Under the natural development (ND) scenario, forest land and water expand most significantly, thereby maximizing ecological space. (2) Under the ND scenario, the number and distribution of ecological source areas and ecological corridors reach their peak, leading to an enhanced ecological network structure that positively contributes to corridor improvement. (3) By comparing the ESP in the ND scenario in 2032 with that in 2022, the number and area of ecological barrier points increase substantially while the number and area of ecological pinch points decrease. These areas should be prioritized for ecological protection and restoration. Based on the scenario simulation results, this study proposes a planning objective for a “one axis, four belts, and four zones” ESP, along with corresponding strategies for ecological protection and restoration. This research provides a crucial foundation for decision-making in enhancing territorial space planning in coal mining subsidence areas with high groundwater levels. Full article

Accurate short-term weather forecasting plays a vital role in disaster response, agriculture, and energy management, where timely and reliable predictions are essential for decision-making. Graph neural networks (GNNs), known for their ability to model complex spatial structures and relational data, have achieved remarkable success in meteorological forecasting by effectively capturing spatial dependencies among distributed weather stations. However, most existing GNN-based approaches rely on pairwise station connections, limiting their capacity to represent higher-order spatial interactions. Moreover, their dependence on supervised learning makes them vulnerable to spatial heterogeneity and temporal non-stationarity. This paper introduces a novel spatial–temporal pretraining framework, Hypergraph-enhanced Meteorological Pretraining (HyMePre), which combines hypergraph neural networks with self-supervised learning to model high-order spatial dependencies and improve generalization across diverse climate regimes. HyMePre employs a two-stage masking strategy, applying spatial and temporal masking separately, to learn disentangled representations from unlabeled meteorological time series. During forecasting, dynamic hypergraphs group stations based on meteorological similarity, explicitly capturing high-order dependencies. Extensive experiments on large-scale reanalysis datasets show that HyMePre outperforms conventional GNN models in predicting temperature, humidity, and wind speed. The integration of pretraining and hypergraph modeling enhances robustness to noisy data and improves generalization to unseen climate patterns, offering a scalable and effective solution for operational weather forecasting. Full article

Scale insects, which belong to the superfamily Coccoidea within the order Hemiptera, encompass more than 8000 species worldwide. The adult females of these species are characterized by their immobility, and often lack wings and legs. Scale insects feed on plant tissues and can cause significant agricultural damage as pests. This study presents the sequencing of five coccoid mitogenomes, revealing detailed annotations and comparisons with other Hemiptera. The sequencing yielded between 73 million and over 121 million reads, allowing for the reconstruction of mitogenomes ranging from 12,821 to 14,446 nucleotides. Notably, a high A + T content was observed across the newly sequenced mitogenomes. Gene rearrangements were identified in all five newly sequenced mitogenomes, with the evolutionary rate analysis indicating that Coccoidea exhibit the highest Ka and Ka/Ks values among the hemipterans. In a phylogenetic context, the mitogenomes of representative species from Coccoidea and Aleyrodoidea exhibit more frequent mitochondrial gene rearrangements than those of other hemipteran groups. The analysis suggests that the frequent mitochondrial gene rearrangements observed in the coccoid species are associated with accelerated nucleotide substitution rates, supporting a connection between genetic evolution and structural variation in mitogenomes. Full article

This study aims to develop a method for analyzing real-world battery degradation in electric vehicles in order to identify the optimal battery management system (BMS) during the early digital phase of vehicle development. Battery management of lithium-ion batteries (LiBs) in electric vehicles is important to ensure a stable output and to counteract degradation and thermal runaway. To design the optimal system, it is most effective to use a 1D (one-dimensional) vehicle system simulation model, which connects each unit model inside the vehicle, due to the system’s complexity. In order to create a long-term degradation simulation in a vehicle system model, it is important to reduce computational load. Therefore, in this paper, we studied a suitable battery degradation calculation for the vehicle system model based on an equivalent circuit model (ECM) and degradation approximation formulas. After implementing these models, we analyzed long-term degradation behavior through the real-world operation of an electric vehicle driver. We first implemented a high-accuracy ECM using transient charge–discharge tests and Bayesian Optimization. Next, we formulated approximation formulas for degradation prediction based on calendar and cycle degradation tests. Finally, we simulated real-world degradation behavior using these models. The simulation results revealed that even for users who frequently use electric vehicles, degradation under storage conditions is the dominant factor in overall degradation. Full article

High-resolution remote sensing imagery plays an essential role in urban management and environmental monitoring, providing detailed insights for applications ranging from land cover mapping to disaster response. Semantic segmentation methods are among the most effective techniques for comprehensive land cover mapping, and they commonly employ ImageNet-based pre-training semantics. However, traditional fine-tuning processes exhibit poor transferability across different downstream tasks and require large amounts of labeled data. To address these challenges, we introduce Denoising Diffusion Probabilistic Models (DDPMs) as a generative pre-training approach for semantic features extraction in remote sensing imagery. We pre-trained a DDPM on extensive unlabeled imagery, obtaining features at multiple noise levels and resolutions. In order to integrate and optimize these features efficiently, we designed a multi-layer perceptron module with residual connections. It performs channel-wise optimization to suppress feature redundancy and refine representations. Additionally, we froze the feature extractor during fine-tuning. This strategy significantly reduces computational consumption and facilitates fast transfer and deployment across various interpretation tasks on homogeneous imagery. Our comprehensive evaluation on the sparsely labeled dataset MiniFrance-S and the fully labeled Gaofen Image Dataset achieved mean intersection over union scores of 42.7% and 66.5%, respectively, outperforming previous works. This demonstrates that our approach effectively reduces reliance on labeled imagery and increases transferability to downstream remote sensing tasks. Full article

In order to verify the rationality and feasibility of a demountable and replaceable fabricated RC beam with bolted connection under mid-span compression, one cast-in-place RC beam and four fabricated RC beams were designed and fabricated. Through the mid-span static loading test and analysis of five full-scale RC beams, the effects of high-strength bolt specifications and stiffeners were compared, and the behavior of the fabricated RC beams with bolted connections was analyzed. The test process was observed and the test results were analyzed. The failure mode, cracking load, yield load, ultimate load, stiffness change, deflection measured value, ductility, and other indicators of the specimens were compared and analyzed. It was shown that the failure mode of the fabricated RC beam was reinforcement failure, which met the three stress stages of the normal section bending of the reinforcement beam. The failure position occurred at 10~15 cm of the concrete outside the bolt connection, and the beam support and the core area of the bolt connection were not damaged. The fabricated RC beam has good mechanical performance and high bearing capacity. In addition, comparing the test value with the simulation value, it is found that they are in good agreement, indicating that ABAQUS software of 2024 can be well used for the simulation analysis of the behavior of fabricated RC beam structure. Full article

In social networks like community Question-Answering (cQA) services, members interact with each other by asking and answering each other’s questions. This way they find counsel and solutions to very specific real-life situations. Thus, it is safe to say that community fellows log into this kind of social network with the goal of satisfying information needs that cannot be readily resolved via traditional web searches. And in order to expedite this process, these platforms also allow registered, and many times unregistered, internauts to browse their archives. As a means of encouraging fruitful interactions, these websites need to be efficient when displaying contextualized/personalized material and when connecting unresolved questions to people willing to help. Here, demographic factors (i.e., gender) together with frontier deep neural networks have proved to be instrumental in adequately overcoming these challenges. In fact, current approaches have demonstrated that it is perfectly plausible to achieve high gender classification rates by inspecting profile images or textual interactions. This work advances this body of knowledge by leveraging lexicalized dependency paths to control the level of abstraction across texts. Our qualitative results suggest that cost-efficient approaches exploit distilled frontier deep architectures (i.e., DistillRoBERTa) and coarse-grained semantic information embodied in the first three levels of the respective dependency tree. Our outcomes also indicate that relative/prepositional clauses conveying geographical locations, relationships, and finance yield a marginal contribution when they show up deep in dependency trees. Full article

Urban tourism has experienced significant growth driven by platforms such as Airbnb, yet the relationship between short-term rental (STR) location and guest mobility remains underexplored. In this study, a structured survey of STR guests in Madrid during 2024 was administered face-to-face through property managers and luggage-storage services to examine factors influencing public transport (PT) use. Responses on bus and metro usage were combined into a three-level ordinal variable and modeled using ordered logistic regression against tourist demographics, trip characteristics, and accommodation attributes, including geocoded location zones. The results indicate that first-time and international visitors are less likely to use PT at high levels, while tourists visiting more points of interest and those who rated PT importance highly when choosing accommodation are significantly more frequent users. Accommodation in the central almond or periphery correlates positively with higher PT use compared to the city center. Distances to transit stops were not significant predictors, reflecting overall network accessibility. These findings suggest that enhancing PT connectivity in peripheral areas could support the spatial dispersion of tourism benefits and improve sustainable mobility for STR guests. Full article

In order to break through the difficulties with a very-low-frequency (VLF) miniaturized antenna with small power capacity and low radiation efficiency, this paper proposes a high-radiation-field-strength magnetic loop antenna based on a nanocrystalline alloy magnetic core. A high-permeability nanocrystalline toroidal core (μ_r = 50,000, B_s = 1.2 T) is used to optimize the thickness-to-diameter ratio (t = 0.08) and increase the effective permeability to 11,000. The Leeds wires, characterized by their substantial carrying capacity, are manufactured through a toroidal winding process. This method results in a 68% reduction in leakage compared to traditional radial winding techniques and enhances magnetic induction strength by a factor of 1.5. Additionally, this approach effectively minimizes losses, thereby facilitating support for kilowatt-level power inputs. A cascaded LC resonant network (resonant capacitance 2.3 μF) and ferrite balun transformer (power capacity 3.37 kW) realize a 20-times amplification of the input current. A series connection of a high-voltage isolation capacitor blocks DC bias noise, guaranteeing the stable transmission of 1200 W power, which is 6 times higher than the power capacity of traditional ring antenna. At 7.8 kHz frequency, the magnetic field strength at 120 m reaches 47.32 dBμA/m, and, if 0.16 pT is used as the threshold, the communication distance can reach 1446 m, which is significantly better than the traditional solution. This design marks the first instance of achieving kilowatt-class VLF effective radiation in a compact 51 cm-diameter magnetic loop antenna, offering a highly efficient solution for applications such as mine communication and geological exploration. Full article