Search Results (745)

The integration of Building Information Modeling (BIM) and Digital Twin (DT) technologies offers new opportunities for enhancing reinforcement design and on-site constructability. This study addresses a current gap in DT applications by introducing an intelligent framework that simultaneously automates rebar layout generation and reduces rebar cutting waste (RCW), two challenges often overlooked during the construction execution phase. The system employs heuristic algorithms to generate constructability-aware rebar configurations and leverages Industry Foundation Classes (IFC) schema-based data models for interoperability. The framework is implemented using Autodesk Revit and Dynamo for rebar modeling and layout generation, Microsoft Project for schedule integration, and Autodesk Navisworks for clash detection. Real-time scheduling synchronization is achieved through IFC schema-based BIM models linked to construction timelines, while embedded clash detection and constructability feedback loops allow for iterative refinement and improved installation feasibility. A case study on a high-rise commercial building demonstrates substantial material savings, improved constructability, and reduced layout time, validating the practical advantages of BIM–DT integration for RC construction. Full article

Under the dual pressures of global climate warming and rapid urbanization, the Yangtze River Basin, as the world’s largest urban agglomeration, is facing intensifying thermal environmental stress. Although river ecosystems demonstrate significant thermal regulation functions, their spatial thresholds of cooling effects and multiscale driving mechanisms have remained to be systematically elucidated. This study retrieved land surface temperature (LST) using the split window algorithm and quantitatively analyzed the changes in the river cold island effect and its driving mechanisms in the Yangtze River Basin by combining multi-ring buffer analysis and the optimal parameter-based geographical detector model. The results showed that (1) forest land is the main land use type in the Yangtze River Basin, with built-up land having the largest area increase. Affected by natural, socioeconomic, and meteorological factors, the summer temperatures displayed a spatial pattern of “higher in the east than the west, warmer in the south than the north”. (2) There are significant differences in the cooling magnitude among different land types. Forest land has the maximum daytime cooling distance (589 m), while construction land has the strongest cooling magnitude (1.72 °C). The cooling effect magnitude is most pronounced in upstream areas of the basin, reaching 0.96 °C. At the urban agglomeration scale, the Chengdu–Chongqing urban agglomeration shows the greatest temperature reduction of 0.90 °C. (3) Elevation consistently demonstrates the highest explanatory power for LST spatial variability. Interaction analysis shows that the interaction between socioeconomic factors and elevation is generally the strongest. This study provides important spatial decision support for formulating basin-scale ecological thermal regulation strategies based on refined spatial layout optimization, hierarchical management and control, and a “natural–societal” dual-dimensional synergistic regulation system. Full article

For complex assembly workshops, optimizing AGV scheduling is critical to enhancing production efficiency and resource utilization. Traditional scheduling methods often rely on fixed priority rules and basic path planning algorithms, which are insufficient to accommodate the dynamic changes in resource availability and task demands. To overcome these limitations, this study proposes a DES-based optimization approach that dynamically adjusts AGV task allocation and path planning to improve scheduling performance in complex manufacturing environments. By integrating lean production principles with intelligent simulation technologies, a comprehensive simulation model was developed using the Plant Simulation platform. This model simulates the coordination between AGVs and workstations while optimizing workstation layout and material flow. Simulation results demonstrate that the proposed approach significantly improves AGV scheduling efficiency and overall production performance. Notably, workstation utilization increased from below 6% to over 28%, while the work-in-progress rate dropped from 94% to under 74%. This study offers a practical and effective AGV scheduling strategy for complex product assembly workshops, with strong potential for real-world implementation. Full article

The optimization of ventilation strategies in high-ceiling theater stage spaces is crucial for improving thermal comfort and energy efficiency. This study addresses the challenge of uneven temperature distribution and airflow stagnation in stage environments by employing computational fluid dynamics (CFD) simulations to evaluate the effectiveness of different ventilation modes, including natural, mechanical, and hybrid systems. Six airflow organization scenarios were designed based on modifications to structural layout, equipment settings, and mechanical disturbances (e.g., fan integration). Key evaluation indicators such as temperature uniformity coefficient, airflow velocity, and exhaust efficiency were used to assess performance. The results show that a multi-dimensional optimization approach combining spatial adjustments and mechanical disturbances significantly reduced the average temperature from 26 °C to 23 °C and the temperature uniformity coefficient from 2.79 to 1.49. This study contributes a comprehensive design strategy for stage ventilation that improves comfort while minimizing energy consumption, offering practical implications for performance space design and HVAC system integration. Full article

With the large-scale global deployment of photovoltaics (PV), traditional monitoring technologies face challenges such as wiring difficulties, high energy consumption, and high maintenance costs in remote or complex terrains, which limit long-term environmental sensing. Therefore, energy-harvesting systems are crucial for the intelligent operation of photovoltaic systems; however, their deployment depends on the accurate mapping of wind energy fields and solar irradiance fields. This study proposes a multi-scale simulation method based on computational fluid dynamics (CFD) to optimize the placement of energy-harvesting systems in photovoltaic power plants. By integrating wind and irradiance distribution analysis, the spatial characteristics of airflow and solar radiation are mapped to identify high-efficiency zones for energy harvesting. The results indicate that the top of the photovoltaic panel exhibits a higher wind speed and reflected irradiance, providing the optimal location for an energy-harvesting system. The proposed layout strategy improves overall energy capture efficiency, enhances sensor deployment effectiveness, and supports intelligent, maintenance-free monitoring systems. This research not only provides theoretical guidance for the design of energy-harvesting systems in PV stations but also offers a scalable method applicable to various geographic scenarios, contributing to the advancement of smart and self-powered energy systems. Full article

This paper presents a wideband low-power/low-voltage 60-GHz low-noise amplifier (LNA) in a 28-nm bulk CMOS technology. The LNA has been designed for high-speed millimeter-wave (mm-wave) communications. It consists of two pseudo-differential amplifying stages and a buffer stage included for 50-Ohm on-wafer measurements. Two integrated input/output baluns guarantee both simultaneous 50-ohm input–noise/output matching at input/output radio frequency (RF) pads. A power-efficient design strategy is adopted to make the LNA suitable for low-power applications, while minimizing the noise figure (NF). Thanks to the adopted design strategy, the post-layout simulation results show an excellent trade-off between power gain and 3-dB bandwidth (BW_3dB) with 13.5 dB and 7 GHz centered at 60 GHz, respectively. The proposed LNA consumes only 11.6 mA from a 0.9-V supply voltage with an NF of 8.4 dB at 60 GHz, including the input transformer loss. The input 1 dB compression point (IP_1dB) of −15 dBm at 60 GHz confirms the first-rate linearity of the proposed amplifier. Human body model (HBM) electrostatic discharge (ESD) protection is guaranteed up to 2 kV at the RF input/output pads thanks to the input/output integrated transformers. Full article

Source inversion optimization using sensor observations is a key method for rapidly and accurately identifying unknown source parameters (source strength and location) in abrupt hazardous gas leaks. Sensor number and location distribution both play important roles in source inversion; however, their combined impacts on source inversion optimization remain poorly understood. In our study, the optimization inversion method is established based on the Gaussian plume model and the generation algorithm. A research strategy combining random sampling and coefficient of variation methods was proposed to simultaneously quantify their combined impacts in the case of a single emission source. The sensor layout impact difference was analyzed under varying atmospheric conditions (unstable, neutral, and stable) and source location information (known or unknown) using the Prairie Grass experiments. The results indicated that adding sensors improved the source strength estimation accuracy more when the source location was known than when it was unknown. The impacts of sensor location distribution were strongly negatively correlated (r ≤ −0.985) with the number of sensors across scenarios. For source strength estimation, the impacts of the sensor location distribution difference decreased non-linearly with more sensors for known locations but linearly for unknown ones. The impacts of sensor number and location distribution on source strength estimation were amplified under stable atmospheric conditions compared to unstable and neutral conditions. The minimum number of randomly scattered sensors required for stable source strength inversion accuracy was 11, 12, and 17 for known locations under unstable, neutral, and stable atmospheric conditions, respectively, and 24, 9, and 21 for unknown locations. The multi-layer arc distribution outperformed rectangular, single-layer arc, and downwind-axis distributions in source strength estimation. This study enhances the understanding of factors influencing source inversion optimization and provides valuable insights for optimizing sensor layouts. Full article

This study explores strategic deception and counter-deception in multi-agent reinforcement learning environments for a police officer–robber game. The research is motivated by real-world scenarios where agents must operate with partial observability and adversarial intent. We develop a suite of progressively complex grid-based environments featuring dynamic goals, fake targets, and navigational obstacles. Agents are trained using deep Q-networks (DQNs) with game-theoretic reward shaping to encourage deceptive behavior in the robber and intent inference in the police officer. The robber learns to reach the true goal while misleading the police officer, and the police officer adapts to infer the robber’s intent and allocate resources effectively. The environments include fixed and dynamic layouts with varying numbers of goals and obstacles, allowing us to evaluate scalability and generalization. Experimental results demonstrate that the agents converge to equilibrium-like behaviors across all settings. The inclusion of obstacles increases complexity but also strengthens learned policies when guided by reward shaping. We conclude that integrating game theory with deep reinforcement learning enables the emergence of robust, deceptive strategies and effective counter-strategies, even in dynamic, high-dimensional environments. This work advances the design of intelligent agents capable of strategic reasoning under uncertainty and adversarial conditions. Full article

Low-altitude unmanned aerial vehicle (UAV) vision is critically hindered by the Sim-to-Real Gap, where models trained exclusively on simulation data degrade under real-world variations in lighting, texture, and weather. To address this problem, we propose RA3T (Region-Aligned 3D Transformer), a novel self-supervised framework that enables robust Sim-to-Real adaptation. Specifically, we first develop a dual-branch strategy for self-supervised feature learning, integrating Masked Autoencoders and contrastive learning. This approach extracts domain-invariant representations from unlabeled simulated imagery to enhance robustness against occlusion while reducing annotation dependency. Leveraging these learned features, we then introduce a 3D Transformer fusion module that unifies multi-view RGB and LiDAR point clouds through cross-modal attention. By explicitly modeling spatial layouts and height differentials, this component significantly improves recognition of small and occluded targets in complex low-altitude environments. To address persistent fine-grained domain shifts, we finally design region-level adversarial calibration that deploys local discriminators on partitioned feature maps. This mechanism directly aligns texture, shadow, and illumination discrepancies which challenge conventional global alignment methods. Extensive experiments on UAV benchmarks VisDrone and DOTA demonstrate the effectiveness of RA3T. The framework achieves +5.1% mAP on VisDrone and +7.4% mAP on DOTA over the 2D adversarial baseline, particularly on small objects and sparse occlusions, while maintaining real-time performance of 17 FPS at 1024 × 1024 resolution on an RTX 4080 GPU. Visual analysis confirms that the synergistic integration of 3D geometric encoding and local adversarial alignment effectively mitigates domain gaps caused by uneven illumination and perspective variations, establishing an efficient pathway for simulation-to-reality UAV perception. Full article

Municipal solid waste (MSW) and refuse-derived fuel (RDF) incineration generate over 20 million tons of residues annually in the EU. These include bottom ash (IBA), fly ash (FA), and air pollution control residues (APCr), which pose significant environmental challenges due to their leaching potential and hazardous properties. While these residues contain valuable metals and reactive mineral phases suitable for carbonation or alkaline activation, chemical, techno-economic, and policy barriers have hindered the implementation of sustainable, full-scale management solutions. Accelerated carbonation technology (ACT) offers a promising approach to simultaneously sequester CO₂ and enhance residue stability. This review provides a comprehensive assessment of waste incineration residue carbonation, covering 227 documents ranging from laboratory studies to field applications. The analysis examines reactor designs and process layouts, with a detailed classification based on material characteristics, operating conditions, investigated parameters, and the resulting pollutant stabilization, CO₂ uptake, or product performance. In conclusion, carbonation-based approaches must be seamlessly integrated into broader waste management strategies, including metal recovery and material repurposing. Carbonation should be recognized not only as a CO₂ sequestration process, but also as a binding and stabilization strategy. The most critical barrier remains chemical: the persistent leaching of sulfates, chromium(VI), and antimony(V). We highlight what we refer to as the antimony problem, as this element can become mobilized by up to three orders of magnitude in leachate concentrations. The most pressing research gap hindering industrial deployment is the need to design stabilization approaches specifically tailored to critical anionic species, particularly Sb(V), Cr(VI), and SO₄²⁻. Full article

This study presents a 3D fracture network modeling approach for designing curtain grouting systems in building foundations, utilizing geological mapping data from the Dongzhuang Project. A one-dimensional Markov chain model is applied to simulate the transitions in fracture density, while fracture orientation and size are characterized using Fisher and statistical distribution models. To enhance the prediction accuracy, a correction method is introduced to refine the transition matrices. The model’s reliability is validated using tunnel wall fracture data and borehole detection, demonstrating strong agreement in both trend and magnitude. In under 100,000 simulations, when the allowable absolute error is set to 1, the optimal accuracy can reach 80%. Reliability analysis confirms the robustness of the approach, with 99.91% of predictions within a ± 2 error margin. The final fracture network model effectively captures spatial heterogeneity and fracture penetration across various foundation layers; the spatial distribution density index of fractures can provide a reference basis for optimizing the layout of impermeable curtains in complex geological conditions. This integrated modeling approach offers a reliable tool for improving grouting strategies in building foundation projects and other civil infrastructure. Full article

Like other mega cities in China, Beijing is undergoing a large-scale urban renewal process. However, in the context of global warming and the goal of promoting human health and well-being, urban renewal should follow the principle of minimal intervention, draw inspiration from the condition of the climate and environment itself, and pursue the goal of common health and development between humans and non-human beings. This study takes the Panjiayuan Antique Market as the research object. Unlike previous studies that focused on the behavior patterns of vendors and buyers, this study focuses on the increase in users’ expectation on environmental thermal comfort when the Panjiayuan Antique Market transforms from a conventional commercial market into an urban public space. This study aimed to find a minimal intervention strategy suitable for urban public space renewal from the perspective of the microclimate, encouraging people to use outdoor public spaces more, thereby promoting physical and mental health, as well as social well-being. We used a mixed-methods approach comprising microclimate measurements, questionnaires (n = 254), and field measurements. Our results show that the mean radiant temperature (T_mrt) is the key factor that affects thermal comfort, and it is a comprehensive concept that is associated with other microclimate factors. Linking the quantitative sun-related factors, such as the solar position angle (SAA), the shadow area ratio (SAR), and direct sun hours (DSHs), we also found that the correlation between the T_mrt and physical spatial characteristics, such as the ratio of the visible sky (SVF), the aspect ratio (H/W), and orientation of the building layout, helped us to generate design strategies oriented by regulating microclimate, such as controlling thermal mass/radiant heating, solar radiation, and air convection. One of the significances of this study is its development of a design method that minimizes intervention in urban public spaces from the perspective of regulating the microclimate. In addition, this study proposes a new perspective of promoting people’s health and well-being by improving outdoor thermal comfort. Full article

Inverse lithography technology (ILT) based on the gradient descent (GD) algorithm, which is a classical local optimal method, can effectively improve the lithographic imaging fidelity. However, due to the low-pass filtering effect of the lithography imaging system, GD, although able to converge quickly, is prone to fall into the local optimum for the information in the corner region of complex patterns. Considering the high-frequency information of the corner region during the optimization process, this paper proposes a resolution layering method to improve the efficiency of GD-based ILT algorithms. A corner-rounding-inspired target retargeting strategy is used to compensate for the over-optimization defect of GD for inversely optimizing the complex pattern layout. Furthermore, for ensuring the manufacturability of masks, differentiable top-hat and bottom-hat operations are employed to improve the optimization efficiency of the proposed method. To confirm the superiority of the proposed method, multiple optimization methods of ILT were compared. Numerical experiments show that the proposed method has higher optimization efficiency and effectively avoids the over-optimization. Full article

Urban blue–green space (UBGS) plays a critical role in mitigating the urban heat island (UHI) effect and reducing land surface temperatures (LSTs). However, existing research has not sufficiently explored the optimization of UBGS spatial configurations or their interactions with urban morphology. This study takes New York City as a case and systematically investigates small-scale urban cooling strategies by integrating multiple factors, including adjustments to the blue–green ratio, spatial layouts, vegetation composition, building density, building height, and layout typologies. We utilize multi-source geographic data, including LiDAR derived land cover, OpenStreetMap data, and building footprint data, together with LST data retrieved from Landsat imagery, to develop a prediction model based on generative adversarial networks (GANs). This model can rapidly generate visual LST predictions under various configuration scenarios. This study employs a combination of qualitative and quantitative metrics to evaluate the performance of different model stages, selecting the most accurate model as the final experimental framework. Furthermore, the experimental design strictly controls the study area and pixel allocation, combining manual and automated methods to ensure the comparability of different ratio configurations. The main findings indicate that a blue–green ratio of 3:7 maximizes cooling efficiency; a shrub-to-tree coverage ratio of 2:8 performs best, with tree-dominated configurations outperforming shrub-dominated ones; concentrated linear layouts achieve up to a 10.01% cooling effect; and taller buildings exhibit significantly stronger UBGS cooling performance, with super-tall areas achieving cooling effects approximately 31 percentage points higher than low-rise areas. Courtyard layouts enhance airflow and synergistic cooling effects, whereas compact designs limit the cooling potential of UBGS. This study proposes an innovative application of GANs to address a key research gap in the quantitative optimization of UBGS configurations and provides a methodological reference for sustainable microclimate planning at the neighborhood scale. Full article

In this study, we present a comprehensive numerical investigation of alternating-current electrothermal (ACET) pumping strategies tailored for energy-efficient microfluidic applications. Using coupled electrokinetic and thermal multiphysics simulations in narrow microchannels, we systematically explore the effects of channel geometry, electrode asymmetry and external heating on flow performance and thermal management. A rigorous mesh convergence study confirms velocity deviations below ±0.006 µm/s across the entire operating envelope, ensuring reliable prediction of ACET-driven flows. We demonstrate that increasing channel height from 100 µm to 500 µm reduces peak temperatures by up to 79 K at a constant 2 W heat input, highlighting the critical role of channel dimensions in convective heat dissipation. Introducing a localized external heat source beneath asymmetric electrode pairs enhances convective circulations, while doubling the fluid’s electrical conductivity yields a ~29% increase in net flow rate. From these results, we derive practical design guidelines—combining asymmetric electrode layouts, tailored channel heights, and external heat bias—to realize self-regulating, low-power microfluidic pumps. Such devices hold significant promises for on-chip semiconductor cooling, lab-on-a-chip assays and real-time thermal control in high-performance microelectronic and analytical systems. Full article