Search Results (7,552)

This study introduces a novel, automated image-to-BIM (Building Information Modeling) workflow designed to generate semantically rich and geometrically useful BIM models directly from RGB images. Conventional scan-to-BIM often relies on specialized, costly, and time-intensive equipment, specifically if LiDAR is used to generate point clouds (PCs). Typical workflows are followed by a separate post-processing step for semantic segmentation recently performed by deep learning models on the generated PCs. Instead, the proposed method integrates vision language object detection (YOLOv8x-World v2) and vision based segmentation (SAM 2.1) with Neural Radiance Fields (NeRF) 3D reconstruction to generate segmented, color-labeled PCs directly from images. The key novelty lies in bypassing post-processing on PCs by embedding semantic information at the pixel level in images, preserving it through reconstruction, and encoding it into the resulting color labeled PC, which allows building elements to be directly identified and geometrically extracted based on color labels. Extracted geometry is serialized into a JSON format and imported into Revit to automate BIM creation for walls, windows, and doors. Experimental validation on BIM models generated from Unmanned Aerial Vehicle (UAV)-based exterior datasets and standard camera-based interior datasets demonstrated high accuracy in detecting windows and doors. Spatial evaluations yielded up to 0.994 precision and 0.992 Intersection over Union (IoU). NeRF and Gaussian Splatting models, Nerfacto, Instant-NGP, and Splatfacto, were assessed. Nerfacto produced the most structured PCs suitable for geometry extraction and Splatfacto achieved the highest image reconstruction quality. The proposed method removes dependency on terrestrial surveying tools and separate segmentation processes on PCs. It provides a low-cost and scalable solution for generating BIM models in aging or undocumented buildings and supports practical applications such as renovation, digital twin, and facility management. Full article

The global food industry is undergoing a critical shift toward sustainability, driven by high postharvest losses—reaching up to 40% for fruits and vegetables—and the need to reduce environmental impact. Sustainable postharvest innovations focus on improving quality, extending shelf life, and minimizing waste through eco-efficient technologies. Advances in non-thermal and minimal processing, including ultrasound, pulsed electric fields, and edible coatings, support nutrient preservation and food safety while reducing energy consumption. Although integrated postharvest technologies can reduce deterioration and microbial spoilage by 70–92%, significant challenges remain, including global losses of 20–40% and the high implementation costs of certain nanostructured materials. Simultaneously, eco-friendly packaging solutions based on biodegradable biopolymers and bio-composites are replacing petroleum-based plastics and enabling intelligent systems capable of monitoring freshness and detecting spoilage. Energy-efficient storage, smart sensors, and optimized cold-chain logistics further contribute to product integrity across distribution networks. In parallel, the circular bioeconomy promotes the valorization of agro-food by-products through the recovery of bioactive compounds with antioxidant and anti-inflammatory benefits. Together, these integrated strategies represent a promising pathway toward reducing postharvest losses, supporting food security, and building a resilient, environmentally responsible fresh produce system. Full article

Urban green space is essential for ecological functions, environmental quality, and human well-being, yet campus expansion can reduce vegetated areas. This study assessed UGS dynamics at Universitas Padjadjaran’s Jatinangor campus from 2015 to 2025 and evaluated an object-based machine learning approach for fine-scale land cover mapping. High-resolution WorldView-2, WorldView-3, and Legion-03 imagery were pan-sharpened, geometrically corrected, normalized, and used to compute NDVI and NDWI indices. Object-based image analysis segmented the imagery into homogeneous objects, followed by random forest classification into six land cover classes; UGS was derived from dense and sparse vegetation. Accuracy assessment included confusion matrices, overall accuracy 0.810–0.860, kappa coefficients 0.747–0.826, weighted F1 scores 0.807–0.860, and validation with 43 field points. The total UGS increased from 68.89% to 74.69%, bare land decreased from 13.49% to 5.81%, and building areas moderately increased from 10.36% to 11.52%. The maps captured vegetated and developed zones accurately, demonstrating the reliability of the classification approach. These findings indicate that campus expansion has been managed without compromising ecological integrity, providing spatially explicit, reliable data to inform sustainable campus planning and support green campus initiatives. Full article

This study presents a well-to-wheel life-cycle assessment (WTW-LCA) comparing battery-electric heavy-duty trucks (BEVs) with conventional diesel trucks, utilizing real-world fleet data from Southern California’s Volvo LIGHTS project. Class 7 and Class 8 vehicles were analyzed under ISO 14040/14044 standards, combining measured diesel emissions from portable emissions measurement systems (PEMSs) with BEV energy use derived from telematics and charging records. Upstream (“well-to-tank”) emissions were estimated using USLCI datasets and the 2020 Southern California Edison (SCE) power mix, with an additional scenario for BEVs powered by on-site solar energy. The analysis combines measured real-world energy consumption data from deployed battery electric trucks with on-road emission measurements from conventional diesel trucks collected by the UCR team. Environmental impacts were characterized using TRACI 2.1 across climate, air quality, toxicity, and fossil fuel depletion impact categories. The results show that BEVs reduce total WTW CO₂-equivalent emissions by approximately 75% compared to diesel. At the same time, criteria pollutants (NO_x, VOCs, SO_x, PM_2.5) decline sharply, reflecting the shift in impacts from vehicle exhaust to upstream electricity generation. Comparative analyses indicate BEV impacts range between 8% and 26% of diesel levels across most environmental indicators, with near-zero ozone-depletion effects. The main residual hotspot appears in the human-health cancer category (~35–38%), linked to upstream energy and materials, highlighting the continued need for grid decarbonization. The analysis focuses on operational WTW impacts, excluding vehicle manufacturing, battery production, and end-of-life phases. This use-phase emphasis provides a conservative yet practical basis for short-term fleet transition strategies. By integrating empirical performance data with life-cycle modeling, the study offers actionable insights to guide electrification policies and optimize upstream interventions for sustainable freight transport. These findings provide a quantitative decision-support basis for fleet operators and regulators planning near-term heavy-duty truck electrification in regions with similar grid mixes, and can serve as an empirical building block for future cradle-to-grave and dynamic LCA studies that extend beyond the operational well-to-wheels scope adopted here. Full article

Quiet drivetrains have become a central requirement in modern electric vehicles, where the absence of engine masking makes even subtle gear tones clearly audible. As a result, manufacturers are looking for more reliable ways to understand how design choices, manufacturing variability, and operating conditions shape gear noise and vibration. Digital Twin (DT) approaches—linking high-fidelity models with measured data throughout the product lifecycle—offer a potential route to achieve this, but their use in gear NVH is still emerging. This review examines recent work from the past decade on DT concepts applied to gears and drivetrain NVH, drawing together advances in simulation, metrology, sensing, and data exchange standards. The survey shows that several building blocks of an NVH-oriented twin already exist, yet they are rarely combined into an end-to-end workflow. Clear gaps remain. Current models still struggle with high-frequency behavior. Real-time operation is also limited. Manufacturing and test data are often disconnected from simulations. Validation practices lack consistent NVH metrics. Hybrid and surrogate modeling methods are used only to a limited extent. The sustainability benefits of reducing prototypes are rarely quantified. These gaps define the research directions needed to make DTs a practical tool for future gear NVH development. A research Gap Map is presented, categorizing these gaps and their impact. For each gap, we propose actionable future directions—from multiscale “hybrid twins” that merge test data with simulations, to benchmark datasets and standards for DT NVH validation. Closing these gaps will enable more reliable gear DTs that reduce development costs, improve acoustic quality, and support sustainable, data-driven NVH optimization. Full article

To achieve a dynamic balance between economic development and ecological protection, it is necessary to analyze the enabling mechanism of new quality productive (NQP) on the green and low-carbon transformation of the construction industry. Based on the panel data of 107 cities in the Yangtze River Economic Belt (YERB) from 2011 to 2023, this study analyzes the spatiotemporal characteristics of carbon emissions in the construction industry (CECI), and explores the impact, mechanism, regional heterogeneity and spatial spillover effect of NQP on carbon emission intensity in the construction industry (CEICI). From 2011 to 2023, CECI increased in low amplitude but weakened the spatial concentration. The overall level of NQP in the YREB region shows a trend of first a slight decline and then a steady increase, and the development disparities among different regions have continued to widen. The NQP is significantly negatively correlated with the CEICI. The impact effect shows a gradient distribution pattern of “upstream > downstream > midstream”, and there is also a spatial spillover effect. Moreover, the mechanism analysis shows that the NQP can influence CEICI by the green technological innovation (GTI) and industrial structure upgrading (ISU). Moreover, the mediating effect of GTI (−0.4019) is greater than ISU (−0.1049). These results can help to formulate policies on NQP for reducing building emissions. Full article

Conventional harvesters usually depend on the operator’s expertise to manually manage the power allocation between the harvesting and traveling system, which results in problems like high subjectivity, labor intensity, and sensitivity to terrain. To overcome issues such as inadequate power and power mismatches between harvesting and traveling on gentle slopes, this study introduces a hydrostatic four-wheel-drive system featuring a single variable pump paired with two variable motors, improving the vehicle’s capability to handle complex terrains. Building on this system, an adaptive power allocation method for traveling is proposed. This method dynamically adjusts the power distribution between traveling and harvesting according to changing terrain conditions, giving priority to harvesting power while controlling vehicle and engine speeds to avoid engine stalls, thereby enhancing operational quality and efficiency. A model of the full vehicle system is created using Amesim 2410, and a comparation of adaptive control and constant speed control is modeled under a hardware-in-the-loop environment. The simulation results show that the proposed control approach effectively manages power distribution across different slopes and speeds, and avoids engine stalling, providing valuable technical guidance for power coordination control in harvesters working on gentle slopes. Full article

High-efficiency farmland production is essential for ensuring national food security and promoting sustainable agriculture in China. This paper aims to systematically analyze the challenges in building a new-quality farmland production system driven by innovative productive forces that emphasizes large-scale operations, optimal integration of farming components, and the application of modern technologies and intangible inputs. To achieve this aim, we conducted a comprehensive review and synthesis of the current literature, national policy documents, and agricultural statistics. Our analysis identifies key challenges, including limited water and land resources, outdated machinery and practices, a shortage of skilled farmers, insufficient innovation, and underdeveloped policy and support systems. Based on this analysis, we propose a series of integrated strategies to enhance farmland productivity. These recommendations include improving soil fertility, developing new crop varieties, promoting modern management models, training farmers in advanced technologies, innovating agricultural policies and infrastructure, and establishing accessible farm credit and insurance systems. We conclude that by integrating the six key elements of quality farmland, superior varieties, skilled farmers, modern technologies, sound policies, and supportive credit systems, China can successfully transition from labor-intensive to technology- and information-intensive farming models, thereby boosting the productivity and resilience of its farmland production systems. Full article

Accurate identification of tool wear conditions is of great significance for extending tool life, ensuring processing quality, and improving production efficiency. Current research shows that signals collected by a single sensor have limited dimensions and cannot comprehensively capture the degradation process of tool wear, while multi-sensor fusion recognition methods cannot effectively handle the complementarity and redundancy between heterogeneous sensor data in feature extraction and fusion. To address these issues, this paper proposes Hi-MDTCN (Hierarchical Multi-scale Dilated Temporal Convolutional Network). In the network, we propose a hierarchical signal analysis framework that processes the signal in segments. When processing intra-segment signals, we design a Multi-channel one-dimensional convolutional network with attention mechanism to capture local wear features at different time scales and fuse them into a unified representation. When processing signal segments, we design a Bi-TCN module to further capture long-term dependencies in wear evolution, mining the overall trend of tool wear over time. Hi-MDTCN adopts a dilated convolution mechanism, which can achieve an extremely large receptive field without building an overly deep network structure, effectively solving problems faced by recurrent neural networks in long sequence modeling such as gradient vanishing, low training efficiency, and poor parallel computing capability, achieving efficient parallel capture of long-range dependencies in time series. Finally, the proposed method is applied to the PHM2010 milling data. Experimental results show that the model’s tool condition recognition accuracy is higher than traditional methods, demonstrating its effectiveness for practical applications. Full article

Building drainage systems are essential for protecting occupant health and indoor air quality. While recent studies have focused on high-rise drainage dynamics and riser offset mitigation, ventilation components—particularly appliance vent pipes—remain underexplored. This study employed a full-scale proportional drainage experimental tower to assess appliance vent pipes on horizontal branches as a strategy for water seal protection in dual-riser systems. Maximum drainage capacities were quantified under varying pipe positions and diameters (DN50, DN75, DN100), alongside analyses of pressure transients and water seal losses. Results indicate that appliance vent pipes increase maximum drainage capacity from 6.5 L/s (baseline cast iron dual-riser) to 7.5 L/s, a 1.0 L/s gain, though improvements are modest. Position does not affect capacity (uniformly 7.5 L/s across configurations) but profoundly influences water seal losses: P-type trap placement yields the lowest losses on most floors, combined P-type trap/floor drain placement achieves intermediate values, and floor drain placement the highest. Thus, the P-type trap is optimal. Diameter similarly has no impact on capacity but shows nuanced effects on seals; DN75 minimizes losses on most floors, outperforming DN50 and DN100, indicating that appliance vent pipe design should adopt a height-zoned approach tailored to anticipated drainage loads and pressure characteristics. Appliance vent pipes effectively dampen positive/negative pressure fluctuations, reducing seal depletion and sewer gas risks. These findings guide engineering designs for healthier indoor environments in high-rise buildings. Full article

Currently, in hydrotechnical engineering, such as oil and gas platform construction, floating docks, and other floating structures, the need to develop new lightweight composite building materials is becoming an important problem. Expanded clay concrete (ECC) is the most common lightweight concrete option for floating structures. The aim of this study is to develop effective composite ECC with improved properties and a coefficient of structural quality (CCQ). To improve the properties of ECC, the following formulation and technological techniques were additionally applied: reinforcement of lightweight expanded clay aggregate by pre-treatment in cement paste (CP-LECA) with the addition of microsilica (MS) and dispersed reinforcement with basalt fiber (BF). An experimental study examined the effect of the proposed formulation and technological techniques on the density and cone slump of fresh ECC and the density, compressive and flexural strength, and water absorption of hardened ECC. A SEM analysis was conducted. The optimal parameters for LECA pretreatment were determined. These parameters are achieved by treating LECA grains in a cement paste with 10% MS and using dispersed reinforcement parameters of 0.75% BF. The best combination of CP-LECA10MS-0.75BF provides increases in compressive and flexural strength of up to 50% and 61.7%, respectively, and a reduction in water absorption of up to 32.8%. The CCQ increases to 44.4%. If the ECC meets the design requirements, it can be used in hydraulic engineering for floating structures. Full article

With the increasing functional and geometric complexity of modern steel buildings, irregular-shaped beam-to-column joints are becoming common in engineering practice. However, their seismic behavior remains insufficiently understood, particularly for configurations with geometric asymmetry and complex stress transfer mechanisms. This study experimentally investigates the seismic performance of irregular steel-beam-to-concrete-filled steel tube (CFST) column joints incorporating inclined internal diaphragms (IIDs), taking unequal-depth beam (UDB) and staggered beam (SB) joints as representative cases. Two full-scale joint specimens were designed and tested under cyclic loading to evaluate their failure modes, load-bearing capacity, stiffness/strength degradation, energy dissipation capacity, strain distribution, and panel zone shear behavior. Both joints exhibited satisfactory strength and initial stiffness. Although diaphragm fracture occurred at approximately 3% drift, the joints retained 45–60% of their peak load capacity, based on the average strength of several loading cycles at the same drift level after diaphragm failure, and maintained stable hysteresis with average equivalent damping ratios above 0.20. Final failure was governed by successive diaphragm fracture followed by the tearing of the column wall, indicating that the adopted diaphragm thickness (equal to the beam flange thickness) was insufficient and that welding quality significantly affected joint performance. Refined finite element (FE) models were developed and validated against the test responses, reasonably capturing global strength, initial stiffness, and the stress concentration patterns prior to diaphragm fracture. The findings of this study provide a useful reference for the seismic design and further development of internal-diaphragm irregular steel-beam-to-CFST column joints. Full article

This systematic review assesses indoor air quality (IAQ) in tropical residences (Köppen Af/Am/Aw), explicitly linking IAQ to ventilation from in situ monitoring and, when relevant, occupant surveys (surveys synthesized qualitatively). This focus is warranted by the scarcity of tropical, housing-specific evidence. Searches were performed exclusively in Google Scholar (25 August 2024–5 August 2025; English/Spanish) under PRISMA, with documented queries/filters; eligible studies reported residential settings, tropical climate, and IAQ–ventilation linkage. Results show a regulatory mosaic with few binding residential limits and heterogeneous protocols that hinder comparison. Robust patterns include cooking-related particle peaks, penetration of traffic dust, humidity-driven VOC/formaldehyde emissions, and mold growth under deficient hygrothermal control. CO₂ is a useful operational indicator of ventilation yet insufficient for risk assessment without PM and VOC monitoring. Evidence supports source control, cross-ventilation and/or on-demand extraction/outdoor-air supply, humidity management, and filtration/purification to avoid particle ingress during ventilation. Reporting of sensor performance (calibration, drift, RH/T effects) is inconsistent, and targeted evaluations of TVOC/formaldehyde and window screens (mesh) are scarce. We conclude that tropical residential IAQ management requires multi-parameter, continuous monitoring, standardized reporting, and trials integrating ventilation, dehumidification, and filtration under real occupancy, alongside adaptive regulation and passive tropical design augmented by light mechanical support and informed occupant behavior. Full article

Innovation in the new energy industry serves not only as a key accelerator for the global green and low-carbon energy transition but also as a core driving force of the ongoing energy revolution. This study utilizes data on publications, patents, and the spatial distribution of representative innovation enterprises in the new energy industry of the Yangtze River Delta urban agglomeration from 2009 to 2023 to construct a multilayer science–technology–industry innovation network. Social network analysis is employed to examine its evolutionary dynamics and structural characteristics, and the Quadratic Assignment Procedure (QAP) is used to investigate the factors shaping intercity innovation linkages. The results reveal that the multilayer innovation network has continuously expanded in scale, gradually forming a multi-core radiative structure with Shanghai, Nanjing, and Hangzhou at the center. At the cohesive subgroup level, the scientific and technological layers exhibit clear hierarchical differentiation, where core cities tend to engage in strong mutual collaborations, while the industrial layer shows a hub-and-spoke pattern combining large, medium, and small cities. In terms of layer relationships, the centrality of the scientific layer increasingly surpasses that of the technological and industrial layers. Inter-layer degree correlations and overlaps also display a strengthening trend. Furthermore, differences in regional higher education scale, urban economic density, and geographic proximity are found to exert significant influences on scientific, technological, and industrial innovation linkages among cities. In response, this study recommends enhancing the leadership role of core cities, leveraging the bridging and intermediary functions of peripheral cities, and promoting application-driven cross-regional innovation collaboration, thereby building efficient science–technology–industry networks and enhancing intercity innovation linkages and the flow of innovation resources, and ultimately promoting the high-quality development of the regional new energy industry. Full article

In the context of new-type urbanization and high-quality development, this study aims to construct a multi-objective synergistic land-use mechanism to tackle the “growth-equity-ecology” trilemma in the Chengdu–Chongqing Urban Agglomeration (CCUA). By building an economic–social–ecological benefit evaluation index system and applying TOPSIS with entropy weighting, the coupling coordination degree (CCD) model, and the spatial Durbin model (SDM), we systematically explore the spatiotemporal patterns of land-use benefit synergies and their driving mechanisms. The results reveal the following: (1) From 2015 to 2023, CCUA’s land-use CCD generally improved but showed marked core–periphery polarization. Chongqing’s economic agglomeration worsened regional gaps, while Sichuan’s intra-regional policies boosted internal balance; cross-jurisdictional collaboration eased border disparities but failed to stop overall polarization. (2) Spatial clustering identified hotspots in Chongqing’s main urban and suburban areas and cold spots in eastern Sichuan, reflecting the coexistence of factor agglomeration and cross-border policy synergy. (3) Road network expansion directly hindered CCD, and neighboring ecological protection triggered resource-competition spillovers, emphasizing the key role of cross-regional governance in balancing the “ecology-development” trade-off. This study puts forward spatially differentiated strategies and cross-jurisdictional coordination mechanisms to optimize land-use structures and advance sustainable development in urban agglomerations. Full article