Search Results (8,696)

In existing non-satellite navigation systems, visual localization is widely adopted for its high precision. However, in scenarios with highly similar building structures, traditional visual localization methods that rely on direct coordinate prediction often suffer from decreased accuracy or even failure. Moreover, as scene complexity increases, their robustness tends to decline. To address these challenges, this paper proposes a Sketch Line Information Consistency Generation (SLIC) model for indirect building localization. Instead of regressing geographic coordinates, the model retrieves candidate building images that correspond to hand-drawn sketches, and these retrieved results serve as proxies for localization in satellite-denied environments. Within the model, the Line-Attention Block and Relation Block are designed to extract fine-grained line features and structural correlations, thereby improving retrieval accuracy. Experiments on multiple architectural datasets demonstrate that the proposed approach achieves high precision and robustness, with mAP@2 values ranging from 0.87 to 1.00, providing a practical alternative to conventional coordinate-based localization methods. Full article

Research on Mesoscale Convective Systems (MCSs) in Ecuador has focused on regional studies. However, it lacks a thorough and general examination of their relationship with the nation’s diverse orography and large-scale phenomena. This study conducts a climatological analysis of MCS occurrence throughout Ecuador’s natural regions. We perform this study using Sen’s Slope and the Mann–Kendall test. Teleconnections from the Pacific and Atlantic Oceans are studied through wavelet decomposition between time series and Pacific and Atlantic oceanic indices. The main factors that control MCS formation depend on the region. The Intertropical Convergence Zone (ITCZ) at the large scale affects the entire territory. In western Ecuador, MCS formation is mostly related to the El Niño current and the Chocó Low-Level Jet (CLLJ). The Orinoco Low-Level Jet (OLLJ) and evapotranspiration and nocturnal convection display the largest roles in the east. A progressive intensification of activity from Highlands-North in SON is detected (0.143 MCSs per year). MCSs contribute 26% of total precipitation on average, with regional variations from Coast-South (16.41%) to Amazon-North (44.13%). The research confirms existing knowledge about El Niño’s strong relationship (ρ = 0.7) with MCS occurrence in coastal areas while uncovering new complex patterns. The Trans-Nino Index (TNI) functions as a critical two-sided modulator that conventional analysis methods fail to detect. It produces null correlations over conventional time series of MCS occurrence yet emerges as a primary driver of low-frequency variability in the proposed six natural zones of Ecuador. Wavelet decomposition reveals contrasting TNI responses: Amazon-North shows positive correlation (0.73) while Amazon-South exhibits negative correlation (−0.70) at low frequencies. This affects Walker circulations dynamics over the Pacific Ocean. This research establishes fundamental knowledge about MCSs in Ecuador. It builds on a database with strong methodology as a backbone. The research provides essential information about the factors leading to convection in the country. This will help improve seasonal forecast accuracy and risk management effectiveness. Full article

This paper presents the development of a high-resolution composite index to monitor and quantify climate-related risks across Italy. The country’s complex climatic variability, extensive coastline, and low insurance penetration highlight the urgent need for robust, locally calibrated tools to bridge the climate protection gap. Building on the methodological framework of existing actuarial climate indices, previously adapted for France and the Iberian Peninsula, the index integrates six standardised indicators capturing warm and cool temperature extremes, heavy precipitation intensity, dry spell duration, high wind frequency, and sea level change. It leverages hourly ERA5-Land reanalysis data and monthly sea level observations from tide gauges. Results show a clear upward trend in climate anomalies, with regional and seasonal differentiation. Among all components, sea level is most strongly correlated with the composite index, underscoring Italy’s vulnerability to marine-related risks. Comparative analysis with European indices confirms both the robustness and specificity of the Italian exposure profile, reinforcing the need for tailored risk metrics. The index can support innovative risk transfer mechanisms, including climate-related insurance, regulatory stress testing, and resilience planning. Combining scientific rigour with operational relevance, it offers a consistent, transparent, and policy-relevant tool for managing climate risk in Italy and contributing to harmonised European frameworks. Full article

The complex environment along railway lines, characterized by low imaging quality, strong background interference, and densely distributed small objects, causes existing detection models to suffer from low accuracy in practical applications. To tackle these challenges, this study aims to develop a robust and lightweight insulator detection model specifically optimized for these challenging railway scenarios. To this end, we release a dedicated comprehensive dataset named complexRailway that covers typical railway scenarios to address the limitations of existing insulator datasets, such as the lack of small-scale objects in high-interference backgrounds. On this basis, we present CutP5-LargeKernelAttention-SIoU (C5LS), an improved YOLOv8 variant with three key improvements: (1) optimized YOLOv8’s detection head by removing the P5 branch to improve feature extraction for small- and medium-sized targets while reducing computational redundancy, (2) integrating a lightweight Large Separable Kernel Attention (LSKA) module to expand the receptive field and improve contextual modeling, (3) and replacing CIoU with SIoU loss to refine localization accuracy and accelerate convergence. Experimental results demonstrate that it reaches 94.7% in mAP@0.5 and 65.5% in mAP@0.5–0.95, outperforming the baseline model by 1.9% and 3.5%, respectively. With an inference speed of 104 FPS and a model size of 13.9 MB, the model balances high precision and lightweight deployment. By providing stable and accurate insulator detection, C5LS not only offers reliable spatial positioning basis for subsequent defect identification but also builds an efficient and feasible intelligent monitoring solution for these failure-prone insulators, thereby effectively enhancing the operational safety and maintenance efficiency of the railway power system. Full article

Reproducibility is a cornerstone of scientific progress, yet in X-ray computed tomography (CT) reconstruction, it remains a critical and unresolved challenge. Current benchmarking practices in CT are hampered by the scarcity of openly available datasets, the incomplete or task-specific nature of existing resources, and the lack of transparent implementations of widely used methods and evaluation metrics. As a result, even the fundamental property of reproducibility is frequently violated, undermining objective comparison and slowing methodological progress. In this work, we analyze the systemic limitations of current CT benchmarking, drawing parallels with broader reproducibility issues across scientific domains. We propose an extended data model and formalized schemes for data preparation and quality assessment, designed to improve reproducibility and broaden the applicability of CT datasets across multiple tasks. Building on these schemes, we introduce checklists for dataset construction and quality assessment, offering a foundation for reliable and reproducible benchmarking pipelines. A key aspect of our recommendations is the integration of virtual CT (vCT), which provides highly realistic data and analytically computable phantoms, yet remains underutilized despite its potential to overcome many current barriers. Our work represents a first step toward a methodological framework for reproducible benchmarking in CT. This framework aims to enable transparent, rigorous, and comparable evaluation of reconstruction methods, ultimately supporting their reliable adoption in clinical and industrial applications. Full article

Retrofitting existing buildings is regarded as a main driver of decarbonization, yet retrofitting activities are lagging behind their ambitious goals. This study explores 86 German construction practitioners’ perceptions of organizational, policy, and social challenges to sustainable retrofitting and how those perceptions relate to age, attitude, and their interaction. The primary analyses used OLS moderation models with HC3-robust standard errors and ordered-logit models, which served as robustness checks. Across outcomes, more pro-environment attitudes were associated with fewer perceived challenges, and older practitioners (41–56+) reported higher barrier perception. The attitude × age interaction results indicate that the protective link of attitude was weaker among older respondents, which was significant for policy and social challenges but only marginal for organizational challenges. The model fit was reasonable, at an Adj. R² between ≈0.56 and 0.72 with acceptable diagnostics. Our results suggest that even motivated professionals can feel constrained, especially among older, senior staff. Practical implications include early tenant engagement to enhance acceptance and foster internal organizational sustainability capacities. Policy instruments such as adult education programmes need to be leveraged to enhance sustainable construction capabilities and reinforce attitudes and behaviours toward sustainable retrofitting. More salient policy communications and guidance can contribute to increasing sustainability orientation and reducing perceived trade-offs with economic goals. Full article

High-quality development highlights the importance of environmental protection and green low-carbon development. The high-quality development of the manufacturing industry is not only the key content for achieving green transformation, but also an important cornerstone for building a modern national industrial system. Current research focuses on companies and governments, ignoring the important value of suppliers and consumers. As a result, existing mechanisms have failed to deliver the desired results. This paper constructs an evolutionary game model involving manufacturing enterprises, local governments, suppliers, and consumers, and systematically analyzes the strategy selection process of the four participating populations. On this basis, the impact of exogenous and endogenous factors on the evolutionarily stable strategy is studied at the microscopic level using numerical simulation methods. The results show that (1) increasing any of the endogenous factors, such as innovative capability, organization building, and industrial resources, can accelerate the evolution of manufacturing enterprises evolve to smart upgrade strategy. (2) Increasing any one of the exogenous factors, such as policy environment, industrial cooperation, and market demand, can accelerate the rate at which manufacturing enterprises choose to adopt the strategy of smart upgrade. The purpose of this paper is to provide a theoretical reference for the behavioral strategies of manufacturing enterprises, and to provide a realistic reference for local governments to build a mechanism to promote the high-quality development of the manufacturing industry. Full article

Semantic segmentation of building facade images has enabled a lot of intelligent support for architectural research and practice in the last decade. However, the classifiers for semantic segmentation usually predict facade elements (e.g., windows) as graphics in irregular shapes. The non-smooth edges and hard-to-define shapes impede the further use of the predicted graphics. This study proposes a method to regularize the predicted graphics following the prior knowledge of composition principles of building facades. Specifically, we define four types of boxes for each predicted graphic, namely minimum circumscribed box (MCB), maximum inscribed box (MIB), candidate box (CB), and best overlapping box (BOB). Based on these boxes, a three-stage process, consisting of denoising, BOB finding, and BOB stacking, was established to regularize the predicted graphics of facade elements into basic rectilinear polygons. To compare the proposed and existing methods of graphic regularization, an experiment was conducted based on the predicted graphics of facade elements obtained from four pixel-wise annotated building facade datasets, Irregular Facades (IRFs), CMP Facade Database, ECP Paris, and ICG Graz50. The results demonstrate that the graphics regularized by our method align more closely with real facade elements in shape and edge. Moreover, our method avoids the prevalent issue of correctness degradation observed in existing methods. Compared with the predicted graphics, the average IoU and F1-score of our method-regularized graphics respectively increase by 0.001–0.017 and 0.000–0.012 across the datasets, while those of previous method-regularized graphics decrease by 0.002–0.021 and 0.002–0.015. The regularized graphics contribute to improving the precision and depth of semantic segmentation-based applications of building facades. They are also expected to be useful for the exploration of data mining on urban images in the future. Full article

Internal insulation plasters enable historic building renovation without altering the external appearance of the wall. However, the use of internal insulation must be verified case-by-case through dynamic hygrothermal simulation, and the influence of input parameters on the results is not always clear. This paper aims to (i) characterize a new lime-based insulating plaster with expanded recycled glass and aerogel through laboratory measurements, (ii) assess the damage criteria of the plaster under different boundary conditions through dynamic simulations, and (iii) identify the most impactful design conditions on the relative humidity behind insulation. This innovative plaster combines highly insulating properties (thermal conductivity of 0.0463 W/mK) with good capillary activity while also integrating recycled components without compromising performance. The relative humidity behind insulation remains below 95% in most simulated scenarios, with cases above this threshold found only in cold climates, particularly under high internal moisture loads. The parametric study shows that (i) in the analyzed stones, the thermal conductivity variation of the existing wall has a greater effect on the relative humidity behind insulation than the variation of the vapor resistance factor, (ii) the effect of insulation thickness on the relative humidity behind insulation depends on the difference in thermal resistance of the insulation and existing masonry layers, and (iii) internal moisture load and external climate directly impact the relative humidity behind insulation. Full article

Urban school environments often face significant thermal discomfort due to extensive paved surfaces, limited vegetation, and outdated building designs. This study examines how green spaces can mitigate temperature extremes and improve thermal comfort at two secondary schools in Coimbra, Portugal: Escola Secundária José Falcão (ESJF) and Escola Secundária D. Dinis (ESDD). Using a mixed-methods approach that combined school community surveys with on-site microclimatic measurements, we integrated user feedback on comfort with data on temperature and humidity variations across different indoor and outdoor spaces. Results revealed that tree-shaded areas consistently maintained lower air temperatures and higher relative humidity than unshaded zones, which experienced intense heat accumulation—up to a 5 °C difference. At ESJF, the older infrastructure and large asphalt surfaces led to severe heat retention, with east-facing classrooms recording the highest indoor temperatures. ESDD’s pavilion-style layout and existing green spaces provided comparatively better thermal conditions, although insufficient vegetation maintenance and limited shade reduced their effectiveness. The findings demonstrate a clear correspondence between the school community’s perceptions of thermal comfort and the measured microclimatic data. Vegetation—particularly deciduous trees—plays a critical role in cooling the school microclimate through shading and evapotranspiration. Strategic interventions such as expanding tree cover in high-exposure areas, installing green roofs and walls, and carefully selecting species can significantly reduce temperature extremes and improve outdoor usability. In addition, fostering environmental education and participatory co-design programs can encourage sustainable behaviors within the school community, underlining the importance of inclusive, nature-based solutions for climate adaptation. This research highlights that integrating green infrastructure in school design and management is a cost-effective strategy for thermal regulation. Green spaces, when co-designed with community involvement, not only enhance climate resilience and student well-being but also contribute to broader sustainable urban development goals. Full article

Existing building management systems face critical limitations in real-time data integration, primarily relying on static models that lack dynamic updates from IoT sensors. To address this gap, this study proposes a novel system integrating MQTT over WebSocket with Three.js visualization, enabling real-time sensor-data binding to Building Information Models (BIM). The architecture leverages MQTT’s lightweight publish-subscribe protocol for efficient communication and employs a TCP-based retransmission mechanism to ensure 99.5% data reliability in unstable networks. A dynamic topic-matching algorithm is introduced to automate sensor-BIM associations, reducing manual configuration time by 60%. The system’s frontend, powered by Three.js, achieves browser-based 3D visualization with sub-second updates (280–550 ms latency), while the backend utilizes SpringBoot for scalable service orchestration. Experimental evaluations across diverse environments—including high-rise offices, industrial plants, and residential complexes—demonstrate the system’s robustness: Real-time monitoring: Fire alarms triggered within 2.1 s (22% faster than legacy systems). Network resilience: 98.2% availability under 30% packet loss. User efficiency: 4.6/5 satisfaction score from facility managers. This work advances intelligent building management by bridging IoT data with interactive 3D models, offering a scalable solution for emergency response, energy optimization, and predictive maintenance in smart cities. Full article

Due to the increasing demand for accurate and robust GNSS positioning for location-based services (LBS) in urban regions, the impacts prevalent in metropolitan areas, like multipath reflections and various interferences, have become persistent challenges. Consequently, developing effective strategies to address these sophisticated influences has become both a primary research focus and a shared priority. In this paper, the authors explore an approach to identify and mitigate the drawbacks arising from multipath effects in urban positioning. Unlike conventional ways for building complex models, an adaptive data-driven methodology is proposed to identify the fingerprints of a multipath in GNSS observations. This approach utilizes the Fourier transform (FT) to examine code multipath and other error sources in terms of frequency, as represented by the power spectrum. Wavelet decomposition and signal spectrum methods are subsequently applied to seek traces of code multipath in multilayer decompositions. Based on the exhibited multipath features, the impacts of multipath in GNSS observations are detected and mitigated in the reconstructed observations. The proposed method is validated for both static and dynamic positioning scenarios, demonstrating seamless integration with existing positioning models. The feasibility has been verified through a series of experiments and tests under urban environments using navigation terminals and smartphones. Full article

Widespread casualties and property damage due to structural failures following devastating earthquakes have once again highlighted the critical significance of evaluating the seismic performance of existing buildings. In this context, a fundamental part of modern pre-disaster management is to evaluate the potential seismic risks of existing structures and implementing the necessary precautions. This study focuses on determining regional risk priorities using a rapid assessment methodology applied to a sample of reinforced-concrete (RC) structures in the Centre of Bitlis city, situated in the high-seismic-risk Lake Van Basin. Risk prioritization was made among the buildings based on the Turkish Rapid Assessment technique revised in 2019 for 100 different RC buildings with one to seven stories. The negative parameters utilized in this method were analyzed both in relation to the 6 February 2023, Kahramanmaraş earthquakes and the assessed building stock. Additionally, the study provides a comprehensive review of the existing building inventory across the region and offers recommendations for potential precautions to mitigate earthquake risks. Full article

Building change detection involves identifying areas where buildings have changed by comparing multi-temporal remote sensing imagery of the same geographical region. Recent advances in Transformer-based methods have significantly improved remote sensing change detection. However, current Transformer models still exhibit persistent limitations in effectively extracting multi-scale semantic features within complex scenarios. To more effectively extract multi-scale semantic features in complex scenes, we propose a novel model, which is the Transformer-based Multi-Scale Semantic Extraction Change Detection Network (MSSE-CDNet). The model employs a Siamese network architecture to enable precise change recognition. MSSE-CDNet comprises four parts, which together contain five modules: (1) a CNN feature extraction module, (2) a multi-scale semantic extraction module, (3) a Transformer encoder and decoder module, and (4) a prediction module. Comprehensive experiments on the standard LEVIR-CD benchmark for building change detection demonstrate our approach’s superiority over state-of-the-art methods. Compared to existing models such as FC-Siam-Di, FC-Siam-Conc, DTCTSCN, BIT, and SNUNet, MSSE-CDNet achieves significant and consistent gains in performance metrics, with F1 scores improved by 4.22%, 6.84%, 2.86%, 1.22%, and 2.37%, respectively, and Intersection over Union (IoU) improved by 6.78%, 10.74%, 4.65%, 2.02%, and 3.87%, respectively. These results robustly substantiate the effectiveness of our framework on an established benchmark dataset. Full article

In order to solve the problem of inconsistent data distribution in machine learning, domain adaptation based on feature representation methods extracts features from the source domain, and transfers them to the target domain for classification. The existing feature representation-based methods mainly solve the problem of inconsistent feature distribution between the source domain data and the target domain data, but only a few methods analyze the correlation of cross-domain features between the original space and shared latent space, which reduces the performance of domain adaptation. To this end, we propose a domain adaptation method with a residual module, the main ideas of which are as follows: (1) transfer the source domain data features to the target domain data through the shared latent space to achieve features sharing; (2) build a cross-domain residual learning model using the latent feature space as the residual connection of the original feature space, which improves the propagation efficiency of features; (3) use a regular feature space to sparse feature representation, which can improve the robustness of the model; and (4) give an optimization algorithm, and the experiments on the public visual datasets (Office31, Office-Caltech, Office-Home, PIE, MNIST-UPS, COIL20) results show that our method achieved 92.7% accuracy on Office-Caltech and 83.2% on PIE and achieved the highest recognition accuracy in three datasets, which verify the effectiveness of the method. Full article