Search Results (1,419)

Timber represents a building material that aligns with the environmental demands on the impact of the construction sector on climate change. The most common engineering solution for modern timber buildings with large spans is glued laminate timber (glulam). This project proposes a tool for a topological optimized geometry generator of structural elements made of glulam that can be used for building a database of topologically optimized glulam beams. In turn, this can be further used to train machine learning models that can embed the topologically optimized geometry and structural behavior information. Topological optimization tasks usually require a large number of iterations in order to reach the design goals. Therefore, embedding this information into machine learning models for structural elements belonging to the same topological groups will result in a faster design process since certain aspects regarding structural behavior such as strength and stiffness can be quickly estimated using Artificial Intelligence techniques. Topologically optimized geometry propositions could be obtained by employing generative machine learning model techniques which can propose geometries that are closer to the topologically optimized results using FEM and as such present a starting point for the design analysis in a reduced amount of time. Full article

Learning management systems (LMS) have become central to modern education, enabling accessible, personalized, and engaging learning experiences. This study aims to investigate Saudi university students’ perception of LMS in order to explore the critical factors that shape their engagement, satisfaction, and acceptance of these platforms. Drawing from the existing literature that points out the usability challenges of LMS, this study hopes to derive actionable insights to optimize e-learning outcomes. Using Kelly’s repertory grid analysis technique, this study systematically captured and analyzed the personal constructs students associate with LMS, focusing on ease of use, interactivity, and content alignment with educational needs. A sample of 20 university students provided insights on their experiences with LMS features related to usability, functionality, and interactivity, which are critical to engagement. Findings indicate that ease of use is a major determinant of acceptance, along with interactivity and relevant content delivery that supports diverse learning preferences. The study identifies key elements to improve LMS platforms, fostering a more engaging digital learning environment and supporting students’ learning needs. The findings highlight the key aspects: usability of LMS and students’ satisfaction through user-friendly interfaces and interactive features. Institutions that incorporate student feedback into LMS development will likely see improved e-learning outcomes. This research contributes to a deeper understanding of LMS user perceptions and implies refinements that can align platforms with pedagogical demands in higher education. Full article

In the context of people-centered and sustainable urban policies, identifying renewal potential based on vitality enhancement is crucial for urban regeneration efforts. This article collected population density data, house price data, and built environment data to examine the spatial pattern characteristics of Harbin’s core area using spatial autocorrelation analysis. Building on these findings, a geographically weighted regression (GWR) model was constructed to further analyze the influencing mechanisms of the relevant factors. The analysis revealed significant spatial development imbalances within Harbin’s core area, characterized by differentiated and uneven development of social and economic vitality between the old city and newly constructed areas. Notably, in certain regions, the construction intensity does not align with the levels of social and economic vitality, indicating potential opportunities for urban renewal. Furthermore, the examination of key influencing factors highlighted that the accessibility of commercial facilities and development intensity had the most substantial positive impact on social vitality. In contrast, the age of construction and the distribution of educational facilities demonstrated a strong positive correlation with economic vitality. By clearly delineating specific areas with urban renewal potential, this study provided a detailed characterization of the urban development pattern in Harbin. Additionally, by depicting the local variations in influencing factors, it established analytical foundations and objective references for urban planning in targeted locations. Ultimately, this research contributes new insights and frameworks for urban renewal analyses applicable to other regions. Full article

For biodiversity conservation and the development of protected areas, it is essential to create strategic plans that ensure the preservation and sustainable use of natural resources. Biogeography plays a crucial role in supporting these efforts by identifying and categorizing geographic areas (regionalization) that represent different biotas, as well as recognizing patterns in biodiversity distribution. Another application of regionalization is in planning species sampling and inventories. Developing a species list is vital for monitoring and understanding diversity patterns. This study focuses on the Palearctic region, specifically the areas between Morocco, the Iberian Peninsula, and France. Its aim is to compare different clustering algorithms—such as K-means++, DBSCAN, PD-clustering, Infomap, and federated heuristic optimization based on fuzzy clustering—with a reference regionalization, using environmental and soil data. Various spatial contiguity approaches were applied, including the third-degree polynomial model and principal coordinates. The results demonstrated that the hybrid approach offers a robust solution in the construction of the regions and that K-means++ and PDC produced regions with strong spatial similarity to the reference regionalization, closely aligning with the expected number of regions, especially at the biome level. Our study shows that a purely statistical regionalization can approximate a global reference regionalization, making it reproducible. Full article

Resilience research is concerned with studying the complex interplay of personal and ecological resources that promote positive adaptation following adversity in different populations. Although much research has investigated adjustment in young persons exposed to intimate partner violence (IPV), most of this research has taken a deficit approach by focusing on the negative cascades of effects that exposure to IPV has on the functioning of this group. In this paper, we discuss a mixed methods integration of two independent strength-based or resilience-focused studies involving Canadian youth exposed to IPV. Study 1 is a qualitative constructive grounded theory study that aimed to identify the coping strategies that youth exposed to IPV use to effectively cope with the traumatic experience of growing up in an IPV-affected family. This study included 13 youths with a history of IPV exposure who completed individual in-depth interviews, the drawing of ecomaps, and photovoice projects. Study 2 is a quantitative population-based study that aimed to identify profiles of adjustment in a cohort of 3886 youth who had previously experienced IPV exposure, as well as the specific risk and promotive factors that significantly predicted membership in the identified adjustment profiles. Both studies independently identified personal and ecological resources that were instrumental in supporting the resilience of study participants. By comparing and contrasting the two sets of findings, the present mixed methods integration provides further evidence on the complex interactions of mechanisms that promote positive adaptation in youth exposed to IPV, which aligns with a multisystemic understanding of resilience in this population. We provided recommendations for practice and policy based on the integrated findings. Full article

The construction industry plays a crucial role in the global economy but faces significant challenges, including inefficiencies, high costs, and environmental impacts. Although Building Information Modeling (BIM) has been widely adopted as a solution to these issues, its practical impact remains limited. This study investigates how manufacturers can enhance their contributions to improve BIM’s effectiveness, proposing that coopetition practices—combining competition and cooperation—can positively influence these contributions, thereby enhancing the benefits of BIM. To explore this hypothesis, an Experimental Coopetition Network was implemented in the Portuguese ornamental stone (POS) sector, utilizing Industrial IoT technology to facilitate collaboration among selected companies. The study assessed the impact of coopetition practices on key performance indicators related to BIM, including on-time delivery, labor productivity, and CO₂ emissions. The findings demonstrate significant improvements in scheduling, operational efficiency, and environmental sustainability, validating the hypothesis that coopetition practices can enhance manufacturers’ contributions to BIM. These results suggest that coopetition practices contribute to better project outcomes, increased competitiveness, and sustainability within the construction industry. Despite the promising results, the study acknowledges limitations such as the scope of the sample size and observation periods, indicating areas for future research. This research contributes to the theoretical framework of coopetition, aligning with the United Nations Sustainable Development Goals (SDGs), and provides valuable insights for industry practitioners and policymakers seeking to implement more sustainable construction practices. Full article

Deformation predictions in high Concrete Face Rockfill Dams tend to underestimate observed settlements due to scale effect and breakage phenomena that cannot be adequately captured by laboratory tests. This paper presents a Visco-Elasto-Perfectly Plastic (VEPP) model for predicting deformations in high Concrete Face Rockfill Dams (CFRDs) that addresses these challenges incorporating explicitly key rockfill parameters like grain size and post-compaction porosity, which influence both the non-linear elastic and plastic behaviors of rockfill. The VEPP model enables deformation prediction while using standard laboratory test results. The model’s effectiveness was demonstrated through its application to the 233 m high Shuibuya Dam, the tallest CFRD in the world. The VEPP model predictions closely align with observed deformations throughout the dam’s construction, impoundment, and early operational stages. By using physically meaningful parameters, the model reduces the uncertainty associated with the empirical assessment of model parameters using back-analysis from similar projects. While the VEPP model offers improved predictive accuracy, particularly during early design phases, further advancements could be achieved by refining the creep formulation and accounting for grain size evolution during construction. This approach has the potential to optimize the design and construction of future high CFRD construction. Full article

Group preferences are crucial for Inspirational Solutions of Automotive Design (ISAD). However, sparse individual purchase behavior hinders the identification of group preferences. Therefore, a novel inspiration recommendation (IR) system based on multi-level mining of user behavior data is proposed. Firstly, the K-means algorithm is employed to cluster users based on a variety of features. The fixed association rule is then applied to filter and identify relevant subsets, forming the foundational basis for constructing a user portrait. The Nonlinear Bayesian Personalized Ranking (NBPR) is constructed to explore common preferences using explicit feedback. Finally, the item preference matrix is enriched with implicit feedback to compile a comprehensive recommendation list that caters to group preferences. Using a multi-user joint evaluation approach, we compare the performance of IR with baseline models across multiple metrics. This comparison demonstrates the robust reliability of the IR system and its ability to prioritize ISAD with preference-aligned groups. Our research overcomes data sparsity in the automotive recommendation system, providing a new method for embedding human elements in decision support systems. Full article

Tree peony (Paeonia suffruticosa), as a popular ornamental plant worldwide, has a unique floral fragrance, and it is important in the pollination, ornamental, food, and fragrance product industries. However, the underlying molecular mechanisms for the synthesis of floral fragrance terpenoids in tree peony are not well understood, constraining their exploitation. P. suffruticosa ‘Oukan’ produces strong floral fragrance terpenoids with high ornamental value and excellent stress resistance and is considered a valuable model for studying tree peony floral fragrance formation. Based on transcriptome data analysis, the PsHMGR1 and PsTPS1 genes associated with floral terpene synthesis were cloned. Then, PsHMGR1 and PsTPS1 were functionally characterized by amino acid sequence analysis, multiple sequence alignment, phylogenetic tree construction, qRT-PCR, and transgenic assay. PsHMGR1 contains two transmembrane structures and a conserved HMG-CoA_reductase_class I domain, and PsTPS1 belongs to TPS-a subfamily. The qRT-PCR analysis showed that the expression levels of PsHMGR1 and PsTPS1 increased and then decreased at different flower development stages, and both were significantly higher in flowers than in roots, stems, and leaves. In addition, the linalool content in PsHMGR1 transgenic lines was significantly higher than that of WT. Germacrene D, which was not found in WT, was detected in the flowers of PsTPS1 transgenic lines. These results indicate that PsHMGR1 and PsTPS1 promote terpene synthesis in plants and provide ideas for the molecular mechanism of enhancing terpene synthesis in tree peony floral fragrance. Full article

Remote sensing techniques provide crucial insights into ancient settlement patterns in various regions by uncovering previously unknown archaeological sites and clarifying the topological features of known ones. Meanwhile, in the northern part of the Southern Levant, megalithic structures remain largely underexplored with these methods. This study addresses this gap by analyzing the landscape around Rujm el-Hiri, one of the most prominent Southern Levantine megaliths dated to the Chalcolithic/Early Bronze Age, for the first time. We discuss the type and extent of the archaeological remains identified in satellite images within a broader context, focusing on the relationships between landscapes and these objects and the implications of their possible function. Our analysis of multi-year satellite imagery covering the 30 km region surrounding the Sea of Galilee reveals several distinct patterns: 40–90-m-wide circles and thick walls primarily constructed along streams, possibly as old as Rujm el-Hiri itself; later-period linear thin walls forming vast rectangular fields and flower-like clusters of ~ 20 m diameter round-shaped fences found in wet areas; tumuli, topologically linked to the linear walls and flower-like fences. Although tumuli share similar forms and likely construction techniques, their spatial distribution, connections to other archaeological features, and the statistical distribution in their sizes suggest that they might serve diverse functions. The objects and patterns identified may be used for further training neural networks to analyze their spatial properties and interrelationships. Most archaeological structures in the region were reused long after their original construction. This involved adding new features, building walls over older ones, and reshaping the landscape with new objects. Rujm el-Hiri is a prime example of such a complex sequence. Geomagnetic analysis shows that since the entire region has rotated over time, the Rujm el-Hiri’s location shifted from its original position for tens of meters for the thousands of years of the object’s existence, challenging theories of the alignment of its walls with astronomical bodies and raising questions regarding its possible identification as an observatory. Full article

To address the challenges of accurately segmenting irregular building boundaries in complex urban environments faced by existing remote sensing change detection methods, this paper proposes a building change detection network based on multilevel geometric representation optimization using frame fields called BuildingCDNet. The proposed method employs a multi-scale feature aggregation encoder–decoder architecture, leveraging contextual information to capture the characteristics of buildings of varying sizes in the imagery. Cross-attention mechanisms are incorporated to enhance the feature correlations between the change pairs. Additionally, the frame field is introduced into the network to model the complex geometric structure of the building target. By learning the local orientation information of the building structure, the frame field can effectively capture the geometric features of complex building features. During the training process, a multi-task learning strategy is used to align the predicted frame field with the real building outline, while learning the overall segmentation, edge outline, and corner point features of the building. This improves the accuracy of the building polygon representation. Furthermore, a discriminative loss function is constructed through multi-task learning to optimize the polygonal structured information of the building targets. The proposed method achieves state-of-the-art results on two commonly used datasets. Full article

Unmanned aerial spraying systems (UASSs) are widely used today for the effective control of pests affecting crops, and more advanced UASS techniques are now being developed. To evaluate such systems, artificial targets are typically used to assess droplet coverage through image processing. To evaluate performance accurately, high-quality binary image processing is necessary; however, this involves labor for sample collection, transportation, and storage, as well as the risk of potential contamination during the process. Therefore, rapid assessment in the field is essential. In the present study, we evaluated droplet coverage on water-sensitive papers (WSPs) under field conditions. A dataset was constructed consisting of paired training examples, each comprising source and target data. The source data were high-quality labeled images obtained from WSP samples through image processing, while the target data were aligned RoIs within field images captured in situ. Droplet coverage estimation was performed using an encoder–decoder model, trained on the labeled images, with features adapted to field images via self-supervised learning. The results indicate that the proposed method detected droplet coverage in field images with an error of less than 5%, demonstrating a strong correlation between measured and estimated values (R² = 0.99). The method proposed in this paper enables immediate and accurate evaluation of the performance of UASSs in situ. Full article

Targeted contingency measures have proven highly effective at reducing the duration and harm caused by incidents. This study utilized the Classification and Regression Trees (CART) data mining technique to predict and quantify the duration of incidents. To achieve this, multisensor data collected from the Hangzhou freeway in China spanning from 2019 to 2021 was utilized to construct a regression tree with eight levels and 14 leaf nodes. By extracting 14 rules from the tree and establishing contingency measures based on these rules, accurate incident assessment and effective implementation of post-incident emergency plans were achieved. In addition, to more accurately apply the research findings to actual incidents, the CART method was compared with XGBoost, Random Forest (RF), and AFT (accelerated failure time) models. The results indicated that the prediction accuracy of the CART model is better than the other three models. Furthermore, the CART method has strong interpretability. Interactions between explanatory variables, up to seven, are captured in the CART method, rather than merely analyzing the effect of individual variables on the incident duration, aligning more closely with actual incidents. This study has important practical implications for advancing the engineering application of machine learning methods and the analysis of sensor data. Full article

Urban planning in China is shifting from an administrative unit-based approach to community life circle planning, aiming to align planning units with residents’ actual activity ranges. As the most fundamental life circle, daily life circle (DLC) planning must adopt a bottom-up approach. However, the widely applicable methods for delineating DLCs remain lacking. This study presents a strategy for delineating DLCs centered on neighborhood commercial areas that aggregate essential daily life services. Correspondingly, a method is proposed for identifying neighborhood commercial areas based on residents’ actual usage of facilities. The method was applied in Qinhuai District, Nanjing, where neighborhood commercial areas were identified and the factors influencing their formation and types were quantitatively analyzed. The results indicate the following: (1) the proposed method accurately identifies neighborhood commercial areas that can serve as DLC central areas; (2) commercial diversity, public transportation stops, and parking spots are the three most influential factors in neighborhood commercial area formation, exhibiting non-linear and threshold effects; and (3) the type of neighborhood commercial areas varies by population density, housing prices, and street betweenness, with betweenness being the most significant factor. These findings provide methods and indicators for DLC delineation and planning, contributing to the realization of the DLC construction concept. Full article

This paper presents a comprehensive analysis of the plastic injection molding process through the integration of data acquisition technologies and classification models. In collaboration with a company specializing in plastic injection, data were extracted directly from the machine during a specific period at the beginning of a shift change. These data were subjected to exploratory analysis to identify correlations between important variables, such as injection time, cycle time, and mold pressures. Additionally, classification models, including Random Forest and Logistic Regression, were constructed to predict and classify the process state based on these variables. The model results demonstrated high predictive performance, with 99.5% accuracy for Random Forest and 97% for Logistic Regression. These results provide a strong foundation for the early identification of potential problems and informed decision making to improve the efficiency of the plastic injection molding process. This study contributes to the advancement of the integration of intelligent technologies in industrial process optimization, aligned with the principles of Industry 4.0. Full article