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Search Results (1,261)

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Keywords = hybrid projection

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16 pages, 11765 KiB  
Article
The European Influence on Qing Dynasty Architecture: Design Principles and Construction Innovations Across Cultures
by Manuel V. Castilla
Heritage 2025, 8(8), 311; https://doi.org/10.3390/heritage8080311 (registering DOI) - 2 Aug 2025
Abstract
The design and planning of Western-style constructions during the early Qing Dynasty in China constituted a significant multicultural encounter that fused technological advancement with aesthetic innovation. This cultural interplay is particularly evident in the imperial garden and pavilion projects commissioned by the Qing [...] Read more.
The design and planning of Western-style constructions during the early Qing Dynasty in China constituted a significant multicultural encounter that fused technological advancement with aesthetic innovation. This cultural interplay is particularly evident in the imperial garden and pavilion projects commissioned by the Qing court, which served as physical and symbolic sites of cross-cultural dialogue. Influenced by the intellectual and artistic movements of the European Renaissance, Western architectural concepts gradually found their way into the spatial and visual language of Chinese architecture, especially within the royal gardens and aristocratic buildings of the time. These structures were not simply imitative but rather represented a selective adaptation of Western ideas to suit Chinese imperial tastes and principles. This article examines the architectural language that emerged from this encounter between Chinese and European cultures, analysing symbolic motifs, spatial design, ornamental aesthetics, the application of linear perspective, and the integration of foreign architectural forms. These elements collectively functioned as tools to construct a unique visual discourse that communicated both political authority and cultural hybridity. The findings underscore that this architectural phenomenon was not merely stylistic imitation, but rather a dynamic convergence of technological knowledge and artistic vision across cultural boundaries. Full article
(This article belongs to the Special Issue Progress in Heritage Education: Evolving Techniques and Methods)
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21 pages, 1353 KiB  
Article
Hydrogen Cost and Carbon Analysis in Hollow Glass Manufacturing
by Dario Atzori, Claudia Bassano, Edoardo Rossi, Simone Tiozzo, Sandra Corasaniti and Angelo Spena
Energies 2025, 18(15), 4105; https://doi.org/10.3390/en18154105 (registering DOI) - 2 Aug 2025
Abstract
The European Union promotes decarbonization in energy-intensive industries like glass manufacturing. Collaboration between industry and researchers focuses on reducing CO2 emissions through hydrogen (H2) integration as a natural gas substitute. However, to the best of the authors’ knowledge, no updated [...] Read more.
The European Union promotes decarbonization in energy-intensive industries like glass manufacturing. Collaboration between industry and researchers focuses on reducing CO2 emissions through hydrogen (H2) integration as a natural gas substitute. However, to the best of the authors’ knowledge, no updated real-world case studies are available in the literature that consider the on-site implementation of an electrolyzer for autonomous hydrogen production capable of meeting the needs of a glass manufacturing plant within current technological constraints. This study examines a representative hollow glass plant and develops various decarbonization scenarios through detailed process simulations in Aspen Plus. The models provide consistent mass and energy balances, enabling the quantification of energy demand and key cost drivers associated with H2 integration. These results form the basis for a scenario-specific techno-economic assessment, including both on-grid and off-grid configurations. Subsequently, the analysis estimates the levelized costs of hydrogen (LCOH) for each scenario and compares them to current and projected benchmarks. The study also highlights ongoing research projects and technological advancements in the transition from natural gas to H2 in the glass sector. Finally, potential barriers to large-scale implementation are discussed, along with policy and infrastructure recommendations to foster industrial adoption. These findings suggest that hybrid configurations represent the most promising path toward industrial H2 adoption in glass manufacturing. Full article
(This article belongs to the Special Issue Techno-Economic Evaluation of Hydrogen Energy)
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10 pages, 1555 KiB  
Article
Lithium-Decorated C26 Fullerene in DFT Investigation: Tuning Electronic Structures for Enhanced Hydrogen Storage
by Jiangang Yu, Lili Liu, Quansheng Li, Zhidong Xu, Yujia Shi and Cheng Lei
Molecules 2025, 30(15), 3223; https://doi.org/10.3390/molecules30153223 (registering DOI) - 31 Jul 2025
Abstract
Hydrogen energy holds immense potential to address the global energy crisis and environmental challenges. However, its large-scale application is severely hindered by the lack of efficient hydrogen storage materials. This study systematically investigates the H2 adsorption properties of intrinsic C26 fullerene [...] Read more.
Hydrogen energy holds immense potential to address the global energy crisis and environmental challenges. However, its large-scale application is severely hindered by the lack of efficient hydrogen storage materials. This study systematically investigates the H2 adsorption properties of intrinsic C26 fullerene and Li-decorated C26 fullerene using density functional theory (DFT) calculations. The results reveal that Li atoms preferentially bind to the H5-5 site of C26, driven by significant electron transfer (0.90 |e|) from Li to C26. This electron redistribution modulates the electronic structure of C26, as evidenced by projected density of states (PDOS) analysis, where the p orbitals of C atoms near the Fermi level undergo hybridization with Li orbitals, enhancing the electrostatic environment for H2 adsorption. For Li-decorated C26, the average adsorption energy and consecutive adsorption energy decrease as more H2 molecules are adsorbed, indicating a gradual weakening of adsorption strength and signifying a saturation limit of three H2 molecules. Charge density difference and PDOS analyses further demonstrate that H2 adsorption induces synergistic electron transfer from both Li (0.89 |e| loss) and H2 (0.01 |e| loss) to C26 (0.90 |e| gain), with orbital hybridization between H s orbitals, C p orbitals, and Li orbitals stabilizing the adsorbed system. This study aimed to provide a comprehensive understanding of the microscopic mechanism underlying Li-enhanced H2 adsorption on C26 fullerene and offer insights into the rational design of metal-decorated fullerene-based systems for efficient hydrogen storage. Full article
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20 pages, 10604 KiB  
Article
A Safety-Based Approach for the Design of an Innovative Microvehicle
by Michelangelo-Santo Gulino, Susanna Papini, Giovanni Zonfrillo, Thomas Unger, Peter Miklis and Dario Vangi
Designs 2025, 9(4), 90; https://doi.org/10.3390/designs9040090 (registering DOI) - 31 Jul 2025
Abstract
The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper [...] Read more.
The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s2, comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article
(This article belongs to the Section Vehicle Engineering Design)
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11 pages, 1219 KiB  
Article
The Church and Academia Model: New Paradigm for Spirituality and Mental Health Research
by Marta Illueca, Samantha M. Meints, Megan M. Miller, Dikachi Osaji and Benjamin R. Doolittle
Religions 2025, 16(8), 998; https://doi.org/10.3390/rel16080998 (registering DOI) - 31 Jul 2025
Viewed by 69
Abstract
Ongoing interest in the intersection of spirituality and health has prompted a need for integrated research. This report proposes a distinct approach in a model that allows for successful and harmonious cross-fertilization within these latter two areas of interest. Our work is especially [...] Read more.
Ongoing interest in the intersection of spirituality and health has prompted a need for integrated research. This report proposes a distinct approach in a model that allows for successful and harmonious cross-fertilization within these latter two areas of interest. Our work is especially pertinent to inquiries around the role of spirituality in mental health, with special attention to chronic pain conditions. The latter have become an open channel for novel avenues to explore the field of spirituality-based interventions within the arena of psychological inquiry. To address this, the authors developed and implemented the Church and Academia Model, a prototype for an innovative collaborative research project, with the aim of exploring the role of devotional practices, and their potential to be used as therapeutic co-adjuvants or tools to enhance the coping skills of patients with chronic pain. Keeping in mind that the church presents a rich landscape for clinical inquiry with broad relevance for clinicians and society at large, we created a unique hybrid research model. This is a new paradigm that focuses on distinct and well-defined studies where the funding, protocol writing, study design, and implementation are shared by experts from both the pastoral and clinical spaces. A team of theologians, researchers, and healthcare providers, including clinical pain psychologists, built a coalition leveraging their respective skill sets. Each expert is housed in their own environs, creating a functional network that has proven academically productive and pastorally effective. Key outputs include the creation and validation of a new psychometric measure, the Pain-related PRAYER Scale (PPRAYERS), an associated bedside prayer tool and a full-scale dissemination strategy through journal publications and specialty society conferences. This collaborative prototype is also an ideal fit for integrated knowledge translation platforms, and it is a promising paradigm for future collaborative projects focused on spirituality and mental health. Full article
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31 pages, 11269 KiB  
Review
Advancements in Semantic Segmentation of 3D Point Clouds for Scene Understanding Using Deep Learning
by Hafsa Benallal, Nadine Abdallah Saab, Hamid Tairi, Ayman Alfalou and Jamal Riffi
Technologies 2025, 13(8), 322; https://doi.org/10.3390/technologies13080322 - 30 Jul 2025
Viewed by 293
Abstract
Three-dimensional semantic segmentation is a fundamental problem in computer vision with a wide range of applications in autonomous driving, robotics, and urban scene understanding. The task involves assigning semantic labels to each point in a 3D point cloud, a data representation that is [...] Read more.
Three-dimensional semantic segmentation is a fundamental problem in computer vision with a wide range of applications in autonomous driving, robotics, and urban scene understanding. The task involves assigning semantic labels to each point in a 3D point cloud, a data representation that is inherently unstructured, irregular, and spatially sparse. In recent years, deep learning has become the dominant framework for addressing this task, leading to a broad variety of models and techniques designed to tackle the unique challenges posed by 3D data. This survey presents a comprehensive overview of deep learning methods for 3D semantic segmentation. We organize the literature into a taxonomy that distinguishes between supervised and unsupervised approaches. Supervised methods are further classified into point-based, projection-based, voxel-based, and hybrid architectures, while unsupervised methods include self-supervised learning strategies, generative models, and implicit representation techniques. In addition to presenting and categorizing these approaches, we provide a comparative analysis of their performance on widely used benchmark datasets, discuss key challenges such as generalization, model transferability, and computational efficiency, and examine the limitations of current datasets. The survey concludes by identifying potential directions for future research in this rapidly evolving field. Full article
(This article belongs to the Section Information and Communication Technologies)
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15 pages, 1247 KiB  
Article
Prioritizing Critical Factors Affecting Occupational Safety in High-Rise Construction: A Hybrid EFA-AHP Approach
by Hai Chien Pham, Si Van-Tien Tran and Ung-Kyun Lee
Buildings 2025, 15(15), 2677; https://doi.org/10.3390/buildings15152677 - 29 Jul 2025
Viewed by 162
Abstract
High-rise construction presents heightened safety risks due to vertical complexity, spatial constraints, and workforce variability. Conventional safety management often proves insufficient, especially in rapidly urbanizing or resource-limited settings. This study proposes a hybrid methodological framework to systematically identify and prioritize the critical factors [...] Read more.
High-rise construction presents heightened safety risks due to vertical complexity, spatial constraints, and workforce variability. Conventional safety management often proves insufficient, especially in rapidly urbanizing or resource-limited settings. This study proposes a hybrid methodological framework to systematically identify and prioritize the critical factors influencing occupational safety in Vietnamese high-rise construction projects. Based on 181 valid survey responses from construction professionals, 23 observed variables were developed through extensive literature review and expert consultation. Exploratory Factor Analysis (EFA) was employed to empirically group 23 validated indicators into five key latent dimensions: (1) Safety Training and Inspection, (2) Employer’s Knowledge and Responsibility, (3) Worker’s Competence and Compliance, (4) Working Conditions and Environment, and (5) Safety Equipment and Signage. These dimensions were then structured into an Analytic Hierarchy Process (AHP) model, with pairwise comparisons conducted by industry experts to calculate consistency ratios and derive factor weights across three high-rise project case studies. The findings provide actionable insights for construction managers, safety professionals, and policymakers in developing and underdeveloped countries, supporting data-driven decision-making for safer and more sustainable urban development. Full article
(This article belongs to the Special Issue Safety Management and Occupational Health in Construction)
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25 pages, 1583 KiB  
Article
Predicting China’s Provincial Carbon Peak: An Integrated Approach Using Extended STIRPAT and GA-BiLSTM Models
by Lian Chen, Hailan Chen and Yao Guo
Sustainability 2025, 17(15), 6819; https://doi.org/10.3390/su17156819 - 27 Jul 2025
Viewed by 380
Abstract
As China commits to reaching peak carbon emissions and achieving carbon neutrality, accurately predicting the provincial carbon peak year is vital for designing effective, region-specific policies. This study proposes an integrated approach based on extended STIRPAT and GA-BiLSTM models to predict China’s provincial [...] Read more.
As China commits to reaching peak carbon emissions and achieving carbon neutrality, accurately predicting the provincial carbon peak year is vital for designing effective, region-specific policies. This study proposes an integrated approach based on extended STIRPAT and GA-BiLSTM models to predict China’s provincial carbon peak year. First, based on panel data across 30 provinces in China from 2000 to 2023, we construct a multidimensional indicator system that encompasses socioeconomic factors, energy consumption dynamics, and technological innovation using the extended STIRPAT model, which explains 87.42% of the variation in carbon emissions. Second, to improve prediction accuracy, a hybrid model combining GA-optimized BiLSTM networks is proposed, capturing temporal dynamics and optimizing parameters to address issues like overfitting. The GA-BiLSTM model achieves an R2 of 0.9415, significantly outperforming benchmark models with lower error metrics. Third, based on the model constructed above, the peak years are projected for baseline, low-carbon, and high-carbon scenarios. In the low-carbon scenario, 19 provinces are projected to peak before 2030, which is 8 more than in the baseline scenario. Meanwhile, under the high-carbon scenario, some provinces such as Jiangsu and Hebei may fail to peak by 2040. Finally, based on the predicted carbon peak year, provinces are categorized into four pathways—early, recent, later, and non-peaking—to provide targeted policy recommendations. This integrated framework significantly enhances prediction precision and captures regional disparities, enabling tailored decarbonization strategies that support China’s dual carbon goals of balancing economic growth with environmental protection. The approach provides critical insights for region-specific low-carbon transitions and advances sustainable climate policy modeling. Full article
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18 pages, 3717 KiB  
Article
A Hybrid LMD–ARIMA–Machine Learning Framework for Enhanced Forecasting of Financial Time Series: Evidence from the NASDAQ Composite Index
by Jawaria Nasir, Hasnain Iftikhar, Muhammad Aamir, Hasnain Iftikhar, Paulo Canas Rodrigues and Mohd Ziaur Rehman
Mathematics 2025, 13(15), 2389; https://doi.org/10.3390/math13152389 - 25 Jul 2025
Viewed by 294
Abstract
This study proposes a novel hybrid forecasting approach designed explicitly for long-horizon financial time series. It incorporates LMD (Local Mean Decomposition), SD (Signal Decomposition), and sophisticated machine learning methods. The framework for the NASDAQ Composite Index begins by decomposing the original time series [...] Read more.
This study proposes a novel hybrid forecasting approach designed explicitly for long-horizon financial time series. It incorporates LMD (Local Mean Decomposition), SD (Signal Decomposition), and sophisticated machine learning methods. The framework for the NASDAQ Composite Index begins by decomposing the original time series into stochastic and deterministic components using the LMD approach. This method effectively separates linear and nonlinear signal structures. The stochastic components are modeled using ARIMA to represent linear temporal dynamics, while the deterministic components are projected using cutting-edge machine learning methods, including XGBoost, Random Forest (RF), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs). This study employs various statistical metrics to evaluate the predictive ability across both short-term noise and long-term trends, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Directional Statistic (DS). Furthermore, the Diebold–Mariano test is used to determine the statistical significance of any forecast improvements. Empirical results demonstrate that the hybrid LMD–ARIMA–SD–XGBoost model consistently outperforms alternative configurations in terms of prediction accuracy and directional consistency. These findings demonstrate the advantages of integrating decomposition-based signal filtering with ensemble machine learning to improve the robustness and generalizability of long-term forecasting. This study presents a scalable and adaptive approach for modeling complex, nonlinear, and high-dimensional time series, thereby contributing to the enhancement of intelligent forecasting systems in the economic and financial sectors. As far as the authors are aware, this is the first study to combine XGBoost and LMD in a hybrid decomposition framework for forecasting long-horizon stock indexes. Full article
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23 pages, 2992 KiB  
Article
Research on Two-Stage Investment Decision-Making in Park-Level Integrated Energy Projects Considering Multi-Objectives
by Jiaxuan Yu, Wei Sun, Rongwei Ma and Bingkang Li
Processes 2025, 13(8), 2362; https://doi.org/10.3390/pr13082362 - 24 Jul 2025
Viewed by 346
Abstract
The scientific investment decision of Park-level Integrated Energy System (PIES) projects is of great significance to energy enterprises for improving the efficient utilization of funds, promoting green and low-carbon transformation, and achieving the goal of carbon neutrality. This paper proposed a two-stage investment [...] Read more.
The scientific investment decision of Park-level Integrated Energy System (PIES) projects is of great significance to energy enterprises for improving the efficient utilization of funds, promoting green and low-carbon transformation, and achieving the goal of carbon neutrality. This paper proposed a two-stage investment framework that integrates a multi-objective 0–1 programming model with a multi-criteria decision-making (MCDM) technique to determine the optimal PIES project investment portfolios under the constraint of quota investment. First, a multi-objective (MO) 0–1 programming model was constructed for typical PIES projects in Stage-I, which considers economic and environmental benefits to obtain Pareto frontier solutions, i.e., PIES project portfolios. Second, an evaluation index system from multiple dimensions was established, and a hybrid MCDM technique was adopted to comprehensively evaluate the Pareto frontier solutions in Stage-II. Finally, the proposed model was applied to an empirical case, and the simulation results show that the decision framework can achieve the best overall benefit of PIES project portfolios with maximal economic benefit and minimum carbon emissions. In addition, the robustness analysis was performed by changing the indicator weights to verify the stability of the proposed framework. This research work could provide a theoretical tool for investment decisions regarding PIES projects for energy enterprises. Full article
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20 pages, 2786 KiB  
Article
Inverse Kinematics-Augmented Sign Language: A Simulation-Based Framework for Scalable Deep Gesture Recognition
by Binghao Wang, Lei Jing and Xiang Li
Algorithms 2025, 18(8), 463; https://doi.org/10.3390/a18080463 - 24 Jul 2025
Viewed by 206
Abstract
In this work, we introduce IK-AUG, a unified algorithmic framework for kinematics-driven data augmentation tailored to sign language recognition (SLR). Departing from traditional augmentation techniques that operate at the pixel or feature level, our method integrates inverse kinematics (IK) and virtual simulation to [...] Read more.
In this work, we introduce IK-AUG, a unified algorithmic framework for kinematics-driven data augmentation tailored to sign language recognition (SLR). Departing from traditional augmentation techniques that operate at the pixel or feature level, our method integrates inverse kinematics (IK) and virtual simulation to synthesize anatomically valid gesture sequences within a structured 3D environment. The proposed system begins with sparse 3D keypoints extracted via a pose estimator and projects them into a virtual coordinate space. A differentiable IK solver based on forward-and-backward constrained optimization is then employed to reconstruct biomechanically plausible joint trajectories. To emulate natural signer variability and enhance data richness, we define a set of parametric perturbation operators spanning spatial displacement, depth modulation, and solver sensitivity control. These operators are embedded into a generative loop that transforms each original gesture sample into a diverse sequence cluster, forming a high-fidelity augmentation corpus. We benchmark our method across five deep sequence models (CNN3D, TCN, Transformer, Informer, and Sparse Transformer) and observe consistent improvements in accuracy and convergence. Notably, Informer achieves 94.1% validation accuracy with IK-AUG enhanced training, underscoring the framework’s efficacy. These results suggest that algorithmic augmentation via kinematic modeling offers a scalable, annotation free pathway for improving SLR systems and lays the foundation for future integration with multi-sensor inputs in hybrid recognition pipelines. Full article
(This article belongs to the Section Algorithms for Multidisciplinary Applications)
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25 pages, 2183 KiB  
Article
Advancing Semantic Enrichment Compliance in BIM: An Ontology-Based Framework and IDS Evaluation
by Tomo Cerovšek and Mohamed Omar
Buildings 2025, 15(15), 2621; https://doi.org/10.3390/buildings15152621 - 24 Jul 2025
Viewed by 366
Abstract
As BIM projects grow in volume and complexity, automated Information Compliance Checking (ICC) is becoming essential to meet demanding regulatory and contractual requirements. This study presents novel controlled vocabularies and processes for the management of information requirements, along with a structured evaluation of [...] Read more.
As BIM projects grow in volume and complexity, automated Information Compliance Checking (ICC) is becoming essential to meet demanding regulatory and contractual requirements. This study presents novel controlled vocabularies and processes for the management of information requirements, along with a structured evaluation of the Information Delivery Specification (IDS) and its associated tools. The controlled vocabularies are important as they provide support to standardization, information retrieval, data-driven workflows, and AI integration. Information requirements are classified by input type and project interaction context (phase, origin, project role, and communication), as well as by applicability (data management function, model granularity, BIM usage, and checkability). The ontology comprises seven categories: identity, geometry, design/performance, fabrication/construction, operation/maintenance, cost, and regulatory category, each linked to verification principles such as uniqueness and consistency. This enables systematic implementation of validation checks aligned with company and project needs. We introduce three ICC workflows in relation to the BIM authoring tools (inside, outside, and hybrid) and suggest key criteria for the functional and non-functional evaluation of IDS tools. Empirical results from a real project using five IDS tools reveal implementation issues with the classification facet, regular expressions, and issue reporting. The proposed ontology and framework lay the foundation for a scalable, transparent ICC within openBIM. The results also provide ICC process guidance for practitioners, a SWOT analysis that can inform enhancements to the existing IDS schema, identify possible inputs for certification of IDS tools, and generate innovative ideas for research and development. Full article
(This article belongs to the Section Construction Management, and Computers & Digitization)
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20 pages, 7720 KiB  
Article
Comparative Evaluation of Nonparametric Density Estimators for Gaussian Mixture Models with Clustering Support
by Tomas Ruzgas, Gintaras Stankevičius, Birutė Narijauskaitė and Jurgita Arnastauskaitė Zencevičienė
Axioms 2025, 14(8), 551; https://doi.org/10.3390/axioms14080551 - 23 Jul 2025
Viewed by 155
Abstract
The article investigates the accuracy of nonparametric univariate density estimation methods applied to various Gaussian mixture models. A comprehensive comparative analysis is performed for four popular estimation approaches: adaptive kernel density estimation, projection pursuit, log-spline estimation, and wavelet-based estimation. The study is extended [...] Read more.
The article investigates the accuracy of nonparametric univariate density estimation methods applied to various Gaussian mixture models. A comprehensive comparative analysis is performed for four popular estimation approaches: adaptive kernel density estimation, projection pursuit, log-spline estimation, and wavelet-based estimation. The study is extended with modified versions of these methods, where the sample is first clustered using the EM algorithm based on Gaussian mixture components prior to density estimation. Estimation accuracy is quantitatively evaluated using MAE and MAPE criteria, with simulation experiments conducted over 100,000 replications for various sample sizes. The results show that estimation accuracy strongly depends on the density structure, sample size, and degree of component overlap. Clustering before density estimation significantly improves accuracy for multimodal and asymmetric densities. Although no formal statistical tests are conducted, the performance improvement is validated through non-overlapping confidence intervals obtained from 100,000 simulation replications. In addition, several decision-making systems are compared for automatically selecting the most appropriate estimation method based on the sample’s statistical features. Among the tested systems, kernel discriminant analysis yielded the lowest error rates, while neural networks and hybrid methods showed competitive but more variable performance depending on the evaluation criterion. The findings highlight the importance of using structurally adaptive estimators and automation of method selection in nonparametric statistics. The article concludes with recommendations for method selection based on sample characteristics and outlines future research directions, including extensions to multivariate settings and real-time decision-making systems. Full article
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30 pages, 459 KiB  
Review
Recent Advances in Long-Term Wind-Speed and -Power Forecasting: A Review
by Jacqueline Muthoni Mbugua and Yusuke Hiraga
Climate 2025, 13(8), 155; https://doi.org/10.3390/cli13080155 - 23 Jul 2025
Viewed by 397
Abstract
This review examines advancements and methodologies in long-term wind-speed and -power forecasting. It emphasizes the importance of these techniques in integrating wind energy into power systems. Covering a range of forecasting timeframes from monthly to multiyear projections, this paper highlights the diversity of [...] Read more.
This review examines advancements and methodologies in long-term wind-speed and -power forecasting. It emphasizes the importance of these techniques in integrating wind energy into power systems. Covering a range of forecasting timeframes from monthly to multiyear projections, this paper highlights the diversity of applications and approaches. These applications and approaches are essential for managing the inherent variability and unpredictability of wind energy. Various forecasting methods, including statistical models, machine-learning techniques, and hybrid models, are discussed in detail. The review demonstrates how these methods improve forecast accuracy and reliability across different temporal and geographical scales. It also identifies significant challenges such as model complexity, data limitations, and the need to accommodate regional variations. Future improvements in wind forecasting include enhancing model integration, employing higher resolution data, and fostering collaborative research to further refine forecasting methodologies. This comprehensive analysis aims to advance knowledge on wind forecasting, facilitate the efficient integration of wind power into global energy systems, and contribute to sustainable energy development goals. Full article
(This article belongs to the Special Issue Wind‑Speed Variability from Tropopause to Surface)
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24 pages, 2698 KiB  
Article
Modelling Nature Connectedness Within Environmental Systems: Human-Nature Relationships from 1800 to 2020 and Beyond
by Miles Richardson
Earth 2025, 6(3), 82; https://doi.org/10.3390/earth6030082 - 23 Jul 2025
Viewed by 225
Abstract
Amid global environmental changes, urbanisation erodes nature connectedness, an important driver of pro-environmental behaviours and human well-being, exacerbating human-made risks like biodiversity loss and climate change. This study introduces a novel hybrid agent-based model (ABM), calibrated with historical urbanisation data, to explore how [...] Read more.
Amid global environmental changes, urbanisation erodes nature connectedness, an important driver of pro-environmental behaviours and human well-being, exacerbating human-made risks like biodiversity loss and climate change. This study introduces a novel hybrid agent-based model (ABM), calibrated with historical urbanisation data, to explore how urbanisation, opportunity and orientation to engage with nature, and intergenerational transmission have shaped nature connectedness over time. The model simulates historical trends (1800–2020) against target data, with projections extending to 2125. The ABM revealed a significant nature connectedness decline with excellent fit to the target data, derived from nature word use in cultural products. Although a lifetime ‘extinction of experience’ mechanism refined the fit, intergenerational transmission emerged as the dominant driver—supporting a socio-ecological tipping point in human–nature disconnection. Even with transformative interventions like dramatic urban greening and enhanced nature engagement, projections suggest a persistent disconnection from nature through to 2050, highlighting locked-in risks to environmental stewardship. After 2050, the most transformative interventions trigger a self-sustaining recovery, highlighting the need for sustained, systemic policies that embed nature connectedness into urban planning and education. Full article
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