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Search Results (1,509)

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Keywords = multidimensional information

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28 pages, 5530 KiB  
Article
Hybrid Metaheuristic Optimized Extreme Learning Machine for Sustainability Focused CO2 Emission Prediction Using Globalization-Driven Indicators
by Mahmoud Almsallti, Ahmad Bassam Alzubi and Oluwatayomi Rereloluwa Adegboye
Sustainability 2025, 17(15), 6783; https://doi.org/10.3390/su17156783 (registering DOI) - 25 Jul 2025
Abstract
The escalating threat of climate change has intensified the global urgency to accurately predict carbon dioxide (CO2) emissions for sustainable development, particularly in developing economies experiencing rapid industrialization and globalization. Traditional Extreme Learning Machines (ELMs) offer rapid learning but often yield [...] Read more.
The escalating threat of climate change has intensified the global urgency to accurately predict carbon dioxide (CO2) emissions for sustainable development, particularly in developing economies experiencing rapid industrialization and globalization. Traditional Extreme Learning Machines (ELMs) offer rapid learning but often yield unstable performance due to random parameter initialization. This study introduces a novel hybrid model, Red-Billed Blue Magpie Optimizer-tuned ELM (RBMO-ELM) which harnesses the intelligent foraging behavior of red-billed blue magpies to optimize input-to-hidden layer weights and biases. The RBMO algorithm is first benchmarked on 15 functions from the CEC2015 test suite to validate its optimization effectiveness. Subsequently, RBMO-ELM is applied to predict Indonesia’s CO2 emissions using a multidimensional dataset that combines economic, technological, environmental, and globalization-driven indicators. Empirical results show that the RBMO-ELM significantly surpasses several state-of-the-art hybrid models in accuracy (higher R2) and convergence efficiency (lower error). A permutation-based feature importance analysis identifies social globalization, GDP, and ecological footprint as the strongest predictors underscoring the socio-economic influences on emission patterns. These findings offer both theoretical and practical implications that inform data-driven Artificial Intelligence (AI) and Machine Learning (ML) applications in environmental policy and support sustainable governance models. Full article
21 pages, 2406 KiB  
Article
Determining Factors for the Diagnosis of Multidimensional Depression and Its Representation: A Composite Indicator Based on Linear Discriminant Analysis
by Matheus Pereira Libório, Angélica C. G. Santos, Marcos Flávio Silveira Vasconcelos D’angelo, Hasheem Mannan, Cristiane Neri Nobre, Ariane Carla Barbosa da Silva, Petr Iakovlevitch Ekel and Allysson Steve Mota Lacerda
Appl. Sci. 2025, 15(15), 8275; https://doi.org/10.3390/app15158275 - 25 Jul 2025
Abstract
This study proposes a novel approach to constructing composite indicators, utilizing discriminant analysis to identify the determining factors for the diagnosis of multidimensional depression and to provide an index that represents the multidimensionality of this construct. By focusing solely on factors that significantly [...] Read more.
This study proposes a novel approach to constructing composite indicators, utilizing discriminant analysis to identify the determining factors for the diagnosis of multidimensional depression and to provide an index that represents the multidimensionality of this construct. By focusing solely on factors that significantly correlate with the diagnosis of multidimensional depression, this method provides a more precise and objective representation of the problem. The application of the method to the 2019 Brazilian Health Survey data demonstrated its effectiveness, resulting in a composite indicator that separates individuals who self-declare as having depression from individuals who self-declare as not having depression. The results highlight individuals who have a limiting chronic condition, high levels of education, less support from friends and family, perform unhealthy work, and are male. In contrast, individuals with the opposite characteristics are associated with a negative multidimensional depression diagnosis. The proposed composite indicator not only improves the measurement accuracy but also offers a new means of visualizing and understanding the multidimensional nature of depression diagnosis, providing valuable information for the formulation of targeted public health policies aimed at reducing the time for which people show symptoms of depression. Full article
(This article belongs to the Special Issue State-of-the-Art of Intelligent Decision Support Systems)
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21 pages, 4388 KiB  
Article
An Omni-Dimensional Dynamic Convolutional Network for Single-Image Super-Resolution Tasks
by Xi Chen, Ziang Wu, Weiping Zhang, Tingting Bi and Chunwei Tian
Mathematics 2025, 13(15), 2388; https://doi.org/10.3390/math13152388 - 25 Jul 2025
Abstract
The goal of single-image super-resolution (SISR) tasks is to generate high-definition images from low-quality inputs, with practical uses spanning healthcare diagnostics, aerial imaging, and surveillance systems. Although cnns have considerably improved image reconstruction quality, existing methods still face limitations, including inadequate restoration of [...] Read more.
The goal of single-image super-resolution (SISR) tasks is to generate high-definition images from low-quality inputs, with practical uses spanning healthcare diagnostics, aerial imaging, and surveillance systems. Although cnns have considerably improved image reconstruction quality, existing methods still face limitations, including inadequate restoration of high-frequency details, high computational complexity, and insufficient adaptability to complex scenes. To address these challenges, we propose an Omni-dimensional Dynamic Convolutional Network (ODConvNet) tailored for SISR tasks. Specifically, ODConvNet comprises four key components: a Feature Extraction Block (FEB) that captures low-level spatial features; an Omni-dimensional Dynamic Convolution Block (DCB), which utilizes a multidimensional attention mechanism to dynamically reweight convolution kernels across spatial, channel, and kernel dimensions, thereby enhancing feature expressiveness and context modeling; a Deep Feature Extraction Block (DFEB) that stacks multiple convolutional layers with residual connections to progressively extract and fuse high-level features; and a Reconstruction Block (RB) that employs subpixel convolution to upscale features and refine the final HR output. This mechanism significantly enhances feature extraction and effectively captures rich contextual information. Additionally, we employ an improved residual network structure combined with a refined Charbonnier loss function to alleviate gradient vanishing and exploding to enhance the robustness of model training. Extensive experiments conducted on widely used benchmark datasets, including DIV2K, Set5, Set14, B100, and Urban100, demonstrate that, compared with existing deep learning-based SR methods, our ODConvNet method improves Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the visual quality of SR images is also improved. Ablation studies further validate the effectiveness and contribution of each component in our network. The proposed ODConvNet offers an effective, flexible, and efficient solution for the SISR task and provides promising directions for future research. Full article
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22 pages, 2974 KiB  
Article
An Enhanced Grasshopper Optimization Algorithm with Outpost and Multi-Population Mechanisms for Dolomite Lithology Prediction
by Xinya Yu and Parhat Zunu
Biomimetics 2025, 10(8), 494; https://doi.org/10.3390/biomimetics10080494 - 25 Jul 2025
Abstract
The Grasshopper Optimization Algorithm (GOA) has attracted significant attention due to its simplicity and effective search capabilities. However, its performance deteriorates when dealing with high-dimensional or complex optimization tasks. To address these limitations, this study proposes an improved variant of GOA, named Outpost [...] Read more.
The Grasshopper Optimization Algorithm (GOA) has attracted significant attention due to its simplicity and effective search capabilities. However, its performance deteriorates when dealing with high-dimensional or complex optimization tasks. To address these limitations, this study proposes an improved variant of GOA, named Outpost Multi-population GOA (OMGOA). OMGOA integrates two novel mechanisms: the Outpost mechanism, which enhances local exploitation by guiding agents towards high-potential regions, and the multi-population enhanced mechanism, which promotes global exploration and maintains population diversity through parallel evolution and controlled information exchange. Comprehensive experiments were conducted to evaluate the effectiveness of OMGOA. Ablation studies were performed to assess the individual contributions of each mechanism, while multi-dimensional testing was used to verify robustness and scalability. Comparative experiments show that OMGOA has better optimization performance compared to other similar algorithms. In addition, OMGOA was successfully applied to a real-world engineering problem—lithology prediction from petrophysical logs—where it achieved competitive classification performance. Full article
(This article belongs to the Special Issue Advances in Biological and Bio-Inspired Algorithms)
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17 pages, 655 KiB  
Review
Passenger Service Time at the Platform–Train Interface: A Review of Variability, Design Factors, and Crowd Management Implications Based on Laboratory Experiments
by Sebastian Seriani, Vicente Aprigliano, Vinicius Minatogawa, Alvaro Peña, Ariel Lopez and Felipe Gonzalez
Appl. Sci. 2025, 15(15), 8256; https://doi.org/10.3390/app15158256 - 24 Jul 2025
Abstract
This paper reviews the variability of passenger service time (PST) at the platform–train interface (PTI), a critical performance indicator in metro systems shaped by the infrastructure design, affecting passenger behavior and accessibility. Despite its operational importance, PST remains underexplored in relation to crowd [...] Read more.
This paper reviews the variability of passenger service time (PST) at the platform–train interface (PTI), a critical performance indicator in metro systems shaped by the infrastructure design, affecting passenger behavior and accessibility. Despite its operational importance, PST remains underexplored in relation to crowd management strategies. This review synthesizes findings from empirical and experimental research to clarify the main factors influencing PST and their implications for platform-level interventions. Key contributors to PST variability include door width, gap dimensions, crowd density, and user characteristics such as mobility impairments. Design elements—such as platform edge doors, yellow safety lines, and vertical handrails—affect flow efficiency and spatial dynamics during boarding and alighting. Advanced tracking and simulation tools (e.g., PeTrack and YOLO-based systems) are identified as essential for evaluating pedestrian behavior and supporting Level of Service (LOS) analysis. To complement traditional LOS metrics, the paper introduces Level of Interaction (LOI) and a multidimensional LOS framework that captures spatial conflicts and user interaction zones. Control strategies such as platform signage, seating arrangements, and visual cues are also reviewed, with experimental evidence showing that targeted design interventions can reduce PST by up to 35%. The review highlights a persistent gap between academic knowledge and practical implementation. It calls for greater integration of empirical evidence into policy, infrastructure standards, and operational contracts. Ultimately, it advocates for human-centered, data-informed approaches to PTI planning that enhance efficiency, inclusivity, and resilience in high-demand transit environments. Full article
(This article belongs to the Special Issue Research Advances in Rail Transport Infrastructure)
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29 pages, 5193 KiB  
Article
Fault Diagnosis Method of Plunger Pump Based on Meta-Learning and Improved Multi-Channel Convolutional Neural Network Under Small Sample Condition
by Xiwang Yang, Jiancheng Ma, Hongjun Hu, Jinying Huang and Licheng Jing
Sensors 2025, 25(15), 4587; https://doi.org/10.3390/s25154587 - 24 Jul 2025
Abstract
A fault diagnosis method based on meta-learning and an improved multi-channel convolutional neural network (MAML-MCCNN-ISENet) was proposed to solve the problems of insufficient feature extraction and low fault type identification accuracy of vibration signals at small sample sizes. The signal is first preprocessed [...] Read more.
A fault diagnosis method based on meta-learning and an improved multi-channel convolutional neural network (MAML-MCCNN-ISENet) was proposed to solve the problems of insufficient feature extraction and low fault type identification accuracy of vibration signals at small sample sizes. The signal is first preprocessed using adaptive chirp mode decomposition (ACMD) methods. A multi-channel input structure is then employed to process the multidimensional signal information after preprocessing. The improved squeeze and excitation networks (ISENets) have been enhanced to concurrently enhance the network’s adaptive perception of the significance of each channel feature. On this basis, a meta-learning strategy is introduced, the learning process of model initialization parameters is improved, the network is optimized by a multi-task learning mechanism, and the initial parameters of the diagnosis model are adaptively adjusted, so that the model can quickly adapt to new fault diagnosis tasks on limited datasets. Then, the overfitting problem under small sample conditions is alleviated, and the accuracy and robustness of fault identification are improved. Finally, the performance of the model is verified on the experimental data of the fault diagnosis of the laboratory plunger pump and the vibration dataset of the centrifugal pump of the Saint Longoval Institute of Engineering and Technology. The results show that the diagnostic accuracy of the proposed method for various diagnostic tasks can reach more than 90% on small samples. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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19 pages, 2689 KiB  
Article
A Multi-Temporal Knowledge Graph Framework for Landslide Monitoring and Hazard Assessment
by Runze Wu, Min Huang, Haishan Ma, Jicai Huang, Zhenhua Li, Hongbo Mei and Chengbin Wang
GeoHazards 2025, 6(3), 39; https://doi.org/10.3390/geohazards6030039 - 23 Jul 2025
Viewed by 130
Abstract
In the landslide chain from pre-disaster conditions to landslide mitigation and recovery, time is an important factor in understanding the geological hazards process and managing landsides. Static knowledge graphs are unable to capture the temporal dynamics of landslide events. To address this limitation, [...] Read more.
In the landslide chain from pre-disaster conditions to landslide mitigation and recovery, time is an important factor in understanding the geological hazards process and managing landsides. Static knowledge graphs are unable to capture the temporal dynamics of landslide events. To address this limitation, we propose a systematic framework for constructing a multi-temporal knowledge graph of landslides that integrates multi-source temporal data, enabling the dynamic tracking of landslide processes. Our approach comprises three key steps. First, we summarize domain knowledge and develop a temporal ontology model based on the disaster chain management system. Second, we map heterogeneous datasets (both tabular and textual data) into triples/quadruples and represent them based on the RDF (Resource Description Framework) and quadruple approaches. Finally, we validate the utility of multi-temporal knowledge graphs through multidimensional queries and develop a web interface that allows users to input landslide names to retrieve location and time-axis information. A case study of the Zhangjiawan landslide in the Three Gorges Reservoir Area demonstrates the multi-temporal knowledge graph’s capability to track temporal updates effectively. The query results show that multi-temporal knowledge graphs effectively support multi-temporal queries. This study advances landslide research by combining static knowledge representation with the dynamic evolution of landslides, laying the foundation for hazard forecasting and intelligent early-warning systems. Full article
(This article belongs to the Special Issue Landslide Research: State of the Art and Innovations)
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19 pages, 6349 KiB  
Article
From Theory to Practice: Assessing the Open Building Movement’s Role in Egypt’s Housing Market over Four Decades
by Rania Nasreldin and Dalia Abdelfattah
Buildings 2025, 15(15), 2600; https://doi.org/10.3390/buildings15152600 - 23 Jul 2025
Viewed by 61
Abstract
This research explores the concept of open building (OB) in the context of low-cost housing, focusing on its historical applications in Egypt during the 1980s. By evaluating past experiences, the study aims to extract key lessons that can inform the design and implementation [...] Read more.
This research explores the concept of open building (OB) in the context of low-cost housing, focusing on its historical applications in Egypt during the 1980s. By evaluating past experiences, the study aims to extract key lessons that can inform the design and implementation of contemporary social housing projects. The goal is to foster resilience and diversity in housing typologies to ensure they align with the evolving needs of residents. To achieve these objectives, the research employed a multi-dimensional strategy, beginning with a comprehensive literature review of the open building movement (OB); then, the study traced the evolution of the OB movement in Egypt using a qualitative analysis approach, which involved analyzing its implementation in low-cost housing projects over the past four decades. Through this historical lens, the study identifies design principles and strategies that can enhance social housing projects by applying OB. Considering the life cycle cost, OB enables an incremental process that would align with users’ financial capacities. The research revealed the substantial capacity of open building (OB) to address Egypt’s social housing challenges, primarily by fostering user-driven flexibility in housing unit design and area selection. This empowers occupants to choose spaces perfectly suited to their family’s evolving needs. Moreover, the findings provide a roadmap for revitalizing the OB movement by analyzing and overcoming past implementation difficulties, consequently balancing the initial cost and long-term economics for citizens and significantly reducing the governmental sector’s expenditure. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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33 pages, 9781 KiB  
Article
Spatial Narrative Optimization in Digitally Gamified Architectural Scenarios
by Deshao Wang, Jieqing Xu and Luwang Chen
Buildings 2025, 15(15), 2597; https://doi.org/10.3390/buildings15152597 - 23 Jul 2025
Viewed by 68
Abstract
Currently, exploring digital immersive experiences is a new trend in the innovation and development of cultural tourism. This study addresses the growing demand for digital immersion in cultural tourism by examining the integration of spatial narrative and digitally gamified architectural scenarios. This study [...] Read more.
Currently, exploring digital immersive experiences is a new trend in the innovation and development of cultural tourism. This study addresses the growing demand for digital immersion in cultural tourism by examining the integration of spatial narrative and digitally gamified architectural scenarios. This study synthesizes an optimized framework for narrative design in digitally gamified architectural scenarios, integrating spatial narrative theory and feedback-informed design. The proposed model comprises four key components: (1) developing spatial narrative design methods for such scenarios; (2) constructing a spatial language system for spatial narratives using linguistic principles to organize narrative expression; (3) building a preliminary digitally gamified scenario based on the “Wuhu Jiaoji Temple Renovation Project” after architectural and environmental enhancements; and (4) optimization through thermal feedback experiments—collecting visitor trajectory heatmaps, eye-tracking heatmaps, and oculometric data. The results show that the optimized design, validated in the original game Dreams of Jiaoji, effectively enhanced spatial narrative execution by refining both on-site and in-game architectural scenarios. Post-optimization visitor feedback confirmed the validity of the proposed optimization strategies and principles, providing theoretical and practical references for innovative digital cultural tourism models and architectural design advancements. In the context of site-specific architectural conservation, this approach achieves two key objectives: the generalized interpretation of architectural cultural resources and their visual representation through gamified interactions. This paradigm not only enhances public engagement through enabling a multidimensional understanding of historical building cultures but also accelerates the protective reuse of heritage sites, allowing heritage value to be maximized through contemporary reinterpretation. The interdisciplinary methodology promotes sustainable development in the digital transformation of cultural tourism, fostering user-centered experiences and contributing to rural revitalization. Ultimately, this study highlights the potential use of digitally gamified architectural scenarios as transformative tools for heritage preservation, cultural dissemination, and rural community revitalization. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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38 pages, 7345 KiB  
Article
Retabit: A Data-Driven Platform for Urban Renewal and Sustainable Building Renovation
by Leandro Madrazo, Álvaro Sicilia, Adirane Calvo, Jordi Pascual, Enric Mont, Angelos Mylonas and Nadia Soledad Ibañez Iralde
Energies 2025, 18(15), 3895; https://doi.org/10.3390/en18153895 - 22 Jul 2025
Viewed by 173
Abstract
The Retabit platform is a data-driven tool designed to bridge the gap between building rehabilitation and urban regeneration by integrating energy, economic, and social dimensions into a single framework. Leveraging multiple public data sources, the platform provides actionable insights to local and national [...] Read more.
The Retabit platform is a data-driven tool designed to bridge the gap between building rehabilitation and urban regeneration by integrating energy, economic, and social dimensions into a single framework. Leveraging multiple public data sources, the platform provides actionable insights to local and national authorities, public housing agencies, urban planners, energy service providers, and research institutions, helping to align renovation initiatives with broader urban transformation goals and climate action objectives. The platform consists of two main components: Analyse, for examining building conditions through multidimensional indicators, and Plan, for designing and simulating renovation projects. Retabit contributes to more transparent and informed decision-making, encourages collaboration across sectors, and addresses long-term sustainability by incorporating participatory planning and impact evaluation. Its scalable structure makes it applicable across diverse geographic areas, policy contexts, and domains linked to sustainable urban development. Full article
(This article belongs to the Special Issue Performance Analysis of Building Energy Efficiency)
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24 pages, 3714 KiB  
Article
DTCMMA: Efficient Wind-Power Forecasting Based on Dimensional Transformation Combined with Multidimensional and Multiscale Convolutional Attention Mechanism
by Wenhan Song, Enguang Zuo, Junyu Zhu, Chen Chen, Cheng Chen, Ziwei Yan and Xiaoyi Lv
Sensors 2025, 25(15), 4530; https://doi.org/10.3390/s25154530 - 22 Jul 2025
Viewed by 161
Abstract
With the growing global demand for clean energy, the accuracy of wind-power forecasting plays a vital role in ensuring the stable operation of power systems. However, wind-power generation is significantly influenced by meteorological conditions and is characterized by high uncertainty and multiscale fluctuations. [...] Read more.
With the growing global demand for clean energy, the accuracy of wind-power forecasting plays a vital role in ensuring the stable operation of power systems. However, wind-power generation is significantly influenced by meteorological conditions and is characterized by high uncertainty and multiscale fluctuations. Traditional recurrent neural network (RNN) and long short-term memory (LSTM) models, although capable of handling sequential data, struggle with modeling long-term temporal dependencies due to the vanishing gradient problem; thus, they are now rarely used. Recently, Transformer models have made notable progress in sequence modeling compared to RNNs and LSTM models. Nevertheless, when dealing with long wind-power sequences, their quadratic computational complexity (O(L2)) leads to low efficiency, and their global attention mechanism often fails to capture local periodic features accurately, tending to overemphasize redundant information while overlooking key temporal patterns. To address these challenges, this paper proposes a wind-power forecasting method based on dimension-transformed collaborative multidimensional multiscale attention (DTCMMA). This method first employs fast Fourier transform (FFT) to automatically identify the main periodic components in wind-power data, reconstructing the one-dimensional time series as a two-dimensional spatiotemporal representation, thereby explicitly encoding periodic features. Based on this, a collaborative multidimensional multiscale attention (CMMA) mechanism is designed, which hierarchically integrates channel, spatial, and pixel attention to adaptively capture complex spatiotemporal dependencies. Considering the geometric characteristics of the reconstructed data, asymmetric convolution kernels are adopted to enhance feature extraction efficiency. Experiments on multiple wind-farm datasets and energy-related datasets demonstrate that DTCMMA outperforms mainstream methods such as Transformer, iTransformer, and TimeMixer in long-sequence forecasting tasks, achieving improvements in MSE performance by 34.22%, 2.57%, and 0.51%, respectively. The model’s training speed also surpasses that of the fastest baseline by 300%, significantly improving both prediction accuracy and computational efficiency. This provides an efficient and accurate solution for wind-power forecasting and contributes to the further development and application of wind energy in the global energy mix. Full article
(This article belongs to the Section Intelligent Sensors)
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24 pages, 3409 KiB  
Article
DepressionMIGNN: A Multiple-Instance Learning-Based Depression Detection Model with Graph Neural Networks
by Shiwen Zhao, Yunze Zhang, Yikai Su, Kaifeng Su, Jiemin Liu, Tao Wang and Shiqi Yu
Sensors 2025, 25(14), 4520; https://doi.org/10.3390/s25144520 - 21 Jul 2025
Viewed by 210
Abstract
The global prevalence of depression necessitates the application of technological solutions, particularly sensor-based systems, to augment scarce resources for early diagnostic purposes. In this study, we use benchmark datasets that contain multimodal data including video, audio, and transcribed text. To address depression detection [...] Read more.
The global prevalence of depression necessitates the application of technological solutions, particularly sensor-based systems, to augment scarce resources for early diagnostic purposes. In this study, we use benchmark datasets that contain multimodal data including video, audio, and transcribed text. To address depression detection as a chronic long-term disorder reflected by temporal behavioral patterns, we propose a novel framework that segments videos into utterance-level instances using GRU for contextual representation, and then constructs graphs where utterance embeddings serve as nodes connected through dual relationships capturing both chronological development and intermittent relevant information. Graph neural networks are employed to learn multi-dimensional edge relationships and align multimodal representations across different temporal dependencies. Our approach achieves superior performance with an MAE of 5.25 and RMSE of 6.75 on AVEC2014, and CCC of 0.554 and RMSE of 4.61 on AVEC2019, demonstrating significant improvements over existing methods that focus primarily on momentary expressions. Full article
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27 pages, 2205 KiB  
Article
Motivation of University Students to Use LLMs to Assist with Online Consumption of Sustainable Products: An Analysis Based on a Hybrid SEM–ANN Approach
by Junjie Yu, Wenjun Yan, Jiaxuan Gong, Siqin Wang, Ken Nah and Wei Cheng
Appl. Sci. 2025, 15(14), 8088; https://doi.org/10.3390/app15148088 - 21 Jul 2025
Viewed by 176
Abstract
This study investigates how university students adopt large language models (LLMs) for online consumption of sustainable products, integrating perceived value theory with the technology acceptance model (TAM). Cross-sectional survey data were analyzed using structural equation modeling (SEM) and artificial neural networks (ANNs). SEM [...] Read more.
This study investigates how university students adopt large language models (LLMs) for online consumption of sustainable products, integrating perceived value theory with the technology acceptance model (TAM). Cross-sectional survey data were analyzed using structural equation modeling (SEM) and artificial neural networks (ANNs). SEM results reveal partial mediation. Performance expectancy value (PEV) and information quality value (IQV) directly shape continue using intention (CUI). They also influence CUI indirectly through perceived ease of use (PEU) and perceived usefulness (PU). Green self-identity value (GSV) influences CUI both directly and via PEU, while trust transfer value (TTV) and green perceived value (GPV) affect CUI only via PEU. ANN findings confirm this hierarchy, as PU (86.7%) and PEU (85.7%) are the strongest predictors of CUI, followed by GSV (73.7%). Convergent evidence from both methods indicates that instrumental utility, effortless interaction, and sustainability identity congruence drive sustained LLM use in the context of online consumption of green products, whereas credibility cues and sustainability incentives play secondary roles. This study extends TAM by incorporating multidimensional value constructs and offers design recommendations for engaging and high-utility AI shopping platforms. Full article
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40 pages, 16352 KiB  
Review
Surface Protection Technologies for Earthen Sites in the 21st Century: Hotspots, Evolution, and Future Trends in Digitalization, Intelligence, and Sustainability
by Yingzhi Xiao, Yi Chen, Yuhao Huang and Yu Yan
Coatings 2025, 15(7), 855; https://doi.org/10.3390/coatings15070855 - 20 Jul 2025
Viewed by 480
Abstract
As vital material carriers of human civilization, earthen sites are experiencing continuous surface deterioration under the combined effects of weathering and anthropogenic damage. Traditional surface conservation techniques, due to their poor compatibility and limited reversibility, struggle to address the compound challenges of micro-scale [...] Read more.
As vital material carriers of human civilization, earthen sites are experiencing continuous surface deterioration under the combined effects of weathering and anthropogenic damage. Traditional surface conservation techniques, due to their poor compatibility and limited reversibility, struggle to address the compound challenges of micro-scale degradation and macro-scale deformation. With the deep integration of digital twin technology, spatial information technologies, intelligent systems, and sustainable concepts, earthen site surface conservation technologies are transitioning from single-point applications to multidimensional integration. However, challenges remain in terms of the insufficient systematization of technology integration and the absence of a comprehensive interdisciplinary theoretical framework. Based on the dual-core databases of Web of Science and Scopus, this study systematically reviews the technological evolution of surface conservation for earthen sites between 2000 and 2025. CiteSpace 6.2 R4 and VOSviewer 1.6 were used for bibliometric visualization analysis, which was innovatively combined with manual close reading of the key literature and GPT-assisted semantic mining (error rate < 5%) to efficiently identify core research themes and infer deeper trends. The results reveal the following: (1) technological evolution follows a three-stage trajectory—from early point-based monitoring technologies, such as remote sensing (RS) and the Global Positioning System (GPS), to spatial modeling technologies, such as light detection and ranging (LiDAR) and geographic information systems (GIS), and, finally, to today’s integrated intelligent monitoring systems based on multi-source fusion; (2) the key surface technology system comprises GIS-based spatial data management, high-precision modeling via LiDAR, 3D reconstruction using oblique photogrammetry, and building information modeling (BIM) for structural protection, while cutting-edge areas focus on digital twin (DT) and the Internet of Things (IoT) for intelligent monitoring, augmented reality (AR) for immersive visualization, and blockchain technologies for digital authentication; (3) future research is expected to integrate big data and cloud computing to enable multidimensional prediction of surface deterioration, while virtual reality (VR) will overcome spatial–temporal limitations and push conservation paradigms toward automation, intelligence, and sustainability. This study, grounded in the technological evolution of surface protection for earthen sites, constructs a triadic framework of “intelligent monitoring–technological integration–collaborative application,” revealing the integration needs between DT and VR for surface technologies. It provides methodological support for addressing current technical bottlenecks and lays the foundation for dynamic surface protection, solution optimization, and interdisciplinary collaboration. Full article
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21 pages, 383 KiB  
Article
Mapping the Unmet Informational Needs of Young Portuguese Female Cancer Survivors: Psychometric Validation of a Multidimensional Scale
by Luana Almeida, Ana Bártolo, Sara Monteiro, Isabel S. Silva, Ana Conde, Alexandra M. Araújo, Luiz Lourenço and Isabel M. Santos
Healthcare 2025, 13(14), 1757; https://doi.org/10.3390/healthcare13141757 - 20 Jul 2025
Viewed by 234
Abstract
Background/Objectives: Young female cancer survivors often face specific informational needs related to the physical and emotional effects of cancer and its impact on life plans, particularly fertility and parenthood. However, few tools are tailored to assess these needs during this critical life stage. [...] Read more.
Background/Objectives: Young female cancer survivors often face specific informational needs related to the physical and emotional effects of cancer and its impact on life plans, particularly fertility and parenthood. However, few tools are tailored to assess these needs during this critical life stage. This study aimed to (i) validate a multidimensional measure—the Satisfaction with Information Provided to Young Oncology Patients Scale (SIPYF-CPS)—to assess the specific informational needs of young adult female cancer survivors; and (ii) explore preferences regarding the provision of information and counseling. Methods: A total of 124 women (M[age] = 38.18; SD = 5.49; range 21–45), 76.6% diagnosed with breast cancer, participated in the study. Psychometric analyses included exploratory factor analysis and correlation coefficients to assess reliability and construct validity. Convergent validity was evaluated through standardized measures of anxiety, reproductive concerns, and quality of life. Results: A final 22-item measure demonstrated strong reliability and validity, capturing four factors: (i) Disease-Related Information, (ii) Symptoms and Functional Limitations, (iii) Implications for Fertility and Parenthood, and (iv) Support Services. Participants expressed low satisfaction with information on fertility preservation, sexual health, and support services. Lower satisfaction was moderately associated with higher anxiety and depression while positively related to quality of life. Most participants preferred phased, face-to-face communication throughout the illness trajectory. Conclusions: The SIPYF-CPS is a valid, multidimensional tool that captures the complex and evolving informational needs of young female cancer survivors. Its clinical use may promote earlier, personalized, and emotionally responsive communication—supporting psychological well-being, informed decision-making, and long-term survivorship care. Full article
(This article belongs to the Special Issue Multidisciplinary Approaches in Cancer Healthcare)
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