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24 pages, 10495 KiB  
Article
Dependence of Soil Moisture and Strength on Topography and Vegetation Varies Within a SMAP Grid Cell
by Joseph R. Bindner, Holly Proulx, Kevin Wickham, Jeffrey D. Niemann, Joseph Scalia, Timothy R. Green and Peter J. Grazaitis
Hydrology 2025, 12(2), 34; https://doi.org/10.3390/hydrology12020034 (registering DOI) - 15 Feb 2025
Viewed by 3
Abstract
Off-road vehicle mobility assessments rely on fine-resolution (~10 m) estimates of soil moisture and strength across the region of interest. Such estimates are often produced by downscaling soil moisture from a microwave satellite like SMAP, then using the soil moisture in a soil [...] Read more.
Off-road vehicle mobility assessments rely on fine-resolution (~10 m) estimates of soil moisture and strength across the region of interest. Such estimates are often produced by downscaling soil moisture from a microwave satellite like SMAP, then using the soil moisture in a soil strength model. Soil moisture downscaling methods typically assume consistent relationships between the moisture and topographic, vegetation, and soil composition characteristics within the microwave satellite grid cells. The objective of this study is to examine whether soil moisture and strength exhibit heterogenous dependencies on topography, vegetation, and soil composition characteristics within a SMAP grid cell. Soil moisture and strength data were collected at four geographically separated regions within a 9 km SMAP grid cell in the Front Range foothills of northern Colorado. Laboratory methods and pedotransfer functions were used to characterize soil attributes, and remote sensing data were used to determine topographic and vegetation attributes. Pearson correlation analyses were used to quantify the direction, strength, and significance of the relationships of both soil moisture and strength with topography, vegetation, and soil composition. Contrary to the common assumption, spatial variations in the slope and correlation of the relationships are observed for both soil moisture and strength. The findings indicate that improved predictions of soil moisture and soil strength may be achievable by soil moisture downscaling procedures that use spatially variable parameters across the downscaling extent. Full article
(This article belongs to the Section Soil and Hydrology)
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14 pages, 7066 KiB  
Article
CSI-Channel Spatial Decomposition for WiFi-Based Human Pose Estimation
by Jie Deng, Kaiqi Chen, Pengsen Jing, Guannan Dong, Min Yang, Aichun Zhu and Yifeng Li
Electronics 2025, 14(4), 756; https://doi.org/10.3390/electronics14040756 (registering DOI) - 15 Feb 2025
Viewed by 157
Abstract
WiFi-based human pose estimation has garnered significant interest in deep learning research. However, due to the varying angles of signal transceivers and the differing sensitivities of signal subcarriers to movement, inaccuracies can arise in WiFi-based human pose estimation. For instance, when a person [...] Read more.
WiFi-based human pose estimation has garnered significant interest in deep learning research. However, due to the varying angles of signal transceivers and the differing sensitivities of signal subcarriers to movement, inaccuracies can arise in WiFi-based human pose estimation. For instance, when a person is within a WiFi field, local changes in one or more channels and directions of structure can be detected. This channel interaction generally involves mutual interference, modifying movement localization, and perception sensitivity. To achieve unambiguous localization and identification, we decompose the properties of the Channel State Information spatial structure and its behavior, demonstrating that dual-view observation—spatial direction and channel sensitivity—is sufficient. Furthermore, we propose a CSI-Channel Spatial Decomposition Strategy (CSDS). Specifically, we introduce the Spatial Orientation Attention Module (SOA), which employs angle-dependent weighting to mitigate the error induced by signal transceiver pairs with deviated angles relative to the human body. Subsequently, the Spatial Sensitivity Enhancement Module (SSE) addresses errors from low-sensitivity signal carriers for motion detection by employing channel decoupling. Applying these two modules enables the model to discern potentially valid human pose information more effectively in WiFi transmission signals. The experimental results on the Wi-Pose public dataset demonstrate the effectiveness of CSDS. Full article
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20 pages, 7217 KiB  
Article
The Field Monitoring and Numerical Simulation of Spatiotemporal Effects During Deep Excavation in Mucky Soft Soil: A Case Study
by Qiang Wu, Jianxiu Wang, Yanxia Long, Xuezeng Liu, Guanhong Long, Shuang Ding, Li Zhou, Huboqiang Li and Muhammad Akmal Hakim bin Hishammuddin
Appl. Sci. 2025, 15(4), 1992; https://doi.org/10.3390/app15041992 - 14 Feb 2025
Viewed by 233
Abstract
The issue of geotechnical hazards induced by excavation in soft soil areas has become increasingly prominent. However, the retaining structure and surface settlement deformation induced by the creep of soft soil and spatial effect of the excavation sequence are not fully considered where [...] Read more.
The issue of geotechnical hazards induced by excavation in soft soil areas has become increasingly prominent. However, the retaining structure and surface settlement deformation induced by the creep of soft soil and spatial effect of the excavation sequence are not fully considered where only elastic–plastic deformation is used in design. To understand the spatiotemporal effects of excavation-induced deformation in soft soil pits, a case study was performed with the Huaxi Park Station of the Suzhou Metro Line S1, Jiangsu Province, China, as an example. Field monitoring was conducted, and a three-dimensional numerical model was developed, taking into account the creep characteristics of mucky clay and spatiotemporal response of retaining structures induced by excavations. The spatiotemporal effects in retaining structures and ground settlement during excavation processes were analyzed. The results show that as the excavation depth increased, the horizontal displacement of the diaphragm walls increased linearly and tended to exhibit abrupt changes when approaching the bottom of the pit. The maximum horizontal displacement of the wall at the west end well was close to 70 mm, and the maximum displacement of the wall at the standard section reached approximately 80 mm. The ground settlement on both pit sides showed a “trough” distribution pattern, peaking at about 12 m from the pit edge, with a settlement rate of −1.9 mm/m per meter of excavation depth. The excavation process directly led to the lateral deformation of the diaphragm walls, resulting in ground settlement, which prominently reflected the time-dependent deformation characteristics of mucky soft soil during the excavation process. These findings provide critical insights for similar deep excavation projects in mucky soft soil, particularly regarding excavation-induced deformations, by providing guidance on design standards and monitoring strategies for similar geological conditions. Full article
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19 pages, 10185 KiB  
Article
Research on Shallow Water Depth Remote Sensing Based on the Improvement of the Newton–Raphson Optimizer
by Yanran Li, Bei Liu, Xia Chai, Fengcheng Guo, Yongze Li and Dongyang Fu
Water 2025, 17(4), 552; https://doi.org/10.3390/w17040552 - 14 Feb 2025
Viewed by 183
Abstract
The precise acquisition of water depth data in nearshore shallow waters bears considerable strategic significance for marine environmental monitoring, resource stewardship, navigational infrastructure development, and military security. Conventional bathymetric survey methodologies are constrained by their spatial and temporal limitations, thus failing to satisfy [...] Read more.
The precise acquisition of water depth data in nearshore shallow waters bears considerable strategic significance for marine environmental monitoring, resource stewardship, navigational infrastructure development, and military security. Conventional bathymetric survey methodologies are constrained by their spatial and temporal limitations, thus failing to satisfy the requirements of large-scale, real-time surveillance. While satellite remote sensing technologies present a novel approach to water depth inversion in shallow waters, attaining high-precision inversion in nearshore areas characterized by elevated levels of suspended sediments and diminished transparency remains a formidable challenge. To tackle this issue, this study introduces an enhanced XGBoost model grounded in the Newton–Raphson optimizer (NRBO–XGBoost) and successfully applies it to water depth inversion investigations in the nearshore shallow waters of the Beibu Gulf. The research amalgamates Sentinel-2B multispectral imagery, nautical chart data, and in situ water depth measurements. By ingeniously integrating the Newton–Raphson optimizer with the XGBoost framework, the study realizes the automatic configuration of model training parameters, markedly elevating inversion accuracy. The findings reveal that the NRBO–XGBoost model attains a coefficient of determination (R2) of 0.85 when compared to nautical chart water depth data, alongside a scatter index (SI) of 21%, substantially surpassing conventional models. Additional validation analyses indicate that the model achieves a coefficient of determination (R2) of 0.86 with field-measured data, a mean absolute error (MAE) of 1.60 m, a root mean square error (RMSE) of 2.13 m, and a scatter index (SI) of 13%. Moreover, the model exhibits exceptional performance in extended applications within the waters of Zhanjiang Port (R2 = 0.90), unequivocally affirming its dependability and practicality in intricate nearshore water environments. This study not only provides a fresh solution for remotely sensing water depth in complex nearshore water settings but also imparts valuable technical insights into the associated underwater surveys and marine resource exploitation. Full article
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25 pages, 1280 KiB  
Article
Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
by Isil Karabey Aksakalli and Levent Bayındır
Appl. Sci. 2025, 15(4), 1985; https://doi.org/10.3390/app15041985 - 14 Feb 2025
Viewed by 156
Abstract
With the rapid advancement of network technologies and the widespread adoption of smart devices, the demand for efficient indoor localization and navigation systems has surged. Addressing the navigation challenge without requiring additional hardware is critical for the broad adoption of such technologies. Among [...] Read more.
With the rapid advancement of network technologies and the widespread adoption of smart devices, the demand for efficient indoor localization and navigation systems has surged. Addressing the navigation challenge without requiring additional hardware is critical for the broad adoption of such technologies. Among various fingerprint-based systems—such as Bluetooth, ZigBee, or FM radio—Wi-Fi-based indoor positioning stands out as a practical solution, due to the pervasiveness of Wi-Fi infrastructure in public indoor spaces. This study introduces an ESP32-based data-collection tool designed to minimize offline training time for Wi-Fi fingerprinting, and it presents a novel dataset incorporating room-to-room transition time, which represents the time taken to move between rooms, alongside Wi-Fi signal strength data. The proposed approach focuses on room-level localization, leveraging Machine Learning (ML) models to predict the most likely room rather than precise (x, y) coordinates. To assess the effectiveness of this feature, three datasets were collected from different residential environments by three different individuals, enabling a comprehensive evaluation across multiple spatial layouts and movement patterns. The experimental results demonstrate that incorporating room-to-room transition time consistently enhanced localization performance across all the datasets, with accuracy improvements ranging from 1.17% to 12.47%, depending on the model and dataset. Notably, the Wide Neural Network model exhibited the highest improvement, achieving an accuracy increase from 82.37% to 94.77%, while the Ensemble-based methods such as Ensemble Bagged Trees also benefited significantly, reaching up to 93.17% accuracy. Despite varying gains across the datasets, the results confirm that integrating room-to-room transition time improves Wi-Fi-based indoor positioning by leveraging temporal movement patterns to enhance classification. Full article
(This article belongs to the Special Issue Current Research in Indoor Positioning and Localization)
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17 pages, 1413 KiB  
Article
Spatial Landscape Structure Influences Cross-Species Transmission in a Rabies-like Virus Model
by Norma Rocio Forero-Muñoz, Gabriel Dansereau, Francois Viard, Emily Acheson, Patrick Leighton and Timothée Poisot
Microorganisms 2025, 13(2), 416; https://doi.org/10.3390/microorganisms13020416 - 14 Feb 2025
Viewed by 210
Abstract
In this study, we simulated biologically realistic agent-based models over neutral landscapes to examine how spatial structure affects the spread of a rabies-like virus in a two-species system. We built landscapes with varying autocorrelation levels and simulated disease dynamics using different transmission rates [...] Read more.
In this study, we simulated biologically realistic agent-based models over neutral landscapes to examine how spatial structure affects the spread of a rabies-like virus in a two-species system. We built landscapes with varying autocorrelation levels and simulated disease dynamics using different transmission rates for intra- and interspecies spread. The results were analysed based on combinations of spatial landscape structures and transmission rates, focusing on the median number of new reservoir and spillover cases. We found that both spatial landscape structures and viral transmission rates are key factors in determining the number of infected simulated agents and the epidemiological week when the highest number of cases occurs. While isolated habitat patches with elevated carrying capacity pose significant risks for viral transmission, they may also slow the spread compared to more connected patches, depending on the modelled scenario. This study highlights the importance of spatial landscape structure and viral transmission rates in cross-species spread. Our findings have implications for disease control strategies and suggest that future research should also focus on how landscape factors interact with pathogen dynamics, especially in those locations where susceptible agents could be more in contact with pathogens with high transmission rates. Full article
(This article belongs to the Special Issue Rabies Virus: Infections, Reservoirs and Vectors)
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25 pages, 12139 KiB  
Article
The Impact of Spatiotemporal Effect and Relevant Factors on the Urban Thermal Environment Through the XGBoost-SHAP Model
by Junqing Wei, Yonghua Li, Liqi Jia, Benteng Liu and Yuehan Jiang
Land 2025, 14(2), 394; https://doi.org/10.3390/land14020394 - 13 Feb 2025
Viewed by 182
Abstract
The urban thermal environment is a critical topic in contemporary urban studies. However, the mechanisms driving the relationships between influencing factors and the urban thermal environment across different spatial scales and temporal dimensions remain unclear, particularly as most of these relationships exhibit nonlinearity. [...] Read more.
The urban thermal environment is a critical topic in contemporary urban studies. However, the mechanisms driving the relationships between influencing factors and the urban thermal environment across different spatial scales and temporal dimensions remain unclear, particularly as most of these relationships exhibit nonlinearity. This study utilizes XGBoost and SHAP models, combined with a partial dependency plot, to analyze the influence of population activities, built environment, urban topography, ecological and climatic conditions, and urban landscape pattern on the diurnal and nocturnal land surface temperature (LST) changes across urban and rural areas of Hangzhou throughout the year. The results indicate that during the daytime, urban topography exerts a strong influence on LST changes in both urban and rural areas of Hangzhou. At nighttime, the influence of population activities becomes more pronounced. Meanwhile, urban landscape patterns show no significant impact on LST in either urban or rural areas, regardless of daytime or nighttime. Additionally, we analyzed the specific nonlinear relationships between influencing factors and LST. Finally, our findings suggest that influencing factors can interact synergistically in pairs to affect LST, with this mechanism being more prominent in urban areas. Overall, the study categorizes and examines the factors contributing to urban thermal environment changes from spatial and temporal perspectives, providing insights for developing urban planning strategies to mitigate urban heat issues in the future. Full article
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25 pages, 913 KiB  
Article
Housing Conditions and the Quality of Life of the Populations of the European Union Countries
by Anna Oleńczuk-Paszel and Agnieszka Sompolska-Rzechuła
Sustainability 2025, 17(4), 1550; https://doi.org/10.3390/su17041550 - 13 Feb 2025
Viewed by 391
Abstract
Quality of life (QoL) as a category, which is an overarching goal of sustainable development, dependent on many factors both objective and subjective, should be subjected to constant monitoring in various spatial, temporal and thematic arrangements. This study assesses the spatial differentiation of [...] Read more.
Quality of life (QoL) as a category, which is an overarching goal of sustainable development, dependent on many factors both objective and subjective, should be subjected to constant monitoring in various spatial, temporal and thematic arrangements. This study assesses the spatial differentiation of European Union countries in terms of QoL and housing conditions (HCs) of their populations. Interactions between the studied phenomena were also determined. A multi-criteria decision-making (MCDM) method—the TOPSIS method—and Spearman rank correlation coefficients were used to achieve the objectives of this study. The analysis was conducted using 2019 and 2022 data from the Eurostat database (including the EU-SILC survey) and TheGlobalEconomy.com. The research showed that the housing conditions and QoL of the populations of EU countries vary spatially, being more favorable in Austria, Ireland and Slovenia and the Scandinavian countries of Denmark, Finland and Sweden and less favorable in Greece and some of the countries that joined the EU in 2004 and in 2007, viz. Bulgaria, Hungary and Romania. This study noted a very strong positive correlation between the positions of countries in the rankings created with QoL in 2019 and 2022 (0.947) and with living conditions in the years under study (0.828), as well as a rather weak correlation between QoL and HCs in both 2019 (0.272) and 2022 (0.292). This article fills a research gap because, to our knowledge, the indicated phenomena have not been analyzed to date in the contexts presented in this article. Full article
(This article belongs to the Special Issue Quality of Life in the Context of Sustainable Development)
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17 pages, 6989 KiB  
Article
Prediction of Tropospheric Ozone Levels from Land Surface Temperature in the Urban Area of Durango, Dgo., Mexico
by Hugo Ramírez-Aldaba, Pablito Marcelo López-Serrano, Emily García-Montiel, Miriam Mirelle Morones-Esquivel, Melissa Bocanegra-Salazar, Carlos Borrego-Núñez and José Manuel Loera-Sánchez
Pollutants 2025, 5(1), 3; https://doi.org/10.3390/pollutants5010003 - 13 Feb 2025
Viewed by 220
Abstract
Air pollution in urban centers comes from anthropogenic activities. Tropospheric ozone (O3) depends on chemical precursors that promote an increase in its production, mainly in wind-dominated and large green areas. It is a gas produced by a series of complex chemical [...] Read more.
Air pollution in urban centers comes from anthropogenic activities. Tropospheric ozone (O3) depends on chemical precursors that promote an increase in its production, mainly in wind-dominated and large green areas. It is a gas produced by a series of complex chemical reactions catalyzed by sunlight in the atmosphere. It can be concentrated to a greater or lesser extent depending on factors such as the amount of volatile organic compounds (VOCs), the amount of nitrogen dioxide (NO2), the intensity of solar radiation, or by climatic conditions such as temperature and other factors. The objective of this study was to predict tropospheric ozone levels from Land Surface Temperature (LST) data of Landsat 8 in the city of Durango, Dgo. Tropospheric O3 and LST values were obtained from 14 sampling points in the urban area of the city of Durango, of which 11 were obtained by collecting from temperature-monitoring station data and the rest from three fixed monitoring stations established in the city, specifically located in Ministry of Natural Resources and Environment (SRNyMA), Durango Institute of Technology (ITD) and Interdisciplinary Research Center for Regional Integral Development Durango Unit (CIIDIR). A correlation analysis was performed for the 12 months of the year 2023. Subsequently, a linear regression analysis was executed for each month. The results showed a greater positive correlation between O3 concentration and temperature for January (r = 0.91); additionally, this period showed a greater goodness of fit in the prediction of O3 (R2 = 0.91; RMSE = 0.65 ppm). The LST allows for the spatial prediction of ozone concentrations in terms of covering complete urban areas without measuring air stations. Full article
(This article belongs to the Section Air Pollution)
19 pages, 4234 KiB  
Article
Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding
by Yelan Wu, Pugang Cao, Meng Xu, Yue Zhang, Xiaoqin Lian and Chongchong Yu
Sensors 2025, 25(4), 1147; https://doi.org/10.3390/s25041147 - 13 Feb 2025
Viewed by 327
Abstract
Decoding motor imagery electroencephalography (MI-EEG) signals presents significant challenges due to the difficulty in capturing the complex functional connectivity between channels and the temporal dependencies of EEG signals across different periods. These challenges are exacerbated by the low spatial resolution and high signal [...] Read more.
Decoding motor imagery electroencephalography (MI-EEG) signals presents significant challenges due to the difficulty in capturing the complex functional connectivity between channels and the temporal dependencies of EEG signals across different periods. These challenges are exacerbated by the low spatial resolution and high signal redundancy inherent in EEG signals, which traditional linear models struggle to address. To overcome these issues, we propose a novel dual-branch framework that integrates an adaptive graph convolutional network (Adaptive GCN) and bidirectional gated recurrent units (Bi-GRUs) to enhance the decoding performance of MI-EEG signals by effectively modeling both channel correlations and temporal dependencies. The Chebyshev Type II filter decomposes the signal into multiple sub-bands giving the model frequency domain insights. The Adaptive GCN, specifically designed for the MI-EEG context, captures functional connectivity between channels more effectively than conventional GCN models, enabling accurate spatial–spectral feature extraction. Furthermore, combining Bi-GRU and Multi-Head Attention (MHA) captures the temporal dependencies across different time segments to extract deep time–spectral features. Finally, feature fusion is performed to generate the final prediction results. Experimental results demonstrate that our method achieves an average classification accuracy of 80.38% on the BCI-IV Dataset 2a and 87.49% on the BCI-I Dataset 3a, outperforming other state-of-the-art decoding approaches. This approach lays the foundation for future exploration of personalized and adaptive brain–computer interface (BCI) systems. Full article
(This article belongs to the Section Biomedical Sensors)
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15 pages, 5216 KiB  
Article
Anomalous Diffusion and Decay of Clusters of Dopants in Lanthanide-Doped Nanocrystals
by Grzegorz Pawlik and Antoni C. Mitus
Materials 2025, 18(4), 815; https://doi.org/10.3390/ma18040815 - 13 Feb 2025
Viewed by 278
Abstract
Upconversion (UC) luminescence in doped lanthanide nanocrystals is associated with the energy migration (EM) process within clusters of dopant ions. The process of the synthesis of core–shell nanocrystals occurs at elevated temperatures, promoting the diffusion of the dopants into the shell accompanied by [...] Read more.
Upconversion (UC) luminescence in doped lanthanide nanocrystals is associated with the energy migration (EM) process within clusters of dopant ions. The process of the synthesis of core–shell nanocrystals occurs at elevated temperatures, promoting the diffusion of the dopants into the shell accompanied by the decay of dopant clusters. The details of this unwanted effect are poorly understood. In this paper, we theoretically study a model of the diffusion of dopant ions in a nanocrystal using Monte Carlo (MC) simulations. We characterize the diffusion, spatial neighboring relations and clustering of dopant ions regarding the function of reduced temperature and MC time of the heating process. The dopants undergo a weak subdiffusion caused by trapping effects. The main results of this study are as follows: (i) the phase diagram of the variables reduced the temperature and MC time, which separates the enhanced and limited cluster-driven EM regimes, and (ii) a dependence of the average nearest distance between Yb ions as a function of reduced temperature, the concentration of Yb ions and MC time was found. In both cases, the requirements for an effective EM are formulated. Full article
(This article belongs to the Special Issue Development and Research on Theoretical Chemistry in Materials)
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21 pages, 4948 KiB  
Article
Simultaneous Localization of Two Talkers Placed in an Area Surrounded by Asynchronous Six-Microphone Arrays
by Toru Takahashi, Taiki Kanbayashi and Masato Nakayama
Electronics 2025, 14(4), 711; https://doi.org/10.3390/electronics14040711 - 12 Feb 2025
Viewed by 258
Abstract
If we can understand dialogue activities, it will be possible to know the role of each person in the discussion, and it will be possible to provide basic materials for formulating facilitation strategies. This understanding can be expected to be used for business [...] Read more.
If we can understand dialogue activities, it will be possible to know the role of each person in the discussion, and it will be possible to provide basic materials for formulating facilitation strategies. This understanding can be expected to be used for business negotiations, group work, active learning, etc. To develop a system that can monitor speech activity over a wide range of areas, we propose a method for detecting multiple acoustic events and localizing sound sources using an asynchronous distributed microphone array arranged in a regular hexagonal repeating structure. In contrast to conventional methods based on sound source direction using triangulation with microphone arrays, we propose a method for detecting acoustic events and determining sound sources from local maximum positions based on estimation of the spatial energy distribution inside the observation space. We evaluated the conventional method and the proposed method in an experimental environment in which a dialogue between two people was simulated under 22,104 conditions by using the sound source signal convolving the measured impulse response.We found that the performance changes depending on the selection of the microphone array used for estimation. Our finding is that it is best to choose five microphone arrays close to the evaluation position. Full article
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16 pages, 287 KiB  
Article
Heat-Flux Relaxation and the Possibility of Spatial Interactions in Higher-Grade Materials
by Vito Antonio Cimmelli
Mathematics 2025, 13(4), 599; https://doi.org/10.3390/math13040599 - 12 Feb 2025
Viewed by 290
Abstract
We investigate the thermodynamic compatibility of weakly nonlocal materials with constitutive equations depending on the third spatial gradient of the deformation and the heat flux ruled by an independent balance law. In such materials, the molecules experience long-range interactions. Examples of biological systems [...] Read more.
We investigate the thermodynamic compatibility of weakly nonlocal materials with constitutive equations depending on the third spatial gradient of the deformation and the heat flux ruled by an independent balance law. In such materials, the molecules experience long-range interactions. Examples of biological systems undergoing nonlocal interactions are given. Under the hypothesis of weak nonlocality (constitutive equations depending on the gradients of the unknown fields), we exploit the second law of thermodynamics by considering the spatial differential consequences (gradients) of the balance laws as additional equations to be substituted into the entropy inequality, up to the order of the gradients entering the state space. As a consequence of such a procedure, we obtain generalized constitutive laws for the stress tensor and the specific entropy, as well as new forms of the balance equations. Such equations are, in general, parabolic, although hyperbolic situations are also possible. For small deformations of homogeneous and isotropic bodies, under the validity of a generalized Maxwell–Cattaneo equation for the heat flux, which depends on the deformation too, we study the propagation of small-amplitude thermomechanical waves, proving that mechanical, thermal and thermomechanical waves are possible. Full article
18 pages, 1573 KiB  
Article
PD-Net: Parkinson’s Disease Detection Through Fusion of Two Spectral Features Using Attention-Based Hybrid Deep Neural Network
by Munira Islam, Khadija Akter, Md. Azad Hossain and M. Ali Akber Dewan
Information 2025, 16(2), 135; https://doi.org/10.3390/info16020135 - 12 Feb 2025
Viewed by 406
Abstract
Parkinson’s disease (PD) is a progressive degenerative brain disease that worsens with age, causing areas of the brain to weaken. Vocal dysfunction often emerges as one of the earliest and most prominent indicators of Parkinson’s disease, with a significant number of patients exhibiting [...] Read more.
Parkinson’s disease (PD) is a progressive degenerative brain disease that worsens with age, causing areas of the brain to weaken. Vocal dysfunction often emerges as one of the earliest and most prominent indicators of Parkinson’s disease, with a significant number of patients exhibiting vocal impairments during the initial stages of the illness. In view of this, to facilitate the diagnosis of Parkinson’s disease through the analysis of these vocal characteristics, this study focuses on exerting a combination of mel spectrogram and MFCC as spectral features. This study adopts Italian raw audio data to establish an efficient detection framework specifically designed to classify the vocal data into two distinct categories: healthy individuals and patients diagnosed with Parkinson’s disease. To this end, the study proposes a hybrid model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for the detection of Parkinson’s disease. Certainly, CNNs are employed to extract spatial features from the extracted spectro-temporal characteristics of vocal data, while LSTMs capture temporal dependencies, accelerating a comprehensive analysis of the development of vocal patterns over time. Additionally, the merging of a multi-head attention mechanism significantly enhances the model’s ability to concentrate on essential details, hence improving its overall performance. This unified method aims to enhance the detection of subtle vocal changes associated with Parkinson’s, enhancing overall diagnostic accuracy. The findings declare that this model achieves a noteworthy accuracy of 99.00% for the Parkinson’s disease detection process. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2024–2025)
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23 pages, 782 KiB  
Review
Associations Between Urban Green Space Quality and Mental Wellbeing: Systematic Review
by Zhengyang Xu, Sofia Marini, Mario Mauro, Pasqualino Maietta Latessa, Alessia Grigoletto and Stefania Toselli
Land 2025, 14(2), 381; https://doi.org/10.3390/land14020381 - 12 Feb 2025
Viewed by 381
Abstract
With the rapidity of urbanisation, concerns about citizens’ mental wellbeing issues are on the rise, and simultaneously, the issue of land use conflicts is becoming increasingly prominent. As a nature-based solution, the role of urban green space has been continually emphasised in the [...] Read more.
With the rapidity of urbanisation, concerns about citizens’ mental wellbeing issues are on the rise, and simultaneously, the issue of land use conflicts is becoming increasingly prominent. As a nature-based solution, the role of urban green space has been continually emphasised in the past decade. In urban areas facing scarce land resources, improving the quality of green spaces appears to be an important approach. This review aimed to systematically elaborate the studies regarding the associations between urban green space (UGS) qualities and mental wellbeing, following the Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Twenty-two articles were included, and most of them had a cross-sectional design. From the analysis of the data, it emerged that the definition of the quality of green space is heterogeneous. Natural elements, particularly vegetation diversity and water features, consistently showed positive associations with mental wellbeing, while the effects of spatial features like accessibility showed mixed results. The impact of facilities and amenities appeared more complex, with their benefits heavily dependent on the design and maintenance. More evidence is needed to determine the mental wellbeing benefits of maintenance and the development of facilities and amenities for UGSs. In addition, the assessment of mental wellbeing relied on various self-reported scales, with different scales targeting different aspects. Instrumental measurements were rarely employed. Future research should employ more rigorous experimental methods and standardised quality assessment tools. Full article
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