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15 pages, 1710 KiB  
Article
Profiling and Bioactivity of Polyphenols from the Omani Medicinal Plant Terminalia dhofarica (syn. Anogeissus dhofarica)
by Jonas Kappen, Luay Rashan, Katrin Franke and Ludger A. Wessjohann
Molecules 2025, 30(4), 952; https://doi.org/10.3390/molecules30040952 - 18 Feb 2025
Abstract
Several polyphenol-rich Terminalia species (Combretaceae) are known to accelerate wound healing. Recently, the Omani medicinal plant Anogeissus dhofarica (now Terminalia dhofarica) was attributed to the genus Terminalia based on phylogenetic studies. Leaves, bark, and extracts of T. dhofarica are traditionally used for [...] Read more.
Several polyphenol-rich Terminalia species (Combretaceae) are known to accelerate wound healing. Recently, the Omani medicinal plant Anogeissus dhofarica (now Terminalia dhofarica) was attributed to the genus Terminalia based on phylogenetic studies. Leaves, bark, and extracts of T. dhofarica are traditionally used for various medicinal purposes, including wound treatment and personal hygiene. In the present study, the phytochemical profile of leaves from T. dhofarica was evaluated by ultra-high-performance liquid chromatography coupled with electrospray ionization high-resolution mass spectrometry (UHPLC-ESI-HRMS) and nuclear magnetic resonance (NMR) spectroscopy. Simple phenolics, polyphenolics (e.g., flavonoids and tannins) and their glucosides were characterized as major metabolite classes. In addition, 20 phenolics were isolated and structurally identified. Nine of these compounds were never described before for T. dhofarica. For the first time, we provide complete NMR data for 1-O-galloyl-6-O-p-coumaroyl-d-glucose (1). Biological screening demonstrated moderate efficacy against the Gram-negative bacterium Aliivibrio fischeri, the phytopathogenic fungus Septoria tritici, and the oomycete Phytophthora infestans. In summary, the data expand the knowledge of the phytochemistry of the underexplored species T. dhofarica and underscore its potential for therapeutic applications, particularly in the context of traditional medicine. Full article
(This article belongs to the Special Issue Natural Polyphenols in Human Health (Volume II))
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22 pages, 10092 KiB  
Article
Study on the Distribution of Gravity Wave (GW) Activity in Six Bay of Bengal Tropical Cyclones
by Kousik Nanda, Sudipta Sasmal, Raka Hazra, Abhirup Datta, Pradipta Panchadhyayee and Stelios M. Potirakis
Atmosphere 2025, 16(2), 235; https://doi.org/10.3390/atmos16020235 - 18 Feb 2025
Abstract
This study explores how the variation of Gravity Waves (GWs) is modified and intensified during tropical cyclones using high-resolution ERA5 reanalysis data. GWs play a vital role in understanding tropical cyclone dynamics due to their connection with energy and momentum transfer in the [...] Read more.
This study explores how the variation of Gravity Waves (GWs) is modified and intensified during tropical cyclones using high-resolution ERA5 reanalysis data. GWs play a vital role in understanding tropical cyclone dynamics due to their connection with energy and momentum transfer in the atmosphere. Different issues related to six tropical cyclones in the Bay of Bengal from 2019 to 2022, spanning different intensities and seasonal conditions, are analyzed. Using temperature and pressure data across 37 vertical levels, several variables like perturbation temperature and potential energy Ep profiles associated with GWs are computed. Spatial temperature distributions and Ep exhibit spiral formations resembling cyclone structures with significant altitude-dependent variations. Temperature signatures are observed at altitudes between 1.4 km and 5.8 km, with Pressure Levels (PLs) of 850 hPa and 500 hPa, respectively, varying by season and intensity, while Ep signatures are prominent between 15.25 km and and 20.77 km, with PLs of 125 hPa and PL 50 hPa, respectively, peaking at 16.58 km and PL 100 hPa for most cyclones, except Cyclone Fani, which peaked at 18.72 km with a PL of 70 hPa. Ep values range from 10 to 25 J/kg, reflecting strong GW–cyclone interactions. These findings highlight the influence of cyclone intensity, seasonality, and atmospheric dynamics on GW behavior, enhancing the understanding of energy transfer processes in the upper troposphere and lower stratosphere. Full article
(This article belongs to the Section Upper Atmosphere)
30 pages, 7505 KiB  
Article
Performance Boundaries and Tradeoffs in Super-Resolution Imaging Technologies for Space Targets
by Xiaole He, Ping Liu and Junling Wang
Remote Sens. 2025, 17(4), 696; https://doi.org/10.3390/rs17040696 - 18 Feb 2025
Abstract
Inverse synthetic aperture radar (ISAR) super-resolution imaging technology is widely applied in space target imaging. However, the performance limits of super-resolution imaging algorithms remain largely unexplored. Our work addresses this gap by deriving mathematical expressions for the upper and lower bounds of cross-range [...] Read more.
Inverse synthetic aperture radar (ISAR) super-resolution imaging technology is widely applied in space target imaging. However, the performance limits of super-resolution imaging algorithms remain largely unexplored. Our work addresses this gap by deriving mathematical expressions for the upper and lower bounds of cross-range resolution in ISAR imaging based on the computational resolution limit (CRL) theory for line spectrum reconstruction. Leveraging these explicit expressions, we first explore influencing factors of these bounds, including the traditional Rayleigh limit, number of scatterers, and peak signal-to-noise ratio (PSNR) of the scatterers. Then, we elucidate the minimum resource requirements in ISAR imaging imposed by CRL theory to meet the desired cross-range resolution, without which studying super-resolution algorithms becomes unnecessary in practice. Furthermore, we analyze the tradeoffs between the cumulative rotation angle, radar transmit energy, and other factors that contribute to optimizing the resolution. Simulations are conducted to demonstrate these tradeoffs across various ISAR imaging scenarios, revealing their high dependence on specific imaging targets. Full article
27 pages, 15796 KiB  
Article
MSFF: A Multi-Scale Feature Fusion Convolutional Neural Network for Hyperspectral Image Classification
by Gu Gong, Xiaopeng Wang, Jiahua Zhang, Xiaodi Shang, Zhicheng Pan, Zhiyuan Li and Junshi Zhang
Electronics 2025, 14(4), 797; https://doi.org/10.3390/electronics14040797 - 18 Feb 2025
Abstract
In contrast to conventional remote sensing images, hyperspectral remote sensing images are characterized by a greater number of spectral bands and exceptionally high resolution. The richness of both spectral and spatial information facilitates the precise classification of various objects within the images, establishing [...] Read more.
In contrast to conventional remote sensing images, hyperspectral remote sensing images are characterized by a greater number of spectral bands and exceptionally high resolution. The richness of both spectral and spatial information facilitates the precise classification of various objects within the images, establishing hyperspectral imaging as indispensable for remote sensing applications. However, the labor-intensive and time-consuming process of labeling hyperspectral images results in limited labeled samples, while challenges like spectral similarity between different objects and spectral variation within the same object further complicate the development of classification algorithms. Therefore, efficiently exploiting the spatial and spectral information in hyperspectral images is crucial for accomplishing the classification task. To address these challenges, this paper presents a multi-scale feature fusion convolutional neural network (MSFF). The network introduces a dual branch spectral and spatial feature extraction module utilizing 3D depthwise separable convolution for joint spectral and spatial feature extraction, further refined by an attention-based-on-central-pixels (ACP) mechanism. Additionally, a spectral–spatial joint attention module (SSJA) is designed to interactively explore latent dependency between spectral and spatial information through the use of multilayer perceptron and global pooling operations. Finally, a feature fusion module (FF) and an adaptive multi-scale feature extraction module (AMSFE) are incorporated to enable adaptive feature fusion and comprehensive mining of feature information. Experimental results demonstrate that the proposed method performs exceptionally well on the IP, PU, and YRE datasets, delivering superior classification results compared to other methods and underscoring the potential and advantages of MSFF in hyperspectral remote sensing classification. Full article
(This article belongs to the Special Issue Machine Learning and Computational Intelligence in Remote Sensing)
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17 pages, 5497 KiB  
Article
High Spatiotemporal Resolution Monitoring of Water Body Dynamics in the Tibetan Plateau: An Innovative Method Based on Mixed Pixel Decomposition
by Yuhang Jing and Zhenguo Niu
Sensors 2025, 25(4), 1246; https://doi.org/10.3390/s25041246 - 18 Feb 2025
Abstract
The Tibetan Plateau, known as the “Third Pole” and the “Water Tower of Asia”, has experienced significant changes in its surface water due to global warming. Accurately understanding and monitoring the spatiotemporal distribution of surface water is crucial for ecological conservation and the [...] Read more.
The Tibetan Plateau, known as the “Third Pole” and the “Water Tower of Asia”, has experienced significant changes in its surface water due to global warming. Accurately understanding and monitoring the spatiotemporal distribution of surface water is crucial for ecological conservation and the sustainable use of water resources. Among existing satellite data, the MODIS sensor stands out for its long time series and high temporal resolution, which make it advantageous for large-scale water body monitoring. However, its spatial resolution limitations hinder detailed monitoring. To address this, the present study proposes a dynamic endmember selection method based on phenological features, combined with mixed pixel decomposition techniques, to generate monthly water abundance maps of the Tibetan Plateau from 2000 to 2023. These maps precisely depict the interannual and seasonal variations in surface water, with an average accuracy of 95.3%. Compared to existing data products, the water abundance maps developed in this study provide better detail of surface water, while also benefiting from higher temporal resolution, enabling effective capture of dynamic water information. The dynamic monitoring of surface water on the Tibetan Plateau shows a year-on-year increase in water area, with an increasing fluctuation range. The surface water abundance products presented in this study not only provide more detailed information for the fine characterization of surface water but also offer a new technical approach and scientific basis for timely and accurate monitoring of surface water changes on the Tibetan Plateau. Full article
(This article belongs to the Special Issue Feature Papers in Remote Sensors 2024)
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9 pages, 8141 KiB  
Case Report
Reflectance Confocal Microscopy Can Help Differentiate Adult Xanthogranulomatous Disease from Xanthelasma—A Case Report
by Larysa Krajewska-Węglewicz, Monika Dźwigała, Piotr Sobolewski, Anna Wasążnik-Jędras and Irena Walecka
J. Clin. Med. 2025, 14(4), 1359; https://doi.org/10.3390/jcm14041359 - 18 Feb 2025
Abstract
Background: Adult xanthogranulomatous disease (AXD) is a rare histiocytic disorder with systemic potential, while xanthelasma palpebrarum (XP) is a common xanthoma often linked to lipid disorders. Differentiating these conditions is challenging due to overlapping features. Reflectance confocal microscopy (RCM), a non-invasive imaging [...] Read more.
Background: Adult xanthogranulomatous disease (AXD) is a rare histiocytic disorder with systemic potential, while xanthelasma palpebrarum (XP) is a common xanthoma often linked to lipid disorders. Differentiating these conditions is challenging due to overlapping features. Reflectance confocal microscopy (RCM), a non-invasive imaging tool, offers high-resolution visualization of skin structures and may aid diagnosis. Methods: We present a 71-year-old woman with periocular lesions. RCM was used to evaluate the lesions, identifying cellular and structural features. The findings were confirmed through histopathology, followed by surgical excision. Postoperative monitoring utilized RCM and LC-OCT. Results: RCM identified Touton giant cells, foamy histiocytes, and fibrosis, helping to distinguish xanthogranuloma from xanthelasma. Histopathology confirmed the diagnosis, and the patient underwent successful lesion excision without complications. Conclusions: This case underscores RCM’s utility as a diagnostic adjunct for differentiating AXD from XP in sensitive regions like the periocular area. The combined use of RCM and LC-OCT enhances monitoring for recurrence. While histopathology remains the diagnostic gold standard, RCM shows promise as a non-invasive tool, warranting further research to validate its role and develop standardized clinical protocols. Full article
(This article belongs to the Section Ophthalmology)
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35 pages, 27811 KiB  
Article
Machine Learning to Retrieve Gap-Free Land Surface Temperature from Infrared Atmospheric Sounding Interferometer Observations
by Fabio Della Rocca, Pamela Pasquariello, Guido Masiello, Carmine Serio and Italia De Feis
Remote Sens. 2025, 17(4), 694; https://doi.org/10.3390/rs17040694 - 18 Feb 2025
Abstract
Retrieving LST from infrared spectral observations is challenging because it needs separation from emissivity in surface radiation emission, which is feasible only when the state of the surface–atmosphere system is known. Thanks to its high spectral resolution, the Infrared Atmospheric Sounding Interferometer (IASI) [...] Read more.
Retrieving LST from infrared spectral observations is challenging because it needs separation from emissivity in surface radiation emission, which is feasible only when the state of the surface–atmosphere system is known. Thanks to its high spectral resolution, the Infrared Atmospheric Sounding Interferometer (IASI) instrument onboard Metop polar-orbiting satellites is the only sensor that can simultaneously retrieve LST, the emissivity spectrum, and atmospheric composition. Still, it cannot penetrate thick cloud layers, making observations blind to surface emissions under cloudy conditions, with surface and atmospheric parameters being flagged as voids. The present paper aims to discuss a downscaling–fusion methodology to retrieve LST missing values on a spatial field retrieved from spatially scattered IASI observations to yield level 3, regularly gridded data, using as proxy data LST from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) flying on Meteosat Second Generation (MSG) platform, a geostationary instrument, and from the Advanced Very High-Resolution Radiometer (AVHRR) onboard Metop polar-orbiting satellites. We address this problem by using machine learning techniques, i.e., Gradient Boosting, Random Forest, Gaussian Process Regression, Neural Network, and Stacked Regression. We applied the methodology over the Po Valley region, a very heterogeneous area that allows addressing the trained models’ robustness. Overall, the methods significantly enhanced spatial sampling, keeping errors in terms of Root Mean Square Error (RMSE) and bias (Mean Absolute Error, MAE) very low. Although we demonstrate and assess the results primarily using IASI data, the paper is also intended for applications to the IASI follow-on, that is, IASI Next Generation (IASI-NG), and much more to the Infrared Sounder (IRS), which is planned to fly this year, 2025, on the Meteosat Third Generation platform (MTG). Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics (Second Edition))
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32 pages, 8827 KiB  
Article
Hybrid Predictive Maintenance for Building Systems: Integrating Rule-Based and Machine Learning Models for Fault Detection Using a High-Resolution Danish Dataset
by Silvia Mazzetto
Buildings 2025, 15(4), 630; https://doi.org/10.3390/buildings15040630 - 18 Feb 2025
Abstract
This study evaluates the effectiveness of six machine learning models, Artificial Neural Networks (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression (LR), for predictive maintenance in building systems. Utilizing a high-resolution dataset collected [...] Read more.
This study evaluates the effectiveness of six machine learning models, Artificial Neural Networks (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression (LR), for predictive maintenance in building systems. Utilizing a high-resolution dataset collected every five minutes from six office rooms at Aalborg University in Denmark over a ten-month period (27 February 2023 to 31 December 2023), we defined rule-based conditions to label historical faults in HVAC, lighting, and occupancy systems, resulting in over 100,000 fault instances. XGBoost outperformed other models, achieving an accuracy of 95%, precision of 93%, recall of 94%, and an F1-score of 0.93, with a computation time of 60 s. The model effectively predicted critical faults such as “Light_On_No_Occupancy” (1149 occurrences) and “Damper_Open_No_Occupancy” (8818 occurrences), demonstrating its potential for real-time fault detection and energy optimization in building management systems. Our findings suggest that implementing XGBoost in predictive maintenance frameworks can significantly enhance fault detection accuracy, reduce energy waste, and improve operational efficiency. Full article
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
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19 pages, 5510 KiB  
Article
Unveiling Population Structure Dynamics of Populus euphratica Riparian Forests Along the Tarim River Using Terrestrial LiDAR
by Alfidar Arkin, Asadilla Yusup, Ümüt Halik, Abdulla Abliz, Ailiya Ainiwaer, Aolei Tian and Maimaiti Mijiti
Forests 2025, 16(2), 368; https://doi.org/10.3390/f16020368 - 18 Feb 2025
Abstract
The Populus euphratica desert riparian forest, predominantly distributed along the Tarim River in northwestern China, has experienced significant degradation due to climate change and anthropogenic activities. Despite its ecological importance, systematic assessments of P. euphratica stand structure across the entire Tarim River remain [...] Read more.
The Populus euphratica desert riparian forest, predominantly distributed along the Tarim River in northwestern China, has experienced significant degradation due to climate change and anthropogenic activities. Despite its ecological importance, systematic assessments of P. euphratica stand structure across the entire Tarim River remain scarce. This study employed terrestrial laser scanning (TLS) to capture high-resolution 3D structural data from 2741 individual trees across 30 plots within six transects, covering the 1300 km mainstream of the Tarim River. ANOVA, PCA, and RDA were applied to examine tree structure variation and environmental influences. Results revealed a progressive decline in key structural parameters from the upper to lower reaches of the river, with the lower reaches showing pronounced degradation. Stand density decreased from 440 to 257 trees per hectare, mean stand height declined from 9.3 m to 5.6 m, mean crown diameter reduced from 4.1 m to 3.8 m, canopy cover dropped from 62% to 42%, and the leaf area index fell from 0.51 to 0.29. Age class distributions varied along the river, highlighting population structures indicative of growth in the upper reaches, stability in the middle reaches, and decline in the lower reaches. Abiotic factors, including groundwater depth, soil salinity, soil moisture, and precipitation, exhibited strong correlations with stand structural parameters (p < 0.05, R2 ≥ 0.69). The findings highlight significant spatial variations in tree structure, with healthier growth in the upper reaches and degradation in the lower reaches, enhance our understanding of forest development processes, and emphasize the urgent need for targeted conservation strategies. This comprehensive quantification of P. euphratica stand structure and its environmental drivers offer valuable insights into the dynamics of desert riparian forest ecosystems. The findings contribute to understanding forest development processes and provide a scientific basis for formulating effective conservation strategies to sustain these vital desert ecosystems, as well as for the monitoring of regional environmental changes. Full article
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19 pages, 15754 KiB  
Article
Time Lag Analysis of Atmospheric CO2 and Proxy-Based Climate Stacks on Global–Hemispheric Scales in the Last Deglaciation
by Zhi Liu and Xingxing Liu
Quaternary 2025, 8(1), 11; https://doi.org/10.3390/quat8010011 - 18 Feb 2025
Abstract
Based on 88 well-dated and high-resolution paleoclimate records, global and hemispheric stacks of the last deglacial climate were synthesized by utilizing the normalized average method. A sequential relationship between the West Antarctic Ice Sheet Divide ice core CO2 concentration and the composited [...] Read more.
Based on 88 well-dated and high-resolution paleoclimate records, global and hemispheric stacks of the last deglacial climate were synthesized by utilizing the normalized average method. A sequential relationship between the West Antarctic Ice Sheet Divide ice core CO2 concentration and the composited proxy-based global–hemispheric climate stacks was detected using the Wilcoxon rank-sum test and wavelet analysis. The results indicate that the climate stack of the Northern Hemisphere started to increase slowly before 22 kabp, possibly due to the enhancement of summer insolation at high northern latitudes, the onset of warming in the Southern Hemisphere occurred around 19 kabp, and the atmospheric CO2 concentration began to raise around 18.1 kabp. This suggests that the change in northern high-latitude summer insolation was the initial trigger of the last deglaciation, and atmospheric CO2 concentration was an internal feedback associated with global ocean circulation in the Earth’s system. Both the Wilcoxon rank-sum test and wavelet analysis showed that during the BØlling–AllerØd and the Younger Dryas periods there was no obvious asynchrony between the global climate and atmospheric CO2 concentration, which perhaps implies a fast feedback–response mechanism. The seesawing changes in interhemispheric climate and the abrupt variations in the atmospheric CO2 concentration could be explained by the influences of Atlantic meridional overturning circulation strength during the BØlling–AllerØd and the Younger Dryas periods. This reveals that Atlantic meridional overturning circulation played an important role in the course of the last deglaciation. Full article
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23 pages, 26773 KiB  
Article
Suitability of CMIP6 Models Considering Statistical Downscaling Based on GloH2O and E-OBS Dataset in River Basin Districts of the Southeastern Baltic Sea Basin
by Vytautas Akstinas, Karolina Gurjazkaitė, Diana Meilutytė-Lukauskienė and Darius Jakimavičius
Atmosphere 2025, 16(2), 229; https://doi.org/10.3390/atmos16020229 - 18 Feb 2025
Abstract
Climate projections based on global climate models (GCMs) are generally subject to large uncertainties, as the models only reflect the local climate in the past to a limited extent. Statistical downscaling is the most cost-effective approach to identify the systematic biases of the [...] Read more.
Climate projections based on global climate models (GCMs) are generally subject to large uncertainties, as the models only reflect the local climate in the past to a limited extent. Statistical downscaling is the most cost-effective approach to identify the systematic biases of the GCMs from the past and eliminate them in the projections. This study seeks to evaluate the effectiveness of GCMs in capturing local climatic characteristics at the river basin district scale by applying gridded statistical downscaling techniques using global and regional datasets. The historical observational datasets E-OBS and GloH2O were selected to downscale the raw data of 17 GCMs from ~1° grid cells to 0.25° resolution. E-OBS is a regional dataset supported by a dense network of meteorological stations in Europe, while GloH2O is a global dataset covering all continents. The results show that the suitability of the GCMs varies depending on the selected parameter. The statistical downscaling revealed the advantages of the performance of E-OBS in representing local climate characteristics during the historical period and emphasized the crucial role of regional datasets for good climate depiction. Such an approach provides the possibility to assess the relative performance of GCMs based on the high-resolution observational and reanalysis datasets, while generating statistically downscaled datasets for the best ranked GCMs. The strategies used in this study can help to identify the most appropriate models to assemble the right ensemble of GCMs for specific studies. Full article
(This article belongs to the Special Issue The Hydrologic Cycle in a Changing Climate)
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19 pages, 8273 KiB  
Article
Fine Identification of Landslide Acceleration Phase Using Time Logarithm Prediction Method Based on Arc Synthetic Aperture Radar Monitoring Data
by Chong Li, Liguan Wang, Jiaheng Wang and Jun Zhang
Appl. Sci. 2025, 15(4), 2147; https://doi.org/10.3390/app15042147 - 18 Feb 2025
Abstract
In the field of slope landslide prevention and monitoring in open-pit mines, addressing the lag issues associated with the traditional GNSS inverse-velocity method, this study introduces a novel strategy that integrates high-spatiotemporal-resolution monitoring data from ArcSAR with a time log model for prediction. [...] Read more.
In the field of slope landslide prevention and monitoring in open-pit mines, addressing the lag issues associated with the traditional GNSS inverse-velocity method, this study introduces a novel strategy that integrates high-spatiotemporal-resolution monitoring data from ArcSAR with a time log model for prediction. The key findings include the following: (1) This strategy utilizes the normal distribution characteristics of deformation velocities to set confidence intervals, accurately identifying the starting point of accelerated deformation. (2) Coupled with coordinate transformation, the time logarithm prediction method was constructed, unifying the units of measurement and resolving convergence issues in data fitting. (3) Empirical research conducted at the Kambove open-pit mine in the Democratic Republic of the Congo demonstrates that this method successfully predicts landslide times four hours in advance, with an error margin of only 0.18 h. This innovation offers robust technical support for slope landslide prevention and control in open-pit mines, enhancing safety standards and mitigating disaster losses. Full article
(This article belongs to the Special Issue Novel Technologies in Intelligent Coal Mining)
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18 pages, 2505 KiB  
Article
MRI in Oral Tongue Squamous Cell Carcinoma: A Radiomic Approach in the Local Recurrence Evaluation
by Antonello Vidiri, Vincenzo Dolcetti, Francesco Mazzola, Sonia Lucchese, Francesca Laganaro, Francesca Piludu, Raul Pellini, Renato Covello and Simona Marzi
Curr. Oncol. 2025, 32(2), 116; https://doi.org/10.3390/curroncol32020116 - 18 Feb 2025
Abstract
(1) Background: Oral tongue squamous cell carcinoma (OTSCC) is a prevalent malignancy with high loco-regional recurrence. Advanced imaging biomarkers are critical for stratifying patients at a high risk of recurrence. This study aimed to develop MRI-based radiomic models to predict loco-regional recurrence in [...] Read more.
(1) Background: Oral tongue squamous cell carcinoma (OTSCC) is a prevalent malignancy with high loco-regional recurrence. Advanced imaging biomarkers are critical for stratifying patients at a high risk of recurrence. This study aimed to develop MRI-based radiomic models to predict loco-regional recurrence in OTSCC patients undergoing surgery. (2) Methods: We retrospectively selected 92 patients with OTSCC who underwent MRI, followed by surgery and cervical lymphadenectomy. A total of 31 patients suffered from a loco-regional recurrence. Radiomic features were extracted from preoperative post-contrast high-resolution MRI and integrated with clinical and pathological data to develop predictive models, including radiomic-only and combined radiomic–clinical approaches, trained and validated with stratified data splitting. (3) Results: Textural features, such as those derived from the Gray-Level Size-Zone Matrix, Gray-Level Dependence Matrix, and Gray-Level Run-Length Matrix, showed significant associations with recurrence. The radiomic-only model achieved an accuracy of 0.79 (95% confidence interval: 0.69, 0.87) and 0.74 (95% CI: 0.54, 0.89) in the training and validation set, respectively. Combined radiomic and clinical models, incorporating features like the pathological depth of invasion and lymph node status, provided comparable diagnostic performances. (4) Conclusions: MRI-based radiomic models demonstrated the potential for predicting loco-regional recurrence, highlighting their increasingly important role in advancing precision oncology for OTSCC. Full article
(This article belongs to the Section Head and Neck Oncology)
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18 pages, 2634 KiB  
Article
Monitoring Fine-Scale Urban Shrinkage Space with NPP-VIIRS Imagery
by Shili Chen and Cheng Cheng
Remote Sens. 2025, 17(4), 688; https://doi.org/10.3390/rs17040688 - 18 Feb 2025
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Abstract
Urban shrinkage is a significant challenge to sustainable urban development. To date, the existing research has yet to analyze urban shrinkage at a fine-scale level. This study addresses this gap by employing nighttime light (NTL) data, which, due to its strong correlation with [...] Read more.
Urban shrinkage is a significant challenge to sustainable urban development. To date, the existing research has yet to analyze urban shrinkage at a fine-scale level. This study addresses this gap by employing nighttime light (NTL) data, which, due to its strong correlation with human activity and high spatial–temporal resolution, offers a robust approach for micro-scale population estimation. This paper aims to explore the characteristics and formation mechanisms of urban shrinkage spaces in Guangzhou, using NTL data and applying ordinary least squares (OLS) and geographically weighted regression (GWR) models. The correlational analysis reveals a marked improvement in model fit with GWR (R2 = 0.91) compared with OLS (R2 = 0.63), confirming the predictive power of NTL-based GWR for population mapping and the spatial delineation of urban shrinkage. We demonstrate that urban shrinkage spaces in Guangzhou are predominantly distributed in the outer suburbs, while urban growth is concentrated within the urban core area and inner suburbs. The formation of urban shrinkage in Liwan District examined as a case study, is primarily influenced by market factors, government actions, and regulatory constraints—a constellation of factors likely generalizable with other contexts of urban shrinkage. A comprehensive understanding of urban shrinkage at a fine-scale level is imperative for policy makers to optimize urban land use planning and mitigate the factors contributing to shrinkage space, thereby promoting sustainable urban development. Full article
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18 pages, 5677 KiB  
Article
Computer Vision-Based Concrete Crack Identification Using MobileNetV2 Neural Network and Adaptive Thresholding
by Li Hui, Ahmed Ibrahim and Riyadh Hindi
Infrastructures 2025, 10(2), 42; https://doi.org/10.3390/infrastructures10020042 - 18 Feb 2025
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Abstract
Concrete is widely used in different types of buildings and bridges; however, one of the major issues for concrete structures is crack formation and propagation during its service life. These cracks can potentially introduce harmful agents into concrete, resulting in a reduction in [...] Read more.
Concrete is widely used in different types of buildings and bridges; however, one of the major issues for concrete structures is crack formation and propagation during its service life. These cracks can potentially introduce harmful agents into concrete, resulting in a reduction in the overall lifespan of concrete structures. Traditional methods for crack detection primarily hinge on manual visual inspection, which relies on the experience and expertise of inspectors using tools such as magnifying glasses and microscopes. To address this issue, computer vision is one of the most innovative solutions for concrete cracking evaluation, and its application has been an area of research interest in the past few years. This study focuses on the utilization of the lightweight MobileNetV2 neural network for concrete crack detection. A dataset including 40,000 images was adopted and preprocessed using various thresholding techniques, of which adaptive thresholding was selected for developing the crack evaluation algorithm. While both the convolutional neural network (CNN) and MobileNetV2 indicated comparable accuracy levels in crack detection, the MobileNetV2 model’s significantly smaller size makes it a more efficient selection for crack detection using mobile devices. In addition, an advanced algorithm was developed to detect cracks and evaluate crack widths in high-resolution images. The effectiveness and reliability of both the selected method and the developed algorithm were subsequently assessed through experimental validation. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Infrastructures)
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