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21 pages, 3146 KiB  
Article
TnP as a Multifaceted Therapeutic Peptide with System-Wide Regulatory Capacity
by Geonildo Rodrigo Disner, Emma Wincent, Carla Lima and Monica Lopes-Ferreira
Pharmaceuticals 2025, 18(8), 1146; https://doi.org/10.3390/ph18081146 - 1 Aug 2025
Viewed by 131
Abstract
Background: The candidate therapeutic peptide TnP demonstrates broad, system-level regulatory capacity, revealed through integrated network analysis from transcriptomic data in zebrafish. Our study primarily identifies TnP as a multifaceted modulator of drug metabolism, wound healing, proteolytic activity, and pigmentation pathways. Results: Transcriptomic profiling [...] Read more.
Background: The candidate therapeutic peptide TnP demonstrates broad, system-level regulatory capacity, revealed through integrated network analysis from transcriptomic data in zebrafish. Our study primarily identifies TnP as a multifaceted modulator of drug metabolism, wound healing, proteolytic activity, and pigmentation pathways. Results: Transcriptomic profiling of TnP-treated larvae following tail fin amputation revealed 558 differentially expressed genes (DEGs), categorized into four functional networks: (1) drug-metabolizing enzymes (cyp3a65, cyp1a) and transporters (SLC/ABC families), where TnP alters xenobiotic processing through Phase I/II modulation; (2) cellular trafficking and immune regulation, with upregulated myosin genes (myhb/mylz3) enhancing wound repair and tlr5-cdc42 signaling fine-tuning inflammation; (3) proteolytic cascades (c6ast4, prss1) coupled to autophagy (ulk1a, atg2a) and metabolic rewiring (g6pca.1-tg axis); and (4) melanogenesis-circadian networks (pmela/dct-fbxl3l) linked to ubiquitin-mediated protein turnover. Key findings highlight TnP’s unique coordination of rapid (protease activation) and sustained (metabolic adaptation) responses, enabled by short network path lengths (1.6–2.1 edges). Hub genes, such as nr1i2 (pxr), ppara, and bcl6aa/b, mediate crosstalk between these systems, while potential risks—including muscle hypercontractility (myhb overexpression) or cardiovascular effects (ace2-ppp3ccb)—underscore the need for targeted delivery. The zebrafish model validated TnP-conserved mechanisms with human relevance, particularly in drug metabolism and tissue repair. TnP’s ability to synchronize extracellular matrix remodeling, immune resolution, and metabolic homeostasis supports its development for the treatment of fibrosis, metabolic disorders, and inflammatory conditions. Conclusions: Future work should focus on optimizing tissue-specific delivery and assessing genetic variability to advance clinical translation. This system-level analysis positions TnP as a model example for next-generation multi-pathway therapeutics. Full article
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17 pages, 2629 KiB  
Article
Recovery of High-Alkali-Grade Feldspar Substitute from Phonolite Tailings
by Savas Ozun, Semsettin Ulutas and Sema Yurdakul
Processes 2025, 13(8), 2334; https://doi.org/10.3390/pr13082334 - 23 Jul 2025
Viewed by 267
Abstract
Phonolite is a fine-grained, shallow extrusive rock rich in alkali minerals and containing iron/titanium-bearing minerals. This rock is widely used as a construction material for building exteriors due to its excellent abrasion resistance and insulation properties. However, during the cutting process, approximately 70% [...] Read more.
Phonolite is a fine-grained, shallow extrusive rock rich in alkali minerals and containing iron/titanium-bearing minerals. This rock is widely used as a construction material for building exteriors due to its excellent abrasion resistance and insulation properties. However, during the cutting process, approximately 70% of the rock is discarded as tailing. So, this study aims to repurpose tailings from a phonolite cutting and sizing plant into a high-alkali ceramic raw mineral concentrate. To enable the use of phonolite tailings in ceramic manufacturing, it is necessary to remove coloring iron/titanium-bearing minerals, which negatively affect the final product. To achieve this removal, dry/wet magnetic separation processes, along with flotation, were employed both individually and in combination. The results demonstrated that using dry high-intensity magnetic separation (DHIMS) resulted in a concentrate with an Fe2O3 + TiO2 grade of 0.95% and a removal efficiency of 85%. The wet high-intensity magnetic separation (WHIMS) process reduced the Fe2O3 + TiO2 grade of the concentrate to 1.2%, with 70% removal efficiency. During flotation tests, both pH levels and collector concentration impacted the efficiency and Fe2O3 + TiO2 grade (%) of the concentrate. The lowest Fe2O3 + TiO2 grade of 1.65% was achieved at a pH level of 10 with a collector concentration of 2000 g/t. Flotation concentrates processed with DHIMS achieved a minimum Fe2O3 + TiO2 grade of 0.90%, while those processed with WHIMS exhibited higher Fe2O3 + TiO2 grades (>1.1%) and higher recovery rates (80%). Additionally, studies on flotation applied to WHIMS concentrates showed that collector concentration, pulp density, and conditioning time significantly influenced the Fe2O3 + TiO2 grade of the final concentrate. Full article
(This article belongs to the Section Separation Processes)
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18 pages, 7598 KiB  
Article
Recovery of Fine Rare Earth Minerals from Simulated Tin Tailings by Carrier Magnetic Separation: Selective Heterogeneous Agglomeration with Coarse Magnetite Particles
by Ilhwan Park, Topan Satria Gumilang, Rinaldi Yudha Pratama, Sanghee Jeon, Carlito Baltazar Tabelin, Theerayut Phengsaart, Muhammad Bilal, Youhei Kawamura and Mayumi Ito
Minerals 2025, 15(7), 757; https://doi.org/10.3390/min15070757 - 19 Jul 2025
Viewed by 324
Abstract
The demand for rare earth elements (REEs) is continuously increasing due to the important roles they play in low-carbon and green energy technologies. Unfortunately, the global REE reserves are limited and concentrated in only a few countries, so the reprocessing of alternative resources [...] Read more.
The demand for rare earth elements (REEs) is continuously increasing due to the important roles they play in low-carbon and green energy technologies. Unfortunately, the global REE reserves are limited and concentrated in only a few countries, so the reprocessing of alternative resources like tailings is of critical importance. This study investigated carrier magnetic separation using coarse magnetite particles as a carrier to recover finely ground monazite from tailings. The monazite and carrier surfaces were modified by sodium oleate (NaOL) to improve the hydrophobic interactions between them. The results of zeta potential and contact angle measurements implied the selective adsorption of NaOL onto the surfaces of the monazite and magnetite particles. Although their hydrophobicity increased, heterogenous agglomeration between them was not substantial. To improve heterogenous agglomeration, emulsified kerosene was utilized as a bridging liquid, resulting in more extensive attachment of fine monazite particles onto the surfaces of carrier particles and a dramatic improvement in monazite recovery by magnetic separation—from 0% (without carrier) to 70% (with carrier). A rougher–scavenger–cleaner carrier magnetic separation can produce REE concentrates with a total rare earth oxide (TREO) recovery of 80% and a grade of 9%, increased from 3.4%, which can be further increased to 23.2% after separating REEs and the carrier. Full article
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18 pages, 6970 KiB  
Article
Study on Lateral Erosion Failure Behavior of Reinforced Fine-Grained Tailings Dam Due to Overtopping Breach
by Yun Luo, Mingjun Zhou, Menglai Wang, Yan Feng, Hongwei Luo, Jian Ou, Shangwei Wu and Xiaofei Jing
Water 2025, 17(14), 2088; https://doi.org/10.3390/w17142088 - 12 Jul 2025
Viewed by 333
Abstract
The overtopping-induced lateral erosion breaching of tailings dams represents a critical disaster mechanism threatening structural safety, particularly in reinforced fine-grained tailings dams where erosion behaviors demonstrate pronounced water–soil coupling characteristics and material anisotropy. Through physical model tests and numerical simulations, this study systematically [...] Read more.
The overtopping-induced lateral erosion breaching of tailings dams represents a critical disaster mechanism threatening structural safety, particularly in reinforced fine-grained tailings dams where erosion behaviors demonstrate pronounced water–soil coupling characteristics and material anisotropy. Through physical model tests and numerical simulations, this study systematically investigates lateral erosion failure patterns of reinforced fine-grained tailings under overtopping flow conditions. Utilizing a self-developed hydraulic initiation test apparatus, with aperture sizes of reinforced geogrids (2–3 mm) and flow rates (4–16 cm/s) as key control variables, the research elucidates the interaction mechanisms of “hydraulic scouring-particle migration-geogrid anti-sliding” during lateral erosion processes. The study revealed that compared to unreinforced specimens, reinforced specimens with varying aperture sizes (2–3 mm) demonstrated systematic reductions in final lateral erosion depths across flow rates (4–16 cm/s): 3.3–5.8 mm (15.6−27.4% reduction), 3.1–7.2 mm (12.8–29.6% reduction), 2.3–11 mm (6.9–32.8% reduction), and 2.5–11.4 mm (6.2–28.2% reduction). Smaller-aperture geogrids (2 mm × 2 mm) significantly enhanced anti-erosion performance through superior particle migration inhibition. Concurrently, a pronounced positive correlation between flow rate and lateral erosion depth was confirmed, where increased flow rates weakened particle erosion resistance and exacerbated lateral erosion severity. The numerical simulation results are in basic agreement with the lateral erosion failure process observed in model tests, revealing the dynamic process of lateral erosion in the overtopping breach of a reinforced tailings dam. These findings provide critical theoretical foundations for optimizing reinforced tailings dam design, construction quality control, and operational maintenance, while offering substantial engineering applications for advancing green mine construction. Full article
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40 pages, 600 KiB  
Article
Advanced Lifetime Modeling Through APSR-X Family with Symmetry Considerations: Applications to Economic, Engineering and Medical Data
by Badr S. Alnssyan, A. A. Bhat, Abdelaziz Alsubie, S. P. Ahmad, Abdulrahman M. A. Aldawsari and Ahlam H. Tolba
Symmetry 2025, 17(7), 1118; https://doi.org/10.3390/sym17071118 - 11 Jul 2025
Viewed by 228
Abstract
This paper introduces a novel and flexible class of continuous probability distributions, termed the Alpha Power Survival Ratio-X (APSR-X) family. Unlike many existing transformation-based families, the APSR-X class integrates an alpha power transformation with a survival ratio structure, offering a new mechanism for [...] Read more.
This paper introduces a novel and flexible class of continuous probability distributions, termed the Alpha Power Survival Ratio-X (APSR-X) family. Unlike many existing transformation-based families, the APSR-X class integrates an alpha power transformation with a survival ratio structure, offering a new mechanism for enhancing shape flexibility while maintaining mathematical tractability. This construction enables fine control over both the tail behavior and the symmetry properties, distinguishing it from traditional alpha power or survival-based extensions. We focus on a key member of this family, the two-parameter Alpha Power Survival Ratio Exponential (APSR-Exp) distribution, deriving essential mathematical properties including moments, quantile functions and hazard rate structures. We estimate the model parameters using eight frequentist methods: the maximum likelihood (MLE), maximum product of spacings (MPSE), least squares (LSE), weighted least squares (WLSE), Anderson–Darling (ADE), right-tailed Anderson–Darling (RADE), Cramér–von Mises (CVME) and percentile (PCE) estimation. Through comprehensive Monte Carlo simulations, we evaluate the estimator performance using bias, mean squared error and mean relative error metrics. The proposed APSR-X framework uniquely enables preservation or controlled modification of the symmetry in probability density and hazard rate functions via its shape parameter. This capability is particularly valuable in reliability and survival analyses, where symmetric patterns represent balanced risk profiles while asymmetric shapes capture skewed failure behaviors. We demonstrate the practical utility of the APSR-Exp model through three real-world applications: economic (tax revenue durations), engineering (mechanical repair times) and medical (infection durations) datasets. In all cases, the proposed model achieves a superior fit over that of the conventional alternatives, supported by goodness-of-fit statistics and visual diagnostics. These findings establish the APSR-X family as a unique, symmetry-aware modeling framework for complex lifetime data. Full article
(This article belongs to the Section Computer)
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20 pages, 6287 KiB  
Article
The Discovery and Delimitation of a New Cryptic Species of Spirinia (Nematoda: Desmodoridae) Using SSU and LSU rDNA Divergence
by Kyeongmoon Son and Raehyuk Jeong
J. Mar. Sci. Eng. 2025, 13(7), 1251; https://doi.org/10.3390/jmse13071251 - 28 Jun 2025
Viewed by 325
Abstract
The cosmopolitan nematode Spirinia parasitifera has long been considered a single, morphologically variable species; however, mounting molecular evidence suggests that it represents a complex of cryptic taxa. In this study, we describe Spirinia koreana sp. nov., a new species collected from intertidal sediments [...] Read more.
The cosmopolitan nematode Spirinia parasitifera has long been considered a single, morphologically variable species; however, mounting molecular evidence suggests that it represents a complex of cryptic taxa. In this study, we describe Spirinia koreana sp. nov., a new species collected from intertidal sediments of the Republic of Korea. The new species exhibits a high degree of morphological resemblance to both S. antipodea and S. parasitifera, with overlapping ranges in most morphological traits. While certain measurements, such as relatively shorter body length, more slender form (higher a ratio), moderately long tail length, and shorter spicule length differ from those in some described populations, no single morphological character alone reliably separates S. koreana from all previously reported specimens of S. parasitifera or S. antipodea. Nevertheless, molecular evidence from multiple genetic markers clearly supports its distinction as a separate species. Molecular data from mitochondrial COI, 18S rRNA, and 28S rRNA genes confirm the genetic distinctness of the Korean specimens from S. parasitifera and S. antipodea. Notably, S. koreana sp. nov. differs from other Spirinia species by 2.1–3.4% in 18S and up to 34.4% in 28S sequences, surpassing thresholds previously used to delimit marine nematode species. Our results emphasize the value of integrative taxonomy combining fine-scale morphology and multi-marker molecular data to uncover hidden diversity in meiofaunal nematodes. Full article
(This article belongs to the Special Issue Biodiversity and Population Ecology of Marine Invertebrates)
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34 pages, 10519 KiB  
Article
A Remote Sensing Image Object Detection Model Based on Improved YOLOv11
by Aili Wang, Zhijia Fu, Yanran Zhao and Haisong Chen
Electronics 2025, 14(13), 2607; https://doi.org/10.3390/electronics14132607 - 27 Jun 2025
Viewed by 466
Abstract
Due to the challenges posed by high resolution, substantial background noise, significant object scale variation, and long-tailed data distribution in remote sensing images, traditional techniques often struggle to maintain both high accuracy and low latency. This paper proposes YOLO11-FSDAT, an advanced object detection [...] Read more.
Due to the challenges posed by high resolution, substantial background noise, significant object scale variation, and long-tailed data distribution in remote sensing images, traditional techniques often struggle to maintain both high accuracy and low latency. This paper proposes YOLO11-FSDAT, an advanced object detection framework tailored for remote sensing imagery, which integrates not only modular enhancements but also theoretical and architectural innovations to address these limitations. First, we propose the frequency–spatial feature extraction fusion module (Freq-SpaFEFM), which breaks the conventional paradigm of spatial-domain-dominated feature learning by introducing a multi-branch architecture that fuses frequency- and spatial-domain features in parallel. This design provides a new processing paradigm for multi-scale object detection, particularly enhancing the model’s capability in handling dense and small-object scenarios with complex backgrounds. Second, we introduce the deformable attention-based global–local fusion module (DAGLF), which combines fine-grained local features with global context through deformable attention and residual connections. This enables the model to adaptively capture irregularly oriented objects (e.g., tilted aircraft) and effectively mitigates the issue of information dilution in deep networks. Third, we develop the adaptive threshold focal loss (ATFL), which is the first loss function to systematically address the long-tailed distribution in remote sensing datasets by dynamically adjusting focus based on sample difficulty. Unlike traditional focal loss with fixed hyperparameters, ATFL decouples hard and easy samples and automatically adapts to varying class distributions. Experimental results on the public DOTAv1, SIMD, and DIOR datasets demonstrated that YOLO11-FSDAT achieved 75.22%, 82.79%, and 88.01% mAP, respectively, outperforming baseline YOLOv11n by up to 4.11%. These results confirm the effectiveness, robustness, and broader theoretical value of the proposed framework in addressing key challenges in remote sensing object detection. Full article
(This article belongs to the Special Issue Machine Learning and Computational Intelligence in Remote Sensing)
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16 pages, 4667 KiB  
Article
Subverting Dominance Hierarchies: Interspecific Submission and Agonistic Interactions Between Golden Jackals and a Red Fox
by Yiannis G. Zevgolis, Christos Kotselis, Babis Giritziotis, Anastasia Lekka and Apostolos Christopoulos
Diversity 2025, 17(7), 454; https://doi.org/10.3390/d17070454 - 26 Jun 2025
Viewed by 307
Abstract
Interspecific interactions among sympatric carnivores are critical for understanding patterns of coexistence, competition, and community structure. Among mesocarnivores, dominance hierarchies are typically shaped by differences in body size, social organization, and competitive ability. The golden jackal (Canis aureus) is generally assumed [...] Read more.
Interspecific interactions among sympatric carnivores are critical for understanding patterns of coexistence, competition, and community structure. Among mesocarnivores, dominance hierarchies are typically shaped by differences in body size, social organization, and competitive ability. The golden jackal (Canis aureus) is generally assumed to dominate the red fox (Vulpes vulpes) across shared landscapes, particularly at high-value resources such as carcasses. However, here, we present rare behavioral evidence that challenges this prevailing assumption. Using motion-triggered camera traps deployed at a carcass in Lake Kerkini National Park, Greece, we recorded a sequence of interactions in which a golden jackal displayed clear submissive behavior toward a red fox, including lowered body posture, tail tucking, and conflict avoidance. Subsequent footage revealed two additional agonistic encounters, during which the same red fox successfully displaced two separate jackals, one of which emitted a distress vocalization while retreating. These findings represent the first documented case of interspecific submission by golden jackals toward a red fox and suggest that context-specific factors—such as immediate carcass possession, individual experience, or body condition—may modulate expected dominance outcomes. Our observations underscore the importance of fine-scale behavioral studies in revealing plasticity in interspecific relationships and contribute to a more nuanced understanding of carnivore competition under semi-natural conditions. Full article
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17 pages, 2367 KiB  
Article
Sustainable Mineral Processing Technologies Using Hybrid Intelligent Algorithms
by Olga Shiryayeva, Batyrbek Suleimenov and Yelena Kulakova
Technologies 2025, 13(7), 269; https://doi.org/10.3390/technologies13070269 - 24 Jun 2025
Viewed by 474
Abstract
This study presents a sustainable and adaptive approach to mineral processing. A hybrid intelligent control system was developed to beneficiate fine chromite ore in a jigging machine. The objective is to enhance separation efficiency and reduce chromium losses through real-time optimization of process [...] Read more.
This study presents a sustainable and adaptive approach to mineral processing. A hybrid intelligent control system was developed to beneficiate fine chromite ore in a jigging machine. The objective is to enhance separation efficiency and reduce chromium losses through real-time optimization of process parameters under variable feed conditions. The method addresses ore composition fluctuations by integrating three components: Physical modeling of particle motion, regression analysis, and neural network-based prediction. The jig bed level and pulsation frequency are used as control variables, while the Cr2O3 content in the feed (Cr) is treated as a disturbance. A neural network predicts the Cr2O3 content in the concentrate (Cc) and in the tailings (Ct), representing chromite-rich and gangue fractions, respectively. The optimization is performed using a constrained Interior-Point algorithm. The model demonstrates high predictive accuracy, with a mean squared error (MSE) below 0.01. The proposed control algorithm reduces chromium losses in tailings from 7.5% to 5.5%, while improving concentrate quality by 3–6%. A real-time human–machine interface (HMI) was developed in SIMATIC WinCC for process visualization and control. The hybrid framework can be adapted to other mineral processing systems by adjusting the model structure and retraining the neural network on new ore datasets. Full article
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20 pages, 2848 KiB  
Article
A Dual-Branch Network for Intra-Class Diversity Extraction in Panchromatic and Multispectral Classification
by Zihan Huang, Pengyu Tian, Hao Zhu, Pute Guo and Xiaotong Li
Remote Sens. 2025, 17(12), 1998; https://doi.org/10.3390/rs17121998 - 10 Jun 2025
Viewed by 361
Abstract
With the rapid development of remote sensing technology, satellites can now capture multispectral (MS) and panchromatic (PAN) images simultaneously. MS images offer rich spectral details, while PAN images provide high spatial resolutions. Effectively leveraging their complementary strengths and addressing modality gaps are key [...] Read more.
With the rapid development of remote sensing technology, satellites can now capture multispectral (MS) and panchromatic (PAN) images simultaneously. MS images offer rich spectral details, while PAN images provide high spatial resolutions. Effectively leveraging their complementary strengths and addressing modality gaps are key challenges in improving the classification performance. From the perspective of deep learning, this paper proposes a novel dual-source remote sensing classification framework named the Diversity Extraction and Fusion Classifier (DEFC-Net). A central innovation of our method lies in introducing a modality-specific intra-class diversity modeling mechanism for the first time in dual-source classification. Specifically, the intra-class diversity identification and splitting (IDIS) module independently analyzes the intra-class variance within each modality to identify semantically broad classes, and it applies an optimized K-means method to split such classes into fine-grained sub-classes. In particular, due to the inherent representation differences between the MS and PAN modalities, the same class may be split differently in each modality, allowing modality-aware class refinement that better captures fine-grained discriminative features in dual perspectives. To handle the class imbalance introduced by both natural long-tailed distributions and class splitting, we design a long-tailed ensemble learning module (LELM) based on a multi-expert structure to reduce bias toward head classes. Furthermore, a dual-modal knowledge distillation (DKD) module is developed to align cross-modal feature spaces and reconcile the label inconsistency arising from modality-specific class splitting, thereby facilitating effective information fusion across modalities. Extensive experiments on datasets show that our method significantly improves the classification performance. The code was accessed on 11 April 2025. Full article
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15 pages, 1589 KiB  
Article
Structural Analysis of Aggregates in Clayey Tailings Treated with Coagulant and Flocculant
by Steven Nieto, Eder Piceros, Elter Reyes, Jahir Ramos, Pedro Robles and Ricardo Jeldres
Minerals 2025, 15(6), 627; https://doi.org/10.3390/min15060627 - 10 Jun 2025
Viewed by 388
Abstract
This study evaluated the combined effect of a cationic coagulant (Magnafloc 1727®) and a high molecular weight anionic flocculant (SNF 604®) on the settling properties, aggregate structure, and rheological behavior of synthetic tailings suspensions composed of kaolinite and quartz [...] Read more.
This study evaluated the combined effect of a cationic coagulant (Magnafloc 1727®) and a high molecular weight anionic flocculant (SNF 604®) on the settling properties, aggregate structure, and rheological behavior of synthetic tailings suspensions composed of kaolinite and quartz in industrial water at pH 11. Settling tests, focused beam reflectance measurement (FBRM), zeta potential measurement, and rheological characterization were used to analyze the system’s performance under different coagulant dosages (0–150 g/t), while keeping the flocculant dosage constant (20 g/t). The results indicated that the coagulant favored surface charge neutralization, shifting the zeta potential from −13.2 mV to +4.0 mV. This resulted in larger, more efficient flocs capturing fines, with a 46% turbidity reduction. FBRM analysis revealed a significant increase in aggregate size and a slight decrease in fractal dimension (from 2.35 to 2.20), consistent with larger volume structures and lower bulk density. Rheologically, a substantial increase in yield stress was observed, especially in 50 wt% suspensions, suggesting the development of a continuous flocculated network with greater mechanical strength. These findings highlight the importance of sequential chemical conditioning in clayey tailings and its impact on clarification efficiency and water recovery under alkaline conditions representative of industrial mining processes. Full article
(This article belongs to the Section Mineral Processing and Extractive Metallurgy)
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33 pages, 3134 KiB  
Article
Physical–Statistical Characterization of PM10 and PM2.5 Concentrations and Atmospheric Transport Events in the Azores During 2024
by Maria Gabriela Meirelles and Helena Cristina Vasconcelos
Earth 2025, 6(2), 54; https://doi.org/10.3390/earth6020054 - 6 Jun 2025
Viewed by 1046
Abstract
This study presented a comprehensive physical–statistical analysis of atmospheric particulate matter (PM10 and PM2.5) and trace gases (SO2 and O3) over Faial Island in the Azores archipelago during 2024. We collected real-time data at the Espalhafatos rural [...] Read more.
This study presented a comprehensive physical–statistical analysis of atmospheric particulate matter (PM10 and PM2.5) and trace gases (SO2 and O3) over Faial Island in the Azores archipelago during 2024. We collected real-time data at the Espalhafatos rural background station, covering 35,137 observations per pollutant, with 15 min intervals. Descriptive statistics, probability distribution fitting (Normal, Lognormal, Weibull, Gamma), and correlation analyses were employed to characterize pollutant dynamics and identify extreme pollution episodes. The results revealed that PM2.5 (fine particles) concentrations are best modeled by a Lognormal distribution, while PM10 concentrations fit a Gamma distribution, highlighting the presence of heavy-tailed, positively skewed behavior in both cases. Seasonal and episodic variability was significant, with multiple Saharan dust transport events contributing to PM exceedances, particularly during winter and spring months. These events, confirmed by CAMS and SKIRON dust dispersion models, affected not only southern Europe but also the Northeast Atlantic, including the Azores region. Weak to moderate correlations were observed between PM concentrations and meteorological variables, indicating complex interactions influenced by atmospheric stability and long-range transport processes. Linear regression analyses between SO2 and O3, and between SO2 and PM2.5, showed statistically significant but low-explanatory relationships, suggesting that other meteorological and chemical factors play a dominant role. This result highlights the importance of developing air quality policies that address both local emissions and long-range transport phenomena. They support the implementation of early warning systems and health risk assessments based on probabilistic modeling of particulate matter concentrations, even in remote Atlantic locations such as the Azores. Full article
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27 pages, 1769 KiB  
Article
Satellite Image Price Prediction Based on Machine Learning
by Linhan Yang, Zugang Chen and Guoqing Li
Remote Sens. 2025, 17(12), 1960; https://doi.org/10.3390/rs17121960 - 6 Jun 2025
Viewed by 882
Abstract
This study develops a comprehensive, data-driven framework for predicting satellite imagery prices using four state-of-the-art ensemble learning algorithms: XGBoost, LightGBM, AdaBoost, and CatBoost. Two distinct datasets—optical and Synthetic Aperture Radar (SAR) imagery—were assembled, each characterized by nine technical and economic features (e.g., imaging [...] Read more.
This study develops a comprehensive, data-driven framework for predicting satellite imagery prices using four state-of-the-art ensemble learning algorithms: XGBoost, LightGBM, AdaBoost, and CatBoost. Two distinct datasets—optical and Synthetic Aperture Radar (SAR) imagery—were assembled, each characterized by nine technical and economic features (e.g., imaging mode, spatial resolution, satellite manufacturing cost, and acquisition timeliness). Bayesian optimization is employed to systematically tune hyperparameters, thereby minimizing overfitting and maximizing generalization. Models are evaluated on held-out test sets (20% of data) using Pearson’s correlation coefficient (R), mean bias error (MBE), root mean square error (RMSE), unbiased RMSE (ubRMSE), Nash–Sutcliffe Efficiency (NSE), and Kling–Gupta Efficiency (KGE). For optical imagery, the Bayesian-optimized XGBoost model achieves the best performance (R=0.9870, RMSE=$3.44/km2, NSE=0.9651, KGE=0.8950), followed closely by CatBoost (R=0.9826, RMSE=$3.83/km2). For SAR imagery, CatBoost outperforms all others after optimization (R=0.9278, RMSE=$9.94/km2, NSE=0.8575, KGE=0.8443), reflecting its robustness to heavy-tailed price distributions. AdaBoost also demonstrates competitive accuracy, while LightGBM and XGBoost exhibit larger errors in high-value regimes. SHapley Additive exPlanations (SHAP) analysis reveals that imaging mode and spatial resolution are the primary drivers of price variance across both domains, followed by satellite manufacturing cost and acquisition recency. These insights demonstrate how ensemble models capture nonlinear, high-dimensional interactions that traditional rule-based pricing schemes overlook. Compared to static, experience-driven price brackets, our machine learning approach provides a scalable, transparent, and economically rational pricing engine—adaptable to rapidly changing market conditions and capable of supporting fine-grained, application-specific pricing strategies. Full article
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28 pages, 4962 KiB  
Article
YOLO-Ssboat: Super-Small Ship Detection Network for Large-Scale Aerial and Remote Sensing Scenes
by Yiliang Zeng, Xiuhong Wang, Jinlin Zou and Hongtao Wu
Remote Sens. 2025, 17(11), 1948; https://doi.org/10.3390/rs17111948 - 4 Jun 2025
Viewed by 768
Abstract
Enhancing the detection capabilities of marine vessels is crucial for maritime security and intelligence acquisition. However, accurately identifying small ships in complex oceanic environments remains a significant challenge, as these targets are frequently obscured by ocean waves and other disturbances, compromising recognition accuracy [...] Read more.
Enhancing the detection capabilities of marine vessels is crucial for maritime security and intelligence acquisition. However, accurately identifying small ships in complex oceanic environments remains a significant challenge, as these targets are frequently obscured by ocean waves and other disturbances, compromising recognition accuracy and stability. To address this issue, we propose YOLO-ssboat, a novel small-target ship recognition algorithm based on the YOLOv8 framework. YOLO-ssboat integrates the C2f_DCNv3 module to extract fine-grained features of small vessels while mitigating background interference and preserving critical target details. Additionally, it employs a high-resolution feature layer and incorporates a Multi-Scale Weighted Pyramid Network (MSWPN) to enhance feature diversity. The algorithm further leverages an improved multi-attention detection head, Dyhead_v3, to refine the representation of small-target features. To tackle the challenge of wake waves from moving ships obscuring small targets, we introduce a gradient flow mechanism that improves detection efficiency under dynamic conditions. The Tail Wave Detection Method synergistically integrates gradient computation with target detection techniques. Furthermore, adversarial training enhances the network’s robustness and ensures greater stability. Experimental evaluations on the Ship_detection and Vessel datasets demonstrate that YOLO-ssboat outperforms state-of-the-art detection algorithms in both accuracy and stability. Notably, the gradient flow mechanism enriches target feature extraction for moving vessels, thereby improving detection accuracy in wake-disturbed scenarios, while adversarial training further fortifies model resilience. These advancements offer significant implications for the long-range monitoring and detection of maritime vessels, contributing to enhanced situational awareness in expansive oceanic environments. Full article
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27 pages, 5226 KiB  
Article
A Novel Pulsation Reflux Classifier Used for Enhanced Preconcentration Efficiency of Antimony Oxide Ore
by Dongfang Lu, Yuxin Zhang, Zhenqiang Liu, Xiayu Zheng, Yuhua Wang and Yifei Liu
Minerals 2025, 15(6), 605; https://doi.org/10.3390/min15060605 - 4 Jun 2025
Cited by 1 | Viewed by 484
Abstract
This study developed a novel pulsation-fluidized bed system, and the device was integrated into a reflux classifier to enhance the preconcentration of antimony oxide ore. The diaphragm-based pulsation device converts a stable upward water flow into a vertically alternating pulsation flow. By precisely [...] Read more.
This study developed a novel pulsation-fluidized bed system, and the device was integrated into a reflux classifier to enhance the preconcentration of antimony oxide ore. The diaphragm-based pulsation device converts a stable upward water flow into a vertically alternating pulsation flow. By precisely controlling the pulsation parameters and optimizing operational conditions, the density-based stratification of particles can be significantly enhanced, thereby improving bed layering and effectively reducing entrainment. An antimony oxide ore from flotation tailings with an Sb grade of 0.8% was used as the feed material to evaluate the performance of the pulsation reflux classifier (PRC). Under optimized conditions, the PRC produced a concentrate with an Sb grade of 5.48% and a recovery of 81.68%, corresponding to a high separation efficiency of 70.97%. The response surface statistical model revealed that the interaction between the fluidization rate and pulsation frequency significantly enhanced the Sb grade of the concentrate, while pulsation stroke was identified as the key factor influencing separation efficiency. Furthermore, the variation in bed profile parameters with changing pulsation characteristics elucidates the interplay between particle suspension, stratification, and fluid disturbances. This study demonstrates that pulsation fluidization significantly enhances the separation performance of the reflux classifier, offering a new approach for the efficient preconcentration of complex fine-grained minerals. Full article
(This article belongs to the Section Mineral Processing and Extractive Metallurgy)
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