Search Results (193,628)

Forecasting fine particulate matter with a diameter of 2.5 μm (PM_2.5) is critically important due to its adverse effects on human health and environmental sustainability. Although ground-based monitoring stations provide accurate measurements, their limited spatial coverage restricts large-scale PM_2.5 assessment, especially in complex urban regions. Consequently, aerosol optical depth (AOD) derived from satellite imagery, combined with advanced deep learning (DL) techniques, has emerged as an effective alternative by offering wide spatial coverage and rich spatio-temporal information. This paper proposed an optimization-driven fuzzy transformer–deep belief network (ODFT-DBN) for accurate PM_2.5 air pollution prediction. The proposed framework integrates a fuzzy inference module to model uncertainty and nonlinear environmental relationships, a transformer encoder to capture long-range spatio-temporal dependencies, and a DBN to extract hierarchical features and improve prediction robustness. In addition, a novel multi-objective gray wolf optimizer (NMOGWO) is employed to jointly optimize the model hyper-parameters and fuzzy membership functions. The proposed approach is implemented for the city of Tehran, Iran, using meteorological variables, topographical features, ground-based PM_2.5 measurements, and satellite-derived AOD data. The ODFT-DBN model is compared with several benchmark methods, including bidirectional encoder representations from transformers (BERT), transformer, long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), DBN, and extreme gradient boosting (XGBoost). Experimental results demonstrate that the proposed framework achieves superior predictive performance, attaining an R² value of 0.94 and root mean square error (RMSE) of 0.8 μg/m³. Scatter plot analyses indicate a strong agreement between predicted and observed PM_2.5 values, while the proposed model exhibits low variance, stable convergence behavior, and acceptable computational time. Overall, the results confirm the effectiveness, robustness, and practical applicability of the proposed ODFT-DBN framework for spatio-temporal PM_2.5 forecasting. Full article

Compact wireless electronic devices require charging interfaces that can support different receiver positions and orientations within limited spaces. In this context, a double-sided mixed-coupling structure can provide independent magnetic- and electric-field power-transfer paths by combining coil-based and plate-based coupling mechanisms. This paper proposes a double-sided mixed-coupling wireless power transfer (DMPT) coupler for compact wireless electronic devices related to the Internet of Things (IoT) and the Internet of Drones (IoD). The proposed coupler integrates an upper coil-based magnetic-field coupling path and a lower stacked-plate-based electric-field coupling path within a single transmitter structure. Through this configuration, inductive wireless power transfer (IPT) and capacitive wireless power transfer (CPT) are implemented as independent double-sided power-transfer paths. To analyze the resonant behavior, a three-port equivalent circuit including mutual inductance and mutual capacitance is developed, and the resonance splitting under the uncompensated condition is investigated using even/odd mode decomposition. The predicted resonant frequencies agree with the ANSYS HFSS results with errors of 0.16% and 1.12%. After series-L compensation, the 60 × 60 × 7.31 mm³ coupler operates at the 6.78 MHz industrial, scientific, and medical band, showing S₁₁ ≈ 0.042, S₂₁ ≈ 0.68, and S₃₁ ≈ 0.64 under the double-sided aligned condition. Field and transient waveform analyses further verify that the upper H-coupling region and lower E-coupling region operate simultaneously while being spatially separated. The proposed DMPT coupler provides a coupler-level design framework for implementing IPT and CPT as independent double-sided coupling paths. Full article

In this study, five Fe-rich scoria-based glass compositions were prepared using natural scoria, recycled glass cullet, limestone, and magnesite through the melt-quenching technique at a temperature of 1400 °C for 2 h. The effect of Fe₂O₃ content (2.9–14.5 wt%) on the thermal behavior, crystallization, density, and predicted optical properties of glass was investigated. Differential thermal analysis revealed that increasing Fe₂O₃ content leads to a variation in glass transition (Tg = 632–669 °C) and an increase in softening temperatures (Ts = 711–737 °C), accompanied by an expanded thermal stability window (∆T = Tx − Tg) up to 254 °C, indicating enhanced resistance to crystallization and improved thermal stability. The density measurement showed a non-monotonic variation with composition, due to the combined effect of Fe₂O₃ enrichment and network structural modification. The crystallization behavior of the Fe-rich scoria-based glass (H50) was further studied after heat treatment at 900 °C and at 950 °C using XRD and SEM analysis. The heated samples exhibited the formation of crystalline phases including diopside, gehlenite, wollastonite, maghemite, and anorthite. While SEM observation revealed progressive crystal growth and microstructural densification with increasing heat treatment temperature, indicating the transformation from glass to glass–ceramic. In addition, a semi-empirical optical assessment based on literature-derived models suggested increased absorptance from 97.26% to 98.83% and reduced reflectance with increasing Fe₂O₃ content. However, these optical parameters show theoretical estimates and require experimental validation. These findings demonstrate the potential of Fe-rich scoria-based glasses as thermally stable materials for high-temperature and energy-related applications while using natural and industrial waste sources. Full article

Whole-plant soybean (WPS) is a high-protein forage resource, but its natural ensiling is often unsatisfactory due to low water-soluble carbohydrate content and high buffering capacity. This study investigated the effects of exogenous melatonin (ME) at 0 (CK), 5 (ME1), 10 (ME2), and 20 (ME3) mg/kg fresh matter on fermentation quality, chemical composition, in vitro rumen fermentation, and bacterial community structure of WPS silage. ME2 and ME3 had lower pH values and higher lactic acid contents than CK, with both treatments achieving pH values below 4.2. Crude protein concentration increased from 15.42% in CK to 19.96% in ME3, while neutral detergent fiber was lower in all ME treatments, and acid detergent fiber was lower in ME2 and ME3 than in CK. At 36 h, no overall treatment effect was detected for cumulative gas production, whereas in vitro dry matter digestibility differed only between ME2 and ME3. 16S rRNA gene sequencing revealed that ME altered the bacterial community, with community-weighted rrn copy number elevated in ME2 and ME3. Random forest analysis identified Enterococcus as the genus with the highest importance for treatment classification, and functional predictions indicated higher predicted abundances of amino acid biosynthesis pathways in ME treatment groups. These results indicate that ME has potential as an additive for improving WPS silage fermentation, but practical dosage recommendations require further validation through aerobic stability, animal performance, economic, and safety assessments. Full article

Transmission towers are fundamental components of electric power networks. However, their structure, scale and background textures vary substantially across remote sensing images acquired from different geographic regions. These discrepancies often reduce the detection accuracy of a model trained in one region when it is applied to another region. This paper proposes an enhanced DINO-based framework for cross-domain transmission tower detection that incorporates three lightweight optimisation modules. First, a Query-level Objectness Gating (QOG) module adaptively reweights decoder queries by estimating per-query objectness scores, thereby suppressing background-dominated queries. Second, MPDIoU regression is used to improve the localisation accuracy of elongated transmission tower targets. Third, a Quality-aware Scoring Module (QSM) calibrates classification confidence using predicted localisation-quality logits, thereby reducing high-confidence false detections caused by poor box alignment. Experiments are conducted on two remote sensing image datasets from different geographic regions. Under the 10% target-domain annotation setting, the proposed method achieves a precision of 0.8947, a recall of 0.8199, an F1-score of 0.8556 and an mAP@0.5 of 0.8684, outperforming the original DINO baseline and mainstream detectors including YOLOv8, YOLOv9 and YOLOv11. The results demonstrate that the proposed framework provides an effective solution for robust cross-domain detection of slender transmission tower targets in remote sensing images. Full article

Background and Objectives: Eustachian tube (ET) dysfunction links adenoidal disease to persistent middle ear dysfunction in children, yet the independent contribution of ET orifice obstruction grade to audiological outcomes and health-related quality of life remains unquantified after adjustment for anatomical and inflammatory confounders. Because conventional anatomical grading (e.g., the Cassano classification) does not directly characterize the degree of ET orifice compromise, it may underestimate the functional threat to middle ear ventilation; this study is the first to quantify the independent predictive value of endoscopic ET obstruction grade. This study aimed to evaluate ET obstruction grade as an independent determinant of hearing thresholds, middle ear pressure, and quality-of-life impairment in children with chronic adenoiditis and otitis media with effusion. Materials and Methods: A retrospective cohort of 236 children (aged 3–12 years) was analyzed. ET orifice obstruction was graded endoscopically as none, partial, or complete. Primary outcomes included pure tone average (PTA), middle ear pressure (MEP), and OSA-18 total score. Multivariate linear and logistic regression models were fitted, adjusting for age, sex, Cassano grade, neutrophil-to-lymphocyte ratio (NLR), allergic status, and acute otitis media frequency. The modifying role of mucosal appearance (edematous versus fibrotic/remodeling) on quality-of-life outcomes was also assessed. Results: ET obstruction was absent in 42 (17.8%), partial in 114 (48.3%), and complete in 80 (33.9%) children. PTA increased progressively across groups (22.2 ± 5.5 to 36.2 ± 6.7 dB; p < 0.001), as did OSA-18 scores (44.9 ± 7.9 to 80.4 ± 10.3; p < 0.001). In adjusted analysis, each obstruction increment independently predicted a 5.57 dB PTA increase (95% CI 4.37–6.77; p < 0.001), a 14.89-point OSA-18 increase (95% CI 12.87–16.92; p < 0.001), and 5.12-fold higher odds of PTA > 30 dB (95% CI 2.84–9.24; p < 0.001). Persistent middle ear dysfunction at six months occurred in 7.1%, 26.3%, and 61.3% across obstruction grades. Among children with complete obstruction, fibrotic mucosa was associated with higher OSA-18 scores than edematous mucosa (82.3 vs. 76.8; p = 0.02). Conclusions: ET obstruction grade independently determines audiological and quality-of-life outcomes in pediatric chronic adenoiditis. Mucosal remodeling further amplifies quality-of-life burden in complete obstruction. These findings support routine ET endoscopic grading in pediatric otorhinolaryngology risk stratification. Full article

Background: Mutations in the KRAS oncogene occur in approximately 40% of colorectal cancers, predominantly within exon 2. Non-exon 2 mutations are less common and remain poorly characterized in terms of their clinical and biological significance. Systemic inflammatory markers are well-established prognostic indicators in colorectal cancer, yet whether their prognostic value differs across KRAS mutation subtypes has yet to be defined. We aimed to characterize and compare the clinicopathological and inflammatory profiles of patients with exon 2 versus non-exon 2 KRAS-mutated colorectal cancer and evaluate their prognostic implications. Methods: This retrospective cohort study analyzed 272 patients with microsatellite stable metastatic colorectal cancer with KRAS mutations, comprising 236 exon 2 and 36 non-exon 2 cases. Clinical, molecular, and laboratory data, including baseline systemic inflammatory markers, were extracted from electronic medical records. Survival outcomes and the prognostic impact of these variables were evaluated with Kaplan-Meier curves and univariable and multivariable Cox proportional hazards regression analyses. Results: Non-exon 2 mutations were significantly more frequent in female patients (64% vs. 45%, p = 0.048) and in left-sided primary tumors (83% vs. 64%, p = 0.035). Median overall survival was 45.7 months for the non-exon 2 group compared to 32.4 months for the exon 2 cohort; KRAS mutation subtype was not significantly associated with overall survival on univariable or multivariable analysis (univariable HR 1.36, 95% CI 0.85–2.16, p = 0.2; multivariable HR 1.376, 95% CI 0.794–2.383, p = 0.255). Systemic inflammation demonstrated distinct prognostic value, with elevated white blood cells, absolute neutrophil count, platelets, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and low albumin levels demonstrating association with worse overall survival in the exon 2 cohort. Conversely, only an elevated neutrophil-to-lymphocyte ratio predicted worse survival in the non-exon 2 group. Conclusions: KRAS exon 2 and non-exon 2 mutated metastatic colorectal cancers exhibit distinct clinical and inflammatory characteristics. Systemic inflammation exerts a significantly greater prognostic impact in exon 2 disease. As the therapeutic landscape for KRAS-mutated CRC continues to evolve, these findings hold promise for informing KRAS mutation-specific approaches to patient stratification and treatment planning. Full article

This study investigates the mechanical properties and uniaxial compression behavior of nano-TiO₂-modified coral concrete (NTCC). Twelve groups of specimens with different nano-TiO₂ contents were prepared and cured in freshwater, seawater, and oxalic acid environments. Cube compressive strength, splitting tensile strength, and uniaxial compression tests were conducted according to relevant standards. The results indicate that nano-TiO₂ significantly enhances the mechanical performance of coral concrete. The compressive and tensile strengths initially increased and then decreased with increasing nano-TiO₂ content, with the maximum strength improvement reaching approximately 22%. Furthermore, increasing the nano-TiO₂ dosage reduced the brittle failure characteristics of NTCC under compression. The curing environment had a significant influence on the performance of NTCC. Specimens cured in seawater exhibited superior early-age strength, whereas those cured in freshwater achieved the highest later-age strength. The stress–strain response of NTCC under uniaxial compression can be divided into three stages: the elastic stage, elastoplastic stage, and descending stage. Based on the experimental results, an empirical constitutive model was proposed for NTCC. The predicted stress–strain curves showed good agreement with the experimental results, demonstrating the applicability of the proposed model for describing the compressive behavior of NTCC. Full article

Replacing Ordinary Portland Cement (OPC) with high volumes of fly ash (FA) offers a practical approach to reducing the environmental impacts associated with cement manufacturing and landfill disposal. However, high FA replacement levels, particularly up to 60%, often lead to lower early-age strength. This study developed a green cement mortar containing 60% FA and waste bottle glass nanoparticles (WBGNPs). The WBGNPs were incorporated at replacement levels of 2%, 4%, 6%, 8%, and 10% by volume of the OPC–FA binder. The findings showed that the addition of 4–6% WBGNPs significantly promoted the formation of dense reaction gels and enhanced compressive strength by 12.5–39.1%. Similar performance trends were observed in both the engineering and microstructural properties. The combined incorporation of FA and WBGNPs also improved drying shrinkage performance by reducing capillary stresses during water evaporation and minimizing crack development within the cement matrix. Additionally, a proposed shrinkage prediction model was validated using experimental data and demonstrated good agreement, with an average prediction error of approximately 8%. Overall, the incorporation of WBGNPs provides an effective method for producing high-volume FA cement mortars with satisfactory engineering properties suitable for concrete applications in tropical environments. This approach further supports sustainability by reducing waste generation, lowering landfill demand, and minimizing environmental pollution. Full article

Global warming is driving large-scale shifts in the climatically suitable habitats of many species. However, climate-only species distribution assessments may overestimate the spatial availability of future suitable habitats when dynamic land-use change is not considered. To assess potential spatial overlaps between climate-driven habitat suitability shifts and human land-use patterns, this study focuses on Quercus L. as a widely distributed keystone forest taxon in China. The genus-level assessment was designed to identify broad-scale habitat–land-use conflict patterns under multiple climate pathways and territorial spatial planning scenarios, rather than to predict species-specific distribution responses. We developed a soft-coupled framework integrating the Maximum Entropy (MaxEnt) model and the Patch-generating Land-Use Simulation (PLUS) model, and applied the Habitat–Land-Use Conflict Index (HLCI) as a categorical spatial overlay framework to classify potential overlaps between projected suitable habitats and future land-use categories across 16 exploratory scenario combinations integrating Shared Socioeconomic Pathway (SSP)-based climate projections and land-use/land-cover (LULC) scenarios for the 2040s at the grid scale. The results indicate that: (1) climate warming may reshape Quercus habitat suitability, characterized by northward/westward expansion and southward contraction in some low-latitude regions; (2) future land-use patterns may reduce the spatial availability of projected suitable habitats by increasing their overlap with built-up land and cultivated land. Under high-emission scenarios, potential newly suitable habitats overlapped with built-up land by up to 5.90 × 10⁴ km² and with cultivated land by up to 36.42 × 10⁴ km²; and (3) the Ecological Protection scenario showed lower overlap with non-ecological land-use categories and a larger area of potentially realizable habitat expansion. This study provides a scenario-based spatial assessment of where future Quercus habitat suitability may overlap with human land-use patterns, offering broad-scale support for adaptive forest conservation and territorial spatial planning. Full article

Among hematological malignancies, lymphoma is associated with an increased incidence of venous thromboembolism (VTE) ranging between 4 and 12%. Although Khorana score was validated for stratifying VTE risk in cancer, its discrimination reliability in lymphoma is reduced by the lack of specific predictors. The aim of this systematic review was to summarize the evidence regarding predictors and available risk assessment models (RAMs) for VTE in patients with lymphoma. A systematic search was conducted on PubMed, Embase and Scopus in order to identify papers published until February 2026, which evaluated predictors and RAMs for VTE in patients diagnosed with lymphoma. Out of 592 evaluated papers, 44 met the inclusion criteria. The widely used Khorana score failed to appropriately identify patients with lymphoma at high risk for VTE, while the Thrombosis Lymphoma predictive score (ThroLy) showed modest improvement. Strong predictors for VTE were a poor performance status, older age, previous history of VTE, the use of central venous catheters, and bulky disease. However, the lack of external validation, the small sample size and bias due to confounding factors limit the generalizability of the results. Therefore, larger studies with external validation cohorts are needed to design lymphoma-specific RAMs and to identify predictors with high discrimination power. Full article

This review evaluates multi-omics applications in dairy research across nutrition, safety, and health. Through multi-omics integration, we reveal nutrient differences driven by species, rearing practices, and processing techniques, identify protein patterns and allergen profiles, and construct adulteration detection fingerprints and species-specific peptide markers, thereby improving the timeliness and accuracy of safety assessment. The coupling of metagenomics and metabolomics effectively predicts spoilage-related microbial risks, enabling better risk control. Furthermore, multi-omics approaches systematically elucidate the functional mechanisms of bioactive peptides (e.g., ACE-inhibitory peptides), clarify the prebiotic effects of functional oligosaccharides, and build interaction networks between dairy components and gut microbiota. The introduction of machine learning enables origin and shelf-life prediction, as well as the discovery of novel biomarkers, promoting personalized nutrition and precision fermentation strategies. However, the field is currently constrained by severe reproducibility issues arising from the absence of standardized operating procedures, excessive optimism regarding machine learning models that rarely generalize across laboratories or product matrices, and a persistent disconnect between laboratory-scale biomarker discovery and industrial implementation. Without rigorous cross-platform validation and openly shared multi-omics reference datasets, most published markers remain unfit for regulatory or industrial application. Future efforts should establish standardized workflows and expand the evidence base to drive the dairy industry toward safer, healthier, and more traceable directions. Full article

Objectives: To assess the feasibility and reproducibility of predicting antimicrobial resistance (AMR) in Escherichia coli from MALDI-TOF mass spectrometry data using a standardized, open-source machine learning (ML) workflow, we systematically compared four ML algorithms, evaluated the impact of culture conditions, extract storage, and spectral preprocessing on model performance, and validated results through nested cross-validation with statistical significance testing. Methods: A total of 282 clinical E. coli isolates were analyzed. Two MALDI-TOF MS datasets were generated from freshly cultured extracts (T₁) and recultured isolates one year later (T₃), yielding 4468 spectra. A third dataset from the T₁ extracts stored at −20 °C for one year (T₂) was evaluated for spectral stability but excluded from primary modeling likely due to storage-induced degradation. Protein spectra (m/z 2000–15,000) were preprocessed using an in-house developed MALDI-TOF preprocessing pipeline (MTPP) comprising variance stabilization, Savitzky–Golay smoothing, SNIP baseline correction, TIC normalization, LOWESS alignment, and MAD-based peak detection (SNR ≥ 3), yielding 121 m/z features. Four classifiers—Random Forest (RF), Logistic Regression, Support Vector Machine, and Gradient Boosting—were trained to predict resistance to 11 antibiotics using nested cross-validation: outer GroupShuffleSplit (5-fold, isolate-level) for evaluation and inner GroupKFold for recursive feature elimination (RFECV) and hyperparameter tuning (RandomizedSearchCV). Classification thresholds were optimized via the precision–recall curve. Model performance was assessed using AUROC, AUPRC, F1-score, Matthews Correlation Coefficient (MCC), and bootstrap 95% confidence intervals (1000 replicates). Pairwise model comparisons were tested with McNemar’s chi-squared test. Results: Among the 12 antibiotics included in the analysis (meropenem excluded for absence of resistance), resistance prevalence ranged from 1.1% (colistin) to 59.9% (amoxicillin). Colistin was subsequently also excluded from ML modeling due to insufficient resistant isolates (n = 3), leaving 11 antibiotics for prediction. The best predictive performance was observed for ciprofloxacin (AUROC 0.76 [95% CI 0.74–0.77]; F1 0.54; MCC 0.38) and ceftazidime (AUROC 0.68 [0.65–0.71]; F1 0.36; MCC 0.29), using 13 and 37 RFECV-selected features, respectively. Amoxicillin achieved the highest F1-score (0.76), driven by high recall (0.98) but modest AUROC (0.58). No meaningful predictive signal was detected for amikacin, cefepime, or tigecycline (AUROC ≤ 0.57, F1 ≤ 0.17), attributable to extreme class imbalance, and no robust multi-peak resistance signature was detected in this dataset. McNemar’s test confirmed that RF significantly outperformed Logistic Regression for all antibiotics (p < 0.01), while Gradient Boosting performed comparably to RF for ciprofloxacin (p = 0.17) and ceftazidime (p = 0.28). Frozen extracts (T₂) produced lower spectral similarity and were excluded from model training; the aligned T₁₊₃ dataset yielded the most stable performance across metrics. Conclusions: Machine learning analysis of MALDI-TOF spectra enables reproducible AMR prediction for selected antibiotics in E. coli, with ciprofloxacin and ceftazidime showing the strongest signal. Nested isolate-level cross-validation, multi-model comparison with statistical testing, and open-source code provide a transparent, reproducible foundation for integrating ML-assisted MALDI-TOF analysis into diagnostic AMR surveillance. Extract storage at −20 °C degrades spectral quality and should be avoided in ML training workflows. Full article

The rules of Bergmann and Rensch are among the macroecological patterns most frequently used to explain body size and sexual dimorphism variations throughout geographic gradients. While Bergmann’s rule predicts an increase in size in higher latitudes and cold regions, Rensch’s rule describes allometric patterns in the expression of sexual dimorphism between males and females. Widely distributed organisms whose growth relies on external resources, such as the hermit crabs that utilize gastropod shells, constitute particularly adequate systems to investigate these patterns. In this study, we evaluate the occurrences of these rules in populations of the hermit crab Clibanarius sclopetarius distributed along the Brazilian shore. The morphologic variation was analyzed through traditional and geometric morphometry, integrating information regarding shape and size as well as comparing males and females from different populations. Regression models and multivariate analyses were employed in order to test associations between morphologic variables, latitude, and microhabitat. Although morphologic variations between populations and sexes have been detected, the patterns observed did not consistently follow the macroecological rules when considering latitude alone. By contrast, differences between analyzed microhabitats, mangrove forests, and rocky shores have been associated with distinct ecological contexts, which influence the expression of body size and sexual dimorphism. Full article