Search Results (3,186)

Thorium has emerged as a promising alternative to uranium in nuclear energy systems due to its higher natural abundance, favorable conversion to fissile ²³³U, and reduced generation of long-lived transuranic waste. This review provides a comprehensive overview of advanced techniques for thorium recovery from primary ores and secondary resources. The main mineralogical carriers—including monazite, thorianite, thorite, and cheralite as well as industrial by-products such as rare-earth processing tailings—are critically examined with respect to their occurrence and processing potential. Physical enrichment methods (gravity, magnetic, and electrostatic separation) and hydrometallurgical approaches (acidic and alkaline leaching) are analyzed in detail, highlighting their efficiencies, limitations, and environmental implications. Particular emphasis is placed on modern separation strategies such as solvent extraction with organophosphorus reagents, diglycolamides, and ionic liquids, as well as extraction chromatography, nanocomposite sorbents, ion-imprinted polymers, and electrosorption on carbon-based electrodes. These techniques demonstrate significant progress in enhancing selectivity, reducing reagent consumption, and enabling recovery from low-grade and secondary feedstocks. Environmental and radiological aspects, including waste minimization, immobilization, and regulatory frameworks, are discussed as integral components of sustainable thorium management. Finally, perspectives on hybrid technologies, digital process optimization, and economic feasibility are outlined, underscoring the need for interdisciplinary approaches that combine chemistry, materials science, and environmental engineering. Collectively, the analysis highlights the transition from conventional practices to integrated, scalable, and environmentally responsible technologies for thorium recovery. Full article

Background/Objectives: Medical nutrition therapy (MNT) is a crucial component of type 1 diabetes (T1D) management; however, the effectiveness of specific dietary approaches in adults remains unclear due to variations in study design, terminology, and reported outcomes. This scoping review summarizes evidence published between 2015 and 2025 on dietary interventions in adults with T1D, focusing on metabolic and psychosocial outcomes and adherence to international nutritional guidelines. Methods: We searched PubMed, Web of Science, Scopus, and Google Scholar, following the PRISMA-ScR recommendations, to identify observational studies, randomized clinical trials (RCTs), and guidelines involving adults (≥18 years) with T1D. Extracted data included metabolic outcomes (glycated hemoglobin A1c (HbA1c), glycemic variability (GV), insulin dose (ID), lipids, blood pressure, body weight, and others), as well as psychosocial indicators (i.e., quality of life, diabetes-related stress, and fear of hypoglycemia). Results: In total, 41 studies met the inclusion criteria, comprising 18 observational, 14 randomized, and 9 studies that evaluated psychosocial aspects. A low-carbohydrate diet (LCD) reduced HbA1c by 0.3–0.9% and total ID by approximately 15–20% without increasing the incidence of severe hypoglycemia. A low-fat vegan diet and structured carbohydrate counting (CC) programs also improved glycemic and lipid profiles. The Mediterranean diet (MedDiet) and plant-based diet mainly improved diet quality and well-being. The results showed an association between better metabolic control and lower carbohydrate (CHO) intake, as well as higher intakes of fiber and protein. In contrast, a Western diet and high intake of sweets were linked to poorer outcomes. Conclusions: Combining an LCD with education, CC, and modern diabetes technology provides the most consistent benefits for adults with type 1 diabetes (T1D adults). The MedDiet and plant-based diet support diet quality and psychosocial well-being, although current evidence remains limited, primarily due to small sample sizes and short follow-up periods. Full article

In recent decades, the consumption of animal proteins has been rethought by consumers. Factors such as improved health and sustainability are key aspects of this scenario. Studies have sought innovative and sustainable technologies to improve protein extraction from alternative sources to increase their competitiveness. In this sense, the aim of this work was to combine the effects of nonconventional extraction methods on the process yield and the resulting techno-functional properties extracted from alternative proteins. The literature contains significant publications regarding the use of ultrasound (US), pulsed electric fields (PEFs), microwaves (MWs) and deep eutectic solvents (DESs) for enhancing protein extraction. Re-emerged techniques such as reverse micelles and aqueous two-phase extraction have also been reported. For this reason, the present study aimed not only to present the obtained results but also to discuss how the mechanisms associated with the aforementioned technologies impact the extraction yield and modification of proteins. In general, US tends to increase protein solubility (20–30%) and emulsifying capacity (35%); MWs can increase protein yield (25%) while reducing extraction time (50–70%); DES-based extraction tends to retain more than ~40% of the native functionality, and PEFs have demonstrated up to a 20% improvement in protein recovery. Nonconventional extraction methods have varying effects on the characteristics and quality of extracted proteins, offering benefits and challenges that should be considered when choosing the most suitable technology. The specificity related to each technology can be used to make possible interesting industrial applications involving nonanimal proteins. Full article

Understanding visitor sentiment is essential for developing effective tourism strategies, particularly as Google Maps reviews have become a key channel for public feedback on tourist attractions. Yet, the unstructured format and dialectal diversity of Arabic reviews pose significant challenges for extracting actionable insights at scale. This study evaluates the performance of traditional machine learning and transformer-based models for aspect-based sentiment analysis (ABSA) on Arabic Google Maps reviews of tourist sites across Saudi Arabia. A manually annotated dataset of more than 3500 reviews was constructed to assess model effectiveness across six tourism-related aspects: price, cleanliness, facilities, service, environment, and overall experience. Experimental results demonstrate that multi-head BERT architectures, particularly AraBERT, consistently outperform traditional classifiers in identifying aspect-level sentiment. Ara-BERT achieved an F1-score of 0.97 for the cleanliness aspect, compared with 0.91 for the best-performing classical model (LinearSVC), indicating a substantial improvement. The proposed ABSA framework facilitates automated, fine-grained analysis of visitor perceptions, enabling data-driven decision-making for tourism authorities and contributing to the strategic objectives of Saudi Vision 20300. Full article

Carrot pomace is the solid residue left after juice extraction from carrots. Carrot pomace, typically seen as waste, is gaining recognition for its sustainability and potential to mitigate food waste while offering essential nutrients (phenolics, carotenoids, and β-carotene), which are recognized for their nutraceutical effects and health benefits. A study was conducted to develop a process for creating an innovative product, specifically a carp roe salad with added value, by incorporating carrot pomace. The innovative aspect is represented by using different proportions of carrot powder, 6% and 12%, when creating new varieties of roe salad. The study assesses the impact of carrot pomace powder on the salad’s antioxidant content, physicochemical properties, color, texture, rheological characteristics, and sensory qualities. The value-added products thus obtained are differentiated by superior phytochemical and nutritional characteristics, especially levels of carotenoids (84.01 ± 3.39–111.01 ± 1.68 mg/100 g DW), and the antioxidant activity (550.66 ± 9.25–588.32 ± 9.41 μM TE/g DW) of the developed salad. The obtained products displayed an improved color and texture profile. The sensory evaluation reveals that the carp roe salad with 12% carrot powder was favorably received by consumers, who valued the nuanced changes in flavor and the improved coloration of the product. Rich in antioxidants, fibers, and natural colorants, carrot pomace enhances the product’s value by increasing antioxidant activity and positively influencing sensory properties such as color and aroma. This research highlights the potential of using food by-products to create innovative, value-added products with improved health benefits. Full article

Background: Microsurgical skill acquisition and development are complex processes, due to the often complex learning curve, limited training possibilities, and growing restrictions on working hours. Simulation-based training programmes, employing various models, have been proposed. Nevertheless, the extent to which these training programmes are supported by scientific evidence is unclear. The aim of this systematic review is to evaluate the extent and quality of the scientific evidence backing validated microsurgical training programmes. Methods: A systematic literature review was conducted, following a study protocol established a priori and in accordance with the PRISMA guidelines. The databases searched were the Web of Science Core Collection (Web of Knowledge), Medline (Ovid), Embase (Embase.com), and ERIC (Ovid). Studies were included if they described microsurgical training programmes and presented a form of validation of training effectiveness. Data extraction included the number of participants, training duration and frequency, validation type, assessment methods, outcomes, study limitations, and a detailed training regimen. The risk of bias and quality were assessed using the Medical Education Research Study Quality Instrument (MERSQI). Validity was assessed using an established validity framework (content, face, construct, and criterion encompassing both concurrent and predictive validity). The Level of Evidence (LoE) and Recommendation (LoR) were evaluated using the Oxford Centre for Evidence-Based Medicine (OCEBM). Results: A total of 25 studies met the inclusion criteria. Training programmes were classified into one-time intensive courses or longitudinal curricula. Face, content, and construct validity were the most commonly assessed aspects, while predictive validity was the least frequently assessed and not properly evaluated. Training models ranged from low-fidelity (silicone tubes, synthetic vessels) to high-fidelity (live animal models). The Global Rating Scale (GRS), the Structured Assessment of Microsurgery Skills (SAMS), and the Objective Structured Assessment of Technical Skills (OSATS) were the most frequently used objective assessment tools for evaluation methods within the programmes. The risk of bias MERSQI score was 12.96, ranging from 10.5 to 15.5, and LoE and LoR scores were moderated. Across the studies, 96% reported significant improvement in microsurgical skills among participants. However, most studies were limited by small sample sizes, heterogeneity in baseline skills, and a lack of long-term follow-up. Conclusions: While validated microsurgical training programmes improve skill acquisition, challenges remain in terms of standardisation and best cost-effective methods. Future research should prioritise evaluating predictive validity, creating standardised objective assessment tools, and focus on skill maintenance. Full article

Traditional deep learning methods for histopathological analysis suffer from a lack of interpretability, which limits their use in the clinic despite their high accuracy. This paper proposes a Topological Data Analysis (TDA) framework for interpretable colorectal cancer tissue classification. We used persistent homology to extract topological features from 5000 histological images representing eight tissue classes, combining persistence landscapes with Support Vector Machine (SVM) classification. This method achieved an overall accuracy rate of 82.70%, while providing biologically interpretable features that are directly related to tissue morphology. Topological features successfully represented cellular connectivity as well as structural patterns, enabling perfect classification of morphologically distinct tissue pairs. This research demonstrates that topological data analysis (TDA) represents a promising alternative to non-transparent methods, offering competitive efficiency while ensuring interpretability, a crucial aspect for its clinical integration in computational pathology. Full article

Due to computational constraints, ocean numerical models are often executed on low-resolution (LR) grids. To maintain consistency between LR simulations and coarsened high-resolution (HR) solutions, a subgrid forcing term is commonly integrated into the LR model as a parameterization scheme. Although numerous data-driven parameterizations have been developed to establish the relationship between resolved LR variables and corresponding subgrid forcing, the accurate extraction of target subgrid forcing remains an open challenge that significantly impacts the performance of such parameterizations. Small-scale dissipation (ssd) operators are widely used to enhance numerical stability while introducing minimal energy dissipation; however, this study demonstrates that these operators critically influence the accurate representation of subgrid forcing: an aspect that has not been adequately addressed. Within a quasi-geostrophic ocean modeling framework, new formulations have been rigorously derived for subgrid forcing that explicitly accounts for ssd effects. Numerical experiments confirm that the proposed forcing enables LR simulations to reproduce coarsened HR results with high fidelity. This work demonstrates that greater attention to the specific numerical discretization scheme is required for the accurate extraction of subgrid forcing from HR simulations. Although these newly developed extraction algorithms are diagnostic in nature, they could provide accurate target data that facilitate the subsequent development of data-driven parameterization schemes. Full article

Earthquakes are the result of complex interactions between tectonic plates, the mantle, and the lithosphere. Complex geodynamic conditions contribute to the occurrence of seismic phenomena. Tectonic plates can collide, move apart, or slide past each other. Mantle convection by internal heat drives plate motions that deform the lithosphere. Rocks deform elastically as stress accumulates and pore fluid pressure changes. Rupture occurs when stress exceeds frictional resistance. The connection between variations in gravity and the magnitude of earthquakes remains unclear. This work aims to examine aspects of this correlation. Three sets of earthquakes, one with events from all over the world, one from broader Greece, and one from the Hellenic Trench in Greece, aiming to cover all cases of geodynamics, from very different to very similar, were employed. Time series of gravity measurements at earthquake epicenters were extracted from GRACE satellite data. Time derivatives of the gravity field, as well as magnitude-dependent variations—reflecting changes relative to earthquake strength—were computed. Multiple linear regression (MLR), partial least squares (PLS) regression, and neural networks (NN) were used to model the relationship between gravity or its derivatives and earthquake magnitude. A correlation between the earthquake magnitude and magnitude derivatives was found. By using the global and Greek datasets, the best accuracy was obtained with MLR, reporting a mean squared error (MSE) of 0.069 with an R² of 0.979, and MSE was 0.011 with R² score of 0.997, respectively. By using the Hellenic Trench set, PLS regression derived the best correlation results, reporting an MSE of 0.004 and an R² of 0.977. Experimental results suggest that gravity, and therefore crustal density, is related to the magnitude of the impending earthquake, but not to its timing. Full article

Vitamin D deficiency is highly prevalent in Pakistan, but there is limited data on its genetic aspects. This case–control pilot study aimed to determine the association of rs782153744, rs200183599, rs118204011, and rs28934604 with vitamin D deficiency along rs7041 which has been studied in our population. The DNA of a total of 600 subjects (300 cases and 300 controls) was extracted and genotyped by tetra ARMS PCR, followed by Sanger DNA sequencing of exon 4 of the CYP2R1 and CYP27B1 genes and exon 8 of the GC gene. SNP Stat was employed to analyze the data, while logistic regression was used to calculate the p-values and odds ratios (ORs). The R package version R studio (2025.05.1) Build 513 was used to statistically analyze rs782153744. In silico modeling of wild and mutant CYP2R1 and GC proteins was performed in Swiss-Model, Swiss-Dock, Discovery Studio, and PyMol using 3c6g and IJ78 as templates to perform binding pocket analysis of vitamin D3. The rs782153744 showed a protective association in the additive (OR: 0.15, 95% CI: 0.08–0.27, p-value < 0.001), recessive (OR: 0.19, 95% CI: 0.10–0.33, p-value < 0.001), and dominant (OR: 0.19, CI = 0.10–0.33, p-value < 0.001) models, while GC rs7041 (T > A, T > G) displayed a p-value < 0.0001 across all genetic models. Sanger sequencing yielded insignificant results, and the SNPs rs200183599, rs118204011, and rs28934604 had no significant association with vitamin D deficiency. The molecular pocket analysis of wild and mutant CYP2R1 proteins carrying rs782153744 polymorphisms revealed no changes. GC proteins carrying the rs7041 polymorphism revealed a shift in their 3D and 2D configuration, as well as a change in the amino acid residue of the binding pocket of VD3. The risk-associated rs7041 and protective rs782153744 variants back genetic screening for vitamin D deficiency risk stratification, allowing targeted supplementation in predisposed subjects and assisting in formulating a genotype-specific therapeutic approach. Full article

Valorization of Phoenix dactylifera L. (date) seeds, an abundant agro-industrial byproduct, offer a sustainable approach to developing multifunctional ingredients for dermocosmetic photoprotection. Rich in polyphenols, flavonoids, and lipophilic antioxidants, date seed extracts and oils demonstrate promising UV-absorbing, anti-inflammatory, and free-radical-scavenging properties. Recent in vitro, ex vivo, and preclinical studies underscore their potential as bioactive agents in sunscreen formulations, supporting both skin barrier integrity and oxidative stress mitigation, although clinical validation is still required. This review consolidates current knowledge on the phytochemical profile and biological efficacy of date seed derivatives, with emphasis on their integration into advanced delivery systems such as nanocarriers, Pickering emulsions, and cyclodextrin complexes to enhance photostability, skin permeability, and esthetic acceptability. Safety aspects, including allergenicity, phototoxicity, and regulatory gaps, are critically examined alongside environmental and ethical advantages, including biodegradability and vegan suitability. The findings advocate for the inclusion of Phoenix dactylifera L. seed actives in next-generation dermocosmetic sunscreens that align with circular bioeconomy principles, consumer demand for “reef-safe” products, and evolving international regulations. Further clinical validation is encouraged to fully translate these botanically derived agents into effective and ethically sound sun care innovations. Full article

The prediction of Remaining Useful Life (RUL) constitutes a vital aspect of Prognostics and Health Management (PHM), providing capabilities for the assessment of mechanical component health status and prediction of failure instances. Recent studies on feature extraction, time-series modeling, and multi-task learning have shown remarkable advancements. However, most deep learning (DL) techniques predominantly focus on unimodal data or static feature extraction techniques, resulting in a lack of RUL prediction methods that can effectively capture the individual differences among heterogeneous sensors and failure modes under complex operational conditions. To overcome these limitations, an adaptive RUL prediction framework named ADAPT-RULNet is proposed for mechanical components, integrating the feature extraction capabilities of attention-enhanced deep learning (DL) and the decision-making abilities of deep reinforcement learning (DRL) to achieve end-to-end optimization from raw data to accurate RUL prediction. Initially, Functional Alignment Resampling (FAR) is employed to generate high-quality functional signals; then, attention-enhanced Dynamic Time Warping (DTW) is leveraged to obtain individual degradation stages. Subsequently, an attention-enhanced of hybrid multi-scale RUL prediction network is constructed to extract both local and global features from multi-format data. Furthermore, the network achieves optimal feature representation by adaptively fusing multi-source features through Bayesian methods. Finally, we innovatively introduce a Deep Deterministic Policy Gradient (DDPG) strategy from DRL to adaptively optimize key parameters in the construction of individual degradation stages and achieve a global balance between model complexity and prediction accuracy. The proposed model was evaluated on aircraft engines and railway freight car wheels. The results indicate that it achieves a lower average Root Mean Square Error (RMSE) and higher accuracy in comparison with current approaches. Moreover, the method shows strong potential for improving prediction accuracy and robustness in varied industrial applications. Full article

Time-domain signal models have been widely applied to single-channel music source separation tasks due to their ability to overcome the limitations of fixed spectral representations and phase information loss. However, the high acoustic similarity and synchronous temporal evolution between vocals and accompaniment make accurate separation challenging for existing time-domain models. These challenges are mainly reflected in two aspects: (1) the lack of a dynamic mechanism to evaluate the contribution of each source during feature fusion, and (2) difficulty in capturing fine-grained temporal details, often resulting in local artifacts in the output. To address these issues, we propose an attention-driven time-domain convolutional network for vocal and accompaniment source separation. Specifically, we design an embedding attention module to perform adaptive source weighting, enabling the network to emphasize components more relevant to the target mask during training. In addition, an efficient convolutional block attention module is developed to enhance local feature extraction. This module integrates an efficient channel attention mechanism based on one-dimensional convolution while preserving spatial attention, thereby improving the ability to learn discriminative features from the target audio. Comprehensive evaluations on public music datasets demonstrate the effectiveness of the proposed model and its significant improvements over existing approaches. Full article

Chitin, one of the most abundant natural polysaccharides, has gained increasing attention for its structural diversity and potential in biomedicine, agriculture, food packaging, and advanced materials. Conventional chitin production from crustacean shell waste faces limitations, including seasonal availability, allergenic protein contamination, heavy metal residues, and environmentally harmful demineralization processes. Chitin from fungi and microalgae provides a sustainable and chemically versatile alternative. Fungal chitin, generally present in the α-polymorph, is embedded in a chitin–glucan–protein matrix that ensures high crystallinity, mechanical stability, and compatibility for biomedical applications. Microalgal β-chitin, particularly from diatoms, is secreted as high-aspect-ratio microrods and nanofibrils with parallel chain packing, providing enhanced reactivity and structural integrity that are highly attractive for functional materials. Recent progress in green extraction technologies, including enzymatic treatments, ionic liquids, and deep eutectic solvents, enables the recovery of chitin with reduced environmental burden while preserving its native morphology. By integrating sustainable sources with environmentally friendly processing methods, fungal and microalgal chitin offer unique structural polymorphs and tunable properties, positioning them as a promising alternative to crustacean-derived chitin. Full article

User preference mining uses rating data, item content or comments to learn additional knowledge to support the prediction task. For the use of rating data, the usual approach is to take rating matrix as data source, and collaborative filtering as the algorithm to predict user preferences. Item content and comments are usually used in sentiment analysis or as auxiliary information for other algorithms. However, factors such as data sparsity, category diversity, and numerical processing requirements for aspect sentiment analysis affect model performance. This paper proposes a hybrid method, which uses the deep neural network as the basic structure, considers the complementarity of text and numeric data, and integrates the numeric and text embedding into the model. In the construction of text-based embedding, extracts the text summary of each text-based review, and uses the Doc2vec to convert the text summary into multi-dimensional vector. Experiments on two Amazon product datasets show that the proposed model consistently outperforms other baseline models, achieving an average reduction of 15.72% in RMSE, 24.13% in MAE, and 28.91% in MSE. These results confirm the effectiveness of our proposed method for learning user preferences. Full article