Search Results (13,630)

Constipation is a prevalent gastrointestinal disorder that significantly impairs quality of life. While pharmacological agents such as loperamide are widely used to induce constipation in experimental models, there is increasing interest in natural alternatives for alleviating intestinal dysfunction. In this study, we investigated the laxative effects of soybean powder fermented by Bacillus subtilis DKU_09 in a loperamide-induced rat model of constipation. The probiotic strain was isolated from cheonggukjang, a traditional Korean fermented soybean paste, and its identity was confirmed through 16S rRNA sequencing. Fermented soybean powder was characterized morphologically via scanning electron microscopy and chemically via HPLC to assess its isoflavone content. Rats were administered loperamide (5 mg/kg) for four days to induce constipation and were then treated with fermented soybean powder at doses of 100, 200, or 300 mg/kg. No pharmacological laxatives (e.g., PEG) were used as a positive control; instead, values from the treatment groups were compared with those from the loperamide-only constipation group. Key outcomes of fecal output, water content, colonic fecal retention, and gastrointestinal transit ratio were measured. The fermented product significantly improved stool frequency and moisture content, reduced colonic fecal retention, and restored gastrointestinal transit in a dose-dependent manner. Notably, the 300 mg/kg group demonstrated nearly complete recovery of fecal parameters without affecting body weight. Statistical analysis was performed using one-way ANOVA followed by Tukey’s post hoc test. These findings suggest that Bacillus subtilis-fermented soybean powder exerts synergistic laxative effects through the combined action of probiotic viability and fermentation-enhanced bioactive compounds such as aglycone isoflavones. This study supports the potential use of fermented soybean-based nutraceuticals as a natural and safe intervention for constipation and gastrointestinal dysregulation. Full article

To address the shortcomings of poor convergence and the ease of falling into local optima when using the traditional gold rush optimization (GRO) algorithm to solve the complex scheduling problem of a combined cooling, heating, and power (CCHP) microgrid system, an optimal scheduling model for a microgrid based on the improved gold rush optimization (IGRO) algorithm is proposed. First, the Halton sequence is introduced to initialize the population, ensuring a uniform and diverse distribution of prospectors, which enhances the algorithm’s global exploration capability. Then, a dynamically adaptive weighting factor is applied during the gold mining phase, enabling the algorithm to adjust its strategy across different search stages by balancing global exploration and local exploitation, thereby improving the convergence efficiency of the algorithm. In addition, a weighted global optimal solution update strategy is employed during the cooperation phase, enhancing the algorithm’s global search capability while reducing the risk of falling into local optima by adjusting the balance of influence between the global best solution and local agents. Finally, a t-distribution mutation strategy is introduced to improve the algorithm’s local search capability and convergence speed. The IGRO algorithm is then applied to solve the microgrid scheduling problem, with the objective function incorporating power purchase and sale cost, fuel cost, maintenance cost, and environmental cost. The example results show that, compared with the GRO algorithm, the IGRO algorithm reduces the average total operating cost of the microgrid by 3.29%, and it achieves varying degrees of cost reduction compared to four other algorithms, thereby enhancing the system’s economic benefits. Full article

This study evaluated the effects of different storage conditions, varying in light exposure, relative humidity (RH), and packaging materials, on the physicochemical stability of Lobosphaera sp. biomass, the retention of bioactive compounds, and the bioactivity of its extracts. Under light and 75% RH, the biomass absorbed moisture over time, reaching 0.779 ± 0.003 g/g dry weight (DW) after three months. This was accompanied by a decline in luminosity, chroma, and hue values. In contrast, samples stored under other conditions showed minimal changes, indicating that high humidity, combined with light exposure, compromises biomass stability. Packaging in metalized polyethylene terephthalate (PETmet/PE) effectively preserved the water content, color, and carotenoid levels during a two-month storage period. Bioactive compounds extracted via hydroethanolic ultrasound-assisted extraction yielded 15.48 ± 1.35% DW. Total phenolic content (TPC) of the extracts declined over time in both PETmet/PE and low-density polyethylene (LDPE) packaging, though the decrease was less pronounced in PETmet/PE. Antioxidant activity, assessed via the ABTS assay, remained stable, regardless of storage duration or packaging. Antimicrobial activity of the extract decreased over time but remained more effective against Gram-positive bacteria (Staphylococcus aureus, Bacillus cereus, and Listeria monocytogenes), with PETmet/PE packaging better preserving antimicrobial efficacy than LDPE. These findings underscore the importance of optimized storage conditions and packaging for maintaining the quality and bioactivity of Lobosphaera sp. biomass and its extracts. Full article

This paper showcases current developments in the use of carbon nanotube (CNT) and nanoparticle-based materials for electromagnetic radiation shielding. Electromagnetic radiation involves different types of radiation covering a wide spectrum of frequencies. Due to their good electrical conductivity, small diameter, and light weight, individual CNTs are good candidates for shielding radio and microwaves. CNTs can be organized into macroscale forms by dispersing them in polymers or by wrapping CNT strands into fabrics or yarn. Magnetic nanoparticles can also be incorporated into the CNT fabric to provide excellent shielding of electromagnetic waves. However, for shielding higher-frequency X-ray and gamma ray radiation, the situation is reversed. Carbon’s low atomic number means that CNTs alone are less effective than metals. Thus, different nanoparticles such as tungsten are added to the CNT materials to provide improved shielding of photons. The goal is to achieve a desired combination of light weight, flexibility, safety, and multifunctionality for use in shielding spacecraft, satellites, nuclear reactors, and medical garments and to support lunar colonization. Future research should investigate the effect of the size, shape, and configuration of nanoparticles on radiation shielding. Developing large-scale low-cost methods for the continuous manufacturing of lightweight multifunctional nanoparticle-based materials is also needed. Full article

Background/Objectives: Osteosarcoma (OS) is the most common malignant bone tumor in children and adolescents; the survival rate is as low as 24%. Accurate prediction of clinical outcomes remains a challenge due to tumor heterogeneity and the complexity of pediatric cases. This study aims to improve predictions of progressive disease, therapy response, relapse, and survival in pediatric OS using MRI-based radiomics and machine learning methods. Methods: Pre-treatment contrast-enhanced coronal T1-weighted MR scans were collected from 63 pediatric OS patients, with an additional nine external cases used for validation. Three strategies were considered for target region segmentation (whole-tumor, tumor sampling, and bone/soft tissue) and used for MRI-based radiomics. These were then combined with clinical features to predict OS clinical outcomes. Results: The mean age of OS patients was 11.8 ± 3.5 years. Most tumors were located in the femur (65%). Osteoblastic subtype was the most common histological classification (79%). The majority of OS patients (79%) did not have evidence of metastasis at diagnosis. Progressive disease occurred in 27% of patients, 59% of patients showed adequate therapy response, 25% experienced relapse after therapy, and 30% died from OS. Classification models based on bone/soft tissue segmentation generally performed the best, with certain clinical features improving performance, especially for therapy response and mortality. The top performing classifier in each outcome achieved 0.94–1.0 validation ROC AUC and 0.63–1.0 testing ROC AUC, while those without radiomic features (RFs) generally performed suboptimally. Conclusions: This study demonstrates the strong predictive capabilities of MRI-based radiomics and multi-region segmentations for predicting clinical outcomes in pediatric OS. Full article

Non-invasive ultrasound diagnosis, combined with deep learning, is frequently used for detecting thyroid diseases. However, real-time detection on portable devices faces limitations due to constrained computational resources, and existing models often lack sufficient capability for small object detection of thyroid nodules. To address this, this paper proposes an improved lightweight small object detection network framework called LISA-YOLO, which enhances the lightweight multi-scale collaborative fusion algorithm. The proposed framework exploits the inherent symmetrical characteristics of ultrasound images and the symmetrical architecture of the detection network to better capture and represent features of thyroid nodules. Specifically, an improved depthwise separable convolution algorithm replaces traditional convolution to construct a lightweight network (DG-FNet). Through symmetrical cross-scale fusion operations via FPN, detection accuracy is maintained while reducing computational overhead. Additionally, an improved bidirectional feature network (IMS F-NET) fully integrates the semantic and detailed information of high- and low-level features symmetrically, enhancing the representation capability for multi-scale features and improving the accuracy of small object detection. Finally, a collaborative attention mechanism (SAF-NET) uses a dual-channel and spatial attention mechanism to adaptively calibrate channel and spatial weights in a symmetric manner, effectively suppressing background noise and enabling the model to focus on small target areas in thyroid ultrasound images. Extensive experiments on two image datasets demonstrate that the proposed method achieves improvements of 2.3% in F1 score, 4.5% in mAP, and 9.0% in FPS, while maintaining only 2.6 M parameters and reducing GFLOPs from 6.1 to 5.8. The proposed framework provides significant advancements in lightweight real-time detection and demonstrates the important role of symmetry in enhancing the performance of ultrasound-based thyroid diagnosis. Full article

Among the approximately 60,000 bone stick fragments unearthed from the Weiyang Palace site of the Han Dynasty, about 57,000 bear inscriptions. Most of these fragments exhibit vertical fractures, leading to a separation between the upper and lower fragments, which poses significant challenges to digital preservation and artifact restoration. Manual matching is inefficient and may cause further damage to the bone sticks. This paper proposes a novel multimodal bone stick matching approach that integrates image, inscription, and archeological information to enhance the accuracy and efficiency of matching fragmented bone stick artifacts. Unlike traditional methods that rely solely on image data, our method leverages large-scale pre-trained models, namely Vision-RWKV for visual feature extraction, RWKV for inscription analysis, and BERT for archeological metadata encoding. A dynamic cross-modal feature fusion mechanism is introduced to effectively combine these features, enabling better interaction and weighting based on the contextual relevance of each modality. This approach significantly improves matching performance, particularly in challenging cases involving fractures, corrosion, and missing sections. The novelty of this method lies in its ability to simultaneously extract and fuse multiple sources of information, addressing the limitations of traditional image-based matching methods. This paper uses Rank-N and Cumulative Match Characteristic (CMC) curves as evaluation metrics. Experimental evaluation shows that the matching accuracy reaches 94.73% at Rank-15, and the method performs significantly better than the comparative methods on the CMC evaluation curve, demonstrating outstanding performance. Overall, this approach significantly enhances the efficiency and accuracy of bone stick artifact matching, providing robust technical support for the research and restoration of bone stick cultural heritage. Full article

Internet of Things (IoT) applications and services have transformed the way people interact with their environment, enhancing comfort and quality of life. Additionally, Machine Learning (ML) approaches show significant promise for detecting intrusions in IoT environments. However, the high dimensionality, class imbalance, and complexity of network traffic—combined with the dynamic nature of sensor networks—pose substantial challenges to the development of efficient and effective detection algorithms. In this study, a multi-objective metaheuristic optimization approach, referred to as MOOIDS-IoT, is integrated with ML techniques to develop an intelligent cybersecurity system for IoT environments. MOOIDS-IoT combines a Genetic Algorithm (GA)-based feature selection technique with a multi-objective Particle Swarm Optimization (PSO) algorithm. PSO optimizes convergence speed, model complexity, and classification accuracy by dynamically adjusting the weights and thresholds of the deployed classifiers. Furthermore, PSO integrates Pareto-based multi-objective optimization directly into the particle swarm framework, extending conventional swarm intelligence while preserving a diverse set of non-dominated solutions. In addition, the GA reduces training time and eliminates redundancy by identifying the most significant input characteristics. The MOOIDS-IoT framework is evaluated using two lightweight models—MOO-PSO-XGBoost and MOO-PSO-RF—across two benchmark datasets, namely the NSL-KDD and CICIoT2023 datasets. On CICIoT2023, MOO-PSO-RF obtains 91.42% accuracy, whereas MOO-PSO-XGBoost obtains 98.38% accuracy. In addition, both models perform well on NSL-KDD (MOO-PSO-RF: 99.66% accuracy, MOO-PSO-XGBoost: 98.46% accuracy). The proposed approach is particularly appropriate for IoT applications with limited resources, where scalability and model efficiency are crucial considerations. Full article

Chemical fertilizers pose significant risks to both human health and the environment. To investigate the effect of substituting nitrogen fertilizer with vermicompost tea on wheat yield, shoot chemical constituents, and grain quality under clay-loam soil conditions, two field experiments were conducted at the Faculty of Agriculture, Cairo University, Egypt, during the winter seasons of 2021–2022 and 2022–2023. A split-plot design in randomized complete blocks with three replications was employed. Vermicompost tea was assigned to the main plots, while wheat cultivars were assigned to the subplots. The cultivars were evaluated under four treatments involving partial substitution of mineral nitrogen (recommended dose of nitrogen (RDN%, 190 kg N ha⁻¹): a control (90% of RDN + 25 kg vermicompost tea), 80% of RDN + 37.5 kg vermicompost tea, and 70% of RDN + 50 kg vermicompost tea. Nitrogen fertilizer (RDN%) was applied at rates of 190 (control), 170 (90%), 150 (80%), and 130 (70%) kg N ha⁻¹. The results indicated that partially substituting mineral nitrogen with vermicompost tea significantly increased grain weight/Ha, chlorophyll A, chlorophyll B, carotenoids, nitrogen, phosphorus (P), and potassium (K) content in shoots, as well as ash, crude protein, crude fiber, total sugar, and N, P, and K content in wheat grains. The grain weight/Ha of the Sakha-95, Giza-171, and Sads-14 cultivars increased by 38.6%, 33.5%, and 39.3%, respectively, when treated with 70% RDN + 50 kg vermicompost tea. The combination of the Sads-14 cultivar and 70% RDN + 50 kg vermicompost tea resulted in the highest values for grain weight/ha (9.43 tons ha⁻¹), chlorophyll A (1.39 mg/g), chlorophyll B (1.04 mg/g), N (5.08%), P (1.63%), and P (2.43%) content in shoots. The same combination also improved ash (2.89%), crude fiber (2.84%), and K (6.05%) content in grains. In conclusion, the application of vermicompost tea in conjunction with chemical fertilizers offers a viable alternative to using chemical fertilizers alone, promoting sustainable agricultural practices and improving wheat production. It is recommended that mineral nitrogen fertilizer be partially replaced with vermicompost tea to enhance both the productivity and grain quality of wheat while minimizing environmental pollution. Full article

Accurately predicting glass density is crucial for designing novel materials. This study aims to develop a robust predictive model for the density of oxide glasses and, more importantly, to investigate how physically informed feature engineering can create accurate and interpretable models that reveal underlying physical principles. Using a dataset of 76,593 oxide glasses from the SciGlass database, three machine learning (ML) models (ElasticNet, XGBoost, MLP) were trained and evaluated. Four distinct feature sets were constructed with increasing physical complexity, ranging from simple elemental composition to the advanced Magpie descriptors. The best model was further analyzed for interpretability using feature importance and SHapley Additive exPlanations (SHAP) analysis. A clear hierarchical improvement in predictive accuracy was observed with increasing feature sophistication across all models. The XGBoost model combined with the Magpie feature set provided the best performance, achieving a coefficient of determination (R²) of 0.97. Interpretability analysis revealed that the model’s predictions were overwhelmingly driven by physical attributes, with mean atomic weight being the most influential predictor. The model learns to approximate the fundamental density equation using mean atomic weight as a proxy for molar mass and electronic structure features to estimate molar volume. This demonstrates that a data-driven approach can function as a scientifically valid and interpretable tool, accelerating the discovery of new materials. Full article

Hyperspectral remote sensing plays a crucial role in guiding and supporting various mineral prospecting activities. Combined with artificial intelligence, hyperspectral remote sensing technology becomes a powerful and versatile tool for a wide range of mineral exploration activities. This study investigates the effectiveness of ensemble learning (EL) algorithms for lithological classification and mineral exploration using EnMAP hyperspectral imagery (HSI) in a semi-arid region. The Moroccan Anti-Atlas mountainous region is known for its complex geology, high mineral potential and rugged terrain, making it a challenging for mineral exploration. This research applies core and heterogeneous ensemble learning methods, i.e., boosting, stacking, voting, bagging, blending, and weighting to improve the accuracy and robustness of lithological classification and mapping in the Moroccan Anti-Atlas mountainous region. Several state-of-the-art models, including support vector machines (SVMs), random forests (RFs), k-nearest neighbors (k-NNs), multi-layer perceptrons (MLPs), extra trees (ETs) and extreme gradient boosting (XGBoost), were evaluated and used as individual and ensemble classifiers. The results show that the EL methods clearly outperform (single) base classifiers. The potential of EL methods to improve the accuracy of HSI-based classification is emphasized by an optimal blending model that achieves the highest overall accuracy (96.69%). The heterogeneous EL models exhibit better generalization ability than the baseline (single) ML models in lithological classification. The current study contributes to a more reliable assessment of resources in mountainous and semi-arid regions by providing accurate delineation of lithological units for mineral exploration objectives. Full article

The chickpea (Cicer arietinum L.) is a key legume crop in Mediterranean agriculture, valued for its nutritional profile and adaptability. However, its productivity is severely impacted by drought stress. To identify microbial solutions that enhance drought resilience, we isolated seven Mesorhizobium strains from chickpea nodules collected in southern Spain and evaluated their cultivar-specific symbiotic performance. Two commercial cultivars (Pedrosillano and Blanco Lechoso) and twenty chickpea germplasms were tested under growth chamber and greenhouse conditions, both with and without drought stress. Initial screening in a sterile substrate using nodulation assays, shoot/root dry weight measurements, and acetylene reduction assays identified three elite strains (ISC11, ISC15, and ISC25) with superior symbiotic performance and nitrogenase activity. Greenhouse trials under reduced irrigation demonstrated that several strain–cultivar combinations significantly mitigated drought effects on plant biomass, with specific interactions (e.g., ISC25 with RR-98 or BT6-19) preserving over 70% of shoot biomass relative to controls. Whole-genome sequencing of the elite strains revealed diverse taxonomic affiliations—ISC11 as Mesorhizobium ciceri, ISC15 as Mesorhizobium mediterraneum, and ISC25 likely representing a novel species. Genome mining identified plant growth-promoting traits including ACC deaminase genes (in ISC11 and ISC25) and genes coding for auxin biosynthesis-related enzymes. Our findings highlight the potential of targeted rhizobial inoculants tailored to chickpea cultivars to improve crop performance under water-limiting conditions. Full article

Protein-bound uraemic toxins (PBUTs), such as indoxyl sulphate (IS) and p-cresyl sulphate (PCS), are poorly cleared by conventional haemodialysis (HD) or haemodiafiltration (HDF). Haemoadsorption combined with HD (HAHD) using the novel pHA130 cartridge may increase PBUT removal, and this trial aimed to compare its efficacy and safety with HDF in patients with end-stage renal disease (ESRD). In this single-centre, open-label trial, 30 maintenance HD patients were randomized (1:1:1) to HDF once every two weeks (HDF-q2w), HAHD once every two weeks (HAHD-q2w), or HAHD once weekly (HAHD-q1w) for 8 weeks, with the primary endpoint being the single-session reduction ratio (RR) of IS. The combined HAHD group (n = 20) demonstrated a significantly greater IS reduction than the HDF-q2w group (n = 10) (46.9% vs. 31.8%; p = 0.044) and superior PCS clearance (44.6% vs. 31.4%; p = 0.003). Both HAHD regimens significantly reduced predialysis IS levels at Week 8. Compared with HDF, weekly HAHD provided greater relief from pruritus and improved sleep quality, with comparable adverse events among groups. In conclusion, HAHD with the pHA130 cartridge is more effective than HDF for enhancing single-session PBUT removal and alleviating uraemic symptoms in patients with ESRD, with weekly application showing optimal symptomatic benefits. Full article

The growing use of remote-sensing technologies has placed greater demands on object-detection algorithms, which still face challenges. This study proposes a hierarchical adaptive feature aggregation network (HAF-YOLO) to improve detection precision in remote-sensing images. It addresses issues such as small object size, complex backgrounds, scale variation, and dense object distributions by incorporating three core modules: dynamic-cooperative multimodal fusion architecture (DyCoMF-Arch), multiscale wavelet-enhanced aggregation network (MWA-Net), and spatial-deformable dynamic enhancement module (SDDE-Module). DyCoMF-Arch builds a hierarchical feature pyramid using multistage spatial compression and expansion, with dynamic weight allocation to extract salient features. MWA-Net applies wavelet-transform-based convolution to decompose features, preserving high-frequency detail and enhancing representation of small-scale objects. SDDE-Module integrates spatial coordinate encoding and multidirectional convolution to reduce localization interference and overcome fixed sampling limitations for geometric deformations. Experiments on the NWPU VHR-10 and DIOR datasets show that HAF-YOLO achieved mAP50 scores of 85.0% and 78.1%, improving on YOLOv8 by 4.8% and 3.1%, respectively. HAF-YOLO also maintained a low computational cost of 11.8 GFLOPs, outperforming other YOLO models. Ablation studies validated the effectiveness of each module and their combined optimization. This study presents a novel approach for remote-sensing object detection, with theoretical and practical value. Full article

Gear wear degrades transmission performance, necessitating highly reliable fault diagnosis methods. To address the limitations of existing approaches—where dynamic models rely heavily on prior knowledge, while data-driven methods lack interpretability—this study proposes an integrated bidirectional verification framework combining dynamic modeling and deep learning for interpretable gear wear diagnosis. First, a dynamic gear wear model is established to quantitatively reveal wear-induced modulation effects on meshing stiffness and vibration responses. Then, a deep network incorporating Gradient-weighted Class Activation Mapping (Grad-CAM) enables visualized extraction of frequency-domain sensitive features. Bidirectional verification between the dynamic model and deep learning demonstrates enhanced meshing harmonics in wear faults, leading to a quantitative diagnostic index that achieves 0.9560 recognition accuracy for gear wear across four speed conditions, significantly outperforming comparative indicators. This research provides a novel approach for gear wear diagnosis that ensures both high accuracy and interpretability. Full article