Search Results (33,893)

Accurate weld defect detection is critical for ensuring structural safety and evaluating welding quality in industrial applications. Manual inspection methods have inherent limitations, including inefficiency and inadequate sensitivity to subtle defects. Existing detection models, primarily designed for natural images, struggle to adapt to the characteristic challenges of weld X-ray images, such as high noise, low contrast, and inter-defect similarity, particularly leading to missed detections and false positives for small defects. To address these challenges, a multi-dimensional feature fusion model (MADet), which is a multi-branch deep fusion network for weld defect detection, was proposed. The framework incorporates two key innovations: (1) A multi-scale feature fusion network integrated with lightweight attention residual modules to enhance the perception of fine-grained defect features by leveraging low-level texture information. (2) An anchor-based feature-selective detection head was used to improve the discrimination and localization accuracy for five typical defect categories. Extensive experiments on both public and proprietary weld defect datasets demonstrated that MADet achieved significant improvements over the state-of-the-art YOLO variants. Specifically, it surpassed the suboptimal model by 7.41% in mAP@0.5, indicating strong industrial applicability. Full article

In most commercial lithium-ion batteries, graphite remains the primary anode material. However, its theoretical specific capacity is only 372 mAh∙g⁻¹, which falls short of meeting the demands of high-performance electronic devices. Silicon anodes, despite boasting an ultra-high theoretical specific capacity of 4200 mAh∙g⁻¹, suffer from significant volume expansion (>300%) during cycling, leading to severe capacity fade and limiting their commercial viability. Currently, silicon-based polymer-derived ceramics have emerged as a highly promising next-generation anode material for lithium-ion batteries, thanks to their unique nano-cluster structure, tunable composition, and low volume expansion characteristics. The maximum capacity of the ceramics can exceed 1000 mAh∙g⁻¹, and their unique synthesis routes enable customization to align with diverse electrochemical application requirements. In this paper, we present the progress of silicon oxycarbide (SiOC), silicon carbonitride (SiCN), silicon boron carbonitride (SiBCN) and silicon oxycarbonitride (SiOCN) in the field of LIBs, including their synthesis, structural characteristics and electrochemical properties, etc. The mechanisms of lithium-ion storage in the Si-based anode materials are summarized as well, including the key role of free carbon in these materials. Full article

Background/Objectives: This study aims to design and evaluate transfer learning strategies that fine-tune multiple pre-trained convolutional neural network architectures based on their characteristics to improve the accuracy and generalizability of colorectal cancer histopathological image classification. Methods: The application of transfer learning with pre-trained models on specialized and multiple datasets is proposed, where the proposed models, CRCHistoDense, CRCHistoIncep, and CRCHistoXcep, are algorithmically fine-tuned at varying depths to improve the performance of colorectal cancer classification. These models were applied to datasets of 10,613 images from public and private repositories, external sources, and unseen data. To validate the models’ decision-making and improve transparency, we integrated Grad-CAM to provide visual explanations that influence classification decisions. Results and Conclusions: On average across all datasets, CRCHistoDense, CRCHistoIncep, and CRCHistoXcep achieved test accuracies of 99.34%, 99.48%, and 99.45%, respectively, highlighting the effectiveness of fine-tuning in improving classification performance and generalization. Statistical methods, including paired t-tests, ANOVA, and the Kruskal–Wallis test, confirmed significant improvements in the proposed methods’ performance, with p-values below 0.05. These findings demonstrate that fine-tuning based on the characteristics of CNN’s architecture enhances colorectal cancer classification in histopathology, thereby improving the diagnostic potential of deep learning models. Full article

The development of non-toxic silver nanoparticles (AgNPs) for medical and other diverse applications is steadily increasing. However, this study specifically aims to determine the cytotoxic effects of AgNPs synthesized via a green chemistry approach using aqueous-ethanol and ethyl acetate extracts of Artemisia terrae-albae. The photophysical, morphological, and size distribution characteristics of the synthesized AgNPs are analyzed using UV-Vis spectroscopy and transmission electron microscopy (TEM). A modified Allium cepa assay is employed to evaluate biological responses, including root growth, root number, and mitotic index. In this assay, the cell cycles of onion bulbs are synchronized and pre-incubated at 4 °C for 72 h prior to treatment. This study reveals that the AgNPs synthesized from the ethanol extract exhibit notable stability and higher cytotoxicity activity, with a root length of 0.6 ± 0.13 cm, root number of 16 ± 6.88, and mitotic index of 25.0 ± 2.6. These values are significantly more cytogenotoxic than those observed for the ethyl-acetate-derived nanoparticles, which show a root length of 0.8 ± 0.17 cm, root number of 18 ± 6.27, and mitotic index of 36 ± 3.6. These findings highlight the potential of green-synthesized AgNPs as effective cytotoxic agents, especially those obtained from ethanol extract, possibly due to a greater influence of the quantity of diverse phenolic compounds present in the complex mixtures than in the ethyl acetate extract, which otherwise enhanced their morphology, shape, and size. These, overall, contributed to the biological activity. Full article

A multilayer linear response model (MLRM) is proposed to generate time-series data based on linear response theory. The proposed MLRM is designed to generate data for anomalous dynamics by extending the conventional single-layer linear response model (SLRM) into multiple layers. While the SLRM is a linear equation with respect to external forces, the MLRM introduces nonlinear interactions, enabling the generation of a wider range of dynamics. The MLRM is applicable to various fields, such as finance, as it does not rely on machine learning techniques and maintains interpretability. We investigated whether the MLRM could generate anomalous dynamics, such as those observed during the coronavirus disease 2019 (COVID-19) pandemic, using pre-pandemic data. Furthermore, an analysis of the log returns and realized volatility derived from the MLRM-generated data demonstrated that both exhibited heavy-tailed characteristics, consistent with empirical observations. These results indicate that the MLRM can effectively reproduce the extreme fluctuations and tail behavior seen during high-volatility periods. Full article

The objectives of this study were to prepare whey protein–quercetin–gellan gum conjugates using the pH-shift method and to evaluate the impacts of varying pH values and quercetin concentrations on the interaction mechanisms and functional characteristics of the complexes. Spectroscopic analyses (fluorescence, UV-vis, and FT-IR) revealed that new complexes formed under alkaline conditions. Notably, an increasing quercetin concentration led to a reduction in complex particle size and an increase in the zeta potential value, with these effects being more pronounced under alkaline conditions. The particle size was 425.7 nm, and the zeta potential value was −30.00 mV at a quercetin addition concentration of 15 umol/g protein. Additionally, the complexes formed under alkaline conditions exhibited superior foaming capacity, emulsification properties, and significantly enhanced free radical scavenging activity. The complex’s DPPH and ABTS radical scavenging rates rose by 41.57% and 57.69%, respectively. This study provides theoretical foundations and practical insights for developing protein—polyphenol systems, offering significant implications for the application of quercetin functional foods and supplements in the food science and pharmaceutical industries. Full article

The coastal developments in the Middle East put low priority on tsunami risk assessment due to the rare occurrence and absence of genuine tsunami track records on the coastline in the past. Tsunami-vulnerable coasts, including the east coast of the UAE, need to prepare for, and pay attention to, the impact of future tsunamis due to increased earthquake activity in the region. This study investigated the tsunami characteristics of the nearshore from hypothetical tsunami conditions by applications of numerical modeling and Artificial Neural Network (ANN) methods. The modeling results showed that the maximum tsunami depth at the shore was highest in Khor Fakkan and Mirbih for the given tsunami boundary conditions, while the tsunami withdrawal was greater on the southern bathymetry compared to that on the northern bathymetry when the tsunami period increased. ANN results confirmed that the still sea depth and seabed slope were more important than the tsunami period when predicting the maximum tsunami depth at the shore. Full article

Discovering, analyzing, and finding a key to understanding the physiological and biochemical responses that Vitis vinifera L. undertakes against drought stress is of fundamental importance for this profitable crop. Today’s considerable climatic fluctuations force researchers and farmers to focus on this issue with solutions inclined to respect the ecosystem. In this academic work, we focused on describing the drought stress consequences on several parameters of secondary metabolites on Vitis vinifera leaves (quercetins, kaempferol, resveratrol, proline, and xanthophylls) and on some ecophysiological characteristics (e.g., water potential, stomatal conductance, and leaf temperature) to compare the answers that diverse agronomic management techniques (i.e., irrigation with and without zeolite, pure zeolite and no application) could instaurate in the metabolic pathway of this important crop with the aim to find convincing and thought-provoking responses to use this captivating and versatile mineral, the zeolite known as the “magic rock”. Stressed grapevines reached 56.80 mmol/m²s gs at veraison and a more negative stem Ψ (+10.63%) compared to plants with zeolite. Resveratrol, in the hottest season, fluctuated from 0.18–0.19 mg/g in zeolite treatments to 0.37 mg/g in stressed vines. Quercetins were inclined to accumulate in response to drought stress too. In fact, we recorded a peak of quercetin (3-O-glucoside + 3-O-glucuronide) of 11.20 mg/g at veraison in stressed plants. It is interesting to note how the pool of metabolites was often unchanged for plants treated with zeolite and for plants treated with water only, thus elevating this mineral to a “stress reliever”. Full article

The (1−x)Ba_0.96Ca_0.04TiO₃−xBi(Li_1/3Zr_2/3)O₃ (x = 0.03–0.15) ceramics were fabricated via the traditional solid reaction method. Characterization results revealed that each component exhibited a pure perovskite structure, and the average grain size significantly diminishes with increasing x. The (1−x)Ba_0.96Ca_0.04TiO₃−xBi(Li_1/3Zr_2/3)O₃ ceramics exhibited prominent relaxor ferroelectric behavior, whose characteristic narrow hysteresis loops effectively enhanced the energy storage performance of the material. Most importantly, the composition with x = 0.10 demonstrated exceptional energy storage properties at 150 kV/cm, achieving a high recoverable energy storage density (W_rec = 1.91 J/cm³) and excellent energy efficiency (η = 90.87%). Under the equivalent electric field, this composition also displayed a superior pulsed discharge performance, including a high current density (871 A/cm²), a high power density (67.3 MW/cm³), an ultrafast discharge time (t_0.9 = 109 ns), and a discharged energy density of 1.47 J/cm³. These results demonstrate that the (1−x)Ba_0.96Ca_0.04TiO₃−xBi(Li_1/3Zr_2/3)O₃ ceramic system establishes a promising design paradigm for the creation and refinement of next-generation dielectrics for pulse power applications. Full article

With the rapid increase in global lithium demand, the exploration of newly discovered lithium in the bauxite of the Wenshan area in southeastern Yunnan has become increasingly important. However, the current research on clay-type lithium in the Wenshan area has primarily focused on local exploration, and large-scale predictive metallogenic studies remain limited. To address this, this study utilized multi-source remote sensing data from ZY1-02D and ASTER, combined with ALOS 12.5 m DEM and Sentinel-2 imagery, to carry out remote sensing mineral identification, structural interpretation, and prospectivity mapping for clay-type lithium in the Wenshan area. This study indicates that clay-type lithium in the Wenshan area is controlled by NW, EW, and NE linear structures and are mainly distributed in the region from north of the Wenshan–Malipo fault to south of the Guangnan–Funing fault. High-value areas of iron-rich silicates and iron–magnesium minerals revealed by ASTER data indicate lithium enrichment, while montmorillonite and cookeite identification by ZY1-02D have strong indicative significance for lithium. Field verification samples show the highest Li₂O content reaching 11,150 μg/g, with six samples meeting the comprehensive utilization criteria for lithium in bauxite (Li₂O ≥ 500 μg/g) and also showing an enrichment of rare earth elements (REEs) and gallium (Ga). By integrating stratigraphic, structural, mineral identification, geochemical characteristics, and field verification data, ten mineral exploration target areas were delineated. This study validates the effectiveness of remote sensing technology in the exploration of clay-type lithium and provides an applicable workflow for similar environments worldwide. Full article

Background/Objectives: Targeting individuals with certain characteristics provides improved precision in many healthcare applications. An alert mechanism for COPD exacerbations has recently been validated. It has been argued that its efficacy improves considerably with stratification. This paper provides an in-depth analysis of the cough data of the stratified cohort to identify options for and the feasibility of improved precision in the alert mechanism for the intended patient group. Methods: The alert system was extended using a system complementary to the existing one to accommodate observed rapid changes in cough trends. The designed system was tested in a post hoc analysis of the data. The trend data were inspected to consider their meaningfulness for patients and caregivers. Results: While stratification was effective in reducing misses, the augmented alert system improved the sensitivity and number of early alerts for the acute exacerbation of COPD (AE-COPD). The combination of stratification and the augmented mechanism led to sensitivity of 86%, with a false alert rate in the order of 1.5 per year in the target group. The alert system is rule-based, operating on interpretable signals that may provide patients or their caregivers with better insights into the respiratory condition. Conclusions: The augmented alert system operating based on cough trends has the promise of increased precision in detecting AE-COPD in the target group. Since the design and testing of the augmented system were based on the same data, the system needs to be validated. Signals within the alert system are potentially useful for improved self-management in the target group. Full article

Effective flood management in agricultural fields has become increasingly important due to the rising frequency and intensity of rainfall events driven by climate change. This study investigates the applicability of urban flood analysis models—SWMM (1D) and K-Flood (2D)—to irrigated agricultural fields with artificial drainage systems. A case study was conducted in a rural area near the Sindae drainage station in Cheongju, South Korea, using rainfall data from an extreme weather event in 2017. The models simulated inland flooding and were validated against flood trace maps provided by the Ministry of the Interior and Safety (MOIS). Receiver Operating Characteristic (ROC) analysis showed a true positive rate of 0.565, a false positive rate of 0.21, and an overall accuracy of 0.731, indicating reasonable agreement with observed inundation. Scenario analyses were also conducted to assess the effectiveness of three improvement strategies: reducing the Manning coefficient, increasing pump station capacity, and widening drainage channels. Among them, increasing pump capacity most effectively reduced flood volume, while channel widening had the greatest impact on reducing flood extent. These findings demonstrate the potential of urban flood models for application in agricultural contexts and support data-driven planning for rural flood mitigation. Full article

This study presents an advanced framework for fabric texture classification by leveraging macro- and micro-texture extraction techniques integrated with deep learning architectures. Co-occurrence histograms, local binary patterns (LBPs), and albedo-dependent feature maps were employed to comprehensively capture the surface properties of fabrics. A late fusion approach was applied using four state-of-the-art convolutional neural networks (CNNs): InceptionV3, ResNet50_V2, DenseNet, and VGG-19. Excellent results were obtained, with the ResNet50_V2 achieving a precision of 0.929, recall of 0.914, and F1 score of 0.913. Notably, the integration of multimodal inputs allowed the models to effectively distinguish challenging fabric types, such as cotton–polyester and satin–silk pairs, which exhibit overlapping texture characteristics. This research not only enhances the accuracy of textile classification but also provides a robust methodology for material analysis, with significant implications for industrial applications in fashion, quality control, and robotics. Full article

Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, with a focus on diverse appliance types that exhibit distinct operational characteristics and user preferences. Initially, a single-objective optimization approach using Genetic Algorithms (GAs) is employed to minimize the total energy cost under a real Time-of-Use (ToU) pricing scheme. This heuristic method allows for the effective scheduling of appliance operations while factoring in their unique characteristics such as power consumption, usage duration, and user-defined operational flexibility. This study extends the optimization problem to a multi-objective framework that incorporates the minimization of CO₂ emissions under a real annual energy mix while also accounting for user discomfort. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized for this purpose, providing a Pareto-optimal set of solutions that balances these competing objectives. The inclusion of multiple objectives ensures a comprehensive assessment of DSM strategies, aiming to reduce environmental impact and enhance user satisfaction. Additionally, this study monitors the Peak-to-Average Ratio (PAR) to evaluate the impact of DSM strategies on load balancing and grid stability. It also analyzes the impact of considering different periods of the year with the associated ToU hourly schedule and CO₂ emissions hourly profile. A key innovation of this research is the integration of detailed, category-specific metrics that enable the disaggregation of costs, emissions, and user discomfort across residential, commercial, and industrial appliances. This granularity enables stakeholders to implement tailored strategies that align with specific operational goals and regulatory compliance. Also, the emphasis on a user discomfort indicator allows us to explore the flexibility available in such DSM mechanisms. The results demonstrate the effectiveness of the proposed multi-objective optimization approach in achieving significant cost savings that may reach 20% for industrial applications, while the order of magnitude of the trade-offs involved in terms of emissions reduction, improvement in discomfort, and PAR reduction is quantified for different frameworks. The outcomes not only underscore the efficacy of applying advanced optimization frameworks to real-world problems but also point to pathways for future research in smart energy management. This comprehensive analysis highlights the potential of advanced DSM techniques to enhance the sustainability and resilience of energy systems while also offering valuable policy implications. Full article

Poly(organo)phosphazenes (PPZs) are increasingly recognized as versatile biomaterials for drug delivery applications in nanomedicine. Their unique hybrid structure—featuring an inorganic backbone and highly tunable organic side chains—confers exceptional biocompatibility and adaptability. Through precise synthetic methodologies, PPZs can be engineered to exhibit a wide spectrum of functional properties, including the formation of multifunctional nanostructures tailored for specific therapeutic needs. These attributes enable PPZs to address several critical challenges associated with conventional drug delivery systems, such as poor pharmacokinetics and pharmacodynamics. By modulating solubility profiles, enhancing drug stability, enabling targeted delivery, and supporting controlled release, PPZs offer a robust platform for improving therapeutic efficacy and patient outcomes. This review explores the fundamental chemistry, biopharmaceutical characteristics, and biomedical applications of PPZs, particularly emphasizing their role in zero-dimensional nanotherapeutic systems, including various nanoparticle formulations. PPZ-based nanotherapeutics are further examined based on their drug-loading mechanisms, which include electrostatic complexation in polyelectrolytic systems, self-assembly in amphiphilic constructs, and covalent conjugation with active pharmaceutical agents. Together, these strategies underscore the potential of PPZs as a next-generation material for advanced drug delivery platforms. Full article