Search Results (12,076)

Few-Shot Industrial Anomaly Detection (FSIAD) is an essential yet challenging problem in practical scenarios such as industrial quality inspection. Its objective is to identify previously unseen anomalous regions using only a limited number of normal support images from the same category. Recently, large pre-trained vision-language models (VLMs), such as CLIP, have exhibited remarkable few-shot image-text representation abilities across a range of visual tasks, including anomaly detection. Despite their promise, real-world industrial anomaly datasets often contain noisy labels, which can degrade prompt learning and detection performance. In this paper, we propose AnomalyNLP, a new Noisy-Label Prompt Learning approach designed to tackle the challenge of few-shot anomaly detection. This framework offers a simple and efficient approach that leverages the expressive representations and precise alignment capabilities of VLMs for industrial anomaly detection. First, we design a Noisy-Label Prompt Learning (NLPL) strategy. This strategy utilizes feature learning principles to suppress the influence of noisy samples via Mean Absolute Error (MAE) loss, thereby improving the signal-to-noise ratio and enhancing overall model robustness. Furthermore, we introduce a prompt-driven optimal transport feature purification method to accurately partition datasets into clean and noisy subsets. For both image-level and pixel-level anomaly detection, AnomalyNLP achieves state-of-the-art performance across various few-shot settings on the MVTecAD and VisA public datasets. Qualitative and quantitative results on two datasets demonstrate that our method achieves the largest average AUC improvement over baseline methods across 1-, 2-, and 4-shot settings, with gains of up to 10.60%, 10.11%, and 9.55% in practical anomaly detection scenarios. Full article

The current study explores the fully distributed containment control problem of fractional-order time-delay multi-agent systems by introducing a novel pull-based dynamic event-triggered approach. Firstly, to reduce communication overhead and mitigate time delays in controller updates, a pull-based dynamic event-triggered strategy is proposed. Secondly, in virtue of a Lyapunov candidate function, the proposed pull-based dynamic event-triggered control protocol exhibits inherent distributed properties enabling agents to operate independently and cooperatively without global information. Thirdly, we design adaptive parameters to ensure containment control convergence and provide a rigorous proof to preclude Zeno behavior. Eventually, numerical simulations are performed to verify the validity of the theoretical analysis. Full article

Timely identification and accurate delineation of ultra-early ischemic stroke lesions in non-contrast computed tomography (CT) scans of the human brain are of paramount importance for prompt medical intervention and improved patient outcomes. In this study, we propose a deep learning-driven methodology specifically designed for segmenting ultra-early ischemic regions, with a particular emphasis on both the ischemic core and the surrounding penumbra during the initial stages of stroke progression. We introduce a lightweight preprocessing model based on convolutional filtering techniques, which enhances image clarity while preserving the structural integrity of medical scans, a critical factor when detecting subtle signs of ultra-early ischemic strokes. Unlike conventional preprocessing methods that directly modify the image and may introduce artifacts or distortions, our approach ensures the absence of neural network-induced artifacts, which is especially crucial for accurate diagnosis and segmentation of ultra-early ischemic lesions. The model employs predefined differentiable filters with trainable parameters, allowing for artifact-free and precision-enhanced image refinement tailored to the challenges of ultra-early stroke detection. In addition, we incorporated into the combined preprocessing pipeline a newly proposed trainable linear combination of pretrained image filters, a concept first introduced in this study. For model training and evaluation, we utilize a publicly available dataset of acute ischemic stroke cases, focusing on the subset relevant to ultra-early stroke manifestations, which contains annotated non-contrast CT brain scans from 112 patients. The proposed model demonstrates high segmentation accuracy for ultra-early ischemic regions, surpassing existing methodologies across key performance metrics. The results have been rigorously validated on test subsets from the dataset, confirming the effectiveness of our approach in supporting the early-stage diagnosis and treatment planning for ultra-early ischemic strokes. Full article

Background/Objectives: Staphylococcus haemolyticus is a common commensal bacterium that has emerged as an important nosocomial pathogen. Its multi-antibiotics resistance presents substantial therapeutic challenges in healthcare settings worldwide. Despite its growing clinical relevance, most investigations into antimicrobial resistance determinants have been focused on Staphylococcus aureus or Staphylococcus epidermidis, leaving S. haemolyticus comparatively understudied. This study aimed to elucidate the genetic basis of multi-drug resistance by characterizing mobile genetic elements associated with predominant S. haemolyticus clones circulating in Taiwan. Methods: From 2010 to 2017, 140 clinical targeted isolates of S. haemolyticus were obtained from individual patients. Two representative strains, SH53 (ST3) and SH51 (ST42), were sequenced using the PacBio^TM platform. The structural organization of SCCmec cassettes and phage-associated resistance islands in the remaining 138 isolates was analyzed by polymerase chain reaction (PCR) using specifically designed primers. Results: Of the 140 isolates, 92 (65.7%) were ST42 and 48 (34.3%) were ST3. PCR analysis showed that over two-thirds harbored heavy metal resistance genes. cadD, cadX, arsC, arsB, and arsR occurred in 90.2% of ST42 isolates, with copA in 71.7%. In ST3, these five genes were present in 89.6%, and copA in 64.6%. Fusidic acid (FA) resistance was more frequent in ST42 (46.7%) than ST3 (22.9%) (p = 0.015). Only one ST42 isolate carried fusC. The remaining 52 FA-resistant isolates contained a type I aj1–leader peptide (LP)–fusB structure downstream of smpB, except for a single ST42 isolate with the type IV structure. Conclusions: MDR ST42 S. haemolyticus carrying SCCmec cassettes with heavy metal resistance genes and phage-related islands carrying type I aj1–leader peptide (LP)–fusB structures may represent emerging opportunistic pathogens in Taiwan. Continued longitudinal surveillance is warranted to track the evolution of resistance-associated mobile elements under selective antimicrobial pressure. Full article

Terpenes, representing one of the most extensive classes of natural products, hold significant value in the fields of pharmaceuticals, fragrances, and biofuels. Extracting these compounds from natural sources is often environmentally unsustainable, and the structural diversity found in nature is inherently limited. Metabolic engineering using microbial hosts offers a scalable and sustainable alternative, utilizing optimized biosynthetic pathways—such as the mevalonate (MVA) and the methylerythritol phosphate (MEP) pathways—to achieve high-yield production of natural terpene scaffolds. This review focuses on the various strategies in developing microbial cell factories, ranging from enhancing precursor supply to optimizing terpene synthase systems. A new and promising frontier is the increase in structural diversity of terpenes by integration of non-biological chemical transformations into engineered biosynthetic pathways. We discuss the use of artificial metalloenzymes such as engineered cytochrome P450 variants that catalyze non-natural carbene transfer reactions (cyclopropanation). The merging of synthetic biology and synthetic chemistry goes beyond the normal synthesizing capabilities found in nature, which may pave the way for the design of “non-natural” terpenoids that contain new additions and better capabilities. Full article

This study aimed to minimize the amount of gelator used in oleogel preparation and enhance the valorization of rubber seeds. Cellulose extracted from rubber seed shells was modified via high-pressure microfluidization (HPM), which significantly enhanced its specific surface area from 0.92 m²/g (CL) to 6.47 m²/g (MCL), along with markedly improved water-holding capacity (WHC increased from 4.92 to 29.37 g/g) and swelling capacity (SC increased from 0.65 to 3.38 mL/g). The modified cellulose (MCL) served as the gelator, while rubber seed oil bodies (OBs), isolated through sucrose-assisted extraction, functioned as the oil phase. A series of OB emulsions containing 0% to 0.75% MCL were prepared and subsequently converted into oleogels by freeze-drying and shearing. Oleogels containing ≥0.45% MCL exhibited excellent oil binding capacity (OBC > 98.6%) and strong gel strength (storage modulus G′ > 10⁵ Pa). Texture profile analysis further confirmed significant improvements in the textural properties of the oleogels with increasing MCL content. These findings demonstrate that MCL, combined with rubber seed OBs, enables the development of high-performance oleogels with minimal gelator requirements. This approach not only reduces gelator usage but also provides a novel strategy for the upcycling of rubber seed shells, offering valuable insights for the design of nutrient-rich functional oleogels. Full article

The escalating crisis of bacterial antimicrobial resistance poses a severe threat to global health, necessitating novel strategies beyond conventional antibiotics. Photothermal therapy (PTT) has emerged as a promising alternative that leverages heat generated by laser irradiation to induce localized cellular damage and eradicate bacteria. Among various photothermal agents, carbon-based nanomaterials like graphene nanoplatelets (GNPs) offer exceptional properties for PTT applications. This study introduces a novel urinary catheter (UC) embedded with GNPs (GNPUC), specifically designed for photothermal sterilization to combat catheter-associated bacterial infections. GNPs were systematically incorporated into polydimethylsiloxane (PDMS) catheters at varying weight percentages (1% to 10%). The fabricated GNPUCs exhibited low wettability, hydrophobic characteristics, and low adhesiveness, properties that are crucial for minimizing bacterial interactions and initial adhesion. Upon exposure to near-infrared (NIR) laser irradiation (808 nm, 1.5 W/cm²), the UC containing 10 weight percent of GNPs (10GNPUC) achieved a significant temperature of 68.8 °C, demonstrating its potent photothermal conversion capability. Quantitative agar plate tests confirmed the enhanced, concentration-dependent photothermal antibacterial activity of GNPUCs against both Gram-negative Escherichia coli (E. coli) and Gram-positive Staphylococcus aureus (S. aureus). Notably, 5% and higher GNP concentrations achieved 100% mortality of S. aureus, while 1% and higher concentrations achieved 100% mortality of E. coli. These findings underscore the significant potential of GNP-embedded catheters as a highly effective photothermal antibacterial platform for future clinical applications in combating catheter-associated infections. Full article

A nanoliter-scale fabrication method was applied to construct a colorimetric lateral flow strip for urea detection (Urea-CLFS). The device involves two main papers: a nitrocellulose membrane (NC-Mb) for urease enzyme immobilization and chromatography paper (CH-PP) containing a phenol red indicator. Urea-CLFS is a tool for detecting urea that is based on enzyme catalysis and the change in color of phenol red when urea is present. The Urea-CLFS fabrication was made possible by the minimal amount of nanoliters used in reagent consumption. The use of small arrays of phenol red dots provides a higher response result compared to single dots applied on CH-PP. To find the most effective design, it analyzed how urease was aligned on NC-Mb horizontally and vertically. According to our findings, the vertical alignment of the urease enzyme on NC-Mb leads to a prolonged reaction time, which leads to higher product production. The optimization process included optimizing various parameters, including the layer number of phenol red on CH-PP, phenol red concentration, urease concentration, reaction time, and sample volume. Under optimal conditions, the Urea-CLFS provided a linear range of 0.25–8.0 mmol L⁻¹ with an LOD of 0.34 mmol L⁻¹, which is sufficient for human health diagnostics. The accuracy of the Urea-CLFS was demonstrated by the recovery of the human urine sample between 95 ± 3% and 103 ± 3% (n = 3). Full article

Hydrologists need to predict extreme hydrological and meteorological events for design purposes, whose magnitude and probability are estimated using a probability distribution function (PDF). The choice of an appropriate PDF is crucial in describing the behavior of the phenomenon and the predictions can differ significantly depending on the PDF. So, the success of the probability distribution function in representing the data of extreme value series of natural events such as hydrology and climatology is of great importance. Depending on whether the series consists of maximum or minimum values, the theoretical probability density function must be appropriately fit to the right or left tail of the extreme data, which contains the most critical information. This study includes a combined evaluation of the performance of four different tests for selecting the appropriate probability distribution of maximum rainfall in Türkiye: Kolmogorov–Smirnov (KS) test, Anderson–Darling (AD) test, Probability Plot Correlation Coefficient (PPCC) test, and L-Moments Z^DIST test. Within the scope of the study, maximum rainfall series of seven rainfall durations from 15 to 1440 min, at rain gauge stations in 81 provinces of Türkiye, were examined. Goodness of fit was performed based on ranking using a combination of four different numerical tests (KS, AD, PPCC, Z^DIST). The probabilistic character of maximum rainfall was evaluated using a large dataset consisting of 567 time series with record lengths ranging from 45 to 80 years. The goodness of fit of distributions was examined from three different perspectives. The first is an examination considering rainfall durations, the second is a province-based examination, and the third is a general country-based assessment. In all three different perspectives, the Wakeby distribution was determined as the best fit candidate to represent the maximum rainfall in Türkiye. Full article

The rise in the number of cancer cases and the dissemination of strains with multiple drug resistance in the world pose a serious threat to public health care and human well-being. The design and study of new chemotherapeutic agents for cancer and infectious diseases are hot topics in science. Hydrazones, a versatile and diverse class of chemical compounds, gained a lot of attention as a promising base for future drugs. In this paper, we report on the synthesis of eight new gold(III) complexes with hydrazones derived from pyridoxal-5′-phosphate and pyridoxal. The complexes are thoroughly characterized using IR, ¹H, ³¹P NMR, and mass spectroscopy. The cytotoxic effect of twelve various hydrazones derived from pyridoxal 5′-phosphate on both immortalized (HEK293T) and tumor (HCT116) human cell lines was estimated using the MTT assay. In addition, this contribution describes the antibacterial action of complexes of gold(III) and pyridoxal and pyridoxal 5′-phosphate-derived hydrazones, as well as the mixtures of the solutions containing tetrachloroaurate(III) and hydrazones, using the zone of inhibition test. Gold(III) complexes exhibit moderate antibacterial activity against both Gram-positive and Gram-negative bacteria, while free hydrazones show low cytotoxicity and thus could be considered relatively safe for humans. Full article

Conventional rubber-based absorbers containing cavities exhibit a decline in acoustic absorption as hydrostatic pressure rises. To improve sound absorption performance under hydrostatic pressure, a lattice-reinforced meta-absorber (LRMA) is proposed in this paper. The rubber layer is embedded with periodic cavities and aluminum pipes as a lattice reinforcement structure. The energy dissipation density, displacement field, and surface acoustic impedance are employed to reveal the sound absorption mechanism of the LRMA. Then, the collaborative design of material and structure for the LRMA is optimized using a differential evolution algorithm. Finally, the experiment verifies that the average sound absorption coefficient is above 0.9 in the frequency range of 500–5000 Hz under hydrostatic pressure of 1 MPa, 2 MPa, and 3 MPa. The results show that the face sheet and the lattice reinforcement structure have good hydrostatic pressure resistance. Full article

One-part geopolymers (OPG) offer a low-carbon alternative to Portland cement, yet mix design remains largely empirical. This study couples machine learning with SHAP (Shapley Additive Explanations) to quantify how mix and curing factors govern performance in Ca-containing OPG. We trained six regressors—Random Forest, ExtraTrees, SVR, Ridge, KNN, and XGBoost—on a compiled dataset and selected XGBoost as the primary model based on prediction accuracy. Models were built separately for four targets: compressive strength at 3, 7, 14, and 28 days. SHAP analysis reveals four dominant variables across targets—Slag, Na₂O, Ms, and the water-to-binder ratio (w/b)—while the sand-to-binder ratio (s/b), temperature, and humidity are secondary within the tested ranges. Strength evolution follows a reaction–densification logic: at 3 days, Slag dominates as Ca accelerates C–(N)–A–S–H formation; at 7–14 days, Na₂O leads as alkalinity/soluble silicate controls dissolution–gelation; by 28 days, Slag and Na₂O jointly set the strength ceiling, with w/b continuously regulating porosity. Interactions are strongest for Slag × Na₂O (Ca–alkalinity synergy). These results provide actionable guidance: prioritize Slag and Na₂O while controlling w/b for strength. The XGBoost+SHAP workflow offers transparent, data-driven decision support for OPG mix optimization and can be extended with broader datasets and formal validation to enhance generalization. Full article

Objectives: To evaluate the impact of different corneal storage containers—with and without corneal holders—on endothelial cell density (ECD) and endothelial cell loss (ECL) during organ culture, following a temporary shortage of Böhnke Donor Corneal Holders at a German eye bank. Methods: A retrospective analysis was conducted on 383 human donor corneas cultured in six types of containers between January and September 2024 at the Eye Bank of Rhineland-Palatinate. ECD was measured at 6.0 ± 1.2 days (ECD1) and again at 14.9 ± 4.4 days (ECD2) after retrieval using standardized organ culture protocols with an inverted light microscope. Group 1 (G1) used the standard 50 mL Corning^® flask with the Böhnke corneal holder. Groups 2–6 used alternative containers, with or without corneal holders. ECL was defined as the difference between ECD2 and ECD1. Results: Mean overall ECD1 was 2478.3 ± 843.4 cells/mm². G1 showed ECD1 < 2000 cells/mm² in 29% of corneas and low ECL (−1%). The 60 mL Ratiolab^® flask with Cornea-Claw^® was the best alternative, showing the lowest incidence of ECD1 < 2000 cells/mm² (7%) and no ECL. Containers without corneal holders, particularly the 100 mL Sterilin™ and 15 mL Cryogenic Tube^®, had significantly higher rates of ECD1 < 2000 (40% and 75%) and greater ECL (9% and 14%). Conclusions: Container design, especially volume and the presence of corneal holders, significantly influences endothelial preservation. Especially a lack of corneal holders led to unacceptable endothelial cell loss. Eye banks should prioritize appropriate flask configurations to minimize tissue deterioration, particularly during supply shortages. Full article

Background and objectives: Large language models (LLMs) show promise in automating open-ended evaluation tasks, yet their reliability in rubric-based assessment remains uncertain. Variability in scoring, feedback, and rubric adherence raises concerns about transparency and pedagogical validity in educational contexts. This study introduces CourseEvalAI, a framework designed to enhance consistency and fidelity in rubric-guided evaluation by fine-tuning a general-purpose LLM with authentic university-level instructional content. Methods: The framework employs supervised fine-tuning with Low-Rank Adaptation (LoRA) on rubric-annotated answers and explanations drawn from undergraduate computer science exams. Responses generated by both the base and fine-tuned models were independently evaluated by two human raters and two LLM judges, applying dual-layer rubrics for answers (technical or argumentative) and explanations. Inter-rater reliability was reported as intraclass correlation coefficient (ICC(2,1)), Krippendorff’s α, and quadratic-weighted Cohen’s κ (QWK), and statistical analyses included Welch’s t tests with Holm–Bonferroni correction, Hedges’ g with bootstrap confidence intervals, and Levene’s tests. All responses, scores, feedback, and metadata were stored in a Neo4j graph database for structured exploration. Results: The fine-tuned model consistently outperformed the base version across all rubric dimensions, achieving higher scores for both answers and explanations. After multiple-testing correction, only the Generative Pre-trained Transformer (GPT-4)—judged Technical Answer contrast remains statistically significant; other contrasts show positive trends without passing the adjusted threshold, and no additional significance is claimed for explanation-level results. Variance in scoring decreased, inter-model agreement increased, and evaluator feedback for fine-tuned outputs contained fewer vague or critical remarks, indicating stronger rubric alignment and greater pedagogical coherence. Inter-rater reliability analyses indicated moderate human–human agreement and weaker alignment of LLM judges to the human mean. Originality: CourseEvalAI integrates rubric-guided fine-tuning, dual-layer evaluation, and graph-based storage into a unified framework. This combination provides a replicable and interpretable methodology that enhances the consistency, transparency, and pedagogical value of LLM-based evaluators in higher education and beyond. Full article

Reuse of wastewater through brackish water reverse osmosis presents a major challenge due to the generation of brine, which contains organic and inorganic compounds to be removed. This study focuses on analyzing and optimizing coagulation conditions for brackish reverse osmosis brine treatment by evaluating pollutant removal efficiencies under various scenarios and leveraging advanced modeling techniques. Jar tests were performed using polyaluminum chloride and ferric chloride, evaluating the removal of total organic carbon, turbidity, UV₅₂₄, and phosphorus. Models were developed using response surface methodology, support vector machines, and random forest. Although the same data sets were used, the characteristics of these models were found to be different: Response surface methodology delivered high-fidelity, smooth response surfaces (R² > 0.92), support vector machine pinpointed sharp threshold regions, and random forest defined robust operating plateaus. By overlaying model-specific optimum contours, the consensus regions were identified for reliable removal across total organic carbon, turbidity, and phosphate. This ensemble strategy enhanced predictive reliability and provided a comprehensive decision-support tool for multi-objective optimization. The findings underscore the potential of ensemble-based modeling to improve the design and control of brackish reverse osmosis brine treatment processes, offering a data-driven pathway for addressing one of the most critical bottlenecks in wastewater reuse systems. Full article