Search Results (14,105)

Traditional thresholding methods are often tailored to specific histogram patterns, making it difficult to achieve robust segmentation across diverse images exhibiting non-modal, unimodal, bimodal, or multimodal distributions. To address this limitation, this paper proposes an automatic thresholding method guided by maximizing homologous isomeric similarity under a unified transformation toward unimodal distribution. The primary objective is to establish a generalized selection criterion that functions independently of the input histogram’s pattern. The methodology employs bilateral filtering, non-maximum suppression, and Sobel operators to transform diverse histogram patterns into a unified, right-skewed unimodal distribution. Subsequently, the optimal threshold is determined by maximizing the normalized Renyi mutual information between the transformed edge image and binary contour images extracted at varying levels. Experimental validation on both synthetic and real-world images demonstrates that the proposed method offers greater adaptability and higher accuracy compared to representative thresholding and non-thresholding techniques. The results show a significant reduction in misclassification errors and improved correlation metrics, confirming the method’s effectiveness as a unified thresholding solution for images with non-modal, unimodal, bimodal, or multimodal histogram patterns. Full article

Oats (Avena sativa L.) are a rich source of β-d-glucans, dietary fibre, proteins, and lipids. However, the behaviour of these components in wheat–oat composite systems during baking, particularly with regard to matrix-dependent analytical responses, remains unclear. This study evaluated the compositional changes, technological performance, and sensory quality of wheat bread enriched with various forms of oat. Composite flours containing 5–15% wholegrain oat flour, commercial oat bran, milled commercial oat flakes, or milled sprouted oat grain (sprouted under laboratory conditions for three days at 25 °C) were prepared using the Slovakian oat cultivar ‘Peter’. The raw materials, flour blends, and baked breads were analysed for β-d-glucans (BG), total dietary fibre (TDF), starch, proteins, and lipids using standardised enzymatic, gravimetric, and polarimetric methods. Bread quality was assessed through loaf volume measurements and a sensory evaluation using a 5-point hedonic scale by seven trained panellists. Multivariate statistical analysis was applied to integrate compositional, technological, and sensory data. Compared to wheat flour (0.24% BG and 3.45% TDF), the incorporation of oats significantly increased the contents of BG, TDF, proteins, and lipids, with oat bran showing the strongest enrichment effect (owing to 15.69% TDF in the raw material). Baking induced oat-form-dependent changes in the measured BG and TDF content. The level of BG diminished in wholegrain oat blends but increased or remained stable in bran-rich systems. This reflects differences in matrix structure and analytical extractability, rather than true compositional gains. Meanwhile, starch content consistently declined across all composite breads. Fibre-rich formulations exhibited reduced loaf volume and altered both bread geometry and morphology, particularly at 15% substitution. Breads containing 5% oat flour or moderate levels of oat bran (5 or 10%) were considered the most acceptable in terms of nutritional enhancement and quality attributes. Germinated oat breads showed the greatest technological impairment and the lowest sensory scores. Overall, moderate oat enrichment strikes a balance between nutritional improvement and technological performance without significantly compromising sensory quality. These findings emphasise the significance of matrix effects when interpreting standard total dietary fibre and β-d-glucans analyses and offer an integrated analytical and technological framework for the rational design of fibre-enriched cereal products. Full article

The classic set covering problem (SCP) is an NP-hard binary integer optimization problem with diverse business and industrial applications. Its primary goal is to consolidate resources by selecting a minimal cost subset of columns in a matrix that covers all required rows. Traditionally, conflicts between selected resources were resolved after generating a solution, often adding managerial effort and inefficiency. Recently, two papers have tried to handle conflict constraints explicitly as part of the SCP solution generation process. This paper focuses on SCPs with soft conflict constraints (SCP-SCC), where violations are allowed but with penalties, and proposes a simple hybrid solution approach that combines a GRASP-based heuristic with Gurobi optimization. Using 360 test instances (160 from the literature and 200 new instances), this hybrid approach results in a 7.4% performance improvement over Gurobi, demonstrating the benefit of integrating heuristic and exact solution methods. In addition, classification tree analysis is applied as an attempt to identify problem features (such as conflict graph density and size) that can be used to predict when SCP-SCC instances will likely be difficult to solve to proven optimality efficiently using Gurobi. These insights provide practical guidance for operations research practitioners, enabling informed decisions among heuristic, exact, or hybrid solution approaches and improving efficiency in real-world applications. Full article

The present study investigates the suitability of the invasive herbaceous species Sosnowsky’s hogweed (Heracleum sosnowskyi) and giant knotweed (Fallopia sachalinensis), together with reed (Phragmites australis), as feedstock for pressed biofuel pellets used alone and as additives to pinewood. Biomass of the three herbaceous species and pinewood was harvested, dried, chopped, milled, and pelletized through a 6 mm die to obtain pure pellets and binary mixtures of each herbaceous biomass with pinewood (25, 50, and 75% by weight of herbaceous share). The pellets were characterized for physical and mechanical properties, elemental composition, calorific value, combustion emissions, and life cycle impacts per 1 GJ of heat. Pellet density ranged from 1145.60 to 1227.47 kg m⁻³, comparable to or higher than pinewood, while compressive resistance satisfied solid biofuel quality requirements. The lower calorific values of all herbaceous and mixed pellets varied between 16.29 and 17.78 MJ kg⁻¹, with increased ash and nitrogen contents at higher herbaceous shares. Combustion tests showed substantially higher CO and NO_x emissions for pure invasive and reed pellets than for pinewood, but all values remained within national regulatory limits. Life cycle assessment indicated the highest global warming and fossil fuel depletion potentials for reed systems, followed by Sosnowsky’s hogweed and giant knotweed, with pinewood consistently exhibiting the lowest impacts. Overall, invasive plants and reed are technically suitable as partial pinewood substitutes in pellet production, supporting simultaneous invasive biomass management and renewable heat generation. Full article

Traditional complementary metal-oxide-semiconductor (CMOS) logic gates serve as the fundamental building blocks of modern computing, operating through the electron charge manipulation wherein binary information is encoded as distinct high- and low-voltage states. However, as physical dimensions approach the quantum limit, conventional logic gates encounter fundamental bottlenecks, including power consumption barriers, memory limitations, and a significant increase in static power dissipation. Consequently, the pursuit of novel low-power computing methodologies has emerged as a research hotspot in the post-Moore era. Logic gates based on magnetic skyrmions constitute a highly promising candidate in this context. Magnetic skyrmions, nanoscale quasiparticles endowed with topological protection, offer ideal carriers for information transmission due to their exceptional stability and mobility. In this work, we provide a concise overview of the current development status and underlying operating principles of magnetic skyrmion logic gates across various magnetic materials, including ferromagnetic, synthetic antiferromagnetic, and antiferromagnetic systems. The introduction of magnetic skyrmion-based logical operations represents a paradigm shift from traditional Boolean logic to architectures integrating memory and computation, as well as brain-inspired neuromorphic computing. Although significant challenges remain in the synthesis of materials, fabrication, and detection, magnetic skyrmion-based logic computing holds considerable potential as a future ultra-low-power computing technology. Full article

Classifying optical water types (OWTs), particularly concerning different phytoplankton bloom types, is critically important because dominant phytoplankton groups govern key marine ecosystem functions and biogeochemical processes, including nutrient cycling and carbon export. This study refines a recent OWT classification method developed for the Second-Generation Global Imager (SGLI), which was originally proposed to discriminate dinoflagellate and diatom blooms. By employing binary logistic regression (bLR) with independent in situ data from Karenia selliformis (dinoflagellate) blooms off the Kamchatka Peninsula and Skeletonema spp. (diatom) blooms in Tokyo Bay, this study establishes more robust and statistically meaningful boundaries between OWTs. The analysis confirms the diagnostic spectral shapes from SGLI data: a trough at 490 nm for K. selliformis blooms and a peak at 490 nm for diatom blooms, validating the consistency of this spectral criterion. The updated method reliably identifies waters dominated by coloured dissolved organic matter and different phytoplankton functional types in mesotrophic waters, and successfully detected a Karenia mikimotoi bloom in the Gulf St. Vincent, South Australia, demonstrating its potential for the global monitoring of red tides. By providing a reliable, satellite-based tool to distinguish between ecologically distinct phytoplankton groups, this refined OWT classification offers a valuable data product to improve the accuracy of marine ecosystem and carbon cycle models, moving beyond bulk chlorophyll-a parameterizations. Full article

Background/Objectives: Arboviral diseases share common vectors, geographic distribution, and symptoms. Developing Machine Learning diagnostic tools for co-circulating arboviral diseases faces data-scarcity challenges. This study aimed to demonstrate that proof of concept using synthetic data can establish computational feasibility and guide future real-world validation efforts. Methods: We assembled a synthetic dataset of 28,000 records, with 7000 for each disease—Dengue, Zika, and Chikungunya—plus Influenza as a negative control. These records were obtained from the existing literature. A binary matrix with 67 symptoms was created for detailed statistical analysis using Odds Ratios, Chi-Square, and symptom-specific conditional prevalence to validate the clinical relevance of the simulated data. This dataset was used to train and evaluate various algorithms, including Multi-Layer Perceptron (MLP), Narrow Neural Network (NN), Quadratic Support Vector Machine (QSVM), and Bagged Tree (BT), employing multiple performance metrics: accuracy, precision, sensitivity, specificity, F1-score, AUC-ROC, and Cohen’s kappa coefficient. Results: The dataset aligns with the PAHO guidelines. Similar findings are observed in other arboviral databases, confirming the validity of the synthetic dataset. A notable performance across all evaluated metrics was observed. The NN model achieved an overall accuracy of 0.92 and an AUC above 0.98, with precision, sensitivity, and specificity values exceeding 0.85, and an average Uniform Cohen’s Kappa of 0.89, highlighting its ability to reliably distinguish between Dengue and Influenza, with a slight decrease between Zika and Chikungunya. Conclusions: These models could accelerate early diagnosis of arboviral diseases by leveraging encoded symptom features for Machine Learning and Deep Learning approaches, serving as a support tool in regions with limited healthcare access without replacing clinical medical expertise. Full article

Background/Objectives: Blood thinners (anticoagulants) remain the first line pharmacotherapy for the management of cardiovascular and thromboembolic disorders. The increased utilization of polypharmacy, likely driven by the greater burden of comorbidities, elevates the risk of potential drug–drug interactions (pDDIs) and creates a significant challenge in anticoagulant management. The aim of the study was to assess the prescribing trend and impact of polypharmacy and pDDIs in patients receiving anticoagulant drug therapy in a public hospital providing secondary care. Methods: A cross-sectional observational study was undertaken between January–June 2023. Data from electronic medical records of prescriptions for anticoagulants were collected, analyzed for prescribing patterns, and checked for pDDIs using Micromedex database 2.0^®. Utilizing binary logistic regression, the relationship between polypharmacy and sociodemographic factors was assessed. Multivariate logistic regression analysis served to uncover determinants linked to pDDIs. Results: Of the total 130 patients, females were predominant (58.46%), with a higher prevalence among those aged 61–90 years. Atrial fibrillation emerged as the main clinical reason and apixaban (51.53%) ranked as the top prescribed anticoagulant in our cohort. Among the 766 pDDIs identified, the majority [401 (52.34%)] were categorized as moderate in severity. Polypharmacy was strongly linked to age (p = 0.001), the Charlson comorbidity index (CCI) (p = 0.040), and comorbidities (p = 0.005) in the binary logistic regression analysis. In the multivariable analysis, the number of medications remain a strong predictor of pDDIs (adjusted OR: 30.514, p = 0.001). Conclusions: Polypharmacy and pDDIs were exhibited in a significant segment of cohort receiving anticoagulant therapy, with strong correlations to age, CCI, comorbidities, and the number of medications. A multidimensional approach involving collaboration among healthcare providers assisted by clinical decision support systems can help optimize the management of polypharmacy, minimize the risks of pDDIs, and ultimately enhance health outcomes. Full article

Glioblastoma multiforme (GBM) is the most aggressive primary brain cancer (with a median survival time of 14.5 months), characterized by heterogeneity. Identifying prognostic molecular subtypes could provide a deeper exposition of GBM biology with potential therapeutic implications. In this study, we classified GBM into two prognostic subtypes, C1-GBM (n = 57; OS: 313 days) and C2-GBM (n = 109; OS: 452 days), using pathway-based signatures derived from RNA-seq data. Unsupervised consensus clustering revealed that only binary classification (cluster number, CN = 2; mean cluster consensus score = 0.84) demonstrated statistically prognostic differences. We characterized C1 and C2 based on oncogenic pathway and immune signatures. Specifically, C1-GBM was categorized as an immune-infiltrated “hot” tumor, with high infiltration of immune cells, particularly macrophages and CD4⁺ T cells, while C2-GBM as an “inherent driving” subtype, showing elevated activity in G2/M checkpoint genes. To predict the C1 or C2 classification and explore therapeutic interventions, we developed a neural network model. By using Weighted Correlation Network Analysis (WGCNA), we obtained the gene co-expression module based on both gene expression pattern and distribution among patients in TCGA dataset (n = 166) and identified nine hub genes as potentially prognostic biomarkers for the neural network. The model showed strong accuracy in predicting C1/C2 classification and prognosis, validated by the external CGGA-GBM dataset (n = 85). Based on the classification of the BP neural network model, we constructed a Cox nomogram prognostic prediction model for the TCGA-GBM dataset. We predicted potential therapeutic small molecular drugs by targeting subtype-specific oncogenic pathways and validated drug sensitivity (C1-GBM: Methotrexate and Cisplatin; C2-GBM: Cytarabine) by assessing IC₅₀ values against GBM cell lines (divided into C1/C2 subtypes based on the nine hub genes) from the Genomics of Drug Sensitivity in Cancer database. This study introduces a pathway-based prognostic molecular classification of GBM with “hot” (C1-GBM) and “inherent driving” (C2-GBM) tumor subtypes, providing a prediction model based on hub biomarkers and potential therapeutic targets for treatments. Full article

Background/Objectives: Child undernutrition remains a major public health in Djibouti, yet conventional anthropometric indicators may underestimate its true burden by failing to capture overlapping forms of malnutrition. The Composite Index of Anthropometric Failure (CIAF) provides a more comprehensive assessment by identifying children experiencing one or multiple anthropometric deficits. This study aimed to estimate the prevalence and determinants of undernutrition among children under five years of age in Djibouti using the CIAF. Methods: This study is a secondary analysis of data from the nationally representative 2023 Multisectoral Survey conducted in Djibouti. A cross-sectional design with a two-stage stratified cluster sampling method was used to collect data on a national random sample (n = 2103) of children aged 6–59 months. Standardized anthropometric measurements were used to derive conventional indicators (stunting, wasting, and underweight) and the CIAF. Binary logistic regression analyses were performed to identify factors associated with anthropometric failures, adjusting for child, household, and contextual characteristics. Results: Based on conventional indicators, 23.4% of children were stunted, 20.0% were underweight, and 9.9% were wasted. Using the CIAF, 36.9% of children experienced at least one anthropometric failure, including 18.8% with multiple concurrent failures. Boys, children aged 6–47 months, those living in nomadic households, and those residing in specific regions had significantly higher risks of undernutrition. Socioeconomic indicators and household food security were not independently associated with undernutrition after adjustment. Conclusions: More than one-third of children under five in Djibouti experience undernutrition when assessed using the CIAF, revealing a substantial hidden burden not captured by conventional indicators alone. Incorporating the CIAF into routine nutrition surveillance could improve identification of vulnerable children and support more targeted, context-specific interventions. Full article

Background: The differentiation of pheochromocytoma (PCC) from other adrenal lesions, particularly in incidentalomas with non-benign radiological characteristics (size > 4 cm or density > 10 HU), remains a clinical challenge. The study aimed to develop and validate an interpretable machine learning (ML) model for pairwise differentiation of PCC from adrenocortical carcinomas (ACCs) and non-functioning adrenal adenomas (NAAs) and to identify the most important clinical features. Methods: We analyzed a dataset of 50 clinical, laboratory, and radiological parameters from 123 patients with histologically verified adrenal tumors (63 PCC, 30 ACC, 30 NAA). Four classifiers—Logistic Regression (LR), Random Forest (RF), Linear Discriminant Analysis (LDA), and Extreme Gradient Boosting (XGBoost)—were trained for binary classification tasks (PCC vs. ACC, PCC vs. NAA, ACC vs. NAA) using a robust nested stratified cross-validation pipeline to ensure generalizability and avoid overfitting. Results: All four models showed strong predictive performance, with discrimination (AUC) more than 0.8. Our analysis, based on the interpretable LR model, identified the key discriminators differentiated PCC from both ACC and NAA: maximum systolic blood pressure, grade 3 hypertension, headache, palpitation, tachycardia, male sex, and concomitant gastric and duodenal ulcers. In contrast, lower back pain and general weakness were strong signs of lower probability of PCC. The tumor density specifically differentiated PCC from NAA, whereas tumor size was an important marker for distinguishing PCC and ACC. Conclusions: We developed robust ML models capable of accurately differentiating PCC from other adrenal tumors in complex cases. The models provide a clinically actionable tool for pre-surgical decision support. Furthermore, the identification of key discriminative features enhances the clinical understanding of PCC and facilitates its differential diagnosis prior to histological verification. Full article

Despite the extensive application of machine learning (ML) methods to educational datasets, few studies have provided a systematic benchmarking of the available algorithms with respect to both predictive performance and interpretability of the resulting models. In this work, we address this gap by comparing a range of supervised learning methods on a freely available dataset concerning two high schools, where the goal is to predict student performance by modeling it as a binary classification task. Given the high feature-to-sample ratio, the problem falls within the small-data learning regime, which often challenges ML models by diluting informative features among many irrelevant ones. The experimental results show that several algorithms can achieve robust predictive performance, even in this scenario and in the presence of class imbalance. Moreover, we show how the output of ML algorithms can be interpreted and used to identify the most relevant predictors, without any a priori assumption about their impact. Finally, we perform additional experiments by removing the two most dominant features, revealing that ML models can still uncover alternative predictive patterns, thus demonstrating their adaptability and capacity for knowledge extraction under small-data conditions. Future work could benefit from richer datasets, including longitudinal data and psychological features, to better profile students and improve the identification of at-risk individuals. Full article

To address the challenges posed by rock bolt corrosion to engineering safety and service life, this study focuses on corrosion detection through integrated image processing, deep learning, and feature extraction methods. An automatic corrosion identification model was constructed based on computer-vision object-detection algorithms. By incorporating a Feature Pyramid Network, the model’s multi-scale object-detection capability was significantly enhanced. The corrosion features were extracted via image binarization and grayscale matrix analysis. The binary image method accurately quantified pitting density, revealing an initial increase followed by a decrease over time. The corrosion morphology was simulated using a Fractional Brownian Motion model, validating the accuracy of fractal feature calculations. The fractal dimension increased significantly with prolonged corrosion time, which not only characterize surface roughness evolution and corrosion rate, but also provide a reliable quantitative indicator for metal corrosion assessment. This research offers a technical framework integrating image processing, deep learning, and fractal theory for rock bolt corrosion monitoring and maintenance. Full article

Human Activity Recognition (HAR) has become crucial across various domains, including healthcare, smart homes, and security systems, owing to the proliferation of Internet of Things (IoT) devices. Several Machine Learning (ML) techniques, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), have been proposed for HAR. However, they are still deficient in addressing the challenges of noisy features and insufficient data. This paper introduces a novel approach to tackle these two challenges, employing a Deep Learning (DL) Ensemble-Based Stacking Neural Network (SNN) combined with Generative Adversarial Networks (GANs) for HAR based on ambient sensors. Our proposed deep learning ensemble-based approach outperforms traditional ML techniques and enables robust and reliable recognition of activities in real-world scenarios. Comprehensive experiments conducted on six benchmark datasets from the CASAS smart home project demonstrate that the proposed stacking framework achieves superior accuracy on five out of six datasets when compared to literature-reported state-of-the-art baselines, with improvements ranging from 3.36 to 39.21 percentage points and an average gain of 13.28 percentage points. Although the baseline marginally outperforms the proposed models on one dataset (Aruba) in terms of accuracy, this exception does not alter the overall trend of consistent performance gains across diverse environments. Statistical significance of these improvements is further confirmed using the Wilcoxon signed-rank test. Moreover, the ASGAN-augmented models consistently improve macro-F1 performance over the corresponding baselines on five out of six datasets, while achieving comparable performance on the Milan dataset. The proposed GAN-based method further improves the activity recognition accuracy by a maximum of 4.77 percentage points, and an average of 1.28 percentage points compared to baseline models. By combining ensemble-based DL with GAN-generated synthetic data, a more robust and effective solution for ambient HAR addressing both accuracy and data imbalance challenges in real-world smart home settings is achieved. Full article

Waste textiles may contain heavy metals, which can originate from dyes, mordants, or other chemical treatments used during manufacturing. To explore the impact of heavy metals on the adsorption properties of activated carbon derived from discarded textiles through pyrolysis and to mitigate heavy metal migration, this study investigated the adsorption behavior of copper-impregnated pyrolytic carbon toward typical pollutants—methylene blue and lead—in simulated dyeing wastewater. Aqueous copper nitrate was used to impregnate the waste pure cotton textiles (WPCTs) to introduce copper species as precursors for creating additional active sites. The study systematically examined adsorption mechanisms, single and binary adsorption systems, adsorption kinetics, adsorption isotherms, adsorption thermodynamics, and the influence of pH. Key findings and conclusions are as follows: Under optimal conditions, the copper-containing biochar (Cu-BC) demonstrated maximum adsorption capacities of 36.70 ± 1.54 mg/g for Pb(II) and 104.93 ± 8.71 mg/g for methylene blue. In a binary adsorption system, when the contaminant concentration reached 80 mg/L, the adsorption capacity of Cu-BC for Pb(II) was significantly enhanced, with the adsorption amount increasing by over 26%. However, when the Pb(II) concentration reached 40 mg/L, it inhibited the adsorption of contaminants, reducing the adsorption amount by 20%. SEM, XRD, Cu LMM, FTIR and XPS result analysis proves that the adsorption mechanism of methylene blue involves π–π interactions, hydrogen bonding, electrostatic interactions, and pore filling. For Pb(II) ions, the adsorption likely occurs via electrostatic interactions, complexation with functional groups, and pore filling. This study supplements the research content on the copper adsorption mechanism supported by biochar for heavy metal adsorption research and broadens the application scope of biochar in the field of heavy metal adsorption. Full article