Search Results (561)

Fuzzing is an essential element of software supply chain security governance. Despite its importance, the widespread adoption of library fuzzing is limited by the significant costs associated with constructing fuzz drivers. Without a clear entry point, the reachable path space of the target library is determined by the interplay of API call sequences, parameter dependencies, and state constraints. As a result, fuzz drivers must achieve not only successful builds but also provide sufficient semantic context to enable exploration of deeper state machine interactions, thereby avoiding premature stagnation at superficial validation logic. To systematically assess advancements in automated fuzz driver generation, this paper develops a taxonomy organized around the primary data sources used to derive driver-generation constraints, categorizing existing approaches into four technological trajectories: Usage Artifact Mining, Source Code Constraint Inference, Binary Semantics Recovery, and Heterogeneous Data Fusion. Large language models are increasingly integrated into these workflows as generators and as components for constraint alignment and repair. To address inconsistencies in experimental methodologies, this paper introduces a bounded comparability-oriented evaluation perspective focused on three dimensions: validity, reachability-related evidence, and reproducibility and cost. Together with a disclosure and reporting protocol for metric comparability, this perspective clarifies the information needed for cross-study comparison and examines the unique features and inherent limitations of each technical trajectory. Based on these findings, three key directions for future research are identified: facilitating structural evolution in response to coverage plateaus to address deep logic unreachability; coordinating dynamic closed-loop orchestration that utilizes on-demand heterogeneous data retrieval to resolve context challenges; and developing language-agnostic driver representations with pluggable adaptation mechanisms to improve cross-ecosystem portability and scalability. Full article

The aim of the present work is to give a mathematical underpinning for the use of quasi-probabilities and pseudo-metrics in infinite-dimensional Banach manifolds. The notion of a continuous binary structure is introduced. It is a triple consisting of a continuous symmetric bilinear form together with a pair of closed linear subspaces of a Banach space. Such binary structures are abundant in Hilbert spaces. In order to confirm their existence in arbitrary Banach spaces, the auxiliary notion is introduced of subspaces that are positive with respect to a given symmetric bilinear form. It is shown that any subspace which is maximally positive with respect to the bilinear form induces a continuous binary structure on the Banach space. The Wigner function of a system of quantum mechanical particles is treated as an example. Full article

The optimization of gate allocation and taxiway routing represents a critical challenge in enhancing airport ground operations performance. To simultaneously address these two closely coupled tasks, their interconnected processes are first modeled as flows in a spatiotemporal graph. On this basis, we develop a multi-objective optimization approach that accounts for both temporal and spatial factors across different operational aspects, effectively balancing the diverse needs of travelers, carriers, and airport authorities. To mitigate differences in scale and preference among various optimization objectives, min-max normalization combined with the linear weighting method is employed to transform the multi-objective problem into a single-objective one, which is solved by binary integer linear programming. Based on the actual operational data of Terminal 1 at Shanghai Pudong International Airport, three typical scenarios of different complexity are constructed for validation purposes. Performance comparisons with the state-of-the-art methods demonstrate the superiority of the proposed model in terms of various operational costs and parameter sensitivity. The integrated scheduling solution offers airport operators a reliable and efficient decision-making tool with practical applicability. Full article

The Zeta-Minimizer Theorem establishes that the Riemann zeta function $\zeta \left(s\right)$ and the primes arise variationally as unique minimizers of a phase functional defined on a symmetric measure space $\left(X,\mu ,G\right)$ equipped with helical operators. Three fundamental axioms—strict concave entropy maximization (Axiom 1), spectral Gibbs minima with non-vanishing ground states (Axiom 2), and irreducible bounded oscillations with flux conservation (Axiom 3)—allow for the selection of the non-proper Archimedean conical helix as the sole topology satisfying all constraints. Primes emerge as indivisible minimal cycles in the associated representation graph $\Gamma$ (via Hilbert irreducibility and Maschke’s theorem), while the Euler product is recovered through the spectral Dirichlet mapping of the helical eigenvalues. The partial zeta product, $Z\left(s\right)=\prod _j{\displaystyle \frac{1}{1-p_j^{-s}}},s\in \mathbb{R}\left\{0\right\},$ constitutes the exact grand partition function of any finite subsystem. Numerical inversion of this product directly recovers the mixture frequency $s$ from any experimental compressibility factor $Z_{\mathrm{m}\mathrm{i}\mathrm{x}}$. Mole fractions $x_i\left(s\right)$, interaction parameters $\Delta \left(x_i\right)$, and the Lyapunov spectrum $\lambda \left(x_i\right)$ then follow deductively via the helical transfer matrix and the closed-form linear ODE for $\Delta$. Occupation numbers $N\left(x_i\right)$ attain sharp maxima precisely at Fibonacci ratios $F_r/F_{r+1}$, leading to the molecular prime-ID rule. For twelve representative purely binary (irreducible) systems spanning atomic noble gases, simple diatomics, polar molecules, and an aromatic ring, the residuals satisfy $|Z\left(s\right)-Z_{\mathrm{m}\mathrm{i}\mathrm{x}}|<1.5\times {10}^{-8}$. The resulting $\lambda \left(x_i\right)$ curves accurately reproduce critical points, liquid ranges, and thermodynamic anomalies with zero adjustable parameters. The Riemann Hypothesis follows rigorously as a theorem: the unique fixed point of the duality functor $s\mapsto 1-s$ that preserves the orthogonality condition $\sum {\mathrm{c}\mathrm{o}\mathrm{s}}^2{\theta }^k=1$ is $\mathrm{R}\mathrm{e}\left(s\right)=1/2$, enforced by Axiom 1 concavity and Axiom 3 irreducibility. The framework is fully deductive and parameter-free and extends naturally to arbitrary mixtures and multiplicities through the helical representation graph. It provides a variational unification of analytic number theory, spectral geometry, thermodynamic phase behavior, and the Riemann Hypothesis from first principles. Full article

In our previous analysis of the eclipse timing variation patterns of eclipsing binaries located in and near the Northern Continuous Viewing Zone (NCVZ) of the TESS space telescope, 135 hierarchical triple star candidates were found. Now, two additional years of TESS observations are available and, hence, we have extended the former analysis with the use of the new observational data. We now detect 168 triple star candidates in the updated and reanalyzed sample. The majority (∼74%) of them are identical to the former triples candidates. For many of them, our new solutions are more certain than the original ones. Therefore, we can now conclude that we have identified at least 66 short-period hierarchical triple stellar systems in the NCVZ with full confidence. In the case of the majority of the remaining systems in our sample, the presence of a close third stellar component appears to be very likely. We also identify additional, longer timescale period variations in 34 systems (20% of the total sample) and conclude that in at least three systems the presence of a fourth stellar component is quite plausible. Finally, we report the complete disappearance of the eclipses in two former EBs and detect eclipse depth variations in seven other EBs as well. We interpret this effect as the consequence of changing orbital inclination caused by a non-coplanar third body. Full article

We confirmed four spectroscopic binary candidates using new observations obtained with SALT. Three SB2 systems (HD 20784, HD 43519A, HD 62153A) exhibit circular orbits with periods shorter than 10 days, whereas one hierarchical triple system (HD 56024) contains a close binary with an inner eccentric orbit with a period of approximately 14 days, composed of nearly identical stellar components, and a rapidly rotating star on an outer eccentric orbit with a period of approximately 400 days. For two additional SB2 candidates (HD 198174 and HD 208433), our new observations do not allow us to derive reliable orbital solutions. Full article

Background/Objectives: This study aimed to automatically classify Zirconia-based fixed restorations and porcelain-fused-to-metal (PFM) restorations on panoramic radiographs using an artificial intelligence-based model. Unlike previous studies that mainly focused on classifying types of restorations (e.g., crowns, fillings, implants), this research concentrated on material-based differentiation, aiming to provide a more specific contribution to clinical decision support systems. Method: Panoramic radiographs obtained from the archive of Süleyman Demirel University Faculty of Dentistry were included in this study. Radiographs with poor image quality or insufficient visibility of the restoration area were excluded. A total of 593 cropped region-of-interest (ROI) images, labeled by expert prosthodontists using ImageJ software (version 1.54r; National Institutes of Health, Bethesda, MD, USA), were included in the analysis. In order to reduce class imbalance, data augmentation was applied only for images in the Zirconia-based fixed restorations class. By using various image processing techniques such as rotation, reflection and brightness change, the number of samples in the zirconia-based restorations class was increased and thus a balanced dataset was obtained with a close number of samples for both classes. For model training, the pre-trained VGG16 architecture was used with a transfer learning method, and the final layers were retrained and fine-tuned. The model was configured specifically for binary classification. The entire dataset was randomly split into 70% training, 20% validation, and 10% testing. Model performance was evaluated using accuracy, F1-score, sensitivity, and specificity. Results: The model correctly classified 90 out of 94 images in the test dataset, achieving an overall accuracy rate of 96%. For both classes, the precision, recall, and F1-score values were measured in the range of 95% to 96%. Additionally, the Area Under the Curve (AUC) of the ROC curve was calculated as 0.994, and the Average Precision (AP) score was determined to be 0.995. According to the confusion matrix results, only 4 images were misclassified, consisting of 2 false positives and 2 false negatives. Conclusions: The deep learning model demonstrated high accuracy in differentiating zirconia and metal-supported porcelain restorations on panoramic radiographs, suggesting that material-based AI classification may support clinical decision-making in restorative dentistry. Full article

Promoting resilience, increasing productivity and sustainability, and profit maximization remain key challenges facing farmers globally. These are exacerbated by factors such as climate change, low to no access to technological advancement, financial constraints, poor technical and management skills, inadequate government support, and limited access to resources. However, there are diverse strategies that abound, including on-farm diversification, that farmers could leverage on to address these numerous and complex challenges. This study investigated the determinants of on-farm diversification strategies among smallholders in Mpumalanga Province. The study employed a quantitative approach using closed-ended survey questionnaires to elicit information from a total of 465 farmers who were randomly sampled from a total population of 14,411. The data gathered were analysed using descriptive statistics to determine the on-farm diversification strategies employed by farmers and the factors influencing the use of these strategies. A binary logistic regression model was employed to establish the relationship between on-farm diversification strategies and the determining factors. More than half of the farmers were female (51.8%), with only 48.2% male. The majority (59.1%) of the farmers were between the ages of 36 and 60, with only 20.2% youth participation in farming. Slightly more than half (50.8%) of the farmers practise mixed farming as their on-farm diversification strategy, while only 4.3% of the farmers practise mono-cropping. The study identified significant variables such as level of education (p = 0.001), secondary source of income (p = 0.057), farmland size (p = 0.022), number of farm assistants (p = 0.016), and on-farm diversification awareness as key determinants of on-farm diversification among smallholder farmers in Mpumalanga Province. Therefore, it is recommended that policies within the agricultural sector be revised to encourage on-farm diversification in order to motivate farmers to transition to agripreneurship for poverty alleviation, food security and rural economic development (RED). Full article

Suicidal behavior among adolescents and young adults represents a growing public health concern due to its high prevalence and its negative impact on psychological well-being. The aim of this study was to examine the associations between emotional regulation, attachment styles, cyberviolence, and suicidal ideation among Chilean university students. A descriptive cross-sectional design was employed with a sample of 1083 participants, using the Suicidal Ideation Frequency Inventory, the Close Relationship Experience Questionnaire (ECR-R), the Spanish Modified Version of the Trait Meta-Mood Scale (TMMS-24) and the Cyber Dating Violence Instrument for Teens (CyDAV-T). Bivariate analyses and binary logistic regression were conducted to identify significant predictors of suicidal ideation. The results revealed a high prevalence of suicidal ideation, particularly among women (19.06%; p < 0.001). Difficulties in emotion regulation were strongly associated with a higher likelihood of suicidal ideation (p < 0.001), whereas adequate (p < 0.001) or excellent (p < 0.01) regulation functioned as a significant protective factor. In addition, a disorganized attachment style was identified as a risk factor (p < 0.05), especially among women (p < 0.01). In conclusion, emotion regulation emerges as a key protective factor against suicidal ideation, underscoring the importance of implementing socioemotional training programs within university settings. Full article

This work presents a method to encapsulate plant extracts within a binary polysaccharide carrier and to characterize the physicochemical and rheological performance of the resulting biocomposites in the context of food use. Using a starch/psyllium matrix, extracts from Sambucus nigra (SN), Aronia melanocarpa (AM), and Echinacea purpurea (EP) were effectively protected and incorporated through a stepwise workflow encompassing matrix preparation, encapsulation, structural verification, and functional assessment. SEM revealed a porous network containing uniformly distributed, extract-loaded spherical structures (~800–1500 nm), while FTIR supported the presence of hydrogen bonding and hydrophobic interactions that contributed to system stability. The prepared nanoemulsions showed shear-thinning (pseudoplastic) behavior, indicating favorable processing characteristics, whereas most physicochemical and bioactivity measurements were performed on lyophilized composites. The dried materials preserved extract-specific color signatures (ΔE > 5) and exhibited distinct thermal responses: AM produced a pronounced plasticizing effect (Tg reduced by >20 °C), while the incorporation of extracts generally delayed thermal degradation, consistent with polyphenol–starch interactions. Phase-transition behavior was also altered, with melting peaks suppressed for SN and AM and melting temperatures lowered for EP. Surface analysis indicated increased hydrophobicity and a reduced polar component of surface free energy, suggesting improved moisture barrier potential. Antioxidant capacity closely tracked total phenolic content (r > 0.94), with caffeic acid contributing strongly, particularly in EP-based systems. Antimicrobial activity depended on extract type (broad-spectrum for EP, selective for SN, minimal for AM), and the comparatively higher sensitivity of Gram-negative bacteria points to improved phenolic availability and membrane interactions upon encapsulation. Collectively, these results highlight the starch/psyllium matrix as a flexible platform for stabilizing plant extracts while enabling tunable functional attributes for functional food applications. Full article

Background: Psychological distress is a common comorbidity in chronic obstructive pulmonary disease (COPD), yet its relationship with disease severity remains incompletely understood. This study aimed to assess depression, anxiety, and stress using the Depression Anxiety Stress Scales–21 (DASS-21) and to examine their distribution across COPD severity stages. Methods: This multicenter, cross-sectional observational study included 285 clinically stable COPD patients enrolled between 2023 and 2025. COPD severity was classified according to Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria. Multinomial and binary logistic regression models were constructed to identify independent predictors of COPD severity and clinically significant psychological distress, adjusting for demographic and clinical covariates. Bayesian independent sample analyses and ANOVA effect size estimates were additionally performed. Results: Smoking exposure was independently associated with advanced COPD stages (GOLD 4 vs. GOLD 1–3: aOR 1.05, p < 0.001), as was dyspnea severity (mMRC: aOR 14.66, p < 0.001). In multivariable models examining psychological outcomes, COPD severity was not independently associated with clinically significant depression (p = 0.899), anxiety (p = 0.460), or stress (p = 0.843). In contrast, symptom burden measured using the COPD Assessment Test (CAT) score was consistently associated with depression (aOR 1.133, p < 0.001), anxiety (aOR 1.179, p < 0.001), and stress (aOR 1.144, p < 0.001). ANOVA effect sizes across GOLD stages were small (η² ≤ 0.047), and Bayesian analyses provided moderate to strong evidence supporting minimal differences in DASS-21-R scores between severity groups. Conclusions: Psychological distress is prevalent across all COPD severity stages and is not independently determined by airflow limitation. Symptom burden rather than spirometric severity appears to be more closely associated with emotional outcomes. Full article

Influenza A virus remains a major cause of respiratory disease worldwide and poses a persistent challenge to vaccine development due to its rapid genetic evolution and antigenic variability. T-cell-based immunity has therefore gained increasing importance, as it can provide broader and more durable protection by targeting conserved viral regions. Accurate identification of T-cell epitopes (TCEs) is a fundamental requirement for epitope-based vaccine design and immunological research. Although numerous computational methods have been proposed, many existing approaches rely on handcrafted physicochemical features, which offer limited ability to capture contextual sequence dependencies. In this study, a transformer-based viroinformatics framework is proposed for the binary prediction of TCEs from Influenza A virus peptide sequences. The framework employs a pretrained Evolutionary Scale Modeling-2 (ESM-2) protein language model (PLM) to generate rich, contextualized embeddings directly from raw amino acid sequences, eliminating the need for manual feature engineering. These embeddings are processed using a lightweight attention-based transformer classifier to learn epitope-specific sequence patterns. The model achieves strong and stable predictive performance, attaining an accuracy of approximately 97% and an AUC close to 0.99 under stratified cross-validation. Ablation analysis further confirms that protein language model representations and self-attention contribute substantially to performance gains over classical machine learning baselines. To enhance practical reliability, Monte Carlo dropout is incorporated during inference to provide uncertainty-aware predictions, enabling differentiation between high-confidence and ambiguous peptide candidates. In addition, attention-based interpretability is used to identify residue-level contributions to model decisions, offering biologically meaningful insights into epitope recognition. Overall, this study demonstrates that PLMs combined with Transformer architectures provide an effective, interpretable, and a promising computational framework for Influenza A TCE discovery and vaccine research. Full article

This article explores how young women of colour in Norway navigate everyday racism and how such negotiations are shaped by the tension between speaking out or maintaining social harmony in a society that largely perceives itself as egalitarian and non-racist. The study draws on qualitative interviews with 13 participants from diverse ethnic backgrounds—some were adopted, and others were children of immigrant parents or immigrants themselves. The analysis examines how anti-racism strategies are shaped by drawing on feminist and postcolonial theory, particularly the concept of the feminist killjoy. The notion of Orientalism, and the notion of cultural repertoires. The findings show that participants demonstrated different reactions from silence to confrontation, all demanding emotional labour. Several participants described the burden of having to choose between remaining polite and educating others, while others chose silence as a protective strategy. Rather than viewing resistance as a binary between silence and confrontation, this study demonstrates that everyday anti-racism is a fluid and context-dependent practice. How women performed anti-racism was also closely linked to their social position, social support, cultural norms, and access to political perspectives. The stories show that, over time, some women became more outspoken or secure in their interpretations of racist encounters, especially when gaining distance from constraining environments. Full article

High-speed turning of AISI 304 stainless steel is limited by rapid tool wear driven by thermal accumulation and tribological instability. This study compares five cooling/lubrication strategies (dry, flood cooling, MQL, internally cooled tools—ICT, and ICT + MQL) under a fixed severe cutting regime (Vc = 400 m/min, f = 0.1 mm/rev, ap = 0.2 mm) and develops a low-complexity tool end-of-life predictor using cutting power as the sole monitoring signal. Dry machining produced the highest cutting forces 26.7 N), whereas lubricated/cooled conditions showed statistically similar force levels (≈11 6 – 118 N). Cutting force and derived power increased monotonically with wear, supporting power as an indirect tool-state indicator. A binary XGBoost classifier trained on statistical and trend descriptors of one-second power windows achieved accuracies of 96.5% (training), 95.9% (test), and 93.3% (validation) with AUC–ROC values of 0.988, 0.993, and 0.959, respectively, despite moderate class imbalance (≈85 % healthy/15% worn). SHAP analysis identified average power and distributional descriptors (skewness and amplitude ratios) as dominant predictors, providing interpretable links between signal statistics and wear progression. The results demonstrate that reliable end-of-life detection can be achieved using a single energetic signal across heterogeneous cooling environments, supporting scalable monitoring compatible with low-fluid and closed-loop cooling strategies. Full article

Background/Objectives: Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is a global health crisis, but current diagnostics are limited. Liver biopsy is invasive, magnetic resonance imaging-proton density fat fraction (MRI-PDFF) is expensive, and quantitative ultrasound methods are low-accuracy, especially in patients with a high body mass index (BMI). This study introduces a novel thermo-acoustic (TA) method that generates ultrasound signals based on tissue electrical conductivity, where lean tissue (high in water and electrolytes) absorbs more radio-frequency (RF) energy than fatty tissue, providing a direct molecular contrast for fat. Methods: A prospective, cross-sectional feasibility study compared a new thermo-acoustic fat fraction (TAFF) score with the reference standard MRI-PDFF in 40 subjects with suspected fatty liver disease. Bland–Altman analysis, Deming regression, and Binary classification performance were tested. To establish system stability, a dedicated Repeatability and Reproducibility (R&R) study (N = 14) evaluated inter-operator and intra-operator consistency using an Intraclass Correlation Coefficient (ICC) derived from a two-way random-effects ANOVA model. Results: TAFF estimates demonstrated a substantial correlation (r = 0.89) with MRI-PDFF and an average absolute error of 3.04% fat fraction. Classification performance was high, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.92 at the 12% fat fraction threshold and 0.99 at the 20% fat fraction threshold. The R&R study confirmed robust stability (intraclass correlation = 0.89) and a negligible mean inter-operator difference of 0.36%. Estimation errors showed no statistically significant correlation with BMI or other body habitus measurements. Conclusions: These findings support thermoacoustics’ potential as an accurate, non-invasive, point-of-care solution that can serve as a new imaging biomarker. By providing predictive values closely aligned with MRI-PDFF across the full MASLD spectrum, TAFF can complement currently available ultrasound methods to address the cost and access constraints of MRI for the assessment, diagnosis, and monitoring of MASLD. Full article