Search Results (3,758)

In this paper, we investigate a class of nonlinear fractional boundary value problems involving the Caputo fractional derivative under two-point boundary conditions. By combining the Green function of the associated linear problem with a generalized Gronwall inequality, we derive pointwise estimates for solutions expressed explicitly in terms of the Mittag–Leffler function. In contrast to existing Lyapunov-type inequalities, which are mainly restricted to linear equations and rely on global supremum norm estimates, our approach preserves the nonlinear structure of the problem and captures the local behavior of solutions. These pointwise estimates lead to a Lyapunov-type inequality for nonlinear fractional equations, extending the classical result of Jleli and Samet beyond the linear framework. Moreover, we show that the obtained Lyapunov condition serves not only as a necessary condition for the existence of nontrivial solutions, but also as a sufficient criterion ensuring Hyers–Ulam stability and uniqueness. An illustrative example is provided to demonstrate the applicability of the theoretical results. Full article

Traditional statistical approaches identify group-level associations between biomarkers and rupture status in intracranial aneurysms (IAs) but often miss nonlinear interactions at the patient level. Methods: The authors retrospectively analyzed 35 saccular IAs in 35 patients (57.1% ruptured) from a single center (2021–2023). Demographics, detailed morphology (e.g., neck width, aspect ratio, VERTI, irregular shape), and multi-site inflammatory/immune markers (CRP; complement C3/C4; IgA/IgG/IgM) were included. After preprocessing (min–max scaling; one-hot encoding), five algorithms (DT, AdaBoost, GBM, XGBoost, RF) were evaluated with stratified five-fold CV and class balancing via random oversampling. The primary model (Random Forest) was tuned with Optuna and explained using global feature importance and LIME. The results showed that baseline RF achieved CV ROC-AUC 0.81 and test ROC-AUC 0.92 (test accuracy 0.857). The tuned RF (with oversampling and Optuna) yielded a mean CV accuracy of 0.85 ± 0.09 and CV ROC-AUC of 0.98 ± 0.07 while maintaining test ROC-AUC of 0.92. The average precision on the test PR curve was 0.97. The most influential predictors combined inflammatory markers (CRP, C3, C4) with morphology (neck width, irregular shape). LIME revealed consistent local patterns: low A.CRP/C.CRP and lower C3/C4 favored Not-Broken, whereas higher CRP/complement with smaller neck and irregular shape pushed toward Broken classifications. It can be concluded that an interpretable machine learning (ML) pipeline captured clinically plausible, nonlinear interactions between inflammation and aneurysm geometry. Integrating explainable ML with conventional statistics may enhance rupture risk stratification, enable patient-level rationale, and inform personalized management. These results could significantly contribute to the quality of treatment for patients with intracranial aneurysms. Full article

We present a supervised learning method to estimate two local descriptors of time-series dynamics, the mean-reversion rate $\theta$ and a heavy-tail estimate $\alpha$, from short windows of data. These parameters summarize recovery behavior and tail heaviness and are useful for interpreting stochastic signals in sensing applications. The method is trained on synthetic, dimensionless Ornstein–Uhlenbeck processes with $\alpha$-stable noise, ensuring robustness for non-Gaussian and heavy-tailed inputs. Gradient-boosted tree models (CatBoost) map window-level statistical features to discrete $\alpha$ and $\theta$ categories with high accuracy and predominantly adjacent-class confusion. Using the same trained models, we analyze daily financial returns, daily sunspot numbers, and NASA POWER climate fields for Austria. The method detects changes in local dynamics, including shifts in the financial tail structure after 2010, weaker and more irregular solar cycles after 2005, and a redistribution in clear-sky shortwave irradiance around 2000. Because it relies only on short windows and requires no domain-specific tuning, the framework provides a compact diagnostic tool for signal processing, supporting the characterization of local variability, detection of regime changes, and decision making in settings where long-term stationarity is not guaranteed. Full article

The rising environmental concerns over cement-based construction materials have led to the development of sustainable alternatives. Among these, geopolymers represent a promising class of low-carbon binders offering environmental benefits and competitive mechanical properties; however, their intrinsic brittleness limits their tensile and post-cracking performance. This study investigates the adoption of flax fibers as natural reinforcement to enhance ductility and post-peak behavior of metakaolin-based geopolymers. The performance of metakaolin-based geopolymers with flax fibers (MKFLAX) was experimentally evaluated in terms of strength, stiffness, toughness, and failure behavior. The addition of flax fibers enhanced ductility, toughness, and post-peak load-carrying capacity while slightly improving stiffness due to the bridging of cracks and the fiber pull-out mechanism. In comparison with the available literature on sisal, flax, and jute fibers, flax fibers showed improved performance due to the better dispersion within the matrix and higher tensile modulus. These findings highlight that flax fiber-reinforced metakaolin geopolymers show enhanced post-cracking behavior at the laboratory scale and could be of interest for sustainable cementitious materials, subject to further validation at the structural scale. Furthermore, a nonlinear finite element model was adopted based on damage mechanics to simulate the damage localization, stress–strain response and post-peak behavior of geopolymer composites. The numerical results showed a reasonable agreement with the experimental trends, particularly in the elastic and early softening phases. The findings are limited to the studied material system, fiber content, and small-scale samples and should be viewed as trend-level observations rather than generalized performance claims. Full article

Photodynamic therapy (PDT) is a technique based on the use of photosensitizers activated by light to destroy cancer cells in the presence of oxygen. This enables localized cancer treatment and, in some settings, fluorescence-guided visualization. However, the efficacy and clinical translation of PDT have been limited by the low specificity of traditional photosensitizers. The aim of the study is to create a ligand-guided PDT approach for pancreatic ductal adenocarcinoma (PDAC) using a peptide-conjugated photosensitizer binding to integrin αvβ6, which is a receptor linked to tumor growth and prevalent in PDAC cells. Current treatment options for this tumor are limited, with surgical resection and chemotherapy only effective when the tumor is detected early. Given the limited treatment options for PDAC, PDT via αvβ6 offers a new pathway for precision treatment. The cyclic peptide cyclo[FRGDLAFp(NMe)K], recognized for its high affinity to αvβ6, was chosen to guide a phthalocyanine-class photosensitizer toward αvβ6-expressing PDAC models. The PDT approach was further refined by developing 3D spheroid models and in vivo BxPc3 xenograft models in NOD/SCID mice, where its therapeutic efficacy was assessed. In the absence of a non-targeted control photosensitizer, a contribution from non-specific accumulation and EPR effects in the in vivo setting cannot be fully ruled out. This study highlights the potential of a peptide-guided photosensitizer, demonstrating uptake and photodynamic activity in spheroids, with moderate in vivo results addressing tumor microenvironment challenges. Optimization of PDT dosing, laser precision, and preclinical models, such as patient-derived xenografts, are crucial to enhance clinical translation. Full article

Context: Code recommendation plays an important role in improving programming efficiency and software quality. Existing approaches mainly focus on method- or API-level recommendations, which limits their effectiveness to local code contexts. From a multi-stage recommendation perspective, class-level code recommendation aims to efficiently narrow a large candidate code space while preserving essential structural information. Objective: This paper proposes RioCC, a class-level code recommendation framework that leverages deep forest-based code clone detection to progressively reduce the candidate space and improve recommendation efficiency in large-scale code spaces. Method: RioCC models the recommendation process as a coarse-to-fine candidate reduction procedure. In the coarse-grained stage, a quick search-based filtering module performs rapid candidate screening and initial similarity estimation, effectively pruning irrelevant candidates and narrowing the search space. In the fine-grained stage, a deep forest-based analysis with cascade learning and multi-grained scanning captures context- and structure-aware representations of class-level code fragments, enabling accurate similarity assessment and recommendation. This two-stage design explicitly separates coarse candidate filtering from detailed semantic matching to balance efficiency and accuracy. Results: Experiments on a large-scale dataset containing 192,000 clone pairs from BigCloneBench and a collected code pool show that RioCC consistently outperforms state-of-the-art methods, including CCLearner, Oreo, and RSharer, across four types of code clones, while significantly accelerating the recommendation process with comparable detection accuracy. Conclusions: By explicitly formulating class-level code recommendation as a staged retrieval and refinement problem, RioCC provides an efficient and scalable solution for large-scale code recommendation and demonstrates the practical value of integrating lightweight filtering with deep forest-based learning. Full article

Automated insulator inspection systems face critical challenges from small object sizes, complex backgrounds, and vulnerability to adversarial attacks, a security concern largely unaddressed in safety-critical power infrastructure. We introduce Faster-YOLOv12n, integrating a FasterNet backbone with SGC2f attention modules and Wise-ShapeIoU loss for enhanced small defect localization. Our architecture achieves 98.9% mAP@0.5 on the CPLID, improving baseline YOLOv12n by 1.3% in precision (97.8% vs. 96.5%), 4.7% in recall (95.1% vs. 90.4%), and 1.8% in mAP@0.5. Through differential data augmentation, we expand training samples from 678 to 3900 images, achieving balanced class distribution and robust generalization across fog, adverse weather, and complex transmission line backgrounds. Comparative evaluation demonstrates superior performance over RT-DETR, Faster R-CNN, YOLOv7, YOLOv8, and YOLOv9, with per-class analysis revealing 99.8% AP@0.5 for defect detection. We provide the first comprehensive adversarial robustness evaluation for insulator defect detection, systematically assessing FGSM, PGD, and C&W attacks across perturbation budgets. Through adversarial training with mixed-batch strategies, our robust model maintains 93.2% mAP@0.5 under the strongest FGSM attacks ($ϵ$ = 48/255), 94.5% under PGD attacks, and 95.1% under C&W attacks ($\tau$ = 3.0) while preserving 98.9% clean accuracy, demonstrating no trade-off between accuracy and robustness. Grad-CAM visualizations demonstrate that attacks disrupt confidence calibration while preserving spatial attention on defect regions, providing interpretable insights into model decision-making under adversarial conditions and validating learned feature representations for safety-critical smart grid monitoring applications. Full article

Persistent fiscal stress in South African municipalities undermines service delivery, yet practical tools for early detection remain limited. This study predicts one-year-ahead municipal financial distress to support risk-based prioritisation. We develop machine learning models using a 2018/19–2022/23 municipality panel, combining 13 financial health indicators from State of Local Government (SoLG) reports with selected socio-economic variables. Penalised logistic regression is benchmarked against random forest and XGBoost under a leakage-aware, time-ordered split into training, validation, and an out-of-time test year; class imbalance is handled through class weighting. Performance is evaluated using PR-AUC, ROC-AUC, calibration, and a capacity-constrained Top-30 rule. All models outperform a naïve last-year baseline on the out-of-time test (PR-AUC 0.934–0.954; ROC-AUC 0.886–0.923), with bootstrap intervals supporting robustness. Random forest performs best overall, while penalised logistic regression remains competitive. Under the Top-30 rule (12.3% workload), precision is high (precision@30 0.967–1.000) while recall is modest (recall@30 0.186–0.192). SHAP values and logistic odds ratios identify liquidity, solvency, cash coverage, and employment deprivation as key drivers. The Top-30 rule corresponds to an annual intensive monitoring portfolio that is reasonable under constrained staffing and budget capacity in national and provincial oversight units, while probability thresholds are reported as conventional benchmarks rather than as policy triggers. Full article

With the rapid growth and diversification of malware, accurate multi-class detection remains challenging due to severe class imbalance and limited labeled data. This work presents an image-based malware classification framework that converts executable binaries into $64\times 64$ grayscale images, employs class-wise DCGAN augmentation to mitigate severe imbalance (initial imbalance ratio >12 across 31 families, $N\approx 9300$), and trains a hybrid CNN–Transformer model that captures both local texture features and long-range contextual dependencies. The DCGAN generator produces high-fidelity synthetic samples, evaluated using Inception Score (IS) $=3.43$, Fréchet Inception Distance (FID) $=10.99$, and Kernel Inception Distance (KID) $=0.0022$, and is used to equalize class counts before classifier training. On the blended dataset the proposed GAN-balanced CNN–Transformer achieves an overall accuracy of 95% and a macro-averaged F1-score of 0.95; the hybrid model also attains validation accuracy of ≈94% while substantially improving minority-class recognition. Compared to CNN-only and Transformer-only baselines, the hybrid approach yields more stable convergence, reduced overfitting, and stronger per-class performance, while remaining feasible for practical deployment. These results demonstrate that DCGAN-driven balancing combined with CNN–Transformer feature fusion is an effective, scalable solution for robust malware family classification. Full article

Meteorological drought is a major natural hazard in semi-arid regions, where high climate variability and strong dependence on precipitation intensify pressure on water resources and socio-economic systems. This study examined the spatiotemporal characteristics of meteorological drought in the Wadi Sly basin (northwestern Algeria) over the period 1967–2022, using long-term monthly precipitation records from seven meteorological stations. The Standardized Precipitation Index (SPI) was calculated at multiple time scales (1-, 3-, 6-, 9-, and 12-month) to characterize drought onset, severity, persistence, and temporal variability. In addition, drought severity probability and frequency analyses were conducted to evaluate the likelihood and recurrence of different drought classes. The results indicate pronounced inter-annual and decadal variability in drought conditions, with severe and prolonged drought episodes occurring during the mid-1980s, early-to-mid-1990s, and late 2010s. During these periods, SPI values frequently fell below −2.0, signifying extreme drought conditions. Spatial analysis reveals strong basin-wide synchronicity of drought events, suggesting the influence of large-scale atmospheric drivers, although localized variations in drought intensity remain evident. Overall, near-normal conditions dominate the record (accounting for approximately 60–70% of observations), while moderately dry conditions occur more frequently than moderately wet conditions at several stations. Drought characteristics exhibit strong scale dependence, with short-term droughts prevailing at shorter SPI time scales, while longer time scales emphasize drought persistence and accumulation. Overall, the findings indicate an increasing prominence of long-duration drought conditions in recent decades, as evidenced by recurrent low SPI values at longer aggregation scales. Such conditions may pose heightened risks to groundwater recharge processes and long-term water resource availability. Despite the limitations inherent in precipitation-based indices, this study provides a robust statistical framework for drought characterization and contributes valuable insights for improved drought monitoring, early warning systems, and climate-resilient water resource management in semi-arid basins. Full article

Corrosion remains one of the major contemporary technological challenges, causing significant economic, environmental, and operational impacts on industrial systems. Although it is a spontaneous process inherent to metals and their alloys, its progression can be significantly mitigated by appropriate protection strategies. Traditionally, synthetic inhibitors have been widely used; however, their toxicity, environmental persistence, and increasing regulatory restrictions have prompted a search for greener alternatives. Biosurfactants stand out as promising green anticorrosive agents, acting through the formation of adsorbed films, reduction in wettability, modification of the metal–medium interface, and, in some cases, antimicrobial effects that inhibit the formation of corrosive biofilms. This review presents an integrated analysis of the main corrosion mechanisms, including uniform, localized, galvanic, and microbiologically influenced corrosion, with an emphasis on critical industrial environments such as the maritime, petrochemical, energy, and infrastructure sectors. Additionally, the main classes of biosurfactants are discussed, along with their key physical and chemical characteristics, including critical micelle concentration, thermal and saline stability, adsorption capacity, and their mechanisms of action in mitigating corrosion. Finally, the article summarizes the advances of the last decade, highlighting experimental studies, emerging applications, and technological trends that consolidate biosurfactants as viable, efficient, and environmentally safe alternatives for industrial corrosion protection. Full article

Planar mirrors play a crucial role in autocollimation testing and optical beam relay systems of telescopes and other fields. However, for the next-generation large-aperture telescopes, typical monolithic planar mirrors fall short in meeting anticipated performance requirements, owing to their high costs and fabrication limitations. Here, a new integrated multimodal testing method for 3–4m-class segmented planar mirrors is proposed. The presented system utilizes an innovative keystone architecture with a central mirror and keystone-shaped segments, which is superior to the traditional hexagonal architecture. To facilitate rapid coarse alignment, a machine vision system based on edge detection is investigated. Furthermore, the dispersed fringe technique is used for robust co-phasing. By using a segmented planar mirror designed with sub-aperture stitching strategy and combining local apertures, the system cost was reduced and high-precision measurement was achieved. Eventually, the alignment, co-focus and co-phasing measurements based on the proposed concept were completed, and the transfer characteristics were determined by analyzing the Optical Transfer Function (OTF). Test data shows co-phasing accuracy of better than 30 nm RMS (root-mean-square) and alignment accuracy less than 10 arcseconds. In addition, the system uses small-aperture mirrors in autocollimation testing to facilitate flexible alignment and testing of individual segments. The test optical path is configured to match the effective focal length of the system under test, and the optical lever effect of reflectors enhances the alignment sensitivity. The method combines autocollimation and wavefront sensing which allows the approach to provide high-precision control of co-focus, co-phasing, and surface errors correction. Full article

Cleanrooms are essential environments for the production of sterile pharmaceuticals and medical devices, where airflow stability and contamination control are critical. In this study, the influence of adjustable worktable height on air cleanliness was examined to determine whether height variation affects unidirectional airflow and particle concentration. Measurements were conducted under laboratory conditions in an ISO Class 6 cleanroom and in an industrial ISO Class 8 production line. Particle concentrations were recorded at table heights between 70 and 120 cm using a calibrated particle counter in accordance with ISO 14644-1 and ISO 21501-4. The measured data were evaluated against the classification limits defined in ISO 14644-1. Across all height levels, particle concentrations remained well below the permissible thresholds. In the ISO Class 6 cleanroom, measurement height showed a statistically significant effect on particles >5 μm, though this represented only a small contribution to the overall variance. In contrast, no significant height effect was observed in the ISO Class 8 cleanroom. Observed local differences were attributed to airflow distribution and the placement of air supply in-/outlets rather than table height. These results confirm that height-adjustable worktables can be implemented without affecting cleanroom classification, provided that uniform FFU placement and furniture positioning are considered during design and qualification. Full article

Predictive pest management in urban sewer networks represents a sustainable alternative to reactive, biocide-based methods. Using data collected through an IoT architecture and validated with manual inspections across eight manholes over 113 days, we implemented a rigorous comparative framework evaluating eleven data mining algorithms, including classical methods (KNN, SVM, decision trees) and advanced ensemble techniques (XGBoost, LightGBM, CatBoost) optimized for unbalanced datasets. Gradient boosting models with explicit handling of class imbalance—where the absence of pests exceeds 77% of observations—showed exceptional performance, achieving a Macro-F1 score above 0.92 and high precision in identifying the minority high-risk class. Explainability analysis using SHAP consistently revealed that elevated CO₂ concentrations are the primary predictor of infestation, enabling early identification of critical zones. This study demonstrates that carbon dioxide (CO₂) acts as the most robust bioindicator for predicting severe infestations of Periplaneta americana, significantly outperforming conventional environmental variables such as temperature and humidity. The implementation of the model in a real-time monitoring platform generates interpretable heat maps that support proactive and localized interventions, optimizing resource use and reducing dependence on biocides. This study presents a scalable, operationally viable predictive system designed for direct integration into municipal asset management workflows, offering a concrete, industry-ready solution to transform pest control from a reactive, labor-intensive process into a data-driven, proactive operational paradigm. This approach not only transforms pest management from reactive to predictive but also aligns with the Sustainable Development Goals, offering a scalable, interpretable, and operationally viable system for smart cities. Full article

Image datasets characterized by high intra-image structural heterogeneity pose significant challenges for supervised classification, particularly when local patterns contribute unevenly to image-level decisions. In such scenarios, direct image-level learning may obscure relevant local variability and introduce bias in both training and evaluation. This study proposes a statistically guided, patch-based computational pipeline for the automatic classification of elementary morphological patterns, with application to bioelectrographic imaging data. The pipeline is progressively refined through explicit statistical diagnostics, including image-level data splitting to prevent data leakage, class imbalance handling, and decision threshold calibration based on validation performance. To further control structural bias across images, a continuous image-level descriptor, denoted as pct_point_true , is introduced to quantify the proportion of point-like structures and support dataset stratification and stability analysis. Experimental results demonstrate consistent and robust patch-level performance, together with coherent behavior under complementary image-level aggregation analysis. Rather than emphasizing architectural novelty, the study prioritizes methodological rigor and evaluation validity, providing a transferable framework for patch-based analysis of structurally heterogeneous image datasets in applied computer vision contexts. Full article