Search Results (9,446)

This study focuses on the formulation and analysis of problems that are dual to those defined by convex set-valued mappings. Various important classes of optimization problems—such as the classical problems of mathematical and linear programming, as well as extremal problems arising in economic dynamics models—can be reduced to problems of this type. The dual problem proposed in this work is constructed on the basis of the duality theorem connecting the operations of addition and infimal convolution of convex functions, a result that has been previously applied to compact-valued mappings. It appears that, under the so-called nondegeneracy condition, this construction serves as a fundamental approach for deriving duality theorems and establishing both necessary and sufficient optimality conditions. Furthermore, alternative conditions that partially replace the nondegeneracy assumption may also prove valuable for addressing other issues within convex analysis. Full article

Reliable fault diagnosis of milling machines is essential for maintaining operational stability and cost-effective maintenance; however, it remains challenging due to limited labeled data and the highly non-stationary nature of acoustic emission (AE) signals. This study introduces an optimized Few-Shot Learning framework (FSL) that integrates time–frequency analysis with attention-guided representation learning and distribution-aware classification for data-efficient fault detection. The framework converts AE signals into Continuous Wavelet Transform (CWT) scalograms, which are processed using a self-attention-enhanced ResNet-50 backbone to capture both local texture features and long-range dependencies in the signal. Adaptive prototype computation with learnable importance weighting refines class representations, while Mahalanobis distance-based matching ensures robust alignment between query and prototype embeddings under limited sample conditions. To further strengthen discriminability, contrastive loss with hard negative mining enforces compact intra-class clustering and clear inter-class separation. Comprehensive experiments under 7-way 5-shot settings and 5-fold stratified cross-validation demonstrate consistent and reliable performance, achieving a mean accuracy of 98.86% ± 0.97% (95% CI: [98.01%, 99.71%]). Additional evaluations across multiple spindle speeds (660 rpm and 1440 rpm) confirm that the model generalizes effectively under varying operating conditions. Grad-CAM++ activation maps further illustrate that the network focuses on physically meaningful fault-related regions, enhancing interpretability. The results verify that the proposed framework achieves robust, scalable, and interpretable fault diagnosis using minimal labeled data, offering a practical solution for predictive maintenance in modern intelligent manufacturing environments. Full article

In ancient civilisations, bitumen was widely used for its multifunctional applications in construction, sealing, and adhesion, evidencing early expertise in material engineering and resource optimisation. Here, Sumerian bitumen-based artefacts were studied through Fourier transform infrared spectroscopy (FTIR) and machine learning to investigate ancient practices for the repair, reuse, and recycling of everyday materials. The materials are dated back to the 3rd millennium BC and come from the archaeological site of Abu Tbeirah (Iraq). Four primary classes were identified based on their molecular composition, which revealed a specific gradient determined by the varying proportions of bitumen and other fillers. These composition-based classes were then applied to predict the classification of the undetermined samples, which constitute 50% of the entire dataset, via a kernel-based support vector machine (SVM). The new findings are consistent with philological sources that reference distinct formulations of use in everyday life. The findings offer a new perspective on the social and historical importance of the circular economy. Full article

Biodiesel is an alternative, sustainable, renewable, and environmentally friendly energy source, which has generated interest from the scientific community due to its low toxicity, rapid biodegradability, and zero carbon footprint. Biodiesel is a biofuel produced by the transesterification of triglycerides or the esterification of free fatty acids (FFA). Both reactions require catalysts with numerous active sites (basic, acidic, bifunctional, or enzymatic) for efficient biodiesel production. On the other hand, since the late 1990s, metal–organic frameworks (MOFs) have emerged as a new class of porous materials and have been successfully used in various fields due to their multiple properties. For this reason, MOFs have been used as heterogeneous catalysts or as a platform for designing active sites, thus improving stability and reusability. This literature review presents a comprehensive analysis of using MOFs as heterogeneous catalysts or supports for biodiesel production. The optimal parameters for transesterification/esterification are detailed, such as the alcohol/feedstock molar ratio, catalyst amount, reaction time and temperature, conversion percentage, biodiesel yield, fatty acid and water content, etc. Additionally, novel methodologies such as ultrasound and microwave irradiation for obtaining MOF-based catalysts are described. It is important to note that most studies have shown biodiesel yields >90% and multiple reuse cycles with minimal activity loss. The bibliographic analysis was conducted using the American Chemical Society (ACS) Scifinder^® database, the Elsevier B.V. Scopus^® database, and the Clarivate Analytics Web of Science^® database, under the institutional license of the Universidad Veracruzana. Keywords were searched for each section, generally limiting the document type to “reviews” and “journals,” and the language to English, and published between 2000 and 2025. Full article

Background: Angle’s dental class asymmetries not associated with crossbite are malocclusions that are often underestimated in pediatric patients. However, they may be associated with alterations in the development of the stomatognathic system. Objective: The objective of this study was to evaluate the prevalence of Angle’s class asymmetries without crossbite in primary-school-aged children and to investigate possible associations with perinatal, clinical, and functional variables. Materials and Methods: This cross-sectional observational study analyzed a sample of 391 children aged 6 to 11 years, attending a primary school in the metropolitan area of Milan, Italy. Data were systematically collected through both clinical examination and patient history, with the aim of identifying significant correlations with the occurrence of dental asymmetries in the absence of crossbite. Results. The results revealed a higher prevalence of occlusal asymmetries associated with factors such as oral breathing, low tongue posture, type of delivery, formula feeding, and systemic diseases during the first three years of life. Advanced carious lesions and inclination of the occlusal plane were significantly associated with asymmetry. Conclusions: The study highlights the importance of early diagnosis and a multidisciplinary approach to prevent malocclusions and complex craniofacial dysfunctions later in life. Full article

Memory forensics is an essential cybersecurity tool that comprehensively examines volatile memory to detect the malicious activity of fileless malware that can bypass disk analysis. Image-based detection techniques provide a promising solution by visualizing memory data into images to be used and analyzed by image processing tools and machine learning methods. However, the effectiveness of image-based data for detection and classification requires high computational efforts. This paper investigates the efficacy of texture-based methods in detecting and classifying memory-resident or fileless malware using different image resolutions, identifying the best feature descriptors, classifiers, and resolutions that accurately classify malware into specific families and differentiate them from benign software. Moreover, this paper uses both local and global descriptors, where local descriptors include Oriented FAST and Rotated BRIEF (ORB), Scale-Invariant Feature Transform (SIFT), and Histogram of Oriented Gradients (HOG) and global descriptors include Discrete Wavelet Transform (DWT), GIST, and Gray Level Co-occurrence Matrix (GLCM). The results indicate that as image resolution increases, most feature descriptors yield more discriminative features but require higher computational efforts in terms of time and processing resources. To address this challenge, this paper proposes a novel approach that integrates Local Interpretable Model-agnostic Explanations (LIME) with deep learning models to automatically identify and crop the most important regions of memory images. The LIME’s ROI was extracted based on ResNet50 and MobileNet models’ predictions separately, the images were resized to 128 × 128, and the sampling process was performed dynamically to speed up LIME computation. The ROIs of the images are cropped to new images with sizes of (100 × 100) in two stages: the coarse stage and the fine stage. The two generated LIME-based cropped images using ResNet50 and MobileNet are fed to the lightweight neural network to evaluate the effectiveness of the LIME-based identified regions. The results demonstrate that the LIME-based MobileNet model’s prediction improves the efficiency of the model by preserving important features with a classification accuracy of 85% on multi-class classification. Full article

Cotton leaf mites are pests that cause irreparable damage to cotton and pose a severe threat to the cotton yield, and the application of unmanned aerial vehicles (UAVs) to monitor the incidence of cotton leaf mites across a vast region is important for cotton leaf mite prevention. In this work, 52 vegetation indices were calculated based on the original five bands of spliced UAV multispectral images, and six featured indices were screened using Shapley value theory. To classify and identify cotton leaf mite infestation classes, seven machine learning classification models were used: random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), K-Nearest Neighbors (KNN), decision tree (DT), and gradient boosting decision tree (GBDT) models. The base model and metamodel used in stacked models were built based on a combination of four models, namely, the XGB, GBDT, KNN, and DT models, which were selected in accordance with the heterogeneity principle. The experimental results showed that the stacked classification models based on the XGB, KNN base model, and DT metamodel were the best performers, outperforming other integrated and single individual models, with an overall accuracy of 85.7% (precision: 93.3%, recall: 72.6%, and F1-score: 78.2% in the macro_avg case; precision: 88.6%, recall: 85.7%, and F1 score: 84.7% in the weighted_avg case). This approach provides support for using UAVs to monitor the cotton leaf mite prevalence over vast regions. Full article

Lorenz-type systems are dissipative dynamical systems that are described by three nonlinear equations with derivatives of the first order and are capable of exhibiting chaotic behavior. The generalization of Lorenz-type equations by using general fractional derivatives (GFDs) and periodical kicks is proposed. GFDs allow us to use the general form of memory functions as operator kernels to describe nonlinear dynamics with memory. The exact analytical solutions of Lorenz-type equations with GFDs are derived in the general case for the wide class of nonlinearity and memory functions. Using the exact solutions, we obtain discrete maps with memory (DMMs) that describe kicked GF Lorenz-type systems with general forms of memory and nonlinearity. The proposed maps describe the exact solution of nonlinear equations with GFDs at discrete time points as the function of all past discrete moments of time. The proposed multi-dimensional DMMs are derived from kicked GF Lorenz-type equations with GFDs without any approximations. The proposed results and the method to derive multi-dimensional DMMs are derived for arbitrary dimensions. The importance and unusualness of the proposed results lies in the fact that obtained solutions for equations of the Lorenz-type system are exact analytical solutions. Full article

Background and Clinical Significance: Immune checkpoint inhibitors (ICIs), a revolutionary class of oncology therapeutics that enhance T cell-mediated antitumor immunity, are associated with various immune-related adverse events (IRAEs). While destructive thyroiditis and hypothyroidism are common, ICI-induced Graves’ disease (GD) is exceedingly rare, and the occurrence of concomitant Graves’ ophthalmopathy (GO) is even rarer. Case Presentation: A 57-year-old man with bladder cancer developed GO after receiving the first dose of the programmed death 1 (PD-1) inhibitor pembrolizumab. He presented with severe proptosis, extraocular muscle enlargement, hyperthyroidism, and significantly increased thyroid-stimulating hormone receptor autoantibodies (TRAb). Following the treatment with glucocorticoids and immunosuppressive therapy, his symptoms improved markedly but relapsed upon dosage reduction. To date, we have not identified any previous reports of GO with confirmed positive thyroid-related antibodies induced by pembrolizumab. Conclusions: This case offers valuable insights into the potential IRAEs, underscoring the importance of thorough clinical evaluation and early recognition to improve patient outcomes and quality of life. A literature review of ICI-induced GO was also performed, with further discussion of the potential pathogenic mechanisms, risk factors, and management strategies. Full article

Objectives: Knowledge of tooth axes is important in orthodontics; however, using just one method for evaluation, e.g., orthopantomograms for tip, is not highly reliable. This study aimed to investigate tooth axes in skeletal class I/II/III using two- and three-dimensional evaluations. Methods: In this retrospective study, lateral cephalometric radiographs, orthopantomograms and digitized models of 107 adolescent patients (Ø 13.5 years; n = 36/33/38 with cI/cII/cIII) prior to orthodontic treatment were analyzed digitally regarding tip and torque of teeth. Statistical analysis was performed using SPSS (p ≤ 0.05), G*power and a multiple testing tool (Bonferroni–Holm/Hochberg). Results: Dental compensation of skeletal cII/cIII was significant acc. to Bonferroni–Holm/Hochberg for the following variables: overjet compensation in cII was seen by more retroinclined upper incisors in cII by −5.9°/−5.3° and by −8.8°/−6.6° (U1-SN/U1-PP) vs. cI/cIII (effect size f = 0.489/0.446, power 0.996/0.988). In cIII, the lower incisors were more retroinclined by −8.5°/−10.9° (L1-MP) vs. cI/cII (f = 0.576, power 1.000) and by −8.5°/−8.9° and −6.0°/−7.0° (three-dimensional analysis: L1/L2) vs. cI/cII (f = 0.522/0.527, power 0.999). Compensation of distal occlusion was found by mesial tipping of L3 by 3.5° in cII (f = 0.242, power 0.591) vs. cIII. CIII showed transversal compensation by buccal tipping of the U5 by 5.9°/4.6° vs. cII/I (f = 0.355, power 0.910) and lingual tipping of L3 by −6.4° vs. cII and −3.8° vs. cI (f = 0.446, power 0.988) and L4 by −4.0°/−2.6° vs. cII/I (f = 0.326, power 0.846). Conclusions: Decompensation, e.g., uprighting of distal tipped canines, and further protrusion of incisors might not be desired in orthodontic treatment of adolescents. Full article

In recent decades, the problem of resistant strains, which present resistance to different types of antimicrobials, has increased. Klebsiella pneumoniae is one of the most important species that exhibits an acquired resistance phenotype to at least one agent in three or more classes of antimicrobials and is thus characterized as a multidrug-resistant bacterium (MDR). 98 nosocomial strains of K. pneumoniae were isolated during the pre-COVID-19 period, and more specifically, from February 2015 to March 2019, were analyzed for the detection of class A, D, and B carbapenemase genes. The existence of KPC, OXA-48 like, IMP, VIM, and NDM carbapenemases has been examined. The immunochromatography showed that NDM carbapenemases are more frequently detected in the samples, reaching a percentage of 30.7%, while correspondingly the percentage for VIM carbapenemases was 7.68% among the strains with resistant phenotypes. No strain with carbapenemase IMP was found. Real-time multiplex polymerase chain reaction (PCR) showed, in contrast to immunochromatography kits, that a high percentage of bacterial isolates (94.26%) carry NDM and VIM carbapenemase genes, while no IMP carbapenemase genes were detected. Regarding the KPC enzymes, the immunochromatography kits showed that KPC positive strains are reaching 53.1%, and OXA-48 positive strains are reaching 3.1% among the strains with resistant phenotypes. Real-time multiplex polymerase chain reaction revealed a much higher percentage of 89.6% KPC positive isolates and a percentage of 14.6% OXA-48 carbapenemase producers. The aforementioned results indicate the dominance of the Multiplex Real-Time PCR as a “gold standard” method. This study could not fully support the usefulness of rapid immunochromatographic tests as a fast and useful diagnostic tool in the laboratory daily routine, as per the results of previous studies. Thus, more studies need to be conducted in this field to introduce these rapid tests safely into the daily laboratory workflow as a screening tool. Additionally, this study underlines the predominance of KPC enzymes from clinical isolates of ICUs and a significant shift over the OXA-48 like enzymes that are not limited to the ICU environment. Full article

Stomach ulcers, a common type of gastrointestinal (GI) disease, pose serious health risks if not diagnosed and treated at an early stage. Therefore, in this research, a method is proposed based on two deep learning models for classification and segmentation. The classification model is based on Convolutional Neural Networks (CNN) and incorporates Q-learning to achieve learning stability and decision accuracy through reinforcement-based feedback. In this model, input images are passed through a custom CNN model comprising seven layers, including convolutional, ReLU, max pooling, flattening, and fully connected layers, for feature extraction. Furthermore, the agent selects an action (class) for each input and receives a +1 reward for a correct prediction and −1 for an incorrect one. The Q-table stores a mapping between image features (states) and class predictions (actions), and is updated at each step based on the reward using the Q-learning update rule. This process runs over 1000 episodes and utilizes Q-learning parameters (α = 0.1, γ = 0.6, ϵ = 0.1) to help the agent learn an optimal classification strategy. After training, the agent is evaluated on the test data using only its learned policy. The classified ulcer images are passed to the proposed attention-based U-Net model to segment the lesion regions. The model contains an encoder, a decoder, and attention layers. The encoder block extracts features through pooling and convolution layers, while the decoder block up-samples the features and reconstructs the segmentation map. Similarly, the attention block is used to highlight the important features obtained from the encoder block before passing them to the decoder block, helping the model focus on relevant spatial information. The model is trained using the selected hyperparameters, including an 8-batch size, the Adam optimizer, and 50 epochs. The performance of the models is evaluated on Kvasir, Nerthus, CVC-ClinicDB, and a private POF dataset. The classification framework provides 99.08% accuracy on Kvasir and 100% accuracy on Nerthus. In contrast, the segmentation framework yields 98.09% accuracy on Kvasir, 99.77% accuracy on Nerthus, 98.49% accuracy on CVC-ClinicDB, and 99.13% accuracy on the private dataset. The achieved results are superior to those of previous methods published in this domain. Full article

Cloud infrastructure supports modern services across different sectors, such as business, education, lifestyle, government and so on. With the high demand for cloud computing, the security of network communication is also an important consideration. Distributed denial-of-service (DDoS) attacks pose a significant threat. Therefore, detection and mitigation are critically important for reliable operation of cloud-based systems. Intrusion detection systems (IDS) play a vital role in detecting and preventing attacks to avoid damage to reliability. This article presents DDoS detection using a convolutional neural network (CNN) and recurrent neural network (RNN) model enhancement with a multi-head attention mechanism for cloud infrastructure protection enhances the contextual relevance and accuracy of the DDoS detection. Preprocessing techniques were applied to optimize model performance, such as information gained to identify important features, normalization, and synthetic minority oversampling technique (SMOTE) to address class imbalance issues. The results were evaluated using confusion metrics. Based on the performance indicators, our proposed method achieves an accuracy of 97.78%, precision of 98.66%, recall of 94.53%, and F1-score of 96.49%. The hybrid model with multi-head attention achieved the best results among the other deep learning models. The model parameter size was moderately lightweight at 413,057 parameters with an inference time in a cloud environment of less than 6 milliseconds, making it suitable for application to cloud infrastructure. Full article

Background/Objectives: Failure to rescue (FTR), defined as the occurrence of a major complication plus death within 30 days, is a key measure of surgical safety. Hypoalbuminemia is a known risk factor for poor outcome in metastatic spinal tumor surgery, yet its association with FTR has not been explored. The purpose of this study is to evaluate serum albumin level as predictor of FTR after surgery for spinal metastases. Methods: A total of 1749 patients with disseminated cancer who underwent oncologic surgery for spinal metastases (identified by CPT codes) and met our inclusion criteria were identified in the ACS-NSQIP database (2018–2023). The primary endpoint was FTR, defined as a major complication plus death occurring within 30 days of surgery. Serum albumin was analyzed both as a continuous and categorical variable (hypoalbuminemia < 3.5 g/dL, normal albumin > 3.5 g/dL). Univariable and multivariable logistic regression was performed, adjusting for demographic and operative variables. Results: The mean preoperative serum albumin level was 3.63 g/dL (standard deviation = 0.642) and the FTR rate was 4% (71 of 1749). After adjusting for potential confounders such as modified Frailty Index 5, ASA class, functional status, emergent case, and reoperation, higher preoperative albumin levels (OR 0.39 [95% CI 0.26–0.61]; p < 0.001) were independently associated with decreased odds of FTR. Conclusions: The findings of this study suggest an association between preoperative serum albumin level and FTR in oncologic surgery for spinal metastases. This highlights the importance of albumin assessment for perioperative prognosis, but the findings require further validation. Full article

Monitoring exercise-induced fatigue is important for maintaining the effectiveness of training and preventing injury. We evaluated a non-contact approach that estimates perceived fatigue from full-body kinematics captured by an Azure Kinect depth camera. Ten healthy young adults repeatedly performed simple, reproducible whole-body movements, and 3D skeletal coordinates from 32 joints were recorded. After smoothing, 24 kinematic features (joint angles, angular velocities, and cycle timing) were extracted. Fatigue labels (Low, Medium, and High) were obtained using the Borg CR10 scale at 30-s intervals. A random forest classifier was trained and evaluated with leave-one-subject-out cross-validation, and class imbalance was addressed by comparing no correction, class weighting, and random oversampling within the training folds. The model discriminated fatigue levels with high performance (overall accuracy 86%; macro ROC AUC 0.98 (LOSO point estimate) under oversampling), and feature importance analysis indicated distributed contributions across feature categories. These results suggest that simple camera-based kinematic analysis can feasibly estimate perceived fatigue during basic movements. Future work will expand the cohort, diversify tasks, and integrate physiological signals to improve generalization and provide segment-level interpretability. Full article