Search Results (15,134)

Integral inclusion systems play a significant role in applied analysis and modeling, providing an effective framework for studying various physical, engineering, and dynamical processes. In this work, the solvability of a multidimensional integral inclusion system is investigated by applying the common fixed point technique to a pair of ${\mathbb{M}}_{\alpha }$-admissible multivalued operators. The analysis is carried out within a novel double-controlled vector-valued metric structure, in which the distance is governed by two independent matrix-valued control operators; this setting strictly extends classical Perov-type and $\mathrm{b}$-metric frameworks and offers a more flexible tool for treating multidimensional and interdependent systems. Existence results are derived under a suitable contractive condition within a generalized metric structure. Several auxiliary theorems are established to support the main conclusions. To illustrate the applicability of the theoretical findings, the obtained results are utilized to ensure the existence of solutions for a multidimensional Urysohn-type integral inclusion system. A simple example demonstrates the validity of the theoretical framework and highlights the effectiveness of the adopted approach. Full article

Peptides from scorpion venom, mainly in species such as Olivierus martensii (formerly Olivierus martensii Karsch, often designated BMK) (BmK) and Tityus serrulatus from the Buthidae family, show real promise as painkillers that skip opioids altogether. They work by hitting specific ion channels and dialing down inflammation. This review gathers information on their molecular setups: disulfide-bridged types and those without, weighing in at 3 to 10 kilodaltons (kDa). Structural features include motifs stabilized by cysteines. In pain signaling, they block voltage-gated sodium channels (NaV) such as NaV1.7 and NaV1.8; take the BmK analgesic–antitumor peptide (BmK-AGAP) for example. Additionally, scorpion venom heat-resistant peptide (SVHRP) reduces microglia activity. Tests on rodents using formalin injections, acetic acid writhing, and chronic constriction injury (CCI) setups reveal pain relief that depends on dose and stacks up to morphine. Pairings like AGAP with lidocaine decrease the effective dose by half. In terms of safety, therapeutic levels have low-toxicity with a median lethal dose (LD50) over 20 mg/kg. Issues crop up with immune responses, unintended targets, and differences in venom batches. Clinical information remains thin, so gaps persist. Engineered versions could change the game for neuropathic pain, inflammatory conditions, and cancer-related discomfort. Standardization plus Phase I studies would help move this forward. Full article

Insulin resistance (IR) promotes a prothrombotic milieu by enhancing platelet hyperactivity, oxidative stress, and endothelial dysfunction, driving both microvascular and macrovascular complications in type 2 diabetes. Our review synthesizes mechanistic evidence showing that insulin-resistant platelets exhibit increased basal activation, elevated sensitivity to agonists, and reduced responsiveness to inhibitory signals, with distinct pro-aggregatory subpopulations amplifying thrombotic risk. Molecular pathways underlying platelet hyperactivation include reactive oxygen species accumulation, advanced glycation end-product signaling, disrupted calcium homeostasis, and impaired nitric oxide/prostacyclin pathways. Clinically, these mechanisms contribute to heightened arterial thrombosis, coronary artery disease, stroke, and microvascular injury, including nephropathy and retinopathy. Nutritional interventions emerge as effective modulators of platelet function and vascular health. Diets such as the Mediterranean, DASH, low-glycemic-index, and plant-based regimens, alongside bioactive compounds—including omega-3 fatty acids, polyphenols, vitamins D, E, C, and minerals like magnesium and zinc—may reduce platelet aggregation, oxidative stress, and systemic inflammation while restoring endothelial function. Clinical and epidemiological evidence demonstrates improvements in flow-mediated dilation, arterial elasticity, and stabilization of atherosclerotic plaques following dietary interventions. Integrating whole-diet strategies with targeted nutrients provides synergistic benefits, suggesting that personalized nutritional approaches can mitigate IR-induced platelet hyperactivity and lower vascular risk. These findings highlight nutrition as a practical, evidence-based adjunct to pharmacotherapy for cardiovascular protection in insulin-resistant populations. Full article

Waxy-leaved mustard (Boreava orientalis Jaub. and Spach.) is an invasive weed that has rapidly spread across wheat fields in the Kurdistan Province, Iran. The germination and phenology of this species were studied through a series of greenhouse and field experiments conducted from 2018 to 2020 to better understand its biology and support effective management strategies. We calculated the growing degree days (GDD) required for each growth stage of B. orientalis and related the calculations to the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale. We also studied whether light affected germination. The results indicated that light significantly reduced germination. The base temperature for germination (4 °C) is identical to that of wheat, and the growth periods were largely similar. Consequently, the maturation of wheat and B. orientalis seeds co-occurred, leading to the dispersal of weed seeds during wheat harvest and increasing field infestation. Understanding the phenological development of B. orientalis provides a valuable basis for developing management strategies and implementing effective control measures to reduce field contamination and prevent further spread. Full article

The scorpion genus Hottentotta Birula, 1908 is widely distributed across Africa and the Middle East, extending to Pakistan, India and Sri Lanka. The processes which resulted in their evolution and diversification across this vast area are poorly understood. The present study investigated the phylogeny and historical biogeography of the genus in the Iranian Plateau and the Zagros Mountains based on nuclear and mitochondrial DNA sequences from four African species, an Arabian species and eight species from the Middle East, most of which are endemic to Iran. Phylogenetic analyses confirmed the monophyly of all species included in the analysis and recovered a clade comprising Iranian and Afro-Arabian species. S-DIVA and BBM analyses demonstrated that the species of Hottentotta occurring in the Iranian Plateau and the Zagros Mountains originated from an African ancestor and then dispersed to their current geographical ranges. Further divergence coincided with the orogeny of the Zagros Mountains and climatic changes during the Miocene epoch. The results support the hypothesis that the Zagros Mountains formed a geographical barrier which promoted vicariance and diversification on the Iranian Plateau. Full article

The rapid evaporation of liquid droplets across a normal shock wave is a phenomenon of critical importance in advanced propulsion and clean energy systems, such as NH₃ supersonic separation. The conventional Spalding d²-law is commonly used to model such phenomena, but it is not suitable for predicting the complete vaporization of sub-micron droplets, particularly as evaporation approaches the free-molecular regime. To address this issue, this paper introduces a novel time-dependent one-dimensional CFD model, which is used to analyze the shock structure, the non-equilibrium heat and mass transfer between the liquid and gas phases, and the evolution of the droplets’ size through the shock. The model describes the evaporation of NH₃ sub-micron droplet sprays across a stationary normal shock for various fractions of the liquid phase. The Gyarmathy evaporation model is utilized to accurately account for the transition from diffusion-governed to free-molecular regimes, alongside a new two-phase Rankine–Hugoniot shock jump formulation. The study reveals the influence of a steady normal shock on the physical structure of a droplet-laden flow, including the existence of an initial droplet size swelling through the shock, and quantifies the subsequent complete evaporation of the suspended droplets. The maximum swelling throughout the shock is up to 17%, which corresponds to the case with 8% liquid phase mass fraction in the flow. The model provides acceptable accuracy in calculating the two-phase parameters in high-speed flows and can be extended for modeling more complex, multidimensional detonation and propulsion systems. Full article

Machine learning (ML), including deep learning, kernel-based classifiers, and ensemble methods, is increasingly used to support clinical diagnosis in medical imaging, biosignal interpretation, and electronic health record (EHR)-based decision support. Despite rapid progress, many diagnostic AI studies still rely on limited retrospective evaluation and single summary measures (e.g., accuracy or AUC), creating a gap between reported model performance and evidence required for safe clinical adoption. This review proposes TRIAGE, a clinically grounded evaluation framework designed to organize diagnostic AI testing as an evidence pipeline aligned with real clinical use cases (screening, triage, second reading, and confirmatory testing). We summarize core discrimination metrics derived from the confusion matrix (sensitivity, specificity, predictive values, likelihood ratios, diagnostic odds ratio, and F-scores) and highlight the importance of prevalence and spectrum effects for interpreting predictive value and clinical workload. We further review evaluation strategies for multi-class and multi-label diagnostic tasks using appropriate aggregation methods (micro, macro, and weighted averaging) and set-based measures such as Hamming loss, exact match ratio, and Jaccard/IoU. Because diagnostic deployment is threshold-dependent, we integrate representation curves (ROC, precision–recall, lift, and cumulative gain) with calibration assessment and clinical utility analysis, including calibration slope, Brier score, and decision-curve analysis. We also address robustness and fairness evaluation, leakage-resistant validation designs (patient-grouped splits, stratified and temporal validation, and external validation), computational constraints relevant to deployment (latency, throughput, and energy use), and statistically sound model comparison with multiplicity control. A structured TRIAGE checklist table summarizing the evaluation parameters described in this review is provided in the main text to support reproducible and clinically interpretable reporting. Full article

In this study, the astigmatid mite genus Troglocoptes Fain, 1966 is proposed as a junior synonym of Stereoglyphus Berlese, 1923. As a part of the project concerning identification of cave-dwelling mites in the Zagros Mountains, all ontogenetic instars of Stereoglyphus iranensis sp. nov. (Sarcoptiformes: Acaridae) are described from Doroodzan Cave, Fars Province, Iran. This is the second record of the genus in caves in the Palearctic region and the fifth described species worldwide. The morphological description is supplemented with DNA barcode data based on the mitochondrial cytochrome c oxidase subunit I (COI) gene, representing the first molecular data for this genus. Additionally, the first Asian record of Stereoglyphus longibursatus (Fain et Mahunka, 1990) is reported from Sahlak Cave, Fars Province, Iran. An identification key to the known species of the genus is provided. The troglobitic status of the new species is discussed, and the modifications of the anterior legs and tarsal setae, along with the partial reduction of idiosomal setation, are interpreted as adaptations to burrowing in bat guano. Full article

In this paper, we propose a distributed admittance control framework for joint manipulation of objects by multiple robotic arms that addresses the challenges of human–robot interaction. The system is developed to control the joint transportation of an object by N Franka Emika Panda robots (validated with up to four in simulations) using external human force estimation in a distributed manner without relying on centralized computation or force sensors. We integrate a hybrid observer by combining a distributed force estimator with a nonlinear disturbance observer (NDOB) to achieve accurate human force estimation and minimize estimation errors in simulations. Adaptive radial basis function neural networks (RBFNNs) are employed to dynamically adjust the damping and inertia parameters, enhancing the system’s adaptability and stability. Event-based communication minimizes network bandwidth usage, while consensus protocols ensure synchronization of state estimates across robots. Unlike conventional methods, the proposed observer operates in a fully sensorless manner: no human-force measurements are required. The estimation relies solely on locally available robot states, maintaining high accuracy while reducing system complexity. The framework demonstrates scalability to multiple robots, enhancing robustness in distributed settings. Simulation results show superior performance in terms of path tracking, force estimation accuracy, and communication efficiency compared to centralized approaches. Specifically, the event-triggered strategy reduces communication messages by approximately 70% compared to always-connected mode while maintaining comparable RMSE in position $(9.97\times {10}^{-5}$ vs. $7.39\times {10}^{-5}$) and velocity $(2.52\times {10}^{-5}$ vs. $3.76\times {10}^{-5})$, outperforming periodic communication. Full article

Bacteria play an important role in addressing challenges in rice production by promoting plant growth and enhancing stress tolerance through multiple mechanisms. Different types of soil bacteria affect rice growth by improving nutrient absorption, managing stress, and enhancing root structure. The relationship between rice plants and bacteria is intricate, as these bacteria can help reduce problems like salt stress, heavy metal toxicity, and infections. This review summarises studies published up to 2025 on how bacteria influence rice roots, including aspects like root length, density, biomass, and volume. Bibliometric analysis shows an increase of over 900% in research interest after 2020, with most studies conducted under controlled conditions and limited field validation. In addition to identifying key bacterial groups such as Bacillus, Pseudomonas, Burkholderia, and Azospirillum, this review identifies research gaps related to context dependency, strain specificity, and scalability. We have also emphasised the need for multi-strain inoculation strategies, field-scale experiments, and integration of microbial selection with rice breeding. The synthesis has highlighted that bacterial strains do not simply stimulate root growth but actively reprogram rice root architecture, modulating elongation, branching, density, and surface area as a response to environmental constraints. These effects are mediated by interconnected mechanisms that include phytohormone production, nutrient solubilisation, deaminase activity, stress-related gene regulation, and microbiome-driven feedback involving root exudation. Overall, viewing bacteria as regulators of root developmental dynamics rather than simple biofertilisers provides new insights for designing climate-adapted and sustainable rice production systems. Full article

Background: Age-related Macular Degeneration (AMD) is a leading cause of irreversible vision loss, particularly in the elderly. Optical Coherence Tomography (OCT), a noninvasive imaging modality, is widely used for retinal disease detection. However, the limited availability of labeled OCT datasets poses a significant challenge, making semi-supervised learning a promising approach. This study introduces a novel Iterative Teacher-Student (ITS) framework, which refines pseudo-labeling strategies to improve AMD detection accuracy, particularly in low-data scenarios. Methods: Initially, an optimal supervised model based on EfficientNet was developed to classify AMD using a dataset from Noor Eye Hospital, consisting of 16,822 OCT images. The dataset size was then progressively reduced to 70%, 50%, 20%, and 5% to evaluate model performance under data scarcity. Unlike conventional semi-supervised learning approaches, our ITS framework iteratively refines pseudo-labels, ensuring more reliable knowledge transfer from teacher to student models. Results: The optimized supervised model achieved 87.14% accuracy in AMD classification. As dataset size decreased to 20% and 5%, accuracy declined to 77.05% and 54.78%, respectively. Implementing the ITS framework improved accuracy to 88.56% at 20% and 64.15% at 5%, outperforming traditional semi-supervised methods. Conclusions: This study highlights the potential of semi-supervised learning, particularly our iterative teacher-student approach, to enhance AMD detection when labeled OCT data are scarce. The proposed framework introduces a novel iterative refinement strategy, which can serve as a foundation for future research in retinal disease diagnosis with limited labeled datasets. Full article

Background/Objectives: Pediatric-onset multiple sclerosis (POMS), defined as onset before age 18, is increasingly recognized as a distinct entity, often associated with a more burdensome disease course and earlier disability milestones than adult-onset MS. Although comorbidities may significantly affect disease progression and outcomes, their prevalence, incidence, risk, and characteristics in POMS remain poorly understood. To date, no systematic review has comprehensively evaluated comorbidities in POMS. The primary aim is to systematically identify and synthesize available evidence on the prevalence, incidence, risk, and characteristics of these comorbidities in POMS populations, as well as any reported effects on disease course, treatment outcomes, and overall clinical management. Methods: We will conduct a systematic review and meta-analysis following a hierarchical and pragmatic analytical strategy tailored to the expected heterogeneity and limited evidence base in POMS. MEDLINE (via PubMed) and Embase (produced by Elsevier) will be searched without date restrictions, combining controlled vocabulary terms (MeSH/Emtree) and relevant keywords for POMS and 15 predefined comorbidity categories. Study selection, abstract and full-text screening, and data extraction will be performed independently by two reviewers using predefined criteria and standardized forms. The primary quantitative outcome will be the pooled prevalence of comorbidities. Where study design and reporting permit, incidence rates will be assessed as secondary outcomes, and risk estimates (e.g., odds ratios) will be evaluated only in studies with appropriate comparator groups. Meta-analyses will be conducted using random-effects models when pooling is feasible. Heterogeneity will be assessed using the I² statistic and Cochran’s Q test, with sensitivity and subgroup analyses performed only when sufficient data are available. When quantitative synthesis is not appropriate due to limited data or substantial heterogeneity, findings will be summarized descriptively. Publication bias will be evaluated using funnel plots and, where applicable, Egger’s and Begg’s tests. This protocol adheres to PRISMA and PRISMA-P guidelines. Discussion: A systematic quantification of comorbidity prevalence, incidence (where available), and risk, together with POMS-specific characteristics and any reported impact on clinical outcomes, is anticipated to provide a crucial evidence base for guiding screening, refining management strategies, and informing future research directions. Ultimately, these findings may improve clinical outcomes and quality of life for children and adolescents with MS. Full article

Background: Breast cancer is among the most common cancers in women, and early diagnosis is critical for better treatment outcomes and reduced mortality. Efficient computer-aided diagnostic (CAD) systems play a crucial role in enhancing diagnostic accuracy and facilitating timely clinical decisions. Methods: This study proposes an automated CAD system for detecting cancerous tumors in mammograms, consisting of four stages: preprocessing, feature extraction, feature selection, and classification. In preprocessing, the region of interest (ROI) is extracted, followed by noise suppression and contrast enhancement to improve image quality. Shape, histogram, and tissue-related features are then computed from each ROI. An Improved Simulated Annealing (ISA) algorithm is employed to adaptively select the most informative features through a flexible process and composite fitness function, effectively reducing dimensionality while preserving high classification accuracy. Finally, classification is performed using a Support Vector Machine (SVM) to distinguish between malignant and benign masses. Results: Evaluation on the CBIS-DDSM and MIAS datasets showed the system achieved accuracies of 99.67% and 98%, sensitivities of 99.33% and 98%, and F1-scores of 99.66% and 97.9%, respectively. These results indicate notable improvements over traditional SA and full-feature approaches. Conclusions: The findings confirm the effectiveness of the ISA algorithm in selecting relevant features, thereby enhancing the performance of breast cancer detection. Full article