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This paper addresses the impact of disruptions in event-triggered control and input communication delays in connected vehicle systems (CVSs). Focusing on vehicle platooning, we develop a controller that compensates for communication delays and external disturbances by modulating position, velocity, and acceleration gains across leader and follower vehicles. Tracking error systems are defined to model the fleet dynamics, accounting for controller disruptions and external factors affecting stability. A quadratic Lyapunov function analysis is used to ensure stability, while an event-triggered communication criterion is implemented to avoid Zeno behavior, triggering updates only when a threshold is surpassed. Through event-triggered mechanisms and stability analysis, this approach provides a robust communication framework for vehicle platooning. Numerical simulations evaluate the system’s resilience to external disturbances, demonstrating stable and reliable vehicle coordination.

Vehicle Exterior Body Damage(VEBD) parts classification is important for claim processing, cost estimation, accident investigation, and vehicle damage assessment. Class imbalance in the VEBD part classification dataset is a primary factor affecting the classification performance of existing classification models. Although the availability of datasets and the system’s capability significantly impact system performance, its damage part classification is still limited due to its dynamic body structure, size, shape, color, and types of damage. In this paper, we propose a novel heterogeneous ensemble learning (HEL) model based on VEBD data (VEBD-HEL) to deal with imbalanced data in VEBD. We validate the effectiveness of VEBD-HEL on two original and generated VEBD datasets. The experimental results demonstrate that compared with current state-of-the-art models, VEBD-HEL has the best comprehensive performance. The proposed model not only achieves good Accuracy (99.93%) rates for both the simple damage and the severe damage but also increases the Area Under Curve (AUC) to 99.83%.

This paper presents a distributed control framework for grid–forming (GFM) distributed generations (DGs), considering the objectives of active/reactive power sharing and load feeder voltage regulation in inverter–based microgrids (MGs). Battery energy storage systems (BESSs) are controlled GFM sources while solar–powered DGs are working in grid–following (GFL) mode to provide active power support. The proposed method simplifies the global optimization problem into multiple sub–optimal problems by virtually segregating each GFM source into two decoupled sources. Each sub–problem, containing at most two GFM sources and multiple GFL sources with a single distributed agent, is solvable independently using local and neighboring node information. This feature substantially minimizes computational and communication resources, allowing for solutions using low–cost digital signal processors (DSPs). Moreover, the highly distributed nature of the proposed search algorithm ensures fast solution convergence and real–time implementation in multi–agent systems (MAS). Compared to existing segregation methods like Alternating Direction Method of Multipliers (ADMM) and Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) schemes, which segregate the network into complex sub–networks involving several GFM sources, the proposed approach is more suitable for small–scale inverter–based MGs. The framework’s effectiveness is validated through analytical formulation, MATLAB simulations, and realistic experimental results within a multi–feeder test MG system, which includes numerous load feeders and sparsely available GFM DGs, a scenario that has received limited attention in existing literature.

Abstract Igneous intrusives in northern Pakistan can provide valuable insights into pre‐Himalayan metaluminous to peraluminous magmatism along the northern boundary of the supercontinent Gondwana and its potential tectonic significance. This study generates new geochronologic, petrographic and geochemical data for intermediate (monzonite, syenite, and foid syenite) and felsic (granite and quartz monzonite) rocks within the NW Himalayan region of Pakistan. Both the intermediate and felsic rocks have values of A/NK > 1.1, implying a metaluminous to peraluminous composition, and are distinguished by high FeOT/(MgO + FeOT) (0.81–1.0), high 10,000 × Ga/Al ratio (2.1–5.1), elevated Nb + Zr + Y + Ce contents (122–1,204 ppm), and negative anomalies of P and Ti, consistent with aluminous A‐type magmatic affinity. These rocks are classified as A1‐type, which is linked to anorogenic intraplate extensional setting. Both rock groups yield high calculated average whole‐rock Zr saturation temperatures (i.e., TZr; 790–823°C), which suggests formation from high‐temperature magmas. Whole‐rock geochemistry, including variable (87Sr/86Sr)i values (0.7034–0.7086), positive εNd(t) (+0.1 to +3.9), high Pb isotopic values (that is, (206Pb/204Pb)i = 18.68 to 19.31, (207Pb/204Pb)i = 15.64 to 15.74, and (208Pb/204Pb)i = 38.93 to 39.78), and variation in zircon εHf(t) values (+0.8 to +7.0), indicates diverse magma sources for the intermediate‐felsic rocks and provide evidence of partial melting of metasomatized lithospheric mantle, producing a primary magma of foid to quartz syenitic composition. Subsequently, this magma was responsible for the partial melting of the overlying juvenile crust, producing granitic, quartz monzonitic and monzonitic magmas. During the magma evolution process of these rocks, this process was primarily determined by partial melting that followed fractionation of K‐feldspar, ilmenite and apatite. TDM2 ages indicate that the parent materials of intermediate‐felsic rocks were generated during the Mesoproterozoic‐Neoproterozoic. LA‐ICP–MS U‐Pb dating of magmatic zircons documents their formation in the Late Paleozoic at ∼278‐268 Ma. The intermediate‐felsic rocks are correlatable with alkaline igneous rocks of the Peshawar Plain, which record the breakup of the supercontinent Gondwana and the subsequent opening of Neo‐Tethys during a Late Paleozoic rifting event. The nepheline syenite records a younger episode during the Cenozoic (37 Ma), corresponding to the collision of the Indian‐Eurasian tectonic plates.

Accurately predicting key engineering properties, such as compressive and tensile strength, remains a significant challenge in high-performance concrete (HPC) due to its complex and heterogeneous composition. Early selection of optimal components and the development of reliable machine learning (ML) models can significantly reduce the time and cost associated with extensive experimentation. This study introduces four explainable Automated Machine Learning (AutoML) models that integrate Optuna for hyperparameter optimization, SHapley Additive exPlanations (SHAP) for interpretability, and ensemble learning algorithms such as Random Forest (RF), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), and Categorical Gradient Boosting (CB). The resulting interpretable AutoML models O-RF, O-XGB, O-LGB, and O-CB are applied to predict the compressive and tensile strengths of HPC. Compared to a baseline model from the literature, O-LGB achieved significant improvements in predictive performance. For compressive strength, it reduced the Mean Absolute Error (MAE) by 87.69% and the Root Mean Squared Error (RMSE) by 71.93%. For tensile strength, it achieved a 99.41% improvement in MAE and a 96.67% reduction in RMSE, along with increases in R2. Furthermore, SHAP analysis identified critical factors influencing strength, such as cement content, water, and age for compressive strength, and curing age, water-binder ratio, and water-cement ratio for tensile strength. This approach provides civil engineers with a robust and interpretable tool for optimizing HPC properties, reducing experimentation costs, and supporting enhanced decision-making in structural design, risk assessment, and other applications.

The performance of asphalt pavements is significantly influenced by the mix design and the binder film thickness (BFT) around aggregates. Adjusting the proportions of binders and aggregates modifies the properties of asphalt mixtures, with different mix designs substantially impacting these properties. This study evaluates the influence of two traditional mix designs (i.e., Marshall and Superpave Gyratory Compacted) and one innovative mix design (the one using BFT as a criterion) on asphalt pavement performance Two methods, which are modifications of the Hveem Surface Area Factor method, were used to calculate the optimum BFT. The mix with 3.5% asphalt content (AC) was selected for the innovative mix design as it showed the optimal BFT. For the Marshall and Superpave mix designs, the optimum binder content was determined using the respective Marshall and Superpave Gyratory Compacted methods, yielding values of 4.3% and 4.4%, respectively. Samples were prepared from each type of mix design and then tested using a Universal Testing Machine (UTM) and a Double Wheel Tracker (DWT). Performance tests showed that the innovative mix design samples had superior rutting resistance under DWT, the Marshall samples had the highest Resilient Modulus (Mr) and moisture resistance, and the Superpave samples exhibited the best fatigue resistance, enduring the most cycles until failure in the Indirect Tensile Fatigue Test (ITFT). These findings underscore the importance of considering BFT as a critical mix design criterion, demonstrating its significant potential to enhance the overall performance and durability of asphalt pavements.

Objectives: Rotator cuff syndrome (RCS) is a common upper extremity musculoskeletal condition in the working population, often leading to prolonged work absences. This study assesses and contrasts the impacts of Mulligan mobilization (MWM) and transverse friction massage in individuals with RCS. Methods: A randomized clinical trial was conducted at Northwest General Hospital and Research Centre in Peshawar from January to June 2023. The trial involved 42 participants in the age range of 30 and 70 years who had grade I/II rotator cuff tear and pain. These participants were randomly divided into two groups. Group A was administered MWM, while group B received transverse friction massage in addition to standard care. Both groups received a six-week treatment and were evaluated at the beginning, third and sixth weeks using the visual analogue scale to measure pain, goniometry to assess shoulder range, and the disabilities of the arm, shoulder and hand (DASH) questionnaire to evaluate physical function. Results: The between-group comparison showed a significant difference in VAS, shoulder ranges, and DASH scores in both groups in the third and sixth weeks (P

Regarding a simple graph $ \Gamma $ possessing $ \nu $ vertices ($ \nu $-vertex graph) and $ m $ edges, the vertex-weight and weight of an edge $ e = uv $ are defined as $ w(v_{i}) = d_{ \Gamma}(v_{i}) $ and $ w(e) = d_{ \Gamma}(u)+d_{ \Gamma}(v)-2 $, where $ d_{ \Gamma}(v) $ is the degree of $ v $. This paper puts forward a novel graphical matrix named the edge-weighted adjacency matrix (adjacency of the vertices) $ A_{w}(\Gamma) $ of a graph $ \Gamma $ and is defined in such a way that, for any $ v_{i} $ that is adjacent to $ v_{j} $, its $ (i, j) $-entry equals $ w(e) = d_{ \Gamma}(v_{i})+d_{ \Gamma}(v_{j})-2 $; otherwise, it equals 0. The eigenvalues $ \lambda_{1}^{w}\ge \lambda_{2}^{w}\ge\ldots\ge \lambda_{\nu}^{w} $ of $ A_w $ are called the edge-weighted eigenvalues of $ \Gamma $. We investigate the mathematical properties of $ A_{w}(\Gamma) $'s spectral radius $ \lambda_{1}^{w} $ and energy $ E_{w}(\Gamma) = \sum_{i = 1}^{\nu}|\lambda_{i}^{w}| $. Sharp lower and upper bounds are obtained for $ \lambda_{1}^{w} $ and $ E_{w}(\Gamma) $, and the respective extremal graphs are characterized. Further, we employ these spectral descriptors in structure-property modeling of the physicochemical properties of polycyclic aromatic hydrocarbons for a set of benzenoid hydrocarbons (BHs). Detailed regression analysis showcases that edge-weighted energy outperforms classical adjacency energy in structure-property modeling of the physicochemical properties of BHs.

Ventricular tachycardia poses a significant therapeutic challenge. It can manifest over a spectrum from minimal palpitation symptoms to sudden cardiac death. This makes large-scale trials on the treatment of ventricular tachycardia difficult to perform. The mechanism of ventricular tachycardia must also be understood before embarking on treatment. Patients with or without structural heart disease will have different mechanisms for the onset and propagation of these arrhythmias. Catheter ablation is an established management option for ventricular tachycardia; however, it is not always successful and anti-arrhythmic medications are often necessary to control these life-threatening arrhythmias. Although anti-arrhythmics can suppress ventricular tachycardias they also carry side effects. In certain substrates, some of these medications can exacerbate arrhythmias or heart failure. For these reasons, a multifaceted approach to treating ventricular tachycardia is necessary. This paper is a comprehensive review of the comprehensive management strategies for ventricular tachycardia. Anti-arrhythmic medications have an important role and their use in various cardiomyopathies and channelopathies is reviewed in detail. We also review the promising effects of gene therapy and artificial intelligence on different substrates for ventricular tachycardia.

This paper addresses the shortcomings of existing leader-following consensus approaches in multi-agent systems, particularly their vulnerability to Byzantine attacks and inefficiencies in communication and control updates. Current methods often struggle to handle sensor and actuator disruptions from Byzantine agents and suffer from excessive controller updates, which lead to communication overhead and the potential for Zeno behavior. To tackle these challenges, we introduce the Hybrid Dynamic Event-Triggered Mechanism (HDETM), designed to minimize unnecessary agent interaction and reduce the frequency of controller updates while maintaining system robustness. By employing regularization techniques that ensure a positive inter-event time, our approach eliminates the risk of Zeno behavior. Additionally, the framework integrates auxiliary variables to capture both flow and jump dynamics, and through Lyapunov consensus analysis, we establish L2 stability with finite gain, ensuring system resilience under Byzantine attacks. This research demonstrates that HDETM optimizes communication efficiency and enhances the system's ability to withstand Byzantine disruptions. Extensive numerical simulations validate the effectiveness of the proposed methodology in achieving robust consensus, highlighting its superiority over existing approaches.

ABSTRACTBackground: Early detection, monitoring, and managing adverse events (AEs) are crucial in optimising treatment for multidrug-resistant tuberculosis (MDR-TB) patients.Objectives: To investigate the incidence, factors, management, and impact of AEs on treatment outcomes in MDR-TB patients.Methods: This study reviewed the medical records of 275 MDR-TB patients at Fatimah Jinnah Institute of Chest Diseases in Quetta, Pakistan. Patient information was collected using a designed data collection form. Mann–Whitney U and Kruskal–Wallis tests examined the difference in AEs occurrences based on patients’ characteristics. Multiple binary logistic regression identified factors associated with unsuccessful outcomes, with statistical significance set at a p-value 60 years(OR = 23.481), baseline body weight of 31–60 kg(OR = 0.180), urban residence(OR = 0.296), and experiencing ototoxicity (OR = 0.258) and hypothyroidism (OR = 0.136) were significantly associated with unsuccessful treatment outcomes.Conclusion: AEs were highly prevalent but did not negatively impact treatment outcomes. Patients at higher risk of developing AEs and unsuccessful outcomes should receive special attention for its early management.

This paper addresses the difficulties with connected vehicle systems (CVSs), particularly with vehicle platooning, are examined in this paper. For leader and follower-connected vehicles, the control protocol (which includes artificial delays, disturbances and proportional gains) is implemented. With tracking error systems, system dynamics are modelled while taking outside influences into consideration. Using creative thinking, a centralized event-triggered control system is implemented to maximize fleet wide communication updates. System stability is guaranteed by this centralized method in combination with quadratic form Lyapunov stability analysis. The risk of zeno behaviour is reduced by an event-triggered communication condition that is activated when a threshold is exceeded. The effectiveness of the centralized event-triggered system in improving stability and resilience in connected vehicle platooning scenarios is evaluated numerically through simulations.

Composite construction materials are extensively employed and have proven to be more effective than their individual components, such as steel and concrete. This is because composites combine the strengths of different materials, resulting in enhanced properties such as increased strength-to-weight ratios, better durability, and improved thermal efficiency. However, despite the strong structural properties and energy-saving benefits of reinforced concrete sandwich panels, their use is restricted in many rural regions of developing nations like Pakistan, largely because of the economic limitations faced by the communities. Therefore, this study introduces cost-effective structural mud insulated panels (SMIPs) that utilize mud wythes and evaluates their mechanical performance. These panels consist of a lightweight Expanded Polystyrene (EPS) foam core, sandwiched between two galvanized steel-reinforced mud layers. The study includes flexural, compression, diagonal tension, and water absorption tests. Flexural tests revealed that the panels have substantial load-bearing capacity under bending forces. Whereas, Compression tests showed that the panels possess adequate strength and stiffness to support moderate structural loads. Additionally, Diagonal tension tests confirmed the panels’ ability to resist significant in-plane loads. Water absorption tests also indicated that the panels demonstrated adequate moisture resistance, staying within the acceptable limit of 15%. These findings highlight the mechanical and hydric properties of SMIPs, underscoring their potential in sustainable construction, especially in economically constrained regions.

This paper investigates the behavior of fractional-order nonlinear multi-agent systems subjected to Byzantine assaults, specifically focusing on the manipulations of both sensors and actuators. We employ weighted graphs, both directed and undirected, to illustrate the system's topology. Our methodology combines algebraic graph theory with fractional-order Lyapunov techniques to develop algebraic requirements for leader-following consensus, providing a robust framework for analyzing consensus dynamics in these complex systems. We present quantitative results demonstrating the effectiveness of our approach, including two numerical examples that validate the proposed requirements for consensus evaluation. Notably, our work highlights the novelty of using fractional-order systems to enhance resilience against adversarial conditions, contributing significantly to the field of multi-agent systems. By clarifying key terms and streamlining our language, we ensure accessibility for a broader audience while emphasizing the implications of our findings for real-world applications.

This article addresses the hostile-based bipartite containment control of nonlinear fractional multi-agent systems (FMASs) with input delays. Several fundamental algebraic criteria have been offered by the use of signed graph theory. To make the controller design more realistic, we assumed that the controller was under some disturbance. For the analysis of bipartite containment control, we used a fixed and switching signed network. The commonly used Lyapunov function approach and the Razumikhin technique were used. The use of these techniques can conquer the challenge brought on by switching, temporal delays, and fractional mathematics. To better elucidate the theoretical results, two examples are provided.

Background: Despite the introduction of bedaquiline (Bdq) containing all-oral regimens for treating patients with rifampicin resistant/multidrug resistant tuberculosis (MDR/RR-TB) in 2019, data on its effectiveness in Pakistan, which has the fifth highest MDR-TB burden, is lacking. This study evaluates treatment outcomes and identifies factors associated with unsuccessful outcomes among MDR/RR-TB patients treated with an all-oral longer treatment regimen (LTR). Methods: This retrospective record review included all microbiologically confirmed pulmonary MDR/RR-TB patients treated with an all-oral LTR between August 2019 and February 2021 across nine PMDT centres in Pakistan. Sociodemographic and clinical data were retrieved from the Electronic Nominal Recording and Reporting System. Treatment outcomes, defined by WHO criteria, were analysed using SPSS and multivariate binary logistic regression to identify factors associated with unsuccessful outcomes. A p-value 5 drugs (OR:3.12, 95 %CI:1.36–11.64, p = 0.013) were significantly associated with death and treatment failure. Whereas, lung cavitation had statistically significant association with LTFU (OR:2.66, 95 %CI:1.10–7.32, p = 0.045). Conclusion: Treatment success rate (70.3 %) in this study fell below the WHO recommended target success rate (>90 %). Enhanced clinical management, coupled with special attention to patients exhibiting identified risk factors could improve treatment outcomes.

Abstract In this study, we present the EEG-GCN, a novel hybrid model for the prediction of time series data, adept at addressing the inherent challenges posed by the data's complex, non-linear, and periodic nature, as well as the noise that frequently accompanies it. This model synergizes signal decomposition techniques with a graph convolutional neural network (GCN) for enhanced analytical precision. The EEG-GCN approaches time series data as a one-dimensional temporal signal, applying a dual-layered signal decomposition using both Ensemble Empirical Mode Decomposition (EEMD) and GRU. This two-pronged decomposition process effectively eliminates noise interference and distills the complex signal into more tractable sub-signals. These sub-signals facilitate a more straightforward feature analysis and learning process. To capitalize on the decomposed data, a graph convolutional neural network (GCN) is employed to discern the intricate feature interplay within the sub-signals and to map the interdependencies among the data points. The predictive model then synthesizes the weighted outputs of the GCN to yield the final forecast. A key component of our approach is the integration of a Gated Recurrent Unit (GRU) with EEMD within the GCN framework, referred to as EEMD-GRU-GCN. This combination leverages the strengths of GRU in capturing temporal dependencies and the EEMD's capability in handling non-stationary data, thereby enriching the feature set available for the GCN and enhancing the overall predictive accuracy and stability of the model. Empirical evaluations demonstrate that the EEG-GCN model achieves superior performance metrics. Compared to the baseline GCN model, EEG-GCN shows an average R2 improvement of 60% to 90%, outperforming the other methods. These results substantiate the advanced predictive capability of our proposed model, underscoring its potential for robust and accurate time series forecasting.

Analysis of hyperspectral imagery is a critical aspect of remote sensing in precision agriculture, for which effective dimensionality reduction (DR) strategies for the inherent complexity and uncertainty of the data are highly necessary. The fusion of fuzzy logic with DR techniques offers potential promises to refine enough feature information from the classification system, which may potentially compromise critical information. However, graph-based deep learning, especially the use of graph attention networks (GATs), has advanced the field by ensuring enhanced spatial and spatial-type feature relationships, which alleviates classification errors. Although graph convolution networks and their predecessors have greatly advanced the use of spatial relationship features, deep-learning algorithms, such as three-dimensional convolutional neural networks (3D-CNNs), have resorted to reducing the need for and reliance on high samples through spatial feature learning and consideration. Therefore, to advance the field, a novel approach, enhanced transformation-enabled fuzzy graph (ETFG) network, was designed, which is a combination of deep fuzzy-based DR, enhanced through the use of 3D-CNN and GAT, with the application of principal component analysis (PCA) for optimized DR. The ETFG model entailed two major processing stages, where the initial stage involved classifying the raw data cube using the 3D-CNN, and then the results processed an algorithm enriched by lightweight GAT-based modules. The ETFG model combines fuzzy C-means clustering and optimized DR using PCA, contributing the best of PCA and GAT capabilities for optimized classification. At high-performance optimal DR, the ETFG model offered optimal multispectral imaging and the analysis and classification of hyperspectral data, which is promising enough to advance the field's needs.

This review aims to explore the evolution, techniques, and outcomes of minimally invasive spine surgery (MISS) within the field of neurosurgery. We sought to address the increasing burden of spine degeneration in a rapidly aging population and the need for optimizing surgical management. This review explores various techniques in MISS, drawing upon evidence from retrospective studies, case series, systematic reviews, and technological advancements in neurosurgical spine treatment. Various approaches, including endonasal cervical, transoral cervical, transcervical, mini-open/percutaneous, tubular, and endoscopic techniques, provide alternatives for current approaches to a range of spinal pathologies. The main findings of this review highlight potential advantages of MISS over traditional open surgery, including reduced complications, shorter hospital stays, and improved patient outcomes. Our research underscores the importance of adopting MISS techniques to optimize patient care in neurosurgical spine treatment.

Abstract Facial expressions serve as crucial indicators of an individual's psychological state, playing a pivotal role in face-to-face communication. This research focuses on advancing collaboration between machines and humans by undertaking a thorough investigation into facial expressions. Specifically, we delve into the analysis of emotional variations related to changes in skin tone across different genders and cultural backgrounds (Black and white). The research methodology is structured across three phases. In Phase I, image data is acquired and meticulously processed from the Chicago face dataset, resulting in 12,402 augmented images across five classes (Normal case, Benign case, Adenocarcinoma, Squamous-cell-carcinoma, Large-cell-carcinoma). Phase II involves the identification of Regions of Interest (ROI) and the extraction of RGB values as features from these ROIs. Various methods, including those proposed by Kovac, Swift, and Saleh, are employed for precise skin identification. The final phase, Phase III, centers on the in-depth analysis of emotions and presents the research findings. Statistical techniques, such as Descriptive statistics, independent sample T-tests for gender and cross-cultural comparisons, and two-way ANOVA, are applied to RED, BLUE, and GREEN pixel values as response variables, with gender and emotions as explanatory variables. The rejection of null hypotheses prompts a Post Hoc test to discern significant pairs of means. The results indicate that both cross-cultural backgrounds and gender significantly influence pixel colors, underscoring the impact of different localities on pixel coloration. Across various expressions, our results exhibit a minimal 0.05% error rate in all classifications. Notably, the study reveals that green pixel color does not exhibit a significant difference between Anger and Neutral emotions, suggesting a near-identical appearance for green pixels in these emotional states. These findings contribute to a nuanced understanding of the intricate relationship between facial expressions, gender, and cultural backgrounds, providing valuable insights for future research in human–machine interaction and emotion recognition.

In this paper, in Riemann-Liouville sense, delayed multi-agent system of fractional-order is being considered, in which we consider corresponding topology is weighted graph. To make controller design more practical, it is assumed that the controller is not implemented as it is and we consider the case, when there is disturbance in controller, which is assumed to be bounded. By applying algebraic graph theory and classical Lyapunov direct method, some algebraic criteria are provided and for analysing consensus, a new method is used. Corresponding Lyapunov function of first order can be considered is major reason for suggested access. For showing validity of our approach, some effective examples are also provided.

Introduced by Gutman & Trinajstić in 1972, the two well-known Zagreb indices efficiently determine the total π-electron energy of alternant hydrocarbons. The hyper-Zagreb index is a variant of the classical first Zagreb index. A tree is an acyclic graphical structure. A segment in a tree is equivalent to a vertex of degree two. This paper derives sharp upper and lower bounds on the hyper-Zagreb index of trees with given number of segments. We also characterize the corresponding trees achieving those bounds. The second part of the paper studies the predictive potential of the hyper-Zagreb index for determining physicochemical properties such as the enthalpies of combustion, formation, sublimation, and vaporization of monocarboxylic acids. Our statistical analysis asserts that the hyper-Zagreb index correlates these physicochemical properties of monocarboxylic acids with correlation coefficient ρ>0.99. The strongest correlation by the hyper-Zagreb index was obtained with the enthalpy of combustion having correlation coefficient ρ=0.999991. This warrants the further employability of the hyper-Zagreb index in QSPR modeling. This potential applicability justifies the construction of the hyper-Zagreb graphical index whose motivation of defining was purely mathematical.

Transcranial direct current stimulation (tDCS) has been studied extensively for its potential to enhance human cognitive functions in healthy individuals and to treat cognitive impairment in various clinical populations. However, little is known about how tDCS modulates the neural networks supporting cognition and the complex interplay with mediating factors that may explain the frequently observed variability of stimulation effects within and between studies. Moreover, research in this field has been characterized by substantial methodological variability, frequent lack of rigorous experimental control and small sample sizes, thereby limiting the generalizability of findings and translational potential of tDCS. The present manuscript aims to delineate how these important issues can be addressed within a neuroimaging context, to reveal the neural underpinnings, predictors and mediators of tDCS-induced behavioral modulation. We will focus on functional magnetic resonance imaging (fMRI), because it allows the investigation of tDCS effects with excellent spatial precision and sufficient temporal resolution across the entire brain. Moreover, high resolution structural imaging data can be acquired for precise localization of stimulation effects, verification of electrode positions on the scalp and realistic current modeling based on individual head and brain anatomy. However, the general principles outlined in this review will also be applicable to other imaging modalities. Following an introduction to the overall state-of-the-art in this field, we will discuss in more detail the underlying causes of variability in previous tDCS studies. Moreover, we will elaborate on design considerations for tDCS-fMRI studies, optimization of tDCS and imaging protocols and how to assure high-level experimental control. Two additional sections address the pressing need for more systematic investigation of tDCS effects across the healthy human lifespan and implications for tDCS studies in age-associated disease, and potential benefits of establishing large-scale, multidisciplinary consortia for more coordinated tDCS research in the future. We hope that this review will contribute to more coordinated, methodologically sound, transparent and reproducible research in this field. Ultimately, our aim is to facilitate a better understanding of the underlying mechanisms by which tDCS modulates human cognitive functions and more effective and individually tailored translational and clinical applications of this technique in the future.

Chalcone represents a vital biosynthetic scaffold owing to its numerous therapeutic effects. The present study was intended to synthesize 17 chalcone derivatives (3a–q) by direct coupling of substituted acetophenones and benzaldehyde. The target chalcones were characterized by spectroscopic analyses followed by their in vitro antimicrobial, and antileishmanial investigations with reference to standard drugs. The majority of the chalcones displayed good to excellent biological activities. Chalcone 3q (1000 µg ml−1) exhibited the most potent antibacterial effect with its zone of inhibition values of 30, 33 and 34 mm versus Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa respectively. The results also confirmed chalcone 3q to be the most potent versus Leishmania major with the lowest IC50 value of 0.59 ± 0.12 µg ml−1. Chalcone 3i (500 µg ml−1) was noticed to be the most potent antifungal agent with its zone of inhibition being 29 mm against Candida albicans. Computational studies of chalcones 3i and 3q supported the preliminary in vivo results. The existence of the amino moiety and bromine atom on ring-A and methoxy moieties on ring-B caused better biological effects of the chalcones. In brief, the investigations reveal that chalcones (3i and 3q) can be employed as building blocks to discover novel antimicrobial agents.

Leader-follower cyber secure consensus problem in cybersecurity is crucial as it directly addresses the need for secure and stable communication in a networked system, which is paramount in safeguarding against cyber threats like denial-of-service attacks. In this article, by utilizing control strategy with distributed delays in denial-of-service attack's presence for discrete-time multi-agent system of fractional-order, the leader-following cyber secure consensus problem is examined. Nonlinear functions have been assumed in the leader and follower equation of the system to study variations due to this function in the system. As multi-agent system is networked environment, in response to numerous threats, their security control becomes critically desirable, for example, denial of service. The consensus performance may be destabilized by resulting topologies which are caused by Denial-of-Service attacks. The connectivity between agents is destroyed, especially under connectivity-broken attacks. In order to overcome these problems, the strategy of novel defense which consists of consensus (with distributed delays) control is introduced. By using Caputo fractional difference operator sufficient criteria which includes the condition in terms LMI is derived and by using Lyapunov function approach, average dwell time and algebraic graph theory for security of addressed system's cyber secure consensus this is done for determining the obtained system of error's stability. Finally, by showing some numerical examples on the introduced systems, the effectiveness of the obtained results is determined.

A convex hull of finitely many points in the Euclidean space Rd is known as a convex polytope. Graphically, they are planar graphs i.e. embeddable on R2. Minimum dominating sets possess diverse applications in computer science and engineering. Locating-dominating sets are a natural extension of dominating sets. Studying minimizing locating-dominating sets of convex polytopes reveal interesting distance-dominating related topological properties of these geometrical planar graphs. In this paper, exact value of the locating-dominating number is shown for one infinite family of convex polytopes. Moreover, tight upper bounds on γl−d are shown for two more infinite families. Tightness in the upper bounds is shown by employing an updated integer linear programming (ILP) model for the locating-dominating number γl−d of a fixed graph. Results are explained with help of some examples. The second part of the paper solves an open problem in Khan (2023) [28] which asks to find a domination-related parameter which delivers a correlation coefficient of ρ>0.9967 with the total π-electronic energy of lower benzenoid hydrocarbons. We show that the locating-dominating number γl−d delivers such a strong prediction potential. The paper is concluded with putting forward some open problems in this area.

Background: Computed tomography to body divergence (CTBD) is one of the main barriers to bronchoscopic techniques for the diagnosis of peripherally located lung nodules. Cone-beam CT (CBCT) guidance is being rapidly adopted to correct for this phenomenon and to potentially increase diagnostic outcomes. In this trial, we hypothesized that the addition of mobile CBCT (m-CBCT) could improve the rate of tool in lesion (TIL) and the diagnostic yield of shape-sensing robotic-assisted bronchoscopy (SS-RAB). Methods: This was a prospective, single-arm study, which enrolled patients with peripheral lung nodules of 1–3 cm and compared the rate of TIL and the diagnostic yield of SS-RAB alone and combined with mCBCT. Results: A total of 67 subjects were enrolled, the median nodule size was 1.7 cm (range, 0.9–3 cm). TIL was achieved in 23 patients (34.3%) with SS-RAB alone, and 66 patients (98.6%) with the addition of mCBCT (p < 0.0001). The diagnostic yield of SS-RAB alone was 29.9% (95% CI, 29.3–42.3%) and it was 86.6% (95% CI, 76–93.7%) with the addition of mCBCT (p < 0.0001). There were no pneumothoraxes or any bronchoscopy-related complications, and the median total dose–area product (DAP) was 50.5 Gy-cm2. Conclusions: The addition of mCBCT guidance to SS-RAB allows bronchoscopists to compensate for CTBD, leading to an increase in TIL and diagnostic yield, with acceptable radiation exposure.

This paper introduces an advanced observer-based control strategy designed for fractional multi-agent systems operating in hostile environments. We take into account the dynamic nature of the agents with nonlinear delayed dynamics and consider external disturbances affecting the system. The manuscript presents an improved observation-based control approach tailored for fractional-order multi-agent systems functioning in challenging conditions. We also establish various applicable conditions governing the creation of observers and disturbance compensation controllers using the fractional Razmikhin technique, signed graph theory, and matrix transformation. Furthermore, our investigation includes observation-based control on switching networks by employing a typical Lyapunov function approach. Finally, the effectiveness of the proposed strategy is demonstrated through the analysis of two simulation examples.

The Kawahara equation exhibits signal dispersion across lines of transmission and the production of unstable waves from the water in the broad wavelength area. This article explores the computational analysis for the approximate series of time fractional Kawahara (TFK) and modified Kawahara (TFMK) problems. We utilize the Shehu homotopy transform method (SHTM), which combines the Shehu transform (ST) with the homotopy perturbation method (HPM). He’s polynomials using HPM effectively handle the nonlinear terms. The derivatives of fractional order are examined in the Caputo sense. The suggested methodology remains unaffected by any assumptions, restrictions, or hypotheses on variables that could potentially pervert the fractional problem. We present numerical findings via visual representations to indicate the usability and performance of fractional order derivatives for depicting water waves in long-wavelength regions. The significance of our proposed scheme is demonstrated by the consistency of analytical results that align with the exact solutions. These derived results demonstrate that SHTM is an effective and powerful scheme for examining the results in the representation of series for time-fractional problems.

This article investigates the resilient-based consensus analysis of fractional-order nonlinear leader-following systems with distributed and input lags. To enhance the practicality of the controller design, an incorporation of a disturbance term is proposed. Our modeling framework provides a more precise and flexible approach that considers the memory and heredity aspects of agent dynamics through the utilization of fractional calculus. Furthermore, the leader and follower equations of the system incorporate nonlinear functions to explore the resulting changes. The leader-following system is expressed by a weighted graph, which can be either undirected or directed. Analyzed using algebraic graph theory and the fractional-order Razumikhin technique, the case of leader-following consensus is presented algebraically. To increase robustness in multi-agent systems, input and distributive delays are used to accommodate communication delays and replicate real-time varying environments. This study lays the groundwork for developing control methods that are more robust and flexible in complex networked systems. It does so by advancing our understanding and practical application of fractional-order multi-agent systems. Additionally, experiments were conducted to show the effectiveness of the design in achieving consensus within the system.

Graham and Pollack in 1971 presented applications of eigenvalues of the distance matrix in addressing problems in data communication systems. Spectral graph theory employs tools from linear algebra to retrieve the properties of a graph from the spectrum of graph-theoretic matrices. The study of graphs with “few eigenvalues” is a contemporary problem in spectral graph theory. This paper studies graphs with few distinct distance eigenvalues. After mentioning the classification of graphs with one and two distinct distance eigenvalues, we mainly focus on graphs with three distinct distance eigenvalues. Characterizing graphs with three distinct distance eigenvalues is “highly” non-trivial. In this paper, we classify all trees whose distance matrix has precisely three distinct eigenvalues. Our proof is different from earlier existing proof of the result as our proof is extendable to other similar families such as unicyclic and bicyclic graphs. The main tools which we employ include interlacing and equitable partitions. We also list all the connected graphs on ν ≤ 6 vertices and compute their distance spectra. Importantly, all these graphs on ν ≤ 6 vertices are determined from their distance spectra. We deliver a distance cospectral pair of order 7, thus making it a distance cospectral pair of the smallest order. This paper is concluded with some future directions.

Quantitative structure-property relationship (QSPR) modeling employs contemporary mathematical tools to predict physicochemical, thermodynamic and biological properties of compounds from their chemical structures. Graphical indices provide those mathematical tools and efficiently correlate physicochemical, thermodynamic and biological properties of compounds. This paper considers the class of benzenoid hydrocarbons (BHs) and investigates the predictive power of commonly occurring graphical temperature indices for determining thermodynamic characteristics of BHs. The entropy (So) & heat capacity (Cp) have been selected to advocate for thermodynamic characteristics. In order to validate the statistical inference, the lower 30 BHs have been opted as test molecules as their experimental data is also publicly available. First, a computer-based method is put forwarded to evaluate temperature indices of a chemical graph. The computational method is, then, employed to compute temperature indices of 30 lower BHs. Certain QPSR models are proposed by utilizing the experimental data of Cp and So for the BHs. Our statistical analysis suggests that the most efficient regression models are, in fact, quadratic. Unexpectedly, some underresearched temperature indices like the general first & second temperature indices perform well having the correlation coefficient >0.9 which is the minimum threshold in a comparative testing. Based on our statistical analysis, three recently proposed temperature indices perform exceptionally well in comparison with all the existing temperature indices. The results suggest their further employability in QSPR modeling. Importantly, our research contributes towards countering proliferation of graphical indices.

In this study, a copper-containing polyphenol oxidase (PPO) enzyme was extracted from the selected agricultural food products, i.e., Mangifera indica (L.), Solanum tuberosum (L.), and Luffa cylindrica (L.), which has efficiently degraded the azo dyes (Methylene blue, Reactive Black-5, and Toluidine Blue-O) into less toxic compounds. After preparing the dye solution at various concentrations, the PPO enzymes extracted with phosphate buffer were incubated with it for 2 hours. The percentage of decolorization was estimated by using the unprocessed dye solution. The degraded products were characterized by UV–vis and FT-IR. All sets of experiments followed pseudo-first-order kinetics. The highest kinetic degradation rate constant was observed at kt = 0.0213 min─1 (R2 = 0.9258) and kt = 0.0244 min─1 (R2 = 0.7627) in reactive black-5 from Mangifera indica (L.) and Solanum tuberosum (L.), respectively. The enzyme extracted from the peel of Solanum tuberosum (L.) decolorized Reactive Black-5 and Toluidine Blue-O dyes to a greater extent, i.e., 83.29 and 70.84% with reaction times of 120 and 90 min, respectively.

The Sombor index, introduced by Gutman in 2021, represents a novel graphical topological descriptor reliant upon graph degree information. It holds promise for elucidating the thermodynamic behavior of compounds. Denoting by Vnk (respectively, Enk) the set encompassing all connected graphs comprising n vertices with a specific vertex-connectivity (correspondingly, edge-connectivity) value of k. In Problem 1 (resp. Problem 2) of Hayat et al. (2022) [33], the question of finding minimum Sombor index of graphs in Vnk (resp. Enk) is proposed. In this note, we solve both of the open problems by characterizing minimizing graphs in Enk and Vnk corresponding to the Sombor index. We employed commonly occurring valency-based graphical indices in a QSPR studies of the physicochemical characteristics for monocarboxylic acids. The proposed statistical models infer that the Sombor index predicts various physicochemical characteristics for monocarboxylic acids having strong predictive coefficients such as ρ=0.99998.

Petrographical characterization and field observations were caried out to evaluate Kamlial Formation in Bagh district, Azad Jammu and Kashmir. Based on detailed petrography, the lithic arenite consisted of quartz (20–25%), feldspar (7–11%), rock fragments (20–37%), cementing materials (11–21%), and accessory minerals. Grains are mostly angular to subrounded and poorly to moderately sorted. The analysis revealed that the lithic arenite is mineralogically immature; also, the current activity during the time of deposition was low. Polycrystalline quartz indicates that the sandstone was derived from metamorphic source, while monocrystalline quartz indicates a granitic origin. Quartz having an angular shape suggests the source rock was near the depositional site, while quartz having a rounded shape represents long transportation. The presence of feldspar in the lithic arenite suggests the rocks were deposited at high relief or cold temperatures. Primary porosity in sandstone was reduced by calcite cements around the grain, while secondary porosity was developed by fracturing of quartz and feldspar. Tectonic uplift in the study area was demonstrated by fractured quartz and mica in thin sections. Field observations of various sedimentary structures were observed such as load casts, ripple marks, and mud cracks, etc. The presence of conglomerates and load casts in the study area indicates that the Kamlial sandstone was deposited by fluvial and shallow marine environment. Furthermore, the ripple marks indicate that the tidal flat environment controlled the deposition of the sediments.

The study aims to assess the health-related Quality of Life (HRQOL) and its association with socio-demographic factors among the Afghan refugees residing in Quetta, Pakistan. For this purpose, a cross-sectional, descriptive study design by adopting Euro QOL five dimensions questionnaire (EQ-5D) for the assessment of HRQOL was conducted by approaching Afghan refugees from the camp and other areas of Quetta, Pakistan. Furthermore, this study also involved descriptive analysis to expound participant's demographic characteristics while inferential statistics (Kruskal-Wallis and Mann-Whitney test, P < 0.05) were used to compare EQ-5D scale scores. All analyses were performed using SPSS v 20. Herein, a total of 729 participants were enrolled and were subsequently (n = 246, 33.7%) categorized based on their age of 22-31 years (31.30 ± 15.40). The results of mean EQ-5D descriptive score (0.85 ± 0.20) and EQ-VAS score (78.60 ± 11.10) indicated better HRQOL in the current study respondents as compared to studies conducted in other refugee camps around the globe. In addition, demographic characteristics including age, marital status, locality, years of living as refugees, life as a refugee residing out of Pakistan, place of residence in Afghanistan, educational qualification, occupation, and arrested for crime were the statistically significant predictors (P < 0.05) of EQ-5D index scores. However, gender, living status, monthly income, preferred place of treatment were non-significant predictors (P > 0.05). The results of current study provided evidence for a model that correlated with participant's socio-demographic information and HRQOL. Moreover, this study also revealed a baseline assessment for the health status of Afghan refugees, interestingly, these results could be applied for improving HRQOL of the given participants. In conclusion, the HRQOL of Afghan refugees residing in Quetta, Pakistan can largely be improved by providing adequate healthcare facilities, education and employment opportunities, mental and social support, and providing adequate housing and basic necessities of life.

In this paper, we deal with a Lévy noise-driven epidemic model reflecting the dynamics of measles infection subject to the effect of vaccination. After model formulation, the feasibility of the system was studied by using the underlying existence and uniqueness theory. Moreover, we discussed the behavior of solution around the infection-free and disease-present steady states. To check the persistence and extinction of the infection, we calculated the threshold parameter $ {\bf R_s} $ and it was determined that the disease vanishes whenever $ {\bf R_s} < 1 $. From January to October 2019, the reported measles cases in Pakistan wear used and the model was fitted against this data by using the well-known fitting techniques. The values of the parameter were estimated and future behavior of the infection was predicted by simulating the model. The model was further simulated and theoretical findings of the study were validated.

SARS-CoV-2 and its variants have been investigated using a variety of mathematical models. In contrast to multi-strain models, SARS-CoV-2 models exhibit a memory effect that is often overlooked and more realistic. Atangana-Baleanu’s fractional-order operator is discussed in this manuscript for the analysis of the transmission dynamics of SARS-CoV-2. We investigated the transmission mechanism of the SARS-CoV-2 virus using the non-local Atangana-Baleanu fractional-order approach taking into account the different phases of infection and transmission routes. Using conventional ordinary derivative operators, our first step will be to develop a model for the proposed study. As part of the extension, we will incorporate fractional order derivatives into the model where the used operator is the fractional order operator of order Ψ1. Additionally, some basic aspects of the proposed model are examined in addition to calculating the reproduction number and determining the possible equilibrium. Stability analysis of the model is conducted to determine the necessary equilibrium conditions as they are also required in developing a numerical setup. Utilizing the theory of nonlinear functional analysis, for the model, Ulam-Hyers’ stability is established. We present a numerical scheme based on Newton’s polynomial in order to set up an iterative algorithm for the proposed ABC system. The application of this scheme to a variety of values of Φ1 indicates that there is a relationship between infection dynamics and the derivative’s order. We present further simulations which demonstrate the importance and cruciality of different parameters, as well as their effect on the dynamics and administer the disease. Furthermore, this study will provide a better understanding of the mechanisms underlying contagious diseases, thus supporting the development of policies to control them.

A neural network is a computer system modeled on the nerve tissue and nervous system. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. Neural networks have diverse applications in computer graphics, artificial intelligence (AI), machine & deep learning, chemistry, material science, among others. Research on structural properties and their classification of favorable or unfavorable for neural networks has been started recently. Employing tools from mathematics and specifically graph theory has been a key research direction in this area. Different graph-theoretic parameters have potential applications in studying topological properties of neural networks. In this paper for the first time, we study some nondeterministic polynomial-time (NP)-complete and NP-hard problems on various neural networks by considering their graphs. We consider classes of 3- and 4-layered probabilistic neural networks (PNNs), cellular neural networks (CNNs), and Tickysym spiking neural networks (TSNNs). In order to study their topological properties, we study their structures of maximal cliques, minimal colorings, maximal independent sets, maximal and perfect matchings, and minimum dominating sets. Computational results on the clique number, chromatic number, independence number, matching ratio and the domination number are reported. For instance, the clique number of the probabilistic neural networks is 2, the Tickysym spiking neural network is 3, whereas, for the cellular neural network, it is 4. These numerical results quantify the incoming/outgoing traffic in these architectures. Similarly, the independence number of 25-nodal 3-layered probabilistic neural network is 15, whereas, the independence number of 25-nodal cellular neural network is just 9. Moreover, the asymptotic matching ratio for PNNs (resp. CNNs) is 12 (resp. 18), whereas, it is 16 for the case of TSNNs. This gives probabilistic neural networks a significant priority in controllability over cellular neural networks. Some open problems are proposed in the end to further this research area.

Atrial fibrillation has been described as a global epidemic with a three-fold increase in prevalence in the last 50 years. As the prevalence of atrial fibrillation continues to grow, multiple landmark trials have been designed to determine the best method to treat atrial fibrillation. Initial trials have stated that rate control was not inferior to rhythm control, however, as the efficacy of rhythm control of atrial fibrillation has improved, a benefit in rhythm control has been shown. Because of this trend towards increased rhythm control, more patients have been placed on anti-arrhythmic medications. This paper will review the epidemiology and clinical impact of the utilization of anti-arrhythmic medications. As we enter the era of rhythm control, increased awareness is needed regarding the monitoring and potential adverse events that can occur with these medications. Providers must balance the increased emphasis on rhythm control with the overall clinical impact on their patients due to drug-to-drug interactions and adverse effects that can occur with different co-morbidities. If the clinical momentum towards rhythm control continues, real-world data analysis will be needed to evaluate the clinical impact of the use, risk, and benefits of anti-arrhythmic medications.

Abstract Background The Biologic Abatement and Capturing Kids’ Outcomes and Flare Frequency in Juvenile Spondyloarthritis (BACK‐OFF JSpA) study is a randomized, pragmatic trial investigating different tumour necrosis factor inhibitor de‐escalation strategies for children with sustained inactive disease. In this project, we elicited concept rankings that aided in the selection of the patient‐reported outcome (PRO) measures that should be examined as part of the BACK‐OFF JSpA trial. Methods We conducted a discrete choice experiment to evaluate individuals' preferences regarding PROs. Stakeholders assessed a discrete list of 21 outcome concepts, each of which had a Patient‐Reported Outcome Measurement Information System (PROMIS) measure associated with it. PROMIS measures are self‐ or proxy‐reported instruments that are universally applicable to the general population and all chronic conditions. Stakeholders were required to make choices instead of expressing the strength of a preference. Results Fourteen caregivers, 12 patients (9–22 years old), 16 rheumatologists and three executives from health insurance companies completed the exercise, which took approximately 10 min. The discrete choice experiment resulted in an estimate of the relative importance of each outcome and rank. All stakeholder groups agreed that the primary PRO should be ‘Pain Interference’, a measure that evaluates the effect of pain on a child's everyday activities, including its impact on social, emotional, mental and physical functioning. Patients and caregivers were mostly aligned in their top priorities, with patients valuing physical health (50% of the top 10) whereas caregivers were more interested in mental health (60% of the top 10). Rheumatologists and health insurance executives were most interested in physical health outcomes, which were ranked 80% and 60% of their top 10 PROs, respectively. Overall, the patients had the most diverse set of prioritized outcomes, including at least one of each category in their top 10 rank order of importance. Patients were also the only stakeholders to prioritize ‘social’ health. Conclusions Patients and caregivers were mostly aligned in their outcome priority rankings. The rank‐order list directly informed the creation of a profile of PRO measures for our upcoming trial. Patient or Public Contribution Stakeholder partners helped with acquisition of data and lead parent partners helped interpret data.

Background: Transcranial direct current stimulation (tDCS) has emerged as a non-invasive neuro-modulation technique. Most studies show that anodal tDCS increases cortical excitability, however, with variable outcomes. Previously, we have shown in computer simulations that our multi-channel tDCS (mc-tDCS) approach, the distributed constrained maximum intensity (D-CMI) method can potentially lead to better controlled tDCS results due to the improved directionality of the injected current at the target side for individually optimized D-CMI montages. Objective: In this study, we test the application of the D-CMI approach in an experimental study to stimulate the somatosensory P20/N20 target source in Brodmann area 3b and compare it with standard bipolar tDCS and sham conditions. Methods: We applied anodal D-CMI, the standard bipolar and D-CMI based Sham tDCS for 10 min to target the 20 ms post-stimulus somatosensory P20/N20 target brain source in Brodmann area 3b reconstructed using combined magnetoencephalography (MEG) and electroencephalography (EEG) source analysis in realistic head models with calibrated skull conductivity in a group-study with 13 subjects. Finger-stimulated somatosensory evoked fields (SEF) were recorded and the component at 20 ms post-stimulus (M20) was analyzed before and after the application of the three tDCS conditions in order to read out the stimulation effect on Brodmann area 3b. Results: Analysis of the finger stimulated SEF M20 peak before (baseline) and after tDCS shows a significant increase in source amplitude in Brodmann area 3b for D-CMI (6–16 min after tDCS), while no significant effects are found for standard bipolar (6–16 min after tDCS) and sham (6–16 min after tDCS) stimulation conditions. For the later time courses (16–26 and 27–37 min post-stimulation), we found a significant decrease in M20 peak source amplitude for standard bipolar and sham tDCS, while there was no effect for D-CMI. Conclusion: Our results indicate that targeted and optimized, and thereby highly individualized, mc-tDCS can outperform standard bipolar stimulation and lead to better control over stimulation outcomes with, however, a considerable amount of additional work compared to standard bipolar tDCS.

Multiple morphological abnormalities of the sperm flagella (MMAF) is a severe form of asthenozoospermia categorized by immotile spermatozoa with abnormal flagella in ejaculate. Whole-exome sequencing (WES) is used to detect pathogenic variants in patients with MMAF. In this study, a novel homozygous frameshift variant (c.6158_6159insT) in dynein axonemal heavy chain 8 (DNAH8) from two infertile brothers with MMAF in a consanguineous Pakistani family was identified by WES. Reverse transcription-polymerase chain reaction (RT-PCR) confirmed DNAH8 mRNA decay in these patients with the DNAH8 mutation. Hematoxylin–eosin staining and transmission electron microscopy revealed highly divergent morphology and ultrastructure of sperm flagella in these patients. Furthermore, an immunofluorescence assay showed the absence of DNAH8 and a reduction in its associated protein DNAH17 in the patients' spermatozoa. Collectively, our study expands the phenotypic spectrum of patients with DNAH8-related MMAF worldwide.

For a graph G=(VG,EG), a degree-based graphical index GId takes the general form GId=∑xy∈EGϕ(dx,dy), where ϕ is a symmetric map and di is the degree of i∈VG. For α∈R, if ϕ=(dxdy)α (resp. ϕ=(dx+dy)α), the index is called the general product-connectivity Rα (resp. general sum-connectivity SCIα) index. In this paper, by formulating an optimization problem, we determine the value(s) of α, for which the linear/multiple correlation coefficient of Rα and SCIα with physicochemical properties of benzenoid hydrocarbons is the strongest. This, in turn, fills some research gaps left by similar studies in this area.

This study proposes an adaptive image augmentation scheme using deep reinforcement learning (DRL) to improve the performance of a deep learning-based automated optical inspection system. The study addresses the challenge of inconsistency in the performance of single image augmentation methods. It introduces a DRL algorithm, DQN, to select the most suitable augmentation method for each image. The proposed approach extracts geometric and pixel indicators to form states, and uses DeepLab-v3+ model to verify the augmented images and generate rewards. Image augmentation methods are treated as actions, and the DQN algorithm selects the best methods based on the images and segmentation model. The study demonstrates that the proposed framework outperforms any single image augmentation method and achieves better segmentation performance than other semantic segmentation models. The framework has practical implications for developing more accurate and robust automated optical inspection systems, critical for ensuring product quality in various industries. Future research can explore the generalizability and scalability of the proposed framework to other domains and applications. The code for this application is uploaded at https://github.com/lynnkobe/Adaptive-Image-Augmentation.git.

Summary: Azoospermia is a significant cause of male infertility, with non-obstructive azoospermia (NOA) being the most severe type of spermatogenic failure. NOA is mostly caused by congenital factors, but our understanding of its genetic causes is very limited. Here, we identified a frameshift variant (c.201_202insAC, p.Tyr68Thrfs∗17) and two nonsense variants (c.1897C>T, p.Gln633∗; c.2005C>T, p.Gln669∗) in KCTD19 (potassium channel tetramerization domain containing 19) from two unrelated infertile Chinese men and a consanguineous Pakistani family with three infertile brothers. Testicular histological analyses revealed meiotic metaphase I (MMI) arrest in the affected individuals. Mice modeling KCTD19 variants recapitulated the same MMI arrest phenotype due to severe disrupted individualization of MMI chromosomes. Further analysis showed a complete loss of KCTD19 protein in both Kctd19 mutant mouse testes and affected individual testes. Collectively, our findings demonstrate the pathogenicity of the identified KCTD19 variants and highlight an essential role of KCTD19 in MMI chromosome individualization.

This research provides an improved theoretical framework of the Kermack-McKendrick system. By considering the general interference function and the polynomial perturbation, we give the sharp threshold between two situations: the disappearance of the illness and the ergodicity of the higher-order perturbed system. Obviously, the ergodic characteristic indicates the continuation of the infection in the population over time. Our study upgrades and enhances the work of Zhou et al. (2021) and suggests a new path of research that will serve as a basis for future investigations. As an illustrative application, we discuss some special cases of the polynomial perturbation to examine the precision of our outcomes. We deduce that higher order fluctuations positively affect the illness extinction time and lead to its rapid disappearance.

Accelerated mining activities have increased water contamination with potentially toxic elements (PTEs) and their associated human health risk in developing countries. The current study investigated the distribution of PTEs, their potential sources and health risk assessment in both ground and surface water sources in mining and non-mining areas of Khyber Pakhtunkhwa, Pakistan. Water samples (n = 150) were taken from selected sites and were analyzed for six PTEs (Ni, Cr, Zn, Cu, Pb and Mn). Among PTEs, Cr showed a high mean concentration (497) μg L−1, followed by Zn (414) μg L−1 in the mining area, while Zn showed the lowest mean value (4.44) μg L−1 in non-mining areas. Elevated concentrations of Ni, Cr and a moderate level of Pb in ground and surface water of Mohmand District exceeded the permissible limits set by WHO. Multivariate statistical analyses showed that the pollution sources of PTEs were mainly from mafic-ultramafic rocks, acid mine drainage, open dumping of mine wastes and mine tailings. The hazard quotient (HQ) was the highest for children relative to that for adults, but not higher than the USEPA limits. The hazard index (HI) for ingestions of all selected PTEs was lower than the threshold value (HIing < 1), except for Mohmand District, which showed a value of HI >1 in mining areas through ingestion. Moreover, the carcinogenic risk (CR) values exceeded the threshold limits for Ni and Cr set by the USEPA (1.0E-04–1.0E-06). In order to protect the drinking water sources of the study areas from further contamination, management techniques and policy for mining operations need to be implemented. HIGHLIGHTS Elevated Ni, Cr and Pb levels exceeded the WHO permissible limits in ground and surface water.; Mafic-ultramafic rocks, acid mine drainage, mine wastes and mine tailings are the main primary factors influencing the distribution of PTEs and their contamination.; Health risk was relatively high for children than for adults in mining and non-mining areas.; Management policies and monitoring strategies need to be implemented for mining operations.;

Monkeypox 2022, a new re-emerging disease, is caused by the Monkeypox virus. Structurally, this virus is related to the smallpox virus and infects the host in a similar way; however, the symptoms of Monkeypox are more severe. In this research work, a mathematical model for understanding the dynamics of Monkeypox 2022 is suggested that takes into account two modes of transmission: horizontal human dissemination and cross-infection between animals and humans. Due to lack of substantial knowledge about the virus diffusion and the effect of external perturbations, the model is extended to the probabilistic formulation with Lévy jumps. The proposed model is a two block compartmental system that requires the form of Itô-Lévy stochastic differential equations. Based on some assumptions and nonstandard analytical techniques, two principal asymptotic properties are proved: the eradication and continuation in the mean of Monkeypox 2022. The outcomes of the study reveals that the dynamical behavior of the proposed Monkeypox 2022 system is chiefly governed by some parameters that are precisely correlated with the noise intensities. To support the obtained theoretical finding, examples based on numerical simulations and real data are presented at the end of the study. The numerical simulations also exhibit the impact of the innovative adopted mathematical techniques on the findings of this work.