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Air pollution has a significant impact on environment resulting in consequences such as global warming and acid rain. Toxic emissions from vehicles are one of the primary sources of pollution. Assessment of air pollution data is critical in order to assist residents in locating the safest areas in the city that are ideal for life. In this work, density-based spatial clustering of applications with noise (DBSCAN) is used which is among the widely used clustering algorithms in machine learning. It is not only capable of finding clusters of various sizes and shapes but can also detect outliers. DBSCAN takes in two important input parameters—Epsilon (Eps) and Minimum Points (MinPts). Even the slightest of variations in the parameter values fed to DBSCAN makes a big difference in the clustering. There is a need to find Eps value in as minimum time as possible. In this work, the goal is to find the Eps value in less time. For this purpose, a search tree technique is used for finding the Eps input to the DBSCAN algorithm. Predicting air pollution is a complex task due to various challenges associated with the dynamic and multifaceted nature of the atmosphere such as meteorological variability, local emissions and sources, data quality and availability, and emerging pollutants. Extensive experiments prove that the search tree approach to find Eps is quicker and efficient in comparison to the widely used KNN algorithm. The time reduction to find Eps makes a significant impact as the dataset size increases. The input parameters are fed to DBSCAN algorithm to obtain clustering results. [ABSTRACT FROM AUTHOR]

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The development of the fifth generation (5G) and sixth generation (6G) wireless networks has gained wide spread importance in all aspects of life through the network due to their significantly higher speeds, extraordinarily low latency, and ubiquitous availability. Owing to the importance of their users, components, and services to our everyday lives, the network must secure all of these. With such a wide range of devices and service types being present in the 5G ecosystem, security issues are now much more prevalent. Security solutions, are not implemented, must already be envisioned in order to deal with a range of attacks on numerous services, cutting-edge technology, and more user information available over the network. This research proposes the dual integrated neural network (DINN) for secure data transmission in wireless networks. DINN comprises two neural networks based on sparse and dense dimensions. DINN is designed for any presence of deep learning-based attack in a physical security layer. DINN is evaluated considering the various machine learning attack such as basic_iterative_method attack, momentum_iterative_method attack, post_gradient_descent attack, and C&W attack; comparison is carried out on existing and DINN, considering attack success rate and MSE. Performance analysis suggests that DINN holds a higher level of security against the above attacks. [ABSTRACT FROM AUTHOR]

Wireless networks have become essential in daily life, with a growing number of base stations and connected devices. However, increasing traffic and energy consumption pose challenges. This research proposes a Dual Step Hybrid Mechanism (DSHM) for energy optimization, incorporating MIMO technologies. The first step introduces an optimal algorithm that iteratively updates the probability distribution to achieve the best solution. The second step focuses on reducing energy consumption while maximizing energy efficiency, using specific techniques and strategies to minimize usage without compromising energy maximization. The proposed approach is evaluated using parameter settings, including block length, path loss, hardware impairments, and bandwidth. The research investigates the impact of hardware impairments on energy efficiency and analyzes performance under different SINR constraints. The study also examines energy efficiency in active user density and base station density, highlighting the superior energy efficiency achieved by MIMO configurations. [ABSTRACT FROM AUTHOR]

Objectives: This study was designed to evaluate, compare the mid-term functional outcome of revision anterior cruciate ligament reconstruction (ACLR) using different autografts and assess the cause of failure of primary ACLR in an Omani population with kneeling customs. Materials and methods: Patients with failed primary ACLR who underwent revision ACLR using autografts were included in this retrospective study. The cause of primary ACLR failure and the functional outcome was assessed using the Tegner-Lyholm knee score and compared among bone patella tendon-bone (BPTB), quadriceps tendon (QT), semitendinosus gracilis (STG) autografts used. Results: One hundred two patients (102 male) were included in the study with a minimum follow-up of 2 years. Thirty-one patients underwent revision with BPTB, 34 with STG and 19 with QT autografts. Majority of the patients (70.23%) achieved good-to-excellent functional outcome based on their Tegner–Lysholm scores. The functional outcome of different autografts was comparable to each other based on Kruskal–Wallis test. The causes of primary ACLR failure were failure due to trauma in 58.33% of patients, technical failure in 22.61% of patients, and nontraumatic failure in 19.04% of patients. Conclusions: The functional outcome of revision ACLR in this Middle Eastern Asian Omani population was good-to-excellent, with the patients experiencing no difficulty in performing activities of daily living, including kneeling activities. The outcome of different autografts, BTPB, QT, STSG is similar in high knee flexion patients with no autograft found to be superior. The findings of this study add to the literature on functional outcomes after primary and revision ACLR in a customary kneeling population. Level of evidence III [ABSTRACT FROM AUTHOR]

The present work involves an experimental analysis on gas-lubricated foil thrust bearing (FTB) that has major applications in food processing units at lower operating speeds. Parametric studies include testing thrust foil bearings with various foil configurations in terms of foil thickness, foil geometry, and foil material. Novel bearing testing apparatus designed and fabricated for the purpose supported a larger diameter (224 mm) thrust foil bearing, operated at speeds ranging from 10,000 rpm to 18,000 rpm. Performance of air foil thrust bearing (AFTB) concerning static structural stiffness and load bearing capability was determined in optimizing the best foil configuration. A novel dimensionless number was formulated that emphasizes the affecting factors of thrust foil bearing with respect to axial loads. [ABSTRACT FROM AUTHOR]

A lectin PCL, from Purpureocillium lilacinum a saprophytic, filamentous fungus was purified from the crude extract of the mycelia using 70% ammonium sulphate precipitation followed by affinity chromatography on mucin-Sepharose 4 B column. PCL is a monomer with an apparent molecular mass of 18.5 kDa as revealed by SDS-PAGE under both reducing and non-reducing conditions. PCL is a blood group non-specific lectin and has highest affinity towards chitin, mucin, asialomucin, fetuin with a MIC of 0.15 µg/mL and also recognizes l-fucose, galactose, lactose, n-acetyl galactosamine, hyaluronic acid. PCL is stable up to 60 °C and within the pH range 4–8. To understand its role in pathogenesis, effect of PCL was evaluated on human corneal epithelial cells (HCECs). PCL showed strong glycan mediated binding to HCECs and PCL showed proinflammatory response at lower concentrations by stimulating secretion of IL-6, 8. In contrast PCL at higher concentrations revealed opposite effect of HCECs growth inhibition. All these results collectively support the involvement of PCL in mediating host pathogen interactions possibly leading to pathogenesis. In addition, considering the entomopathogenic effect of Purpureocillium lilacinum, PCL may be attributed for this beneficiary effect, which needs to be explored. [ABSTRACT FROM AUTHOR]

Cloud computing has become the most challenging research field in the current information technology scenario. In this, a set of user tasks are scheduled and allocated to numerous kinds of heterogeneous virtual machines (VMs) in cloud data centers (CDCs), and these VMs are hosted by diverse types of heterogeneous physical machines (PMs). It extends several features, encompassing elasticity, safety, sustainability, and even adequate maintenance compared to traditional data centers. There are numerous techniques available for VM scheduling and allocation. However, it still requires the existence of new mechanisms that can reduce the execution time (ET) of the tasks, improve the optimization of energy usage and resource utilization (RU), and reduce time consumption. Along with optimization, VM scheduling (VMS) and VM allocation (VMA) are two-level issues that need to be considered with essential policies to govern these mechanisms. Hence, for executing optimal VMS and VMA in the data center, new optimization methodologies, such as enhanced shark smell optimization algorithm (ESSOA) at the first level and Brownian movement-centered gravitation search algorithm (BMGSA) at the second level, are proposed in this work to define the policies. Firstly, the user requests for VMs are reserved on the most appropriate PM by the proposed ESSOA, which has the lowest execution cost within deadline limits, and the proposed BMGSA decides the allocation of the chosen VM on the most appropriate PM within the resource limitations at the second level. To demonstrate the proposed algorithm's efficiency, the simulations are carried out using the Java language-based CloudSim simulator, and the proposed mechanism outcomes acquired are compared with the existing approaches. The simulation results show that the suggested algorithm is efficient in terms of the execution cost, degree of imbalance (DOI), make span (MS), and resource utilization (RU). [ABSTRACT FROM AUTHOR]

Fog computing domain plays a prominent role in supporting time-delicate applications, which are associated with smart Internet of Things (IoT) services, like smart healthcare and smart city. However, cloud computing is a capable standard for IoT in data processing owing to the high latency restriction of the cloud, and it is incapable of satisfying needs for time-sensitive applications. The resource provisioning and allocation process in fog-cloud structure considers dynamic alternations in user necessities, and also restricted access resources in fog devices are more challenging. The global adoption of IoT-driven applications has led to the rise of fog computing structure, which permits perfect connection for mobile edge and cloud resources. The effectual scheduling of application tasks in fog environments is a challenging task because of resource heterogeneity, stochastic behaviours, network hierarchy, controlled resource abilities, and mobility elements in IoT. The deadline is the most significant challenge in the fog computing structure due to the dynamic variations in user requirement parameters. In this paper, Mayfly Taylor Optimisation Algorithm (MTOA) is developed for dynamic scheduling in the fog-cloud computing model. The developed MTOA-based Deep Q-Network (DQN) showed better performance with energy consumption, service level agreement (SLA), and computation cost of 0.0162, 0.0114, and 0.0855, respectively. [ABSTRACT FROM AUTHOR]

India is a densely populated country where traffic is a major issue in our country. We can see traffic accidents are increased day by day and becoming more pathetic nowadays. Even though the government are running so many campaigns to raise awareness in public but still we can't able to reduce the death rate due to road accident are not decreased at all. Main cause of the accident is a high speed, rash driving and mental pressure Some of the papers deals with how to track accidents before it could happen and alert the driver to maintain some speeds limit so that person can avoid an accident by taking early precaution to avoid the accident but this paper focuses on how to rescue person life by immediately admitted to a nearby hospital after meeting with an accident. the paper deals with analysing an image uploaded by the end-users once the image is uploaded into the module. once it is uploaded it should satisfy many criteria if these criteria are satisfied only then it continues the process otherwise it terminates the process .criteria to be satisfied to continue the execution one it is a recently uploaded image or it should not be downloaded image, second it is real or fake, third it should be major injury if it is a minor injury then it will terminate the process .these all things happen in the second module i.e. image processing. once the image processing is done it will track the exact location where the accident has met through GPSs send the exact address and ping to the ambulance to arrive at that exact location. [ABSTRACT FROM AUTHOR]

The ever increasing burden of an aging population and pandemic of metabolic syndrome worldwide demands further understanding of the modifiable risk factors in reducing disability and morbidity associated with these conditions. Disuse skeletal muscle atrophy (sometimes referred to as "simple" atrophy) and insulin resistance are "non-pathological" events resulting from sedentary behavior and periods of enforced immobilization e.g., due to fractures or elective orthopedic surgery. Yet, the processes and drivers regulating disuse atrophy and insulin resistance and the associated molecular events remain unclear--especially in humans. The aim of this review is to present current knowledge of relationships between muscle protein turnover, insulin resistance and muscle atrophy during disuse, principally in humans. Immobilization lowers fasted state muscle protein synthesis (MPS) and induces fed-state "anabolic resistance." While a lack of dynamic measurements of muscle protein breakdown (MPB) precludes defining a definitive role for MPB in disuse atrophy, some proteolytic "marker" studies (e.g., MPB genes) suggest a potential early elevation. Immobilization also induces muscle insulin resistance (IR). Moreover, the trajectory of muscle atrophy appears to be accelerated in persistent IR states (e.g., Type II diabetes), suggesting IR may contribute to muscle disuse atrophy under these conditions. Nonetheless, the role of differences in insulin sensitivity across distinct muscle groups and its effects on rates of atrophy remains unclear. Multifaceted time-course studies into the collective role of insulin resistance and muscle protein turnover in the setting of disuse muscle atrophy, in humans, are needed to facilitate the development of appropriate countermeasures and efficacious rehabilitation protocols. [ABSTRACT FROM AUTHOR]