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Wireless sensor network (WSN) plays a crucial role in many industrial, commercial, and social applications. However, increasing the number of nodes in a WSN increases network complexity, making it harder to acquire all relevant data in a timely way. By assuming the end node as a base station, we devised an Artificial Ant Routing (AAR) method that overcomes such network difficulties and finds an ideal routing that gives an easy way to reach the destination node in our situation. The goal of our research is to establish WSN parameters that are based on the biologically inspired Ant Colony Optimization (ACO) method. The proposed AAR provides the alternating path in case of congestion and high traffic requirement. In the event of node failures in a wireless network, the same algorithm enhances the efficiency of the routing path and acts as a multipath data transmission approach. We simulated network factors including Packet Delivery Ratio (PDR), Throughput, and Energy Consumption to achieve this. The major objective is to extend the network lifespan while data is being transferred by avoiding crowded areas and conserving energy by using a small number of nodes. The result shows that AAR is having improved performance parameters as compared to LEACH, LEACH-C, and FCM-DS-ACO.

Scott Buzin, Dhruv Shankar, Kinjal Vasavada, Thomas Youm Department of Orthopaedic Surgery, NYU Langone Health, New York, NY, USACorrespondence: Thomas Youm, Department of Orthopaedic Surgery, NYU Langone Health, New York, NY, USA, Tel +1 212-348-3636, Email thomas.youm@nyulangone.orgAbstract: Femoroacetabular impingement (FAI) has emerged as a common cause of hip pain, especially in young patients. While the exact cause of FAI is unknown, it is thought to result from repetitive microtrauma to the proximal femoral epiphysis leading to abnormal biomechanics. Patients typically present with groin pain that is exacerbated by hip flexion and internal rotation. Diagnosis of FAI is made through careful consideration of patient presentation as well as physical exam and diagnostic imaging. Use of radiographs can help diagnose both cam and pincer lesions, while the use of MRI can diagnose labral tears and cartilage damage associated with FAI. Both non-operative and surgical options have their role in the treatment of FAI and its associated labral tears; however, hip arthroscopy has had successful outcomes when compared with physical therapy alone. Unfortunately, chondral lesions associated with FAI have had poorer outcomes with a higher conversion rate to arthroplasty. Capsular closure following hip arthroscopy has shown superior clinical outcomes and therefore should be performed if possible. More recently, primary labral reconstruction has emerged in the literature as a good option for irreparable labral tears. While non-operative management may have its role in treating patients with FAI, hip arthroscopy has developed a successful track record in being able to treat cam and pincer lesions, chondral damage, and labral injuries.Keywords: femoroacetabular impingement, cam lesion, pincer lesion, labral tear, hip arthroscopy, labral reconstruction

Wireless sensor network is a dynamic field of networking and communication because of its increasing demand in critical Industrial and Robotics applications. Clustering is the technique mainly used in the WSN to deal with large load density for efficient energy conservation. Formation of number of duplicate clusters in the clustering algorithm decreases the throughput and network lifetime of WSN. To deal with this problem, advance distributive energy-efficient adaptive clustering protocol with sleep/wake scheduling algorithm (DEACP-S/W) for the selection of optimal cluster head is presented in this paper. The presented sleep/wake cluster head scheduling along with distributive adaptive clustering protocol helps in reducing the transmission delay by properly balancing of load among nodes. The performance of algorithm is evaluated on the basis of network lifetime, throughput, average residual energy, packet delivered to the base station (BS) and CH of nodes. The results are compared with standard LEACH and DEACP protocols and it is observed that the proposed protocol performs better than existing algorithms. Throughput is improved by 8.1% over LEACH and by 2.7% over DEACP. Average residual energy is increased by 6.4% over LEACH and by 4% over DEACP. Also, the network is operable for nearly 33% more rounds compared to these reference algorithms which ultimately results in increasing lifetime of the Wireless Sensor Network.

The developmental anomaly of the middle cerebral artery (MCA) is a rare occurrence. Rete MCA, also known as "unfused," "twig-like," and aplastic MCA, is an even rarer anomaly. These immature rete vessels are prone to flow-related aneurysms which rupture themselves presenting with intraparenchymal hematoma. We here with present a case of rete MCA with a ruptured aneurysm and discuss its pathophysiology and management. [ABSTRACT FROM AUTHOR]

Byline: Nidhi. Sharma, Sandipan. Dhar, Abhishek. De, Kiran. Godse, D. Krupa Shankar, Vijay. Zawar, Mukesh. Girdhar, Bela. Shah Atopic dermatitis (AD) is a chronic, relapsing inflammatory skin disorder affecting 15-20% [...]

The intrusion detection system (IDS) plays an imperative role in defending the network from attacks. But, the IDS data is imbalanced, making the process complex for detecting the attacks accurately. According to these problems, this study proposes a network intrusion detection system based on an enhanced synthetic minority oversampling technique (SMOTE) and lightweight hybrid CAE (convolutional auto encoder)-ELM (extreme learning machine) model. Initially, the normalization of the original data is performed to avoid the influence of the maximum and minimum values. Secondly, an enhanced SMOTE is employed for solving the issues of the less detection rate of minority-attacks because of less training data. Here, the war strategy optimization (WSO) is incorporated with the SMOTE to design a novel WSO-based SMOTE technique for balancing the dataset. The three major steps utilized in WSO-based SMOTE are WSO based clustering, filtering and over-sampling. Then, the Information gain and fisher score based features extraction is employed for the reduction of computational complexity prior to the intrusion detection. Finally, the lightweight hybrid CAE-ELM model is executed for attack detection. CAEs are one of the most commonly used learning models because of their ability to construct a higher-level feature representation from the input data. Another model used in intrusion detection is the ELM, which provides an acceptable discrimination performance as well as a fast speed of learning. The performance of the proposed NIDS model is tested on the two benchmark datasets and achieved better accuracies of 99.21% and 99.15% on the UNSW-NB15 and CSE-CIC-IDS2018 datasets. [ABSTRACT FROM AUTHOR]

Abstract Populations in many coastal urban areas are increasingly exposed to climate-related hazards. At the same time, the number of people residing in coastal cities is growing, and, especially in the Global South, these cities are characterised by rapid urbanisation and social inequality. However, the progress of adaptation is lagging, and there is a limited understanding of how future socioeconomic urban developments will affect cities’ social vulnerability and challenges to adaptation. We use the case study of Mumbai to apply a participatory scenario approach, in which we downscale the global Shared Socioeconomic Pathway (SSP) narratives to the local level. Our results stress the relevance of addressing social inequality in urban change processes across different sectors, including labour, housing, transport, and health and streamlining urban planning across different governance scales. Our study lays the ground for integrated modelling of future vulnerability and exposure scenarios and the development of local adaptation pathways.