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In mobile ad hoc networks the congestion is a major issue,which affects the overall performance of the networks. The load balancing in the network alongside the congestion is another major problem in mobile ad hoc network (MANET) routing due to difference in link cost of the route. Most of the existing routing protocols provide solutions to load balancing or congestion adaptivity separately. In this paper, a congestion adaptive routing along with load balancing, that is, load balanced congestion adaptive routing (LBCAR), has been proposed. Transferring of load from congested nodes to less busy nodes and involvement of other nodes in transmission that can take part in route can improve the overall network life. In the proposed protocol two metrics, traffic load density and link cost associated with a routing path, have been used to determine the congestion status. The route with low traffic load density and maximum life time is selected for packet transmission using this protocol. Performance of the network using LBCAR has been analyzed and compared with congestion adaptive routing protocol (CRP) for packet delivery ratio, average end-to-end delay, and normalized routing overhead. [ABSTRACT FROM AUTHOR]

A Wireless ad-hoc network is a temporary network set up by wireless mobile nodes moving arbitrary in the places that have no network infrastructure. However, due to security vulnerabilities of the routing protocols, wireless ad-hoc networks are unprotected to attacks of the malicious nodes. One of these attacks is the Black Hole Attack against network integrity absorbing all data packets in the network. Since the data packets do not reach the destination node on account of this attack, data loss will occur. Our aim to protect the Mobile ad-hoc network through Black Hole Attack, Intrusion Detection System aimed to securing the AODV protocol using our Intrusion Detection system. We conclude that AODV performs well at all mobility rates and movement speed. In this, the work has been extended and the proposed protocol is called IDSAODV (Intrusion Detection System AODV).We proposed an IDS solution to eliminate the Black Hole effects in the AODV network. We implemented the solution into the NS-2. And evaluated the results as we did in Black Hole implementation. [ABSTRACT FROM AUTHOR]

Objectives: 1) To investigate in-hospital factors associated with delayed tolerance of full volume enteral nutrition and 2) To assess longitudinal growth in a contemporary population of infants with gastroschisis. Design: Retrospective single-center study of all infants with gastroschisis Setting: Level III neonatal intensive care unit in a free-standing Children’s Hospital Duration: 13.5 years Materials & Methods: Detailed data regarding demographics, nutritional support, growth, and infant outcomes was collected for all infants with gastroschisis. Linear regression was used to investigate in-hospital factors associated with feeding intolerance and poor growth. Results: For 52 infants, the median gestational age at birth was 36 weeks, the median postnatal age to achieve full feeds was 22 days, and median in-hospital weight gain was 18 gm/day. With linear regression, there was a positive association between time to full feeds and both hospital length of stay (adjusted R2=0.503, p

CAD systems for lung cancer diagnosis and detection can significantly offer unbiased, infatiguable diagnostics with minimal variance, decreasing the mortality rate and the five-year survival rate. Lung segmentation and lung nodule detection are critical steps in the lung cancer CAD system pipeline. Literature on lung segmentation and lung nodule detection mostly comprises techniques that process 3-D volumes or 2-D slices and surveys. However, surveys that highlight 2.5D techniques for lung segmentation and lung nodule detection still need to be included. This paper presents a background and discussion on 2.5D methods to fill this gap. Further, this paper also gives a taxonomy of 2.5D approaches and a detailed description of the 2.5D approaches. Based on the taxonomy, various 2.5D techniques for lung segmentation and lung nodule detection are clustered into these 2.5D approaches, which is followed by possible future work in this direction., Competing Interests: Declaration of competing interest None Declared, (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Lung nodule segmentation is an essential step in any CAD system for lung cancer detection and diagnosis. Traditional approaches for image segmentation are mainly morphology based or intensity based. Motion-based segmentation techniques tend to use the temporal information along with the morphology and intensity information to perform segmentation of regions of interest in videos. CT scans comprise of a sequence of dicom 2-D image slices similar to videos which also comprise of a sequence of image frames ordered on a timeline. In this work, Farneback, Horn-Schunck and Lucas-Kanade optical flow methods have been used for processing the dicom slices. The novelty of this work lies in the usage of optical flow methods, generally used in motion-based segmentation tasks, for the segmentation of nodules from CT images. Since thin-sliced CT scans are the imaging modality considered, they closely approximate the motion videos and are the primary motivation for using optical flow for lung nodule segmentation. This paper also provides a detailed comparative analysis and validates the effectiveness of using optical flow methods for segmentation. Finally, we propose methods to further improve the efficiency of segmentation using optical flow methods on CT scans.