CAPITALIZE ON P2P HEADING CONTACT ACCESSIBILITY NOW PORTABLE AD HOC SYSTEMS STILL IMITATION FOR COMPETENT FOLDER DIVISION

Replication of files is a superb approach to improve file convenience minimizing file querying delay. The raised contemplation on mobile programs of file speaking about motivates study peer-to-peer file speaking about on mobile random systems. Within our work we inspect the intricacy of allocating restricted sources for file replication for global optimal effectiveness of file searching in mobile random systems. We initiate a manuscript idea of resource intended for file replication that sights node storage additionally to node meeting capacity and keep focused resource allotment effect on common querying delay and get optimal file rule of replication that assign sources towards each file on foundation its recognition additionally to size. We submit data replication strategies which pulls on rule, which estimate least global querying delay within the completely distributed approach

A new secure multipath routing using sub-set of overlay nodes to minimize delay

We study overlay architecture for dynamic routing, such that only a subset of devices (overlay nodes) need to make the dynamic routing decisions. We determine the essential collection of nodes that must bifurcate traffic for achieving the maximum multi-commodity network throughput.  We apply our optimal node placement algorithm to several graphs and the results show that a small segment of overlay nodes is sufficient for achieving maximum throughput. we propose a threshold-based policy (BP-T) and a heuristic policy (OBP),which dynamically control traffic bifurcations at overlay nodes. Policy BP-T is proved to maximize throughput for the case when underlay paths do no overlap

Optimizing the Load Frequency of a Two-Area Interlinked Power System using Artificial Intelligence Techniques

Power costs are increasing on a daily basis, generating changes in system frequency and causing serious concerns with system stability. It has become a major problem to offer customers with uninterrupted and high-quality power. To mitigate these issues, a linked power system's load distribution and network frequency should be constantly reviewed. Load frequency control adjusts the generator's energy output and tie line power between prescribed limits. As well as regulating generator output power, load frequency control also adjusts tie line power. The disturbance in the frequency due to different load changes is regulated using the proposed scheme. In this article, a two-area load frequency control system is constructed and evaluated using various control approaches, including a proportional integral derivative (PID) controller, a proportional integral (PI) controller, a fuzzy logic-based controller, and an Artificial Neural Network (ANN). The goal is to assess the power system's resilience under different loading conditions with these control schemes. The performance of the controllers is compared based on peak-undershoot, peak-overshoot, and settling time, focusing on tie line power and frequency response. To achieve this, the design is implemented using MATLAB/SIMULINK software

One-Bone Forearm Procedure for Acquired Pseudoarthrosis of the Ulna Combined with Radial Head Dislocation in a Child: A Case with 20 Years Follow-Up

This report describes a 6 year-old boy who was treated with one-bone forearm procedure for acquired pseudoarthrosis of the ulna combined with radial head dislocation after radical ulna debridement for osteomyelitis. At more than 20 years of follow-up, the patient had a nearly full range of elbow movements with a few additional surgical procedures. Pronation and supination was restricted by 45°, but the patient had near-normal elbow and hand functions without the restriction of any daily living activity. This case shows that one-bone forearm formation is a reasonable option for forearm stability in longstanding pseudoarthrosis of the ulna with radial head dislocation in a child

Predicting California Bearing Ratio of Lateritic Soils Using Hybrid Machine Learning Technique

The increase in population has made it possible for better, more cost-effective vehicular services, which warrants good roadways. The sub-base that serves as a stress-transmitting media and distributes vehicle weight to resist shear and radial deformation is a critical component of the pavement structures. Developing novel techniques that can assess the sub-base soil’s geotechnical characteristics and performance is an urgent need. Laterite soil abundantly available in the West Godavari area of India was employed for this research. Roads and highways construction takes a chunk of geotechnical investigation, particularly, California bearing ratio (CBR) of subgrade soils. Therefore, there is a need for intelligent tool to predict or analyze the CBR value without time-consuming and cumbersome laboratory tests. An integrated extreme learning machine-cooperation search optimizer (ELM-CSO) approach is used herein to predict the CBR values. The correlation coefficient is utilized as cost functions of the CSO to identify the optimal activation weights of the ELM. The statistical measures are separately considered, and best solutions are reported in this article. Comparisons are provided with the standard ELM to show the superiorities of the proposed integrated approach to predict the CBR values. Further, the impact of each input variable is studied separately, and reduced models are proposed with limited and inadequate input data without loss of prediction accuracy. When 70% training and 30% testing data are applied, the ELM-CSO outperforms the CSO with Pearson correlation coefficient (R), coefficient of determination (R2), and root mean square error (RMSE) values of 0.98, 0.97, and 0.84, respectively. Therefore, based on the prediction findings, the newly built ELM-CSO can be considered an alternative method for predicting real-time engineering issues, including the lateritic soil properties