Sharda University

About: Sharda University is a education organization based out in Greater Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Medicine. The organization has 1276 authors who have published 2012 publications receiving 16188 citations.

Analysing Mobile Random Early Detection for Congestion Control in Mobile Ad-hoc Network

Abstract: This research paper suggests and analyse a technique for congestion control in mobile ad hoc networks. The technique is based on a new hybrid approach that uses clustering and queuing techniques. In clustering, in general cluster head transfers the data, following a queuing method based on a RED (Random Early Detection), the mobile environment makes it Mobile RED (or MRED), It majorly depends upon mobility of nodes and mobile environments leads to unpredictable queue size . To simulate this technique, the Network Simulator 2 (or NS2) is used for various scenarios. The simulated results are compared with NRED (Neighbourhood Random Early Detection) queuing technique of congestion control. It has been observed that the results are improved using MRED comparatively.

Advancement and current scenario of engineering and design in transparent supercapacitors: electrodes and electrolyte

Abstract: Smart devices such as smartphones, smart watches, smart glasses, and transparent control circuits have captured most of the electronics market worldwide The various smart applications and supporting accessories need an energy storage system with high energy density, highly efficient and transparent energy in nature Transparent conducting supercapacitors (TCSs) are the most anticipated energy storage system for all such applications The electrode and electrolyte are the key components of TCSs, which are solely responsible for delivering high energy density and high performance at load In this regard, in the present review, the basic principle, requirement, and the performance parameters of TCSs are discussed in detail In addition, comprehensive analysis has been given on recent advances in different materials such as carbon-based material, graphene, metal oxides, and conducting polymers and their composites used as electrodes for TCSs The focus of this review is concerned with the major difficulties arising in fabrication of transparent electrodes for TCSs In concern with the electrode and electrolyte, we critically discussed the different methods for enhancing the electrochemical performance and energy density of TCSs Finally, we have highlighted the few shortcomings and future scope of the TCSs for current energy storage systems

Analgesic Effect of Piracetam on Peripheral Neuropathic Pain Induced by Chronic Constriction Injury of Sciatic Nerve in Rats

Abstract: Despite immense advances in the treatment strategies, management of neuropathic pain remains unsatisfactory. Piracetam is a prototype of nootropic drugs, used to improve cognitive impairment. The present study was designed to investigate the effect of piracetam on peripheral neuropathic pain in rats. Neuropathic pain was induced by the chronic constriction injury of the sciatic nerve. Following this, piracetam was intraperitoneally administered for 2 weeks in doses of 50, 100 and 200 mg/kg, and pain was assessed by employing the behavioural tests for thermal hyperalgesia (hot plate and tail flick tests) and cold allodynia (acetone test). After the induction of neuropathic pain, significant development of thermal hyperalgesia and cold allodynia was observed. The administration of piracetam (50 mg/kg) did not have any significant effect on all the behavioural tests. Further, piracetam (100 mg/kg) also had no effect on the hot plate and tail flick tests; however it significantly decreased the paw withdrawal duration in the acetone test. Piracetam in a dose of 200 mg/kg significantly modulated neuropathic pain as observed from the increased hot plate and tail flick latencies, and decreased paw withdrawal duration (in acetone test). Therefore, the present study suggests the potential use of piracetam in the treatment of neuropathic pain, which merits further clinical investigation.

Ameloglyphics: A possible forensic tool for person identification following high temperature and acid exposure.

Abstract: Introduction: Forensic odontology is a branch that is evolving over time and has opened newer avenues that may help in the identification of individuals. Tooth prints are the enamel rod end patterns on tooth surface and they are considered as a hard tissue analog to fingerprints. Teeth have the highest resistance to most environmental effects like fire, desiccation, and decomposition, and may be used as a forensic evidence. Aims and Objectives: The aim of the study was to evaluate if the tooth prints could be used for an individual's identification and reproducibility and permanency of these tooth prints after exposing the teeth to acid and various degrees of temperature. Materials and Methods: 90 tooth prints from 20 freshly extracted maxillary premolar teeth were obtained. Cellophane tape technique was used to record enamel rod end patterns on tooth surface. Ten teeth (one from each patient) were immersed in 36.46% hydrochloric acid and the tooth prints were obtained at various intervals (5 min, 10 min, and 20 min). The other 10 teeth (one from each patient) were incinerated and impression was made at various intervals (80o C, 400o C, 600o C, and 750o C). Tooth prints obtained from different teeth (total of 90 tooth prints) were analyzed using Verifinger® standard SDK version 5.0 software. Results: All the 20 original tooth prints were distinct from each other and no inter-individual or intra-individual similarity was found. The tooth prints from the same tooth after it was exposed to acid or heat were reproducible and showed high to very high similarity with the original tooth print of that particular tooth stored in the database. Conclusion: Tooth prints may be used as an effective aid in person identification even in adverse conditions such as burn and acid attack injuries.

Dorsal Hand Vein-Biometric Recognition Using Convolution Neural Network

Abstract: In this paper, a convolution neural network (CNN)-based vein recognition approach is used for dorsal hand vein patterns. Apart from using pre-trained version, VGG Net-16 model is fine-tuned on four datasets of dorsal hand vein images (good quality, medium quality, and low quality) and augmented images (between the two images from genuine matching or false matching). All four datasets consist of dorsal hand vein images of left and right hands. The comparison of results proposed model is done with other CNN models like VGG Face and VGG-19 (with and without fine-tuned) along with a recent work based on transfer learning. The accuracy of recognition of proposed work using fine-tuned VGG Net-16 model obtained is 99.60% for good quality images; for medium quality images, it is 98.46%, and 97.99%; for low-quality images, it is found to be.