ZnxCd1-x (O) Thin Film Nanorods for PV Applications

ZnxCd1-x (O) nanorods (NRs) thin films were deposited on ITO glass substrate by using a single step controlled electrodeposition process. Thin films of crystalline nature with zinc and cadmium concentration changing from 10% - 90% were electrodeposited onto ITO conductive glass substrates. XRD analysis confirms a hexagonal wurtzite structure having grain size 37 nm. The FESEM images of ZnxCd1-x (O) shows that hexagonal nanorods were first time synthesized via electrodeposition technique at temperature of 90 oC and the size of each regular plane of hexagonal nanorods is about 63nm. The Cd content of ZnxCd1-xO nanorods was as high as (about) 16.7 at% which as calculated by EDX. Remarkably, the ultra-violet (UV) near-band-edge (NBE) emission was red-shifted from 3.21 eV to 3.04 eV due to the direct modulation of band gap caused by Cd substitution, revealed by UV visible spectroscopy. Temperature is deemed as a key parameter for the formation of different morphologies of ZnxCd1-x (O) nanostructures. Finally, ZnxCd1-xO hexagonal nanorods thin film is used as one electrode in photovoltaic cells to produce energy by absorbing the energy from the sun, this single junction cells have been put forward as a potential low-cost alternative to the widely used solar cells. However, the cost effectiveness is due to the ZnxCd1-xO nanorods electrode. Copyright © IJNST, all rights reserved

Word-Graph Construction Techniques for Context Analysis

A Nomo-Word Graph Construction Analysis Method (NWGC-AM) is used to graph let the corresponding construction phrases into essential and non-essential citation groups. NMCS-NR, or Nomo Maximum Common Sub-graph edge resemblance, Maximum Common Subgraph Directed Edge resemblance (MCS-DER), and Maximum Common Subgraph Resemblance. The graph resemblance metrics used in this work are called Undirected Edges Resemblance (MCS-UER). The tests included five distinct classifiers: Random Forest, Naive Bayes, K-Nearest Neighbors (KNN), Decision Trees, and Support Vector Machines (SVM).Four sixty one (361) citations made up the annotated dataset used for the studies.  The Decision Tree classifier exhibits superior performance, attaining an accuracy rate of 0.98

A study on management of tennis elbow by local platelet rich plasma injection

Background: Lateral epicondylitis (tennis elbow), a familiar term used to describe myriad symptoms around the lateral aspect of the elbow can occur during activities that require repetitive supination and pronation of the forearm with the elbow in near full extension. This condition can cause severe discomfort to the patient resulting in debilitation and impairment of routine activities. The purpose of this study was to evaluate the effectiveness of local autologous platelet rich plasma injection in the treatment of tennis elbow.Methods: This was a prospective observational study conducted on 50 patients of either sex with an average age of 45.92 years, presenting to the Orthopaedic OPD of SHKM Government Medical College Hospital, Nalhar, NUH, Haryana between November 2016 and February 2018, with a diagnosis of lateral epicondylitis. All the patients were treated with local platelet rich plasma injection and the results were analysed through the assessment of visual analog score (VAS) and disability of arm shoulder and hand (DASH) score. The patients were followed up for a period of 6 months after the local injection of platelet rich plasma.Results: Majority of the patients had significant relief with this method. The VAS and DASH score improved from the pre-treatment values of 8.7 and 74.6 to 2.6 and 29.8 respectively, which was found to be statistically significant (p<0.001).Conclusions: Thus results of our study demonstrate that the local injection of platelet rich plasma is a safe and effective method of treatment of lateral epicondylitis

A study on management of paediatric supracondylar humerus fractures with lateral percutaneous Kirschner wire fixation

Background: Supracondylar fracture of the humerus in children is a common injury encountered in orthopaedic practice. Undisplaced fractures can be managed conservatively, however displaced fractures need proper reduction and adequate fixation for attainment of optimal functional and cosmetic outcomes. The purpose of this study was to evaluate the effectiveness of lateral percutaneous Kirschner (K) wire fixation in the management of displaced supracondylar fractures in relation to achievement of union and functional results.Methods: This was a prospective observational study conducted on 70 patients of either sex with an average age of 5.98 years, presenting to the Orthopaedic Department of S.H.K.M. Government Medical College Hospital, Nalhar, Nuh, Haryana between February 2016 and February 2018, with displaced supracondylar fractures of humerus. All the patients were managed by closed reduction and percutaneous lateral K wire fixation. The patients were followed up for a period of 1 year. The patients were analyzed for union and functional results.Results: All the fractures united with an average time of union of 3.8 weeks. Functional results were assessed using Flynn’s criteria, which were excellent in 58 (82.86%), good in 7 (10%), fair in 3 (4.28%) and poor in 2 (2.86%) patients.Conclusions: Thus results of our study demonstrate that the lateral percutaneous K wire fixation is a safe and effective method of treatment of displaced paediatric supracondylar humerus fractures

Intelligent Cyber-Security System for IoT-Aided Drones Using Voting Classifier

Developments in drones have opened new trends and opportunities in different fields, particularly in small drones. Drones provide interlocation services for navigation, and this interlink is provided by the Internet of Things (IoT). However, architectural issues make drone networks vulnerable to privacy and security threats. It is critical to provide a safe and secure network to acquire desired performance. Small drones are finding new paths for progress in the civil and defense industries, but also posing new challenges for security and privacy as well. The basic design of the small drone requires a modification in its data transformation and data privacy mechanisms, and it is not yet fulfilling domain requirements. This paper aims to investigate recent privacy and security trends that are affecting the Internet of Drones (IoD). This study also highlights the need for a safe and secure drone network that is free from interceptions and intrusions. The proposed framework mitigates the cyber security threats by employing intelligent machine learning models in the design of IoT-aided drones by making them secure and adaptable. Finally, the proposed model is evaluated on a benchmark dataset and shows robust results

Preparation of Zn0.6Cd0.4o nanorods and its characterization

Hexagonal nanorods of Zn0.6Cd0.4O thin films were synthesized by electrodeposition technique using 0.6% of ZnCl2 and 0.4% of CdCl2 electrolytes. The synthesized Zn0.6Cd0.4O nanorods have uniform hexagonal crystallographic planes, and their diameters are about 100 nm. The ultra-violet (UV) near-band-edge (NBE) emission was red-shifted from 2.75 eV to 3.02 eV due to the direct tailoring of band gap caused by Cd/Zn substitution. The FESEM images of Zn0.6Cd0.4O confirms that hexagonal nanorods were first time synthesized via electrodeposition technique at temperature of 90 oC and the size of each regular face of hexagonal nanorods is about 63.6 nm. The characterizations for Zn0.6Cd0.4O were analyzed and the temperature is a key parameter for the formation of different morphologies of Zn0.6Cd0.4O nanostructures

Oral Health Status of Pregnant Women and their Referral to Dentist during Antenatal Period

AbstractObjective: The aim of this study was to assess oral health status of pregnant women and theirreferral to dentist during antenatal period.Methodology: This descriptive cross-sectional study was conducted in Outpatient Department,(Gynae and Obs.) Jinnah Hospital Lahore, Pakistan after ethical committee approval. The sample sizewas calculated using formula for finite population and found to be 340 pregnant women who wereconveniently sampled and interviewed by using a self-administered questionnaire following verbaland written consent. Oral health status was examined clinically using dental mirror and Michiganprobe. Scoring was done using WHO oral health assessment form for adults, 2013 includingCommunity periodontal index, DMFT, and Dean's fluorosis index. Data was statistically analyzedusing SPSS version 24. Chi-square test was applied to compare categorical variables (p≤ 0.05 assignificant).Results: Total 236 (69.4%) out of 340 pregnant women had oral health problems. Only 11 (3.2%)participants were referred by the gynaecologist to dentist for their oral health issue. Out of 236participants, 67% had dental caries, 80.9% had gingival bleeding, 58.8% had gingival pockets, 41.7%had loss of attachment, 1.8% had dental erosions, 63.8% had dental fluorosis and 1.5% had oralmucosal lesions. The majority of pregnant women who participated in this study had lowsocioeconomic status (mean income PKR 20300±10304.14) reported with more oral health problems.Conclusion: As reported in this study, majority of pregnant women had oral health problems andonly few of them were referred to dentist by gynaecologist.Key Words: Pregnant women, Oral health status, Gynaecologist, Antenatal perio

Enhancing Security and Energy Efficiency in Wireless Sensor Network Routing with IOT Challenges: A Thorough Review

Wireless sensor networks (WSNs) have emerged as a crucial component in the field of networking due to their cost-effectiveness, efficiency, and compact size, making them invaluable for various applications. However, as the reliance on WSN-dependent applications continues to grow, these networks grapple with inherent limitations such as memory and computational constraints. Therefore, effective solutions require immediate attention, especially in the age of the Internet of Things (IoT), which largely relies on the effectiveness of WSNs. This study undertakes a comprehensive review of research conducted between 2018 and 2020, categorizing it into six main domains: 1) Providing an overview of WSN applications, management, and security considerations. 2) Focusing on routing and energy-saving techniques. 3) Reviewing the development of methods for information gathering, emphasizing data integrity and privacy. 4) Emphasizing connectivity and positioning techniques. 5) Examining studies that explore the integration of IoT technology into WSNs with an eye on secure data transmission. 6) Highlighting research efforts aimed at energy efficiency. The study addresses the motivation behind employing WSN applications in IoT technologies, as well as the challenges, obstructions, and solutions related to their application and development. It underscores that energy consumption remains a paramount issue in WSNs, with untapped potential for improving energy efficiency while ensuring robust security. Furthermore, it identifies existing approaches' weaknesses, rendering them inadequate for achieving energy-efficient routing in secure WSNs. This review sheds light on the critical challenges and opportunities in the field, contributing to a deeper understanding of WSNs and their role in secure IoT applications

Understanding Public Opinions on Social Media about ChatGPT – A deep Learning Approach for Sentiment Analysis

User-generated multimedia content—photos, text, videos, and audio—is becoming more and more common on social networking sites to allow individuals to express their thoughts. One of the largest and most advanced social media platform discussing ChatGPT is Twitter. This is because Twitter updates are constantly being produced and have a limited duration. The deep learning method for sentiment analysis of Twitter data about ChatGPT evaluation is presented in this research. This study used 4-class labels (sadness, joy, fear, and anger) from public Twitter data stored in the Kaggle database. The proposed deep learning strategy significantly improves the efficiency metrics determined by the use of the attention layer in current LSTM-RNN approaches, increasing accuracy by 20% and precision by 10-12%, but recall only 12-13%. Out of 18000 ChatGPT-related tweets, positive, neutral, and negative sentiments accounted for a respective 45%, 30%, and 35%. It is determined that the suggested deep learning technique for ChatGPT review sentiment categorization is effective, realistic, and fast to deploy