A comprehensive review: SnO2 for photovoltaic and gas sensor applications

184-193Tin oxide is remarkable material in today’s research era due to its unique properties in electrical and optical fields. Due to its wide band gap (3.6 eV), it has been used as a core material in many important applications in the field of optoelectronics, spintronics, photovoltaic, thin-film transistors, photocatalysis, dielectrics, sensors and transparent electronic devices. Thin film technology provides many advantages towards photovoltaic area which includes low cost, less material and energy consumption and easy to access. Fabrication of photovoltaic cells by SnO2 thin films can open the different technological routes for future generation with excellent conversion efficiencies which may range 15% to 20%. It is one of the best candidates for gas sensor applications too with highest sensitivity and selectivity behavior, good oxidizing power, strong chemical bonding, non toxicity and unique transport properties. Tin oxide thin films with various combinations of materials can be synthesized by chemical and physical routes. The detailed advancement in various preparation methods and characterization techniques including X-ray diffraction, atomic force microscopy and X-ray photoelectron spectroscopy have been presented and discussed by authors. Characteristics measurement by Valence Band Structure, Photoluminence Intensity and Scanning Electron Microscope has been also reported with their performance, effect of solar energy conversion efficiency and quick response time in case of gas sensors. Prospective areas of SnO2 research for photovoltaic and gas sensor applications has been discussed and summarized by the authors. The obtained results will illustrate the possibilities of scheming Physical, chemical, magnetic and optical properties of SnO2 for sensing devices and photovoltaic applications

Deep Learning Multi-Agent Model for Phishing Cyber-attack Detection

Phishing attacks have become one of the most prominent cyber threats in recent times, which poses a significant risk to the security of organizations and individuals. Therefore, detecting such Cyber attacks has become crucial to ensure a secure digital environment. In this regard, deep learning techniques have shown promising results for the detection of phishing attacks due to their ability to learn and extract features from raw data. In this study, we propose a deep learning-based approach to detecting phishing attacks by using a combination of convolutional neural networks (CNN) and long short-term memory (LSTM) networks. Our proposed model extracts features from the URL and email content to detect phishing attempts. We evaluate the proposed approach on a real-world dataset and achieve an accuracy of over 95%. The results indicate that the proposed approach can effectively detect phishing attacks and can be utilized in real-world applications to ensure a secure digital environment

People’s Perception about Gender Equity at RHTC, Naila, Jaipur

Gender equality refers to the equal rights, responsibilities, and opportunities of women and men, as well as girls and boys (United Nations Women, 2012). Women in India have suffered gender disparities since ages; although addressed at all fronts (social, political) for last few decades yet we can find scars here and there in the form of gender violence, honor-killing, rape, and social policing. Changes toward equitable gender roles and relations in the community as well as household are a prerequisite to gender equality Promotion of gender equality and empowering of women is one of the eight Millennium Development Goals (MDG) to which India is a signatory. Gender equality and women‘s empowerment are two sides of the same coin: progress toward gender equality requires women‘s empowerment and women‘s empowerment requires increases in gender equality evident by pairing of them in MDG

A comprehensive review: SnO2 for photovoltaic and gas sensor applications

Tin oxide is remarkable material in today’s research era due to its unique properties in electrical and optical fields. Due to its wide band gap (3.6 eV), it has been used as a core material in many important applications in the field of optoelectronics, spintronics, photovoltaic, thin-film transistors, photocatalysis, dielectrics, sensors and transparent electronic devices. Thin film technology provides many advantages towards photovoltaic area which includes low cost, less material and energy consumption and easy to access. Fabrication of photovoltaic cells by SnO2 thin films can open the different technological routes for future generation with excellent conversion efficiencies which may range 15% to 20%. It is one of the best candidates for gas sensor applications too with highest sensitivity and selectivity behavior, good oxidizing power, strong chemical bonding, non toxicity and unique transport properties. Tin oxide thin films with various combinations of materials can be synthesized by chemical and physical routes. The detailed advancement in various preparation methods and characterization techniques including X-ray diffraction, atomic force microscopy and X-ray photoelectron spectroscopy have been presented and discussed by authors. Characteristics measurement by Valence Band Structure, Photoluminence Intensity and Scanning Electron Microscope has been also reported with their performance, effect of solar energy conversion efficiency and quick response time in case of gas sensors. Prospective areas of SnO2 research for photovoltaic and gas sensor applications has been discussed and summarized by the authors. The obtained results will illustrate the possibilities of scheming Physical, chemical, magnetic and optical properties of SnO2 for sensing devices and photovoltaic applications

Prevalence and risk factor analysis for post-partum depression in women: a cross-sectional study at tertiary care centre

Background: Postpartum depression (PPD) is non-psychotic depressive episode that occurs between postpartum to fourteen months of childhood. It has adverse effect on mother and child health. Aim of this study was to analyze prevalence and risk factors for postpartum depression at tertiary care centre.Methods: This cross-sectional study was done in obstetrics and gynaecology department where 175 women between 10 days to 1 year of delivery were assessed using Edinberg postpartum depression scale. A score of 10 or more were taken as sign of postpartum depression. Various socio-demographic and obstetrics variables were assessed using SPSS (Statistical Package for the Social Sciences).Results: Prevalence of PPD was found in 11.4% patients. Common risk factors associated were intrauterine death (IUD) or early neonatal death, postpartum complications and lack of family support.Conclusions: Postpartum is common among postnatal women and is associated with various factors which can be modified. So early detection of associated risk factors is needed for early intervention and prevents its impact on mother and child health.

Uncovering Semantic Inconsistencies and Deceptive Language in False News Using Deep Learning and NLP Techniques for Effective Management

In today's information age, false news and deceptive language have become pervasive, leading to significant challenges for individuals, organizations, and society as a whole. This study focuses on the application of deep learning and natural language processing (NLP) techniques to uncover semantic inconsistencies and deceptive language in false news, with the aim of facilitating effective management strategies. The research employs advanced deep learning models and NLP algorithms to analyze large volumes of textual data and identify patterns indicative of deceptive language and semantic inconsistencies. By leveraging the power of machine learning, the study aims to enhance the detection and classification of false news articles, enabling proactive management measures. The proposed approach not only examines the superficial aspects of false news but also delves deeper into the linguistic nuances and contextual inconsistencies that are characteristic of deceptive language. By employing advanced NLP techniques, such as sentiment analysis, topic modeling, and named entity recognition, the study strives to identify the underlying manipulative strategies employed by false news purveyors. The findings from this research have far-reaching implications for effective management. By accurately detecting semantic inconsistencies and deceptive language in false news, organizations can develop targeted strategies to mitigate the spread and impact of misinformation. Additionally, individuals can make informed decisions, enhancing their ability to critically evaluate news sources and protect themselves from falling victim to deceptive practices. In this research study, we suggest a hybrid system for detecting fake news that incorporates source analysis and machine learning techniques. Our system analyzes the language used in news articles to identify indicators of fake news and evaluates the credibility of the sources cited in the articles. We trained our system using a large dataset of news articles manually annotated as real or fake and evaluated its performance measured by common metrics like F1-score, recall, and precision. In comparison to other advanced fake news detection systems, our results show that our hybrid method has a high level of accuracy in detecting false news

Leveraging Multiscale Adaptive Object Detection and Contrastive Feature Learning for Customer Behavior Analysis in Retail Settings

Multiscale adaptive object detection is a powerful computer vision technique that holds great potential for customer behavior analysis in various domains. By accurately detecting and tracking objects of interest, such as customers or products, at different scales, this approach enables detailed analysis of customer behavior. It allows businesses to track customer movements, interactions with products, and dwell times, providing valuable insights into shopping patterns and preferences. The application of multiscale adaptive object detection in customer behavior analysis offers businesses the opportunity to optimize store layouts, product placements, and marketing strategies, leading to enhanced customer experiences and improved business performance. In this paper, we introduce an innovative technique for object detection that leverages contrastive feature learning to augment the efficacy of multiscale object detection. Our methodology incorporates a contrastive loss function to extract discriminative features that exhibit resilience to scale and perspective disparities. This empowers our model to precisely detect objects across a broad range of sizes and viewpoints, even in arduous scenarios encompassing partial occlusion or low contrast against the background. Through comprehensive experiments conducted on benchmark datasets, we demonstrate that our approach surpasses state-of-the-art methodologies in terms of both accuracy and efficiency

Comparative evaluation of colposcopy, cytology and histopathology for diagnosis of cervical lesions

Background: Cervical cancer is the fourth most frequent cancer globally in women with an estimated 570,000 new cases and 311,000 deaths in 2018 representing 6.6% of all female cancers. To correlate the cytology, colposcopy and histopathology of cervical lesions in patients attending gynaecology OPD in a tertiary care centre, Ghaziabad.Methods: 208 women were enrolled from Gynaecology OPD of Santosh Medical College and Hospital, Ghaziabad, irrespective of their chief complaints. Women aged 19-80 years were included in the study group. Those with pregnancy and already diagnosed or treated with CIN, Cervical cancer or Cervical HPV infection were excluded from the study. PAP-smear was taken for all the patients followed by colposcopy without waiting for PAP-smear report. Cervical biopsy was taken from patients with abnormal colposcopic findings (90 patients).Results: Majority of women were in age group 30-39 years. 37.5% had unhealthy, 21.6% had hypertrophied cervix and only 9.1% had normal cervix. It was found that PAP -smear has a sensitivity of 33.33%, specificity of 92.59%, accuracy of 68.89%, positive predictive value of 75% and negative predictive value of 67.57%. Test parameters calculated for colposcopy revealed that it has sensitivity of 73.33%, specificity of 92%, PPV of 64.7%, NPV of 94.52% and accuracy of 88.89%.Conclusions: The results from the current study conclude that it is better to use cytology and colposcopy together as part of routine screening for cervical cancer rather than pap smear alone in order to detect maximum number of lesions

Ginseng: Pharmacological Action and Phytochemistry Prospective

Ginseng, the root of Panax species is a well-known conventional and perennial herb belonging to Araliaceae of various countries China, Korea, and Japan that is also known as the king of all herbs and famous for many years worldwide. It is a short underground rhizome that is associated with the fleshy root. Pharmacognostic details of cultivation and collection with different morphological characters are discussed. Phytocontent present is saponins glycosides, carbohydrates, polyacetylenes, phytosterols, nitrogenous substances, amino acids, peptides, vitamins, volatile oil, minerals, and enzymes details are discussed. The main focusing of the bioactive constituent of ginseng is ginsenosides are triterpenoid saponin glycosides having multifunctional pharmacological activities including anticancer, anti-inflammatory, antimicrobial, antioxidant and many more will be discussed. Ginseng is helpful in the treatment of microbial infection, inflammation, oxidative stress, diabetes, and obesity. Nanoparticles and nanocomposite film technologies had developed in it as novel drug delivery for cancer, inflammation, and neurological disorder. Multifaceted ginseng will be crucial for future development. This chapter review pharmacological, phytochemical, and pharmacognostic studies of this plant

Retrospective study to assess the prevalence of overweight and obese women delivering at tertiary care centre in Pune

Background: Obesity is a modern-day epidemic affecting women in reproductive age group. Increase in obesity in pregnant women is associated with increased rates of complications.Methods: A retrospective study was conducted based on available hospital data between January 2020 and March 2020. Pregnant women who have had their first visit at this centre before 20 weeks period of gestation were included and were classified into normal body mass index (BMI), overweight and obese. The rates of caesarean delivery, hypertensive disorders of pregnancy (HDP), gestational diabetes mellitus (GDM), assisted reproductive techniques (ART) for conception and neonatal intensive care unit (NICU) admissions were studied.Results: A total of 582 pregnant women were included in the study. The estimated prevalence of obesity was 29.3% (n=171) whereas 27.8% (n=162) were overweight. There was statistically significant association seen between obesity and caesarean delivery rates, HDP, NICU admission.Conclusions: The results reveal high prevalence of obesity in pregnant women. There is a need for a comprehensive and clinically effective approach to tackle obesity.