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Sexual Relationship Decision Making Based on Entertainment Media: A Qualitative Perspective Among Young Couples
As important as physical, mental, or social health is sexual health. Teenage pregnancy, STDs/STIs, and unsafe abortions are just a few of the population health issues that can arise from the absence of adequate sex education for young people. The purpose of this study is to investigate the process of sexual decision-making as influenced by media intervention among couples. Entertainment education (EE) is an approach that uses storytelling to influence large-scale behaviour change. EE has been used as a potent tool to educate, enlighten, and influence society and individual behaviour change worldwide. Through entertainment education, people have been taught about themes like HIV, family planning, pregnancy and child health, violence against women, and other subjects. Web series or movies that are accessible on the online subscription service, Netflix was taken into consideration for this study. Although there is a great deal of research on adolescent sexuality, studies of sexual decision-making have traditionally been gendered, meaning that men and women have been examined separately. This study is designed for a qualitative investigation using a phenomenological approach. Thematic analysis was employed to analyse semi-structured interviews of couples in a heterosexual romantic relationship. The findings will reveal the influence of entertainment education on young couples choices in their intimate relationships. 2024, The Author(s), under exclusive licence to Springer Nature Switzerland AG. -
Advanced Applications of Python Data Structures and Algorithms
Data structures are essential principles applicable to any programming language in computer science. Data structures may be studied more easily with Python than with any other programming language because of their interpretability, interactivity, and object-oriented nature. Computers may store and process data at an extraordinary rate and with outstanding accuracy. Therefore, it is of the utmost importance that the data is efficiently stored and is able to be accessed promptly. In addition, data processing should take as little time as feasible while maintaining the highest possible level of precision. Advanced Applications of Python Data Structures and Algorithms assists in understanding and applying the fundamentals of data structures and their many implementations and discusses the advantages and disadvantages of various data structures. Covering key topics such as Python, linked lists, datatypes, and operators, this reference work is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students. 2023 by IGI Global. All rights reserved. -
Improved Crypto Algorithm for High-Speed Internet of Things (IoT) Applications
Modern technologies focus on integrated systems based on the Internet of Things (IoT). IoT based devices are unified with various levels of high-speed internet communication, computation process, secure authentication and privacy policies. One of the significant demands of present IoT is focused on its secure high-speed communication. However, traditional authentication and secure communication find it very difficult to manage the current need for IoT applications. Therefore, the need for such a reliable high-speed IoT scheme must be addressed. This proposed title introduces an enhanced version of the Rijndael Cryptographic Algorithm (Advanced Encryption Standard AES) to obtain fast-speed IoT-based application transmission. Pipeline-based AES technique promises for the high-speed crypto process, and this secure algorithm targeted to fast-speed Field Programmable Gate Array (FPGA) hardware. Thus, high-speed AES crypto algorithms, along with FPGA hardware, will improve the efficiency of future IoT design. Our proposed method also shows the tradeoff between High-Speed communications along with various FPGA platforms. 2020, Springer Nature Switzerland AG. -
Urban cooling optimization in Ahmedabad: Defining optimal radius for the thermal performance of water bodies and green spaces
Urban water bodies and vegetation are integral components of urban landscapes. They contribute to thermal comfort, providing essential cooling effects that alleviate the impacts of rapid urbanization. The study emphasizes the importance of planning and performance assessment of these landscapes to achieve maximum cooling and extend their influence effectively. It is well-documented that urban vegetation and water bodies reduce local temperatures which can be evaluated through various landscape indices suggesting that the shape and configuration of these areas greatly impact their cooling capabilities and influence. To explore this further, a spatio-temporal analysis focusing on Land Surface Temperature (LST) is conducted by using high-resolution satellite imagery in 39 water bodies and 130 dense vegetation sites in Ahmedabad, Gujarat to identify thermal patterns and assess the cooling performance of landscape features. The analysis aimed to understand the relationship between temperature changes and the radius of landscape sites leading to the identification of the Radius of Saturation (R_sat) which is the maximum distance around a water body or green space where its cooling effect is most effective. The results indicated that the R_sat is 150 meters for water bodies and 130 meters for dense vegetation. These radii mark the points at which further increases in size do not significantly enhance the cooling effect, signifying the saturation point for thermal influence. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). -
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Non-invasive glucometer /
Patent Number: 201941025125, Applicant: CHRIST (Deemed To Be University) -
Augmented Realities: Unlocking Consumer Engagement and Brand Advocacy Through Comprehensive AR Strategies
Augmented reality (AR) is an emerging concept having an impact on a wide range of industries, including marketing, business, tourism, gaming, human-computer interface, and manufacturing. Consumer engagement and brand advocacy are critical goals for companies looking to build long-lasting relationships with their target consumers in today's digitally driven environment. In this environment of shifting consumer preferences and technology breakthroughs, AR has become a potent instrument for revolutionizing the way brands communicate with their customers. AR presents exceptional chances to captivate audiences, enhance experiences, and foster brand loyalty like never before by seamlessly integrating virtual aspects with the real world. This study has used extensive review of relevant literature of the past nine years from 2016 to 2024. By creating close connections and evoking intense emotions in customers, AR acts as a catalyst to promote brand advocacy. It is important for scholars and AR managers to keep an eye on the most recent developments in AR. One of the most exciting uses of AR is the capability to virtually test things before making a purchase. This book chapter shall cover an exhaustive list of AR attributes' benefits, values, and their interconnections and significance. Thus, managers can design appropriate AR strategies by using these identified characteristics and benefits. According to past studies, consumer engagement is a critical factor influencing sales, profits, customer satisfaction, and loyalty. 2025 by Gajalakshmi N. S. and Seranmadevi R. All rights reserved. -
Combinational edge detection using multiple color channels and GrabCut
Identification of edges is very important in feature extraction and pattern recognition. An edge of an image detected by converting it from RGB format to a Grayscale image and can sometimes be inefficient and inaccurate. This inefficiency is caused due to various color differences that get erased or rewritten during the process of grayscale conversion. Edge locations in a colored image are derived by analysing the variations in the multiple color channels and merging the gradients in these channels to compute a single edge snapping vector field, which is derived using the Euclidean distances between two distinct pixels in an image. This lets to retain the various multidimensional characterizations of color channels of an image as the color differences can be closely calculated using human perceptions. Hence this method is proved to provide more accuracy in edge detection. Furthermore, the results are improved when combined with a finer edge detection method GrabCut, which allows the user to detect the edges in an image using reduced iterations. The proposed paper uses a combinational approach to efficiently acquire edges in the image by enhancing the color properties of an image and then using the GrabCut method to retrieve the edges present in an image. IAEME Publication. -
Etiology and Advanced Detection Techniques for Fetal Brain Abnormalities: A Comprehensive Study
In the modern world, women's health deserves greater focus, particularly in a country like India and particularly during pregnancy. The health of mother's as well as baby's is important throw-out this process. According to a survey, three cases out of a thousand have abnormalities in fetal brain. The likelihood of survival of mother and child significantly increased by early identification of these diseases. Several procedures, including fetal ultrasound, MRI, fetal echocardiography etc. must be carried out during pregnancy in order to monitor fetal development. Pregnancyrelated MRI-scans always practiced to identify and treat fetal brain disorders in former stages. It is possible to identify, examin fetal brain problems early on by doing a prenatal MRI examination. The diagnosis of issues with fetal brain MRI imaging involves several crucial procedures. Among these are image segmentation; image analysis, which comprises extracting characteristic characteristics, improving image quality, identifying relevant patterns, and categorising images based on predefined standards. Classification determines if an anomaly is present or not. Analysing pictures can be difficult due to the wide range of shapes, spatial arrangements, and intensities that are present. The primary subjects of this work are the review and comparison of various fetal brain malformations, as well as their causes and commonalities. 2025 IEEE. -
IN SEMICONDUCTOR MEDIA CAUSED BY DYNAMIC LOADING THROUGH MEMORY EFFECTS AND NONLOCAL FRAMEWORKS
We investigate a novel meticulous heat transfer model to capture the photo-thermal-elastic interactions efficiently inside a nonlocalized semiconductor material affected from a dynamic thermal loading. For the purpose of apprehending memory and nonlocal effects during complex diffusion processes inside the semiconductor, the Atangana-Baleanu fractional derivative is established on the linearized coupled thermoelastic theory which involves thermal displacement gradient and temperature gradient among the constitutive variables. Laplace transform methodology is acquired for solving the problem. Later on, a suitable algorithm of numerical inversion of the Laplace transform is employed for achieving the computational results in physical domain. As per the graphical results, conclusions about the influences of significant parameters such as fractional parameter, photo-generated carrier life-span and the velocity of dynamic heat source on the dimensionless physical fields like temperature, displacement, stress and carrier density are constructed. Further, the utility of the current advanced heat transfer model is established by comparing the graphical results of physical fields under the current heat transfer theory with the old developed theories of heat transfer models having two phase lags and single phase lag parameter. All the graphical results are evaluated against distinct values of depth of the semiconductor media. We believe that this fine study will support researchers for obtaining promising and optimum results of real world problems where the photo-thermal effects inside the semiconductor are taken into account. 2026 The Authors, under license to MSP (Mathematical Sciences Publishers). -
An Examination of Methodological Approaches for Segmentating Fetal Brain MRI Images - Analysis
In today's world and in the country like India, Women's health needs more care. Especially the women's health during the pregnancy period plays a vital role in both the mother as well as the baby's care. As per a survey, among thousands three of them found to have fetal brain abnormalities. If these abnormalities are predicted at the early stage, then it will be an added advantage in saving both the life of mother and baby. During the pregnancy number of tests have to be performed to monitor fetal development. Tests like fetal ultrasound, Chorionic Villus Sampling, Amniocentesis, Fetal Echocardiogram, Fetal MRI imaging SCAN etc. The fetal brain abnormality can be predicted as well as treated at the early stage by analyzing the fetal brain MRI during the gestational period. Identifying abnormalities in fetal brain MRI images involves several essential steps, including image segmentation, analyzing images involves extracting distinctive features, refining their quality, identifying relevant patterns, and categorizing them based on specific criteria. The process of classification determines whether an abnormality is present or not. Analyzing images presents a complex undertaking owing to the diversity in shapes, spatial arrangements, and intensity levels within the images. This paper focuses on reviewing and comparing various segmentation techniques, highlighting their respective strengths and weaknesses. 2024 IEEE. -
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Pluronic F127Folic Acid Modified Nickel Oxide Nanocomposites via a Facile One-Pot Approach for InVitro Anticancer, Antibacterial, and DPPH Radical Scavenging Studies
Drug-resistant bacteria and cancer remain major challenges in healthcare, highlighting the need for multifunctional nanomaterials. In this study, folic acid- and Pluronic F127-modified nickel oxide nanocomposites (NiOPF127FA) were synthesized via a one-pot method, and their invitro antibacterial, antioxidant, and anticancer properties were evaluated. XRD analysis showed a crystallite size of 19.42 nm for NiOPF127FA, while PL spectra exhibited a green emission peak at 507 nm, indicative of structural defects in the NiO lattice. NiOPF127FA displayed enhanced antibacterial activity against MRSA and Candida albicans compared to bare NiO, as evidenced by larger inhibition zones and lower MIC and MBC values. The DPPH assay demonstrated improved radical scavenging activity of the modified nanocomposites, likely related to their smaller size, higher surface area, and surface defect-mediated electron transfer. Invitro anticancer studies using rat C6 glioblastoma cells revealed dose-dependent decreases in cell viability, with IC50 values of 12.3 ?g/mL for NiO and 9.6 ?g/mL for NiOPF127FA. Fluorescence staining with AO/EB and DAPI indicated morphological changes in nuclei and alterations in MMP, consistent with induction of cell death. Overall, these findings suggest that NiOPF127FA nanocomposites exhibit improved invitro biological activity, providing a foundation for further preclinical investigations of their potential biomedical applications. 2026 John Wiley & Sons Ltd. -
The Fusion of CNN and MLP Algorithm as High-Performance Classification for Identification of Healthy and Unhealthy Leaves
In this study, it encapsulates the results of the work carried out with the Convolutional Neural Network, Multi-layer Perceptron, and hybrid of CNN and MLP classifier for the recognition of a tea leaf. The leaves are categorized into distinct classes by analysis and identification and recognition, which benefits both the buyer and the farmer by enabling the seller to sell tea leaves based on the quality of the leaf. Nowadays there is more advancement in the field of agriculture. But it is always the latest subject to study in the field of agriculture for the analysis and to identify quality of leaves. Many AI methodologies can be used for identification and recognition and further their fusion with different techniques or methods that can be applied to address the issue and to acquire the better accuracy. In this CNN, MLP and the hybrid of CNN-MLP are employed for determining the accuracy and this can further help in classifying the leaf in different grades like best quality, average quality, and worst quality, as for the future scope. Then the feature selection algorithms are implemented based on the different selection methods such as ANOVA, information gain, feature importance, and the random forest, which will reduce the number of parameters, at the end classification is carried out for identification of the leaf. 2026, Greater Mekong Subregion Academic and Research Network, Asian Institute of Technology. All rights reserved. -
A COMPUTATIONAL MODEL FOR TEA LEAF PRICE PREDICTION BASED ON QUALITY FACTORS USING HYBRID MACHINE LEARNING TECHNIQUES
This document reflects the effort made to calculate and identify the grade of the tea leaves based on the assessment of the leaves' size and color. The leaves were classified based on their severity with the help of HSV. The leaves were further classified using the k prototypes clustering once their length and width were established. The leaves were then further categorized in line with that. Light, medium, and dark are the three-color categories into which it belongs. The leaves were further sorted according to their quality so that the farmer could sell the produce at a better price. With the machine learning method for the categorization part, we were able to show its values. All of the healthy leaves were considered in a different dataset, and the images were obtained using the feature selection method. The length and width of each individual leaf, along with its color and shape, were then measured using those leaves. We were able to differentiate between the various leaf grades based on the findings. The healthy leaves were separated from the diseased leaves using the textual features. Additionally, we were able to use the other criteria to obtain higher-grade leaves. Little Lion Scientific. -
Identification Of Quality Of Tea Leaves By Using Artificial Intelligence Techniques: A Review
This paper summarizes the outcome of the survey carried out for quality identification of a tea leaf and eventually price prediction. Quality identification can allow to categorizing leaf in different grades, which helps the buyer and seller to acquire suitable quality to their need. Price prediction is an important feature, which can bring certainty at price and farmers can be benefitted more for their good quality. Additionally, if the leaf disease is identified at the initial stage that would also allow farmers to timely resolve the concerned issues and save their corps. In the field of agriculture, this has been always a research area to identify and predict the quality of tea leaves. Various artificial intelligence techniques are hot topics in the field of recognition and their effective combination can not only solve the problem but also enhance recognition accuracy. Therefore, there is an imminent need for a detailed survey on compiling techniques used for the identification of different varieties of tea plants. In this research, we aim to propose a review of the various techniques which can be utilized for determining the quality and price prediction. The Survey is hybrid with a combination of different artificial techniques, which is a suitable approach to target effective tea leaf identification. Further for the classification of tea leaf images, various algorithms can be combined as well to obtain better results and different algorithms can be used for feature extraction based on texture extraction, color extraction, and shape extraction. The Electrochemical Society -
Image Pre-Processing Algorithms for the Quality Detection of Tea Leaves
This Identification and prediction of the tea quality is the essential research focus nowadays in the field of agriculture. Nowadays the Artificial Intelligence has become the latest topic in the region of pattern recognition. The various combination and permutation of the different techniques has resulted in proper solving the problem as well as have better accuracy in recognition. Therefore, there is urge need of a detailed survey AI techniques used for the identification of the tea leaf quality for the different grades of tea plants. In this paper, we aim on the various methods used for the pre- processing of the input image to extract the processed image which will further be useful for the feature extraction and the classification of the proposed image. It is very important to get the effective and accurate processed data which will further act as an input for the next level modules. This paper shows various methods of edge detection are applied on the image like Canny, Sobel and Laplacian are used. The further results are compared for quality metrics parameters such as the Mean Square Error (MSE) & Structural Similarity Index Metric (SSIM). The main agenda of this paper is to perform the edge detection and to check the quality measure of the processed image. The software used here is python. 2022 IEEE. -
Navigating Technological Advancement in the VUCA and BANI World
Navigating technological advancement in todays VUCA (Volatile, Uncertain, Complex, Ambiguous) and BANI (Brittle, Anxious, Nonlinear, Incomprehensible) world presents both opportunities and challenges for organizations and societies. Rapid innovation in fields like AI, automation, and digital connection transform industries, redefine work, and reshape human interaction. These advancements also introduce new aspects of unpredictability, ethical dilemmas, and systemic risks. Leaders must encourage agility, resilience, and foresight, embracing technology as a tool for efficiency and as a catalyst for adaptive and sustainable growth. Navigating Technological Advancement in the VUCA and BANI World explores how rapid technological advancements interact with the risks of the modern world. It examines how organizations and individuals can adapt, innovate, and build resilience to navigate the challenges and opportunities presented by digital and systemic change. This book covers topics such as digital technology, ethics and law, and information security, and is a useful resource for business owners, engineers, policymakers, academicians, researchers, and scientists. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Facebook as a Socialising Agent and its Impact on Academic Achievements on an Individual
Golden Research Thoughts Vol.2,Issue 9,pp.1-3 ISSN No. 2231-5063