bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing VANET Communication through Realistic Traffic Simulation
Authors: Devendra Kumar Pallavi Singh, Rajesh Keshavrao Deshmukh,
Published: December 2023

This article explores the optimization of communication in Vehicular Ad-Hoc Networks (VANETs) through the use of realistic traffic simulation in Raipur City, located in the state of Chhattisgarh. VANETs are a specialized type of Mobile Ad-Hoc Networks (MANETs) that enable vehicles to communicate with each other and with roadside infrastructure, improving road safety, traffic efficiency, and providing additional services. However, VANETs encounter challenges due to the dynamic nature of vehicular environments, including high mobility, intermittent connectivity, and varying traffic conditions. To address these challenges, this study proposes the Integration to Message Exchange (IME) method, which incorporates realistic traffic simulation into VANET communication optimization strategies. Realistic traffic simulation models vehicle behavior and interactions with the road network, considering factors such as traffic flow, congestion, and road conditions. By integrating this simulation into VANET communication optimization algorithms, the performance of the proposed IME method and other routing strategies is evaluated under realistic traffic scenarios. The results demonstrate that the IME method significantly improves message delivery rates and reduces message latency compared to the D-LAR and AODV routing methods. In comparison to the DLAR method, the IME method increases the packet delivery ratio by 4.82%, reduces end-to-end delay by 4.0%, increases throughput by 4.85%, decreases routing overhead by 8.79%, and normalizes routing load by 7.71%. The city of Raipur in Chhattisgarh is the geographical context for this research. Raipur is a rapidly growing urban area with diverse traffic patterns and road conditions. By conducting experiments and simulations in this specific setting, the findings of this study can directly address the challenges faced by VANETs in Raipur City.
Quadrasophic Fuzzy Sets and its Applications in OCD-Decision Making Analysis
Authors: A. Anthoni Amali G. Aruna, J. Jesintha Rosline,
Published: December 2023

Various tools and extensions of fuzzy sets have been used to deal the ambiguous environment. Quadrasophic Fuzzy set (QFS) is the new model to handle the imprecise information in an efficient manner. QFS is one of the extensions of fuzzy set with new parameters. In this artifact, we establish the idea of QFS with operations, theorems and relations with examples. To validate our proposed method comparison study with other existing models is also established. Further, the application of QFS in the process of decision making is also illustrated in determining the OCD patients through the real- life data using SPSS.
Analysis of Breathing Rate Estimation Using Adaptive Techniques
Authors: B. Leela Kumari Akkim .Sree Amrutha ,
Published: December 2023

Breathing rate (BR) is an important physiological factor that is commonly measured in many treatment settings. Though it is still routinely measured by hand. In this study, a novel approach for determining the BR from an ECG, photoplethysmogram, or blood pressure signal is proposed. To extract respiratory signals from time and frequency domain data, the framework employs Discrete Wavelet Transform and Empirical Mode Decomposition techniques. Because we used a Robust Kalman Filter with a Signal Quality Index, our technique worked effectively even when the signals were severely damaged. The output signals have been integrated by state vector fusion, and the BR has been established. Two openly available clinical databases, the MIT-BIH Polysomnographic and the BIDMC datasets are used. The mean absolute percentage error was used to assess performance. The results were very accurate; PPG signals had MAPEs of 7% and BP signals of 5.4%, whereas ECG signals on the two databases had MAPEs of 4% and 4%, respectively.Additionally,the results revealed an astounding robustness to noise at 0 db. In light,this technique may be beneficial for BR monitoring in noisy areas.
Psychological Constraints of the Post-Pandemic Learning and Development Delivery Modalities: Narratives of School Principals
Authors: Gever G. Jauro, Suzette G. Talaman,,Nordy D. Siason, Jr.,
Published: December 2023

This qualitative research situated in the School Division of Guimaras delves into the narratives and experiences of five (5) school principals, with insights from the Division Human Resource Training and Development Focal Person. Grounded in Hustler's cognitive psychological and professional development theories and Vygotsky's zone of proximal development, the study explores alternative approaches to continuous professional development, aiming to enhance teacher learning effectively. Using a validated researcher-made interview guide, the study gathered data on learning and development initiatives, participant practices, and challenges faced during the pandemic. The findings revealed that school principals navigated the challenges posed by COVID-19 restrictions by crafting and implementing Basic Education-Learning Continuity plans. They addressed learning and development modalities by creating School Plans for Professional Development, enabling them to conduct various activities tailored to teachers' needs. Four delivery modalities were employed: Learning Action Cell, Relationship and Discussion-based Learning, Job-Embedded Learning, and Formal Learning. Each modality had its unique practices and challenges, reflecting the diverse contexts and needs of the school principals. To overcome these challenges, school principals demonstrated resilience leadership skills, characterized by passion, vision, creativity, interaction, and empowerment. Building upon existing theories and the 70-20-10 Learning Model, the study introduced the Learning and Development Model for Better Learning Outcomes (LDM for BLOs). This model emphasizes the roles of self-directed and interactive learners for both teachers and school principals in learning and development activities. The study contributes significantly to the field by introducing the Theory of Educational Intention and the LDM for BLOs, both of which warrant further exploration in future research. These findings shed light on effective strategies for enhancing teacher professional development and learning outcomes in challenging contexts.
Equipped Upon Evaluation: Online Delivery Preparedness as an Input for Management and Psychosocial Support
Authors: Siason Jr. Nordy D.
Published: December 2023

This quantitative study aimed to assess the level of students' readiness for online instructional delivery and to investigate the factors contributing to it. This study provided a comprehensive analysis of the phenomenon, shedding light on student’s readiness characteristics and its contributing elements. The study employed a two-stage probability sampling method, beginning with a random selection of 388 participants from a total population of 1,782 students enrolled in a state university in the Philippines. Subsequently, a probability-stratified random sampling technique was applied to ensure representation across year levels and gender categories within each college. The findings of this study indicated that students exhibited a high level of readiness for online instructional delivery. This readiness was consistent across five dimensions, namely: Computer and Internet Skills (CIS), Self-Directed Learning (SDL), Learning Commitment (LC), Motivation for Online Learning (MFL), and Online Communication Skills (OCS). Moreover, the analysis revealed that when students were grouped by sex or year level, there were no significant differences in their readiness levels. These results suggest that students possess a strong potential to embrace online instruction not only during times of significant disruptions but also as a regular mode of the teaching and learning process. This study contributes valuable insights for educational institutions and policymakers, highlighting the readiness of students to engage in online learning, which can be leveraged to enhance educational practices and facilitate a seamless transition to online instruction when needed.
Diagnosis of Brain Tumor Using Deep Learning Stacked Classifier -Ml Algorithms
Authors: B. Leela Kumari B. Mani Kumari ,
Published: December 2023

Brain tumours are very lethal, and radiologists have a difficult time classifying them due to the varied characteristics of tumour cells. Recently, computer-aided diagnostic (CAD) systems have shown promise as an enabling tool for detecting brain tumours using MRI. The bottom layers of natural photos and medical images are distinct, although both are used in modern applications of pre-trained models. This research presents a strategy for early identification of brain tumours that involves the extraction and concatenation of many characteristics at different levels. These results are credible since VGG-16 is a pretrained deep learning model. These two models were used to compare two scenarios for identifying and categorising brain tumours. To begin classifying brain tumours, characteristics were first retrieved from various VGG modules using the pretrained VGG_16 model. Second, we used PCA, LDA, and ICA to derive features. These merged characteristics were then fed into a hybrid classifier to determine the kind of brain tumour. Finally, the presence of brain tumour is be identified.The results obtained using matlab tool are compared in terms of various parameters evaluated.
Law of Magnetic Fields Via Bianchi Identities
Authors: NVN Babu S.M Bhati, Murali G, Ch.Sanjay,
Published: December 2023

This article deal with the study of magnetic fields via bianchi identities. In the present study we discuss second order tensor for the magnetic flux and obtain the law of magnetic fields in the space time system w.r.t. to any frame of reference by using Bianchi identities of Riemannian Geometry. Finally, it notice  that the time variant Maxwell’s law of magnetic fields will include as a special case our results.
Object Detection and Localization in GPR B- Scan images using Hyperbola Fitting Algorithm
Authors: P Anbhazagan S.J. Savita,  Andhe Pallavi,
Published: December 2023

Ground-penetrating radar [GPR] is a non-invasive method, that generates an electromagnetic wave to map a subsurface object. It finds applications in various fields such as civil / structural engineering, environmental, Geotechnical and Military applications. This research is conducted at the Civil Engineering laboratory, IISc, Bangalore to collect the experimental data, using the Mala Pro-ex ground-coupled antenna GPR system from Mala Geoscience. GPR technology emits electromagnetic pulses into the ground and captures the reflected echoes from the target,enabling a detailed visualization of the subsurface targets.This paper presents the Hyperbola fitting algorithm for accurate detection and localization of the underground objects such as metallic and nonmetallic. It involves several steps, including preprocessing, identifying hyperbolas, fitting hyperbolas, determining target locations and depths, and validating the results. Following the fitting of the hyperbola, the algorithm proceeds to estimate the target's position and depth using the hyperbola's parameters. These estimated values for the target location and depth are subsequently validated through a model generated using the gprMax numerical modeling tool and is also tested on experimental data. There are numerous iterations of hyperbola fitting algorithms, and their implementation varies according to the attributes of the GPR data and the subject of interest. This method provides significant advantages for engineers before excavation of the ground. The algorithm has been developed and implemented using MATLAB. It has been rigorously tested using both experimental data and simulated data generated through gprMax Numerical modeling tool. The algorithm achieves a minimum error of 0.02 in the lateral position (x) and 0.01 in the depth (Y) direction. These results demonstrate the algorithm's accuracy and reliability in identifying and locating buried objects.
Rural Digital Transformation in Indonesia: A Policy Analysis
Authors: Andi Amran Sulaiman Andi Ilham , Ahmad Munir , Ambo Ala ,
Published: December 2023

The purpose of this investigation is to appraise the adequacy of existing policies in supporting digital transformation efforts within Indonesia's rural agricultural sector. The study focuses on a) identifying policies influencing rural digital transformation, b) strategizing solutions to rural challenges, and c) proposing a digital transformation model for Indonesia's rural agricultural sector. A descriptive qualitative approach is employed, utilizing policy study methods such as SWOT analysis (Strengths, Weaknesses, Opportunities, Treatment) and the TOWS Matrix for evaluating digital transformation policy strategies. The study reveals several influential policies, including the obligation to achieve the SDGs and various laws and regulations like the Village Law. The strategy to address rural issues involves the digital transformation of existing resources. The proposed digital transformation model involves the application of Technology 4.0 in the agricultural sector, supported by institutional backing, funding, and digital literacy programs.
Design of Efficient routing protocol achieving high QoS and energy Efficiency based for Wireless Sensor Network
Authors: SIDDESH.G.K Maheshkumar Patil ,
Published: December 2023

Wireless sensor networks (WSN), which until recently were primarily employed to convey modest amounts of data, have started to be used in recent years for multimedia applications. The routing protocol needs to be streamlined and improved in order to handle the demand for higher throughput and real-time characteristics of multimedia data. The smart, greedy forwarding algorithm based on throughput and energy-awareness (HBOA-Honey Badger optimization Algorithm) that we provide in this research offers minimal end-to-end packet delay and low packet loss. Instead of always choosing the shortest path to the sink, this method chooses high-throughput paths to transport multimedia packets over a WSN. A network void-bypass is used when it runs into network gaps to increase network reliability. Energy management is also used to improve the lifespan of sensor nodes by lowering the amount of radio coverage to a sufficient range that the sensor nodes can access. The simulation findings demonstrate that, compared to other algorithms, HBOA reduces end-to-end time by 8%, packet loss ratio by 18% and energy consumption by maintaining. . Load balancing occurs as a result of energy management, which makes the remaining energy of sensor nodes uniformly dispersed throughout the entire network. Through Simulation, the performance of HBOA is also compared to that of AGEM, TPGF, GPSR, and AODV.the esults suggests the HBOA performance better under conditions that are similat to these, such as low packet end-to-end- delay, low packet loss, and load balancing of routing channels.
Microbiological Activities Of Para - Bromophenoxy Acetic Acid
Authors: V. Senthilkumar K. K. Ilavenil,
Published: December 2023

Para-bromophenoxyacetic acid's antimicrobial activity showed promise as it inhibited the growth of various microorganisms, including bacteria and fungi. The compound's bromo group appeared to have enhanced its effectiveness against these microorganisms, resulting in larger lethal zones compared to the standard drug, gentamicin. The antimicrobial assay demonstrated notable inhibitory effects on Bacillus subtilis, Enterobacter, E. coli, Klebsiella pneumoniae, Candida albicans, and Trichoderma. The potential modes of action involved the disruption of cell membranes, interference with cell wall synthesis, inhibition of protein or nucleic acid synthesis, or induction of reactive oxygen species. The compound's antimicrobial properties provided an intriguing avenue for future research into novel treatments against microbial infections.
Study of Gandhian ideology and its main Principles
Authors: Manisha, Dr. Sumeshta
Published: December 2023

The life and teachings of Mahatma Gandhi, a towering figure in the history of civil rights, nonviolent resistance, and social justice, can be better understood via the study of Gandhian ideology and its fundamental principles. This article presents an in-depth analysis of Gandhi's political philosophy, delving into its fundamental tenets such as nonviolence (ahimsa), truth (satyagraha), self-reliance (swadeshi), welfare for all (sarvodaya), equality, simplicity, decentralisation of power, constructive work, fearlessness, and cleanliness. This research sheds insight on the ongoing legacy of Gandhian ideology by examining the historical context in which these ideas developed, how they were applied in the movement for Indian independence, and how they continue to be relevant in modern global efforts for justice and peace. The ideology of Mahatma Gandhi serves as a timelessly relevant source of motivation for people and movements committed to social reform. It provides a road map for ethical leadership, conflict resolution, and the quest of a world that is more equal and compassionate.
An analysis of Heptoprotective effects of plant extract on albino rats by Deep Learning analysis using ResNet and VGG16 algorithms
Authors: Velmurugan R, Asaithambi V
Published: December 2023

The hepatoprotective and anti-inflammatory effects of Terminalia catappa L. and Pergularia daemia hydroalcoholic extracts in albino rats with CCl4-induced liver damage (HAETC and HAEPD, respectively). The extracts significantly reduced the harm induced by CCl4 when given orally at a dosage of 200mg/kg. They also showed strong hepatoprotective and antioxidant properties. Wistar albino rats in good health were divided into seven groups, each with six animals. Group I acted as the control and received no treatment, whereas Group II was given 30% CCl4 (1ml/kg, intraperitoneally) to cause hepatotoxicity. After administering Silymarin (25 mg/kg, orally) as per protocol, Group III underwent CCl4 therapy. The CCl4-induced rats showed significant reductions in antioxidant enzymes like SOD, catalase, glutathione peroxidase (GPX), and glutathione-S-transferase (GST), as well as increases in serum marker enzymes like SGOT, SGPT, alkaline phosphatase (ALK), ACP, LDH, and lipid peroxidation (LPO) activity. However, rats given HAEPD and HAETC showed significant improvements, returning these parameters to levels that are almost normal (P 0.001).The hepatoprotective and antioxidant benefits of Terminalia catappa L. (HAETC) and Pergularia daemia (HAEPD) against CCl4-induced hepatotoxicity in rats were further validated by histopathological analysis of liver tissues. It encourages the use of pre-trained CNN architectures, such as VGG16 and ResNet (Residual Network), in the proposed method, which uses convolutional neural networks (CNNs) for histopathological image analysis to assess the hepatoprotective properties of Pergularia daemia and Terminalia catappa L. leaf extracts against CCl4-induced hepatotoxicity. These well-known CNN models may be modified for precise and effective histopathological image interpretation, assisting in the evaluation of the impact of plant extracts on liver health.
Eye Disease Diagnosis and Classification using Deep Convolution Neural Network
Authors: Dr. N. Rajkumar, Dr. S. Megala, S. Muthukumaran
Published: December 2023

Human eye plays a vital role in everyone’s life. If retinal diseases occur it could cause complete or partial vision loss. Objective: To overcome that situation, the fundus images can be utilized by the ophthalmologists to automatically diagnose the retinal diseases. At the same time, the development of latest Artificial Intelligence (AI) techniques, especially Deep Learning (DL) models finds useful for object detection in several application areas, including medical imaging. Methods: In this view, this paper presents an automated DL based multi-retinal disease diagnosis and classification model. The presented technique comprises three processes, such as pre-processing, feature extraction, and classification. For feature extraction purposes, AlexNet and Residual Network (ResNet) models are employed. Besides, Convolutional Neural Network (CNN) and Deep Neural Network (DNN) models are applied to classify the retinal fundus images into different retinal diseases. The incorporation of DL models for feature extraction and classification resulted to enhanced diagnostic performance. Findings: A series of experiments were conducted on benchmark dataset and the experimental results are examined under different aspects. The experimental values exhibited that the ResNet-CNN model has achieved better performance with the accuracy of 98.84%, precision of 98.84%, recall of 98.17%, and F1-score of 98.48%. Novelty: With the help of such methods and techniques those latest findings indicate that some human eye diseases which can be alleviated by pharmacological interventions in an easy manner.
Study of Digital Platforms in Primary Healthcare and Competencies for Better Healthcare Services
Authors: Mohammed Shadab Aslam, Dr. Sakhi John, Dr. Shibu John
Published: December 2023

Digital platforms are revolutionizing primary healthcare delivery in the fast changing healthcare landscape. This study examines how digital platforms affect primary healthcare and what skills providers need to use them. Mixed-methods research combines quantitative study of digital platform usage statistics with qualitative insights from healthcare professionals and patients. The main goals are to examine the presence and types of digital platforms used in primary healthcare, evaluate their usefulness in improving patient outcomes, and determine the critical competencies needed to use these platforms effectively. Electronic health records, telemedicine platforms, and health-related mobile apps are analyzed in the quantitative phase. Digital platforms' impact on primary healthcare will be measured by patient involvement, health outcomes, and service delivery efficiency. Interviews and focus groups with healthcare practitioners, administrators, and patients comprise the qualitative phase. Digital platform usage, problems, and benefits will be discussed. The competencies healthcare practitioners need to use these platforms efficiently will also be studied. Digital platforms improve primary healthcare accessibility, efficiency, and patient involvement, according to preliminary research. However, data security, interoperability, and the digital divide must be addressed. Healthcare providers need digital literacy, virtual communication, data interpretation, and technology adaptation. This study affects healthcare policy, education, and practice. Understanding the skills needed to use digital platforms in primary care can drive training programs and help governments build supportive infrastructure. This research advances the seamless integration of digital technologies into primary healthcare to ensure high-quality, patient-cantered treatment.
Research Paper on Analysis Load Balancing Algorithm using Cloud Computing
Authors: Gagandeep kaur , Dr Sahista Nawaz
Published: December 2023

Cloud load balancing is nothing but distributing the work load among many servers & computing property. It can manage work load demand as well as application demand. This process is done with the help of distributing resources among numerous computers. Cloud load balancing consist of circulation of workload traffic & on time demand of internet. Various techniques were represented in literature to improve performance, quality of services, cargo distribution etc. The issue of cargo balancing is same as cloud computing which is data over loading & under loading. We have to apply proper cargo balancer for cloud terrain. The study represents view of problem faced by current cargo balancing ways. The researchers make more effective algorithm. Cloud computing refers to a services  & application that can accessible throughout the world from data center.all services are accessible through online. There  are several issues in cloud computing such as scheduling, load balancing, quality of services & storage resources. Here we study load balancing. When many clients send request to server, server has already n number of task. In that case server is over loaded for this condition we have to apply load balancing algorithm. Cloud computing is a internet based network technology that share rapid growth in communication technology, by providing services to customer of various requirement with the help of online computing resources. It has both hardware & software application along with software development platform. Cloud computing becomes a popular word in the area of high performance distributed computing. It provide on demand access to share pool of resources over internet. Cloud computing is still much research is required for broad array of topic. One of the important research issue which need to be focus for effective performance isload balancing. Cloud computing aim is to gives computation & resources over the internet via dynamic provision of services. There are many challenges and issues associated with the implementation of cloud computing. This research paper deliberates one of cloud computing main problem that is load balancing.  The main aim of load balancing is equally distribute the load on cloud server such that no host is under load & over load. Several load balancing algorithm have been implemented in literature to satisfied customer request for appropriate cloud node.
Challenges and Prospects Entrepreneurship Education among Indian Youth: A Study
Authors: Sunny Masand, Sandeep Saxena, CS Maulik Chandnani
Published: December 2023

In India, there is a prevailing belief that entrepreneurship is an inherent trait in individuals. However, it is also true that individuals how receive formal education in entrepreneurship have an advantage hen embarking on entrepreneurial endeavours. This educational background can significantly reduce the risk of failure in entrepreneurship. With this perspective in mind, this paper aims to shed light on entrepreneurship education programs and the challenges they face within the Indian context. To achieve a comprehensive understanding of entrepreneurship education, we conducted a thorough review of relevant literature. Additionally, e examined specific literature and data pertaining to youth to uncover the obstacles and barriers hindering youth entrepreneurship. Consequently, this paper provides a comprehensive overview of entrepreneurship education for the youth in India and the challenges it encounters.
Human Values among Adolescents – An Empirical Study
Authors: Dr. Meenakshi Sharma, Abhishek Soni
Published: December 2023

Human values constitute the foundational elements of our humanity and the guiding principles of life, playing a crucial role in shaping commendable human conduct. These values typically emerge from one's individual interests, desires, impulses, and preferences, often regarded as universal and shared by people irrespective of religious, racial, cultural, or personal differences. The dual objectives of this study revolved around comprehending the significance of individuals' values in life and delineating the values held by the younger generation.

The research, employing a random sampling method, delved into the perspectives of 50 young individuals . A combination of primary and secondary data was harnessed to fulfill the study's requirements. The formulation of the questionnaire drew inspiration from Schwarz's cost research, and subsequent analysis involved employing statistical tools like simple percentages, two-way tables, chi-square tests, and correlation assessments.

Within the study's hypotheses, the absence of a correlation between age and endeavors to enhance human outcomes, as well as the absence of a link between gender and changes in human benefits over time, were explored. The findings revealed a positive and significant association between age and the initiation of human development. Conversely, no discernible association surfaced between gender and alterations in human characteristics across the years.

In conclusion, the study underscores the imperative role of parents, schools, and communities in imparting the value of humanity to the younger generation. Such an educational approach is posited as essential for fostering a healthy and fulfilling life, thereby emphasizing the critical role of instilling human values in the formative years.
Identifying the Factors of E-service Quality among Indian Millennial Streaming Service Users
Authors: Mr. K.K. Bajaj
Published: December 2023

This research sought to explore the preferences of Indian Millennial in the realm of streaming services, specifically focusing on music. The goal was to uncover the intricate connections between various quality dimensions within six services: web design, performance, personalization, trust, satisfaction, and repurchase intention. Employing an e-service quality measurement and a quantitative research methodology, the study distributed questionnaires to 400 Indian millennial known for their heavy usage of music streaming services, gathered from local private colleges and universities. Data analysis involved descriptive analysis and partial least squares for structural modeling. The study's findings indicated significant associations between web design, customization, and repurchase intention. Performance factors, such as reliability and responsiveness, played a crucial role in satisfaction, and a link between satisfaction and repurchase intention was established. Notably, trust did not show a significant influence on purchase intention. These findings contribute valuable insights that can be applied not only to streaming music services but also to other e-services, aiding in the development of quality metrics to assess customer-centric effectiveness. The suggestion for future research includes exploring different types of e-services with varied quality dimensions for a comprehensive and comparative analysis.Top of Form
Synthesis and Characterization Conducting Polymer Supported CDS-ZNS Nano Composites Used for Various Applications
Authors: Ms. Dipali Nagnath Hodade, Dr. Mohan N Giriya
Published: December 2023

Keys include undertaking polymer synthesis and physical property research. Conductive is polyaniline.  The presence of pristine nano CdS particles in the matrix was demonstrated by X-ray diffraction (XRD).

In this study, four sol-gel techniques are used to prepare nanostructured polyaniline (PANI). PANI molecular structure was characterized using X-ray diffraction to assess the morphology of PANI samples.

The results of the prepared PANI's characterization reveal that the unconventional synthesis methods utilized to create it drastically changed its morphology, chemical composition, crystallinity, conductivity, and surface area. The outcomes of the characterization supported this. The manufacturing and properties of polyaniline conducting polymers are examined in this research. The classic and grafted techniques of polyaniline synthesis have been examined. The relationship between structure and properties is established by this high-accuracy development procedure. In the future, polyaniline with altered structure may be employed.

PANI/CdS nanocomposites have higher DC conductivity than PANI, according to observations.

In situ production of conducting polyaniline nanocomposites with CdS nanoparticles was achieved by oxidizing aniline with cadmium sulphate at 3.5 pH. Electrical conductivity of polyaniline is influenced by CdS-nanoparticles. In contrast to pure polyaniline, the electrical conductivity of polyaniline nano-composite is increased by the homogenous intercalation of CdS nanoparticles, which results in a cooperative phenomenon between polyaniline and the nanoparticles.
Development and Validation of Headspace-Gas Chromatography (HS-GC) Method for the Determination of Potential Genotoxic Impurities in Meloxicam
Authors: Amit B Bhosale , Ravi Yadev, Seema Kothari
Published: December 2023

The development and validation of a specific Headspace-Gas Chromatography (HS-GC) method for the determination of genotoxic impurities (isopropyl chloride, propionaldehyde, 2-chloropropanol, and chloromethane) in Meloxicam. The chromatographic separation of these impurities was accomplished using a nitrogen carrier gas on a DB-624 (6% cyanopropyl phenyl + 94% dimethyl polysiloxane fused silica) (75 m x 0.53 mm, 3.0 µ) GC analytical column. The Flame Ionization Detector (FID) served as the detector with optimized temperatures set at 260°C for the detector and 240°C for the injector port. Additionally, an injection volume of 1.0 mL with a split ratio mode was employed. The performance of this method was assessed through parameters, including system suitability, specificity, linearity, range, accuracy, limit of detection (LOD), limit of quantification (LOQ) and precision. The limit of detection and limit of quantification for isopropyl chloride, propionaldehyde, 2-chloropropanol, and chloromethane were determined to be 2 and 4 ppm, respectively, and for chloromethane 0.8 and 1.6 respectively. The correlation coefficient of the linearity experiment was 0.999 for propionaldehyde, 2-chloropropanol, chloromethane and 1 for isopropyl chloride compounds. Furthermore, the accuracy assessment involved spiking the genotoxic impurities at various levels, demonstrating favourable recovery percentages well within the acceptable range of 80–120% of the limit level. These compelling results substantiate the suitability of the proposed HS-GC method for the accurate determination of isopropyl chloride, propionaldehyde, 2-chloropropanol, and chloromethane in Meloxicam.
An Adaptive Consumer Centric Management Model For Central Bank Digital Currency With Special Reference To Coimbatore District
Authors: Dr. V. Abirami, V. Ambika
Published: December 2023

A revolutionary turning point in the financial services industry has been reached with the introduction of Central Bank Digital Currency (CBDC). This research proposes an adaptable Consumer-Centric Management Model (CCMM) designed for the distinct financial environment of Coimbatore District, with the potential to transform digital transactions and consumer interaction. The model uses insights from customer experiences and preferences, with a focus on consumer protection, privacy, and security. To collect both quantitative and qualitative data for the study, a mixed-method approach is employed, which involves conducting stakeholder interviews and surveys. The Coimbatore District's financial institutions, central banks, and regulatory bodies can incorporate CBDC in a way that puts the interests of their customers first by following the guidelines provided by the CCMM. It covers stakeholder cooperation, risk management, consumer awareness, and regulatory compliance.
Prediction Of Soccer Playing Ability from Selected Fundamental Skill Performances and Anthropometric Measurements of Soccer Players
Authors: NS. Kirankumar, Dr. R. Rajaram, Dr. P. Kulothungan
Published: December 2023

The intension of the study was prediction of soccer playing ability from selected fundamental skill performances and anthropometric measurements of soccer players. To attain this aim, the investigator selected one hundred and sixty male inter-collegiate level Bangalore north university soccer players as subjects. Random group design was used for this investigation, as it is considered most suitable. The age of the selected subjects ranged from eighteen to twenty five years. In this study one criterion (soccer playing ability) and eight determinant variables are included. Pearson product moment correlation was utilized to verify the association between criterion (soccer playing ability) and determinant shooting ability, dribbling ability, passing ability, height, weight, leg length, calf girth and thigh girth variables respectively. The relationship between criterion and determinant variables as well as inter-correlations among determinant variables was calculated by using Pearson product-moment correlation formula. To test the hypothesis 0.05 level of confidence was fixed. The selected fundamental skill performance and anthropometric measurements such as shooting ability, calf girth, dribbling ability and weight were highly significantly correlated with soccer playing ability of Bangalore north university affiliated inter collegiate level soccer players.
Green Marketing and Its Influence on Consumer Purchasing Behaviour
Authors: Ms. Rajeswari.K, Dr. R. V. Suganya
Published: December 2023

As global environmental concerns continue to mount, businesses have increasingly turned to green marketing strategies to align with consumer demand for eco-friendly products and services. This study examines the intricate relationship between green marketing and consumer purchasing behavior, shedding light on the factors influencing environmentally-conscious consumer choices.

The research explores the multifaceted dynamics of green marketing, considering various elements such as environmental messaging, sustainability labels, product design, and corporate social responsibility. It investigates how these factors impact consumer perceptions, attitudes, and behaviors toward green products, while also addressing the prevalence of green washing in the marketplace.

Through a comprehensive review of existing literature and empirical studies, this research delves into the complexities of consumer decision-making processes in the context of green marketing. It examines the role of personal values, social influences, cultural factors, and trust in shaping consumer responses to sustainability-oriented marketing efforts.

The study also highlights the long-term implications of green marketing, focusing on consumer loyalty to green brands and sustained environmentally responsible purchasing behavior. It emphasizes the potential for green marketing to drive lasting positive changes in consumer behavior and contribute to the broader sustainability agenda.

Furthermore, the research considers the ethical and policy dimensions of green marketing, emphasizing the importance of transparent and responsible marketing practices. It discusses the role of government regulations and educational initiatives in fostering a marketplace where green marketing aligns with genuine environmental commitments.

In an era marked by increasing environmental awareness and calls for sustainability, understanding how green marketing influences consumer choices is not only of academic interest but also holds significant practical implications for businesses, policymakers, and consumers themselves. This study provides valuable insights into the evolving landscape of green marketing and its transformative potential in promoting sustainable consumption patterns.
Analysis Of Two-Layered Model Of Blood Flow Through Stenosed Tube With Permeable Walls In The Presence Of Magnetic Field Considering Blood As Couple Stress Fluid With Variable Viscosity And Slip Velocity
Authors: Neha Phogat, Sumeet Gill, Rajbala Rathee, Jyoti
Published: December 2023

The paper investigates the “two-layered model of blood flow” as steady incompressible couple stress fluid through one-dimensional channel in multi-stenosed blood vessel. Within the core region, the blood flow is conceptualized as a dynamic entity exhibiting the attributes of a couple stress fluid, distinguished by variable viscosity orchestrated in accordance with the precepts delineated by the Einstein relation and the peripheral region of the tube comprises of plasma which is considered as “Newtonian fluid with constant viscosity”. The governing equations of the blood flow are solved using the Frobenius technique using the slip boundary condition and the expressions are derived for peripheral and central velocities along with shear stress and pressure gradient. The effects of various parameters on the flow variables have been emphasised. MATLAB programming software is used to visualize the results regarding shear stress and pressure gradient. The study further interrogates the effect of change in thickness of peripheral layer on pressure gradient under the influence of magnetic field and slip velocity.
Artificial Neural Network based prediction of Specific Fuel Consumption in various Compression Ratio CI Engine Fueled with Various Plastic Composition Pyrolysis oil and Diesel
Authors: Maulik A. Modi, Tushar M. Patel
Published: December 2023

This study looks at the usage of Artificial Neural Network Modeling for predicting Specific Fuel Consumption for compression ignition engines running on Diesel, Low-Density Polyethylene Pyrolysis Oil (LDPE PO), High-Density Polyethylene Pyrolysis Oil (HDPE PO), and Polypropylene Pyrolysis Oil (PP PO). Using preliminary practical findings, The Model of ANN was built to estimate SFC by adjusting the parameters of the engine. SFC is anticipated by adjusting the parameters and utilizing the orthogonal array. The experiment is then carried out using an orthogonal array. The outcomes of experimental work are used to create an ANN model. In the case of a non-linear mapping between input & output, the classic back-propagation method and a multi-layer perception network are utilized. The values of Mean Square Error (MSE), Root Mean Square Error (RMSE), and Regression Coefficient (R2) for LM10TP architecture are 1.5143×10–06, 0.0012 & 1 in training & the validation values 1.2185×10–06, 0.0011 and 0.9999 respectively.  It was discovered that neural networks are useful tools for prediction since they performed well in specific fuel consumption prediction in both training and validation.
Performance and emission characteristics of diesel engine with hemp biodiesel blends
Authors: Dodda Hanamesha, D. Madhu, Rudresh B M
Published: December 2023

The increased population in the world has resulted in excessive use of conventional fossil fuel resources especially petroleum diesel over the world wide which in turn made every nation to find out the possible alternate energy sources for replacement of petroleum diesel. This article investigates the suitability of hemp biodiesel blends generated from hemp vegetable oil can be used as a fuel a single-cylinder, four-stroke diesel engine. The conventional transesterification procedure is used to transform hemp vegetable oil to hemp biodiesel. The fuel qualities of biodiesel and its blends have been analyzed and determined to be equivalent to those of petroleum diesel, in accordance with the criteria set by ASTM. The purpose of this work was to examine the performance and emission characteristics of a diesel engine employing hemp biodiesel blends and compare them with those corresponding to conventional diesel fuel which was employed as a reference fuel. The results showed that as the load on the diesel engine increased, the brake specific fuel consumption (BSFC) decreases, however the brake thermal efficacy (BTE) as well as exhaust gas temperatures (EGT) increased for the blends of hemp biodiesel. In the emission test, as load increases, there is an increase in the emissions of CO2, UBHC and NOX but decreases in CO. Additionally, the emission of smoke exhibits an inverse relationship with load, increasing at lower loads as well as decreasing at higher loads. Hemp biodiesel blends provide diesel engine efficiency and emission characteristics that are comparable to those produced by diesel engine utilizing standard diesel fuel.
Earthworm population and Community structure with reference to Soil ecological parameters from different agroecosystem in Cuddalore district, Tamil Nadu, India
Authors: S. Devanathan, S. Senthilmurugan
Published: December 2023

Earthworms are fundamental to the dynamics of our ecosystems. The six species of earthworms (Pondoscolex corethrurus (Muller, 1856), Perionyx ceylanensis (Michaelsen, 1904), Perionyx excavates (Perrier, 1872), Lambitomauritti (Kinberg, 1867), Amynthas gracilis (Kinberg, 1867) and Drawida willsi (Michaelsen, 1907)) belonging to three families (Glossoscolecidae, Megascolecidae, Moniligastridae), five Genera (Pondoscolex, Perionyx, Lambito, Amynthas, Drawida) and three orders (Opisthopora, Heplotaxida, Moniligastrida) were identified from the study site in Cuddalore district, Tamil Nadu, India.Selected two types of cultivated lands in present study: Wet Lands (Paddy, Banana, Sugarcane), Dry Lands (Coconut, Groundnut, Oil palm).Totally six species find in the study sites, two species (Perionyx excavates, Perionyx ceylanensis) were epigeic category, one species (Pondoscolex corethrurus) were endogeic category, one species (Lambito mauritti) anecic category, one species (Amynthas gracilis) were epiendogeic and last one species (Drawida willsi) were epianecic category. The wet land cultivable fieldshave three types of earthworm category (epigeic, endogeic, epianecic) and cultivable dry land fields have two types of earthworm category (anecic and epiendogeic).The statistical analysis of data was also performed by using ANOVA software and different types of soil ecological parameters (pH, moisture, temperature, organic carbon, nitrogen, phosphorous, potassium, C:N ratio, microbial activity, earthworm density, earthworm biomass, relative density, frequency, richness index, diversity index, dominance index, evenness index, Lm/Oratioa, Pe/O ratiob).
Automatic Detection of Covid-19 in CT Images Using an Optimized Deep Neural Network Classifier
Authors: G. Prabakaran, Dr. K. Jayanthi
Published: December 2023

An exact and quick finding of Covid-19 patients plays a significant part in the initial period of medicinal treatment and prevention. Automatic recognition of COVID-19 cases using CT images may help lessen the effect of this infection on human civilization. In recent years, computer vision is the major solution for diagnosing the covid-19 disease by using CT images. Besides, many researchers had presented efficient artificial intelligence techniques for identifying Covid-19 disease. Nevertheless, the accuracy and time consumption of the model further to be improved. Thus, in this work, we propose a Covid-19 disease classification using a hybrid deep neural network with handcrafted features. The proposed approach consists of three stages namely, pre-processing, handcrafted feature extraction, and classification. Initially, the images are given to the pre-processing stage to remove the noise present in the input images. Then, we extract the hand-crafted features (Gray-Level Co-Occurrence Matrix (GLCM)) from each image. After that, the pre-processed image is given to the input of the optimized deep neural network classifier to classify an image as normal or abnormal. The proposed optimized deep neural network is a combination of a convolution neural network (CNN) and adaptive artificial jelly optimization (A2JO) algorithm. To enhance the performance of classifier accuracy, the extracted handcrafted features are fused with the hybrid deep neural network. The performance of the proposed approach was analyzed based on different metrics and performance compared with other techniques.