bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Analysis of Scaling Parameter Estimation for the Lindley Distribution in Wait Time Analysis: Simulated and Real Data Applications
Authors: Sadeq Taha Abdulazeez Sameera Abdulsalam Othman, Sebar Taher Qadir,
Published: December 2023

This research focuses on estimating the scale parameter for the Lindley distribution using various methods, including Maximum Likelihood, least squares, and Linear Quantile-Moment. These methods were employed to identify the most effective approach for estimating the distributional parameters. To determine the optimal parameter estimation approach, a comparison was conducted among these estimators based on the mean square error (MSE) criteria, which involved different simulation experiments. The results demonstrated the superiority of Maximum Likelihood estimation across all sample sizes. The best estimator was then utilized to study and analyze the waiting time for Zanko Bank/University of Duhok.
A Review on Cloud Computing Elasticity
Authors: Tejas P. Patalia Hirenkumar R. Patel,
Published: December 2023

Cloud computing is an internet-based computing environment where computing resources (hardware, software) are managed, pooled, and provided on demand by the cloud service providers to cloud service users. Users may rent resources with fixed capacity by considering the application's peak workload, but the application workload may vary. The workload variation may depend on many situations like time of the day, day of the week, etc., So resources may be underutilized when the workload is lower than the peak workload and overutilized when the workload is higher than the peak workload so some strategies are required to manage resources that can add or remove resource capacity as per the variation in workload. For managing cloud resource capacity elasticity plays a vital role. Elasticity is the dynamic property of the cloud and is used for the provisioning and de-provisioning of cloud resources to map between workload and cloud resources. This study reviews the elasticity for “VM only”, “Container only” and “VM and containers together”.
Identification of Solanum melongena Linn. Accessions using the RAPD method: A case study
Authors: B.S. Jyothsna, Mrittika Das, Priya Deori
Published: December 2023

Brinjal (Solanum melongena L.) is an important solanaceous vegetable crop in many countries. It is a native of India and widely grown in Karnataka, West Bengal, Odisha, Gujarat, Madhya Pradesh, Chhattisgarh, Maharashtra, Andhra Pradesh, Haryana, Tamil Nadu, Uttar Pradesh, Jharkhand and Tamil Nadu.  Arka Navneet is a popular brinjal variety known for its high yield and adaptability to different climatic conditions. Although many molecular markers have been developed for it, not much data is available for the accession of Tamil Nadu. The present study aimed to identify different accessions of Kanyakumari district, Tamil Nadu, India through RAPD analysis.  A total of 15 accessions were collected from various locations and named B1 to B15. They were identified differently based on morphological and taxonomical characteristics. Molecular characterization was done by using 15 universal RAPD primers. Fifteen RAPD primers generated 1466 amplicons, of which 42 (2.86%) were monomorphic. OPL 15 could identify all fifteen accessions, so, it was used for phylogenetic analysis. The dendrogram showed a minimum distance between B3 and B9 followed by B5 and B10. Accession B11 was noted most distant from all other accessions. We conclude that RAPD technique is efficient for the characterization of Solanum melongena accession from Tamil Nadu, India. Even This technique was noted as efficient for phylogenetic identification and assessing diversity.
A Study on the Ramifications of Silicon as a Semiconductor Being Used Heavily In the Practice of Cosmetic Surgery
Authors: Gajanan C Upadhye Vinay H Singh,
Published: December 2023

This study has explained the features and importance of the usage of silicon as a semiconductor in cosmetic surgery and its impact on analyze the importance of the ramifications of silicon as a semiconductor being used heavily in the practice of cosmetic surgery and social services of securing the self-esteem of any individual. This includes a detailed literature review to represent the different matters related to the meaning of cosmetic surgery to give new confidence, uplift self-esteem, and gain a new look. This also gives importance to the personal and private impact of cosmetic surgery. This analyses the responsibilities of medical departments and the government to care about the need of the client who wants to have the surgery. The function of silicon that acts as a dressing of occlusive, holding the skin moisture and keeping bacteria out is the reason behind the heavy usage in cosmetic surgery. The inflammatory process that occurs in any incision may modify the time of healing. So, the end goal of giving a soft and smooth, and beautiful flat as much as possible can be done with the use of silicon.
The Role of Public Management in Dealing with Crises in the Public Sector in Israel
Authors: Jadallah Subh
Published: December 2023

Effective public management is the most critical prerequisite for an organization amid a crisis. By providing answers to the research questions, this study gave Israel's public managers the knowledge they needed to be effective crisis leaders. The study demonstrated how each essential leadership characteristic influences how effectively the public sector in Israel manages crises by employing the SEM statistical analysis method. This study builds on prior research on how to manage crises in the public sector and how to provide perceived effective public management leadership during a crisis. Although previous studies investigated collaboration in crisis management, they did not concentrate on leadership in crisis management in the public sector. This study is anticipated to shed further light on how to network and collaborative leadership theories are applied to crisis management in Israel's public sector.
Egg Yolk Basti – A Boon to Heal Accidental Burn Wound
Authors: Harish Daga, Varsha Chaudhary Dharmesh Chauhan, Hemant Toshikhane,
Published: December 2023

Burn injuries are frequent, horrifying, and life-threatening conditions that are still challenging to cure. Complications like scarring, keloid formation, and contractures are the main challenges for the treating surgeon. Burn injuries can occur from fire, moist heat, electrical source, chemical agents, radiations, etc. The Ayurvedic classics explain different treatment modalities for the management of burn wounds In this case study, A Case of 54 years old male patient came to OPD of Shalyatantra Department Parul institute of Ayurveda with complains of accidental burn injury, wound over right foot and burning pain over right leg. In past history we have found that patient burned from hot liquid before 15 days. He had taken contemporary medicines and iv fluids for Initial Management. Treatment protocol given for this patient is wound care, debridement and wound healing. Wound healing was promoted with the help of nutrient enemata in the form of egg yolk basti. Document supports that the absorptive capacity of the colonic mucosa could be used to provide partial nutrition who at present require central lines for intravenous feeding. This would avoid the many metabolic, infective, and technical complications associated with the parenteral route.
Econometric Modeling of the Investment Capacity of Industrial Enterprises in the Conditions of the Digital Economy
Authors: Alisherovna Mukhitdinova Kamola
Published: December 2023

As a result of the scientific research, the article proved that the most important factor positively influencing the change of investments in fixed capital is the growth of transportation, storage, information and communication services and foreign trade turnover. Also, it was concluded based on the Engel Grinder correlation cointegration test that the increase of investments causes the long-term growth of the production of industrial products through the SEM model.
A Novel Rel-DASA Model for Reliable Data Analysis in IoT Based Smart Agriculture
Authors: R.Chinnaiyan R.Divya,
Published: December 2023

Agriculture today depends largely on these technologies to supply food for human consumption because to their widespread usage. The Internet of Things bridges the gap by providing farmers with critical tools to predict crop growth, availability of moisture in the soil, soil status, and so on. We describe a comprehensive agricultural monitoring system for accurate crop growth management. This proposed research investigates how supplying farmers with real-time data such as weather forecasts through sensor integration might enhance decision-making. Internet of Things, Agricultural Sensors, Wi-Fi Module, Android App, and Raspberry Pi are all integrated.
Generalized Eccentricity k^th Power Sum Adjacency Energy of Graphs (EGE^k SA(G))
Authors: A.R. Agnes Porselvi M. Deva Saroja,  M. Mutharasi  ,
Published: December 2023

Let  be a finite, simple and undirected graph. For any integer , generalized eccentricity  power sum adjacency matrix of  is  matrix with its  entry as  and zero otherwise, where  is the eccentricity of  the vertex v of a graph G. In this paper, the new energy of graph          under the name as generalized eccentricity  power sum adjacency energy of   has been introduced. Generalized eccentricity  power sum adjacency energy   of some standard graphs and regular graphs obtained  by complete graph.

Let  be a finite, simple and undirected graph. For any integer , generalized eccentricity  power sum adjacency matrix of  is  matrix with its  entry as  and zero otherwise, where  is the eccentricity of  the vertex v of a graph G. In this paper, the new energy of graph          under the name as generalized eccentricity  power sum adjacency energy of   has been introduced. Generalized eccentricity  power sum adjacency energy   of some standard graphs and regular graphs obtained  by complete graph.
Investigative Studies on Thermal performance of Heatpipe with suspended Nanoparticles
Authors: Shivaprasad H Prasad CSMV, Venkataraja Mohan S D, Supritha R. M,
Published: December 2023

This paper presents an experimental investigation regarding the use of solid nanoparticles added to water as a working ﬂuid. Tests were made on a thermosyphon heat pipe.  The experiment was performed in order to measure the temperature distribution and compare the heat transfer rate of the thermosyphon heat pipe with nanoﬂuid and with DI-water. The iron oxide nanoparticles were obtained by the laser pyrolysis technique. The tested concentration level of nanoparticles is 0%, 2%, and 5.3%. Results show that the addition of 5.3% (by volume) of iron oxide nanoparticles in water presented improved thermal per-formance compared with the operation with DI-water.
Holistic Approach in the Management of Venous Ulcer: A Case Study
Authors: Aquib Hemant Toshikhane, Harish Daga,
Published: December 2023

Introduction: Venous ulcers (stasis ulcers, varicose ulcers) are wounds occurring due to inappropriate functioning of venous valves, usually of the legs. It is one of the most serious chronic venous insufficiency complications. When a venous valve gets damaged, it prevents the backflow of blood, which causes pressure in the veins that leads to hypertension and, in turn, venous ulcers.

Incidence: Between 1.5 and 3.0/1000 people have active leg ulcers. Prevalence increases with age to about 20/1000 people aged over 80 years.

Material and Methods: Varicose veins were defined as dilated, tortuous, subcutaneous veins usually seen in an upright posture. An unbiased consultant surgeon assessed any suspected chronic venous insufficiency on clinical examination.  The severity of varicose vein was assessed by clinical grading from CEAP (Clinical-Etiological-Anatomical-Pathophysiological) system in which there are six classes.

Discussion: Due to the lekahana effect of kshara is does srotoshodhana and hence increases the peripheral circulation and helps in woundbed healing by increasing the blood flow to the wound bed to improve the vitalityand metabolites supply to aid healing. the patient was given manjista which by virtue of its rakta sodhaka and praasdana property will increase the metabolites in the blood and helps in providing sufficient substrates in the wound bed for its healing.

Conclusion:Chronic venous ulcers that did not heal after a few months of multiple courses of antibiotics & and anti-inflammatory treatment healed within 20 days after Ayurveda treatment. This indicates that Ayurveda therapy is effective in healing chronic venous wounds. Non-reoccurrence of ulcer after a 3-month break from medicines indicates a complete reversal of venous level pathology. However, a more in-depth study with a larger sample size will help to develop treatment protocols in such cases.
Fracture Mechanics and Fatigue Analysis in Structural Engineering
Authors: Mahesh Kumar, Rahul Kumar Gupta, Vipin Kumar, Praveen Bhatt
Published: December 2023

Fatigue analysis is a critical component of structural engineering, focusing on the response of materials and structures to cyclic loading. This abstract provides a concise overview of its significance. Fatigue analysis is applied in the design and selection of materials to prevent unexpected failures, extend the life of structures, and reduce maintenance costs. It predicts fatigue life, ensuring the safe operation of structures enduring thousands of load cycles. Real-time structural health monitoring enhances safety by detecting fatigue-related damage, while insights from fatigue analysis inform the development of stronger materials. From bridge maintenance to automotive engineering, fatigue analysis underpins the reliability and safety of structures in diverse industries.
A review on Construction and Performance Investigation of Three-Phase Solar PV
Authors: Raman Kumar, Raushan Kumar, Amit Kumar
Published: December 2023

The levelized cost of electricity (LCOE) of solar photovoltaic (PV) systems is continuously declining in many countries, which is largely responsible for the current geometric increase in the global deployment of PV modules at utility-scale and residential roof-top systems. Other factors contributing to this trend are the modules' affordability, scalability, and long-term warranty. Furthermore, as the world's energy portfolio continues to transition towards cleaner energy technologies, PV adoption is anticipated to maintain this increasing trend. Nevertheless, during outdoor deployment, the PV modules are subjected to a broad variety of climatic conditions, regardless of the kind, material, and component technology. The modules are frequently exposed to intense chemical, photochemical, and thermo mechanical stress under these hostile environmental conditions. These circumstances, in addition to manufacturing flaws, significantly increase the ageing rate and faults of PV modules. This research looks at the application of Unified Power Quality Conditioners (UPQC) to reduce grid power quality issues and harmonics caused by non-linear loads. In this work, photovoltaic (PV) and battery energy storage systems (BESS) assist the UPQC. In most cases, the load receives its active power from the PV system. On the other hand, the BESS kicks in and supplies electricity if the PV is unable to do so, particularly during the longer-lasting voltage disruption. Due to its considerable dependence on the environment and instability, a hybrid PV-BESS system is more dependable than a solo PV-UPQC system.
A Study on Prediction for Crop Area, Production, and Yield Analys is Using Machine Learning Approaches
Authors: A. Murugan J. Karthikeyan,
Published: December 2023

Analyzing crop area, production, and yield typically involves data collection from farmers, the application of remote sensing technologies like satellite imagery, and on-site surveys. This gathered data is then used to assess crop performance, identify trends, and offer recommendations to improve agricultural practices.Data mining finds extensive application across diverse sectors such as business, healthcare, finance, marketing, and scientific research, enabling the extraction of knowledge and insights from data that may not be readily discernible using conventional approaches.This paper considers Crops Production between 2006 to 2011. The machine learning approaches which is used to analysis and predict the dataset usinglinear regression, multilayer perceptron, SMOreg, random forest, random tree, and REP tree. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.
MT-MRANBrainTumorNet: A Novel Brain Tumour and Severity Level Classification Framework using Meta-heuristic-assisted Transformer based Multiscale Residual Attention Network
Authors: R.Aishwarya, Sumathi Ganesan, Tks Rathish Babu
Published: December 2023

Brain tumor is generally structured by the subtle summation of anomalous cells because it categorized as a mass of tissue, and it is very crucial to categorize brain tumors from Magnetic Resonance Imaging (MRI) for diagnosis purpose. The investigation of brain tumors by humans is a routine process for detecting and classifying MRI brain tumors. Due to the low contrast of the images, poor boundaries and noises, the image classification techniques provide poor performance in medical images. Hence, a new classification model for detecting brain tumors is needed to solve the critical challenges in existing approaches. This decreases human intervention while making decisions about brain tumors. Therefore, a new brain tumor classification model is investigated to solve the challenges in existing approaches with deep learning. The proposed framework consists of two main different phases (i) classifying normal and abnormal images and (b) Segmentation and severity classification of abnormal images. The first step is comprised of pre-processing and classification. Firstly, the required images are gathered from the benchmark databases. These collected images are undergone pre-processing, which is done by filtering methods and Contrast Limited Adaptive Histogram Equalization (CLAHE). Subsequently, the pre-processed image is fed into the new classification model as a Transformer-based Multi-scale Residual Attention Network (TMRAN), where the normal and abnormal images are obtained. While in the second case, the classified abnormal image is considered as the input for the segmentation process. The segmented image is obtained by Convolutional Neural Network (CNN), Modified UNet and UNet, in turn the parameters in UNet is optimized by using the Modified Controlling Parameters-based African Vultures Optimization Algorithm (MCP-AVOA), termed as Modified UNet. At the final stage, these segmented images are applied as input to the novel TMRAN model, where the hyper parameters are tuned optimally using improved MCP-AVOA in order to acquire the optimal severity classified results. The performance is validated using diverse measures and compared over other conventional methodologies. Thus, the findings elucidate that the proposed model attains higher classification results.
A Study on the Rheology of Scc with the Addition of Fly Ash in Opc, Ppc and, Psc Cements with Different Mixing Times
Authors: M M Premchand, I V Ramana Reddy
Published: December 2023

In this paper, a study was performed on the rheological properties of self-compacting concrete by the addition of Supplementary Cementitious Material (SCM) as Fly Ash with various percentages of 15%, 20%, 25%, and 30% in the different types of cement OPC, PPC, and PSC. The mix designs of self-compacting concrete were done by two methods of constants: Water-Binder Ratio Constant and Weight and Volume of Aggregates Constant for varying percentages of addition of Fly Ash with a fixed cement content of 380 kg per cubic meter. Flow ability (Flow Table and V Funnel) and Passing Ability (L-Box and U-Box) tests were carried out on fresh concrete in accordance to EFNARC with three different mixing times 5(initial), 30, and 90 minutes were done to find the self-compactibility of designed concrete mixes. Varying dosages of Super Plasticizer (Auromix 450) were added to obtain the required flow ability and passing ability of SCC without bleeding of water and segregation of aggregates. Self-compactibility of the designed SCCs was obtained for 20%, 25%, and 30% addition of SCM in Water-Binder Ratio Constant Method and 15%, 20%, and 30% addition of SCM in Weight and Volume Constant Method.
Fear of predicament of the Afghans- A Transformation of life in Nadia Hashimi’s When The Moon is Low
Authors: Dr. S. Regin Sam
Published: December 2023

Nadia Hashimi is a pediatrician and an outstanding novelist. Her clear and lucid style achieved the clear manipulation of ideas and words gives a comprehensive picture of Afghan reality to a larger audience outside the country. In her novel When The Moon is Low she deals with the problems face by the Afghans in Taliban’s regimes and their migrating experience. This study focus on the crisis faces by the Afghans in the name of violence, suffering due to the incredible power of the Taliban regime. Due to their hazardous violence, many Afghans wish to flew from their country. It also exposes the realistic pictures of the Taliban regime in Kabul, which affects the Afghans.
Assessment of Face Image Quality using Machine Learning
Authors: Parul Choudhary, Phalguni Gupta, Pooja Pathak
Published: December 2023

Face recognition systems have gained significant attention in recent years due to their applications in various domains such as security, authentication and surveillance. However, the performance of these systems heavily relies on the quality of input face images. This paper presents a quality assessment technique for face images with the aim of enhancing the accuracy and reliability of face recognition systems. There are several methods used for Face Image Quality Assessment (FIQA), thus measuring the quality of images is likely to filter out poor-quality images, which can improve the performance of downstream tasks. Therefore, a novel FIQA framework is proposed to select the best facial image for better recognition. In our proposed methodology, firstly measures various image quality factors then apply fusion techniques to combine all quality factors for classifying the face quality. In addition, conducting a statistical test to check the level of confidence of proposed model. The technique is evaluated on the Color-Feret and KinectFace datasets. The correlation determine between the proposed system and human score is 0.95.
Developing Methodologies for Navigation using Multimedia to help the Visually Impared
Authors: Vinod Biradar, Karuna C. Gull
Published: December 2023

The objective of research into navigation assistance is to facilitate autonomous living for individuals who are visually impaired. Although numerous navigation aids employ contemporary technologies and methodologies, they do have certain drawbacks, including portability, object detection, convenience, and the need for extensive training. When developing a navigation aide for individuals with visual impairments, portability and user-friendliness without the need for extensive training are additional crucial factors. Certain navigation systems might fail to furnish precise details regarding the nature of hurdles that can be identified, a critical component for making well-informed decisions while in real-time travel. This research proposes the EfficientNet with entirely contorted Convolutional Neural Networks, for the development of a navigation assistance system in order to circumvent all of these challenges. The system's EfficientNet is a portable object detection model that permits the execution of multiple operations, including uniform expansion, resolution, depth, and breadth. The completely convolutional CNN, a pre-trained model, finds extensive application in object detection and computer vision tasks. The evaluation of the proposed system is conducted using the metrics of Distance, Mean Average Precision (mAP), and Accuracy.
Adjacent Line Graph in Ferrers Graph
Authors: R. Chenthil ThangaBama, S. Sujitha
Published: December 2023

A simple graph
Utilization Of Kota Stone Slurry Powder and Recycled Aggregate for Rigid Pavement
Authors: Aditya Jakhar, Mahendra Chaudhary
Published: December 2023

This research paper highlights the use of waste materials such as recycled coarse aggregate (RCA) and stone slurry powder (SSP) in the production of concrete, which offers significant environmental and economic benefits. The aim of this study was to investigate the impact of substituting cement with SSP on the consistency, initial and final setting time, and strength properties of concrete, as well as replacing natural coarse aggregate (NCA) with RCA and cement with SSP on the fresh and hardened properties of concrete. The experimental results showed that the substitution of cement with SSP and NCA with RCA improved the compressive strength, split tensile strength, and flexural strength of the concrete. This research contributes to sustainable construction practices by providing an alternative to depleting natural resources and reducing waste generation from stone-cutting activities. The findings of this study suggest that using SSP and RCA in concrete production has the potential to reduce the environmental impact of the construction industry while also providing a cost-effective and sustainable alternative to traditional concrete production methods. However, the use of SSP resulted in a decrease in slump, which may require more water and absorb water from the mix, leading to reduced workability. Therefore, further research is needed to investigate the long-term durability and sustainability of concrete made with SSP and RCA. In conclusion, this study demonstrates the potential for using SSP and RCA as alternative materials in concrete production to achieve stronger, more sustainable, and cost-effective construction.
Container Security Intelligence: Leveraging Machine Learning for Anomaly Detection in Containerized Applications
Authors: V. Mahavaishnavi, Dr. R. Saminathan, R. Prithviraj
Published: December 2023

This research explores the application of Machine Learning (ML) techniques for anomaly detection in containerized applications. The proposed approach utilizes by applying machine learning techniques for anomaly detection in containerized applications. The proposed methodology integrates container runtime metrics, system call analysis, and network traffic patterns to build a predictive model capable of identifying abnormal behavior within containers. To evaluate the effectiveness of this approach, extensive experiments were conducted using real-world containerized applications and datasets. The experiment focused on assessing the accuracy of anomaly detection and the computational efficiency of the proposed model. The results demonstrated that this approach achieved a remarkable accuracy rate of 95% in detecting anomalies, while maintaining high computational efficiency, with minimal overhead on the container runtime environment. This research contributes valuable insights into bolstering the security of containerized applications, offering practical solutions for real-world deployment scenarios. The research also evaluates the accuracy and efficiency of the proposed approach and discusses its potential impact on enhancing container security in real-world scenarios.
Artificial Intelligence based Covid19 detection using CTScan and Chest X-Ray Images: Machine Learning and Deep Learning techniques
Authors: G. G. Rajput, Ashvini Alashetty
Published: December 2023

COVID-19, also known as the coronavirus disease 2019 is a highly contagious respiratory illness caused by SARS-COV-2 virus which spread form china globally resulting in pandemic. With advances in diagnostic tools, radiologic imaging is widely used for COVID-19 pneumonia diagnosis other than usual clinical and laboratory testing. In this paper, several deep learning and machine learning enhanced techniques are applied to X-Ray and CT-Scan medical images for the detection of covid-19 and also a clinical data for the prediction of the covid19. The images are preprocessed and trained using U-Net model, a popular architecture for image segmentation tasks. The accuracy and F1-score were found to be above 98% in the diagnosis of COVID-19 using CT-scan images. Further, transfer learning techniques were applied to overcome the insufficient data and to improve the training time. The binary and multi-class classification of X-ray images tasks were performed by utilizing enhanced CNN deep transfer learning architecture. An accuracy of 99% was achieved by enhanced CNN in the detection of X-ray images from COVID-19 and pneumonia.Further,using clinical dataset we compared the performance of supervised machine learning algorithms: logistic regression, random forest classifier, and XGBoost classifier. These algorithms were trained and evaluated on the preprocessed and feature-selected dataset to predict COVID-19 cases. The results showed that XGBoost classifier outperformed logistic regression and random forest classifier in predicting COVID-19 cases based on symptoms, age, gender, and test indications.
A Noval Approach on Picture Fuzzy Graceful Labeling in Fluid flows
Authors: N. Sujatha
Published: December 2023

Graph labeling is one of the best network models in the study of fluid dynamics in the flow of particles. But the said labeling is not provided the better results to represent in any of network model having neutral membership degree in case of ambiguity. To overcome the above mentioned problem ,Picture fuzzy graph labeling is playing a major role in  the framing of network model in fluid dynamics in case of neutrality degree which  have been represented by a picture fuzzy graph.

  In this paper, the researcher have shown picture fuzzy graph labeling on star graph which is used to represent the flow of particles in fluid dynamics. Also they have extended the concepts towards the existence of picture fuzzy graceful labeling and picture fuzzy ant magic labeling in fuzzy star graph  which will be helpful to analyze the properties of flow of particles in the fluid flow.
Trajectory Controllability of Artificial Satellite Under the Effect of Oblateness of Earth
Authors: Jaita Sharma, B. S. Ratanpal, Shivam Munshi, Vishant Shah
Published: December 2023

This article explores the trajectory controllability of artificial satellites, taking into account the gravitational force of Earth and the force resulting from Earth’s oblateness. Controllability in this context is investigated by transforming the equations of motion into a cylindrical coordinate system and leveraging the concept of Lipschitz continuity within the nonlinear terms, coupled with the application of functional analysis.
21 Level Asymmetric Inverter without Inversion Circuit with Reduced Switch Count for BLDC Motor Drive
Authors: Amit B. Jadhav, Dr. Mahadev S. Patil
Published: December 2023

Design for asymmetrical Multi-Level Inverter without H-bridge and with minimal number of switch is presented in this paper. The proposed circuit is capable of generating 21 levels with only 10 switches and 3 unequal D.C. voltage sources. Operation of the proposed circuit is discussed in detail in this paper and there by framing the switching sequence for the proposed circuit. Simulation of the presented inverter is done for loads like resistive, reactive and induction motor. From the simulation results, inverter performance is analyzed in terms of THD.
Enhancing Healthcare Delivery: AI Applications In Remote Patient Monitoring
Authors: Dr. Rahul Pulimamidi, Prabu Ravichandran
Published: December 2023

Research in Remote Patient Monitoring Systems (RPMS) is highly valued since it directly impacts people's health and wellbeing. The number of cases being treated with RPMS has increased since the beginning of the pandemic. However, there are still challenges to be met, such as mobility, heterogeneous networks, standardisation of RPMSs, automation, and Quality of Service (QoS), despite the rise in their use. The incorporation of artificial intelligence (AI) into remote patient monitoring (RPM) is causing a revolution in the healthcare industry by improving the quality of care provided to patients, boosting operational effectiveness, and making it possible to intervene earlier. RPM makes use of technology to monitor the health of patients in a remote location, hence minimising the frequency of in-person visits that are required. The addition of AI algorithms further enhances the potential of RPM by analysing huge volumes of patient data to identify patterns, abnormalities, and potential problems. In this study, we will investigate the most potentially game-changing applications of AI in remote patient monitoring in 2023 and beyond.
Exploring Resilience in Social Work Trainees
Authors: Ms. Divya J, Dr. K. Sathyamurthi
Published: December 2023

Social work students are at risk for burnout and stress due to the demanding nature of their education and the challenging populations they serve. Resilience is the ability to adapt and bounce back from adversity, and it is essential for social work students to develop in order to succeed in their studies and future careers. Social work education often involves long hours of coursework and fieldwork. This paper through a descriptive research design discusses the resilience among social work students and the role of social work education in fostering the same. The under graduate trainees were chosen for the study and the data was collected using Connor-Davidson Resilience Scale. The scale comprises 25 items reflecting five general factors: personal competence, tolerance of negative affect, acceptance of change, control and spiritual influences. Psycholo the individual difference factor was identified that have an effect on resilience is understood through examining their psychological needs satisfaction. Research revealed that social work students are resilient and they are able to cope with stress and adversity. There is a significant negative relationship found between resilience and psychological distress The various interventions based on the findings to promote resilience among social work trainees is discussed in the paper.
Understanding The Mechanisms Of Catalytic Reactions On K10 Clay: A Comprehensive Study
Authors: Anjali Dhiman, Gurmeet Kaur
Published: December 2023

This systematic review delves into the mechanisms of catalytic reactions on K10 clay, synthesizing a wealth of experimental and computational insights to offer a comprehensive understanding. The catalytic versatility of K10 clay is dissected across diverse reactions, including esterification, aldol condensation, methane activation, biomass conversion, redox reactions, ester hydrolysis, and tandem reactions. Thematic analysis unravels the broad spectrum of applications for K10 clay, emphasizing the need for a nuanced understanding of reaction-specific mechanisms.

A critical comparative analysis of experimental and computational approaches showcases their complementary roles in unraveling the intricacies of K10 clay catalysis. The quality assessment process underscores the methodological rigor of individual studies, guiding the interpretation of findings. Practical implications for environmental remediation, fine chemical synthesis, and industrial processes are discussed, accompanied by identified gaps that delineate future research directions.

This systematic review not only advances the understanding of catalytic reactions on K10 clay but also serves as a template for future explorations in the dynamic realm of catalytic materials. With a foundation built on rigorous analysis and thoughtful integration of diverse findings, this review aims to inspire continued innovation and investigation in the field of catalysis research.
Evolution of the Scholarly Landscape in Teacher Leadership Research: A Bibliometric Examination of Literature from 2014 To 2023
Authors: Ahmad Zabidi Abd Razak, Mazzlida Mat Deli Suhaibah Mukhtar,
Published: December 2023

Over the past decade, there has been a notable surge in the attention directed toward teacher leadership, where it has assumed a pivotal role in initiatives aimed at reforming and enhancing the realm of education. This bibliometrics analysis delves into the scholarly terrain of teacher leadership research available within the Scopus database, spanning the years 2014 to 2023. The primary objective of this study is to discern emerging trends, identify existing research lacunae, and outline potential implications for future inquiry and educational practice in this dynamic field. This comprehensive review consolidates insights derived from a diverse array of studies that scrutinize multiple facets of teacher leadership. It investigates the impact of various leadership styles on teacher morale, student achievement, and overall school improvement. Moreover, this analysis probes into the role of educators in nurturing positive school climates, orchestrating change, and fostering collaborative relationships within the educational community. The data collection process commenced by retrieving 1048 articles using the keyword "teacher leadership." To rigorously analyze this wealth of information, a variety of tools were employed, including Microsoft Excel for frequency analysis, data visualization, and Harzing's Publish or Perish for citation metrics and in-depth analysis. By dissecting the existing body of literature, this paper seeks to enhance our comprehension of the multifaceted dimensions of teacher leadership, offering valuable insights to both researchers and practitioners. The study encompasses an array of bibliometric indicators, encompassing language, subject area, publication year-based research trends, prominent countries of contribution, influential institutions, active source titles, citation patterns, authorship characteristics, and keyword distributions. The results underscore a significant upswing in teacher leadership literature from 2014 to 2023, with the United States emerging as the primary contributor to educational leadership research, closely followed by Turkey. In conclusion, this bibliometrics analysis provides a comprehensive panorama of teacher leadership research spanning the years 2014 to 2023. The discerned trends and identified research gaps present valuable insights for researchers, educators, and policymakers, offering a platform for the continued advancement of teacher leadership as a potent instrument for enhancing educational outcomes and nurturing professional development in the 21st century.
Effect of Chemical reaction on hyperbolic tangent Williamson-Nanofluid flow past an exponentially porous stretching sheet: MHD and slip effects
Authors: R. S. DurgaRao, R. Vijayakumar, V. Vasudeva Murthy
Published: December 2023

In this research investigation, Runge-Kutta method solutions of dual-dimensional, steady, viscous, incompressible, chemically reacting, electrically conducting, hyperbolic tangent Williamson- flow of nanofluid past a uni-directional an while a magnetic field is present and several slip effects, an exponentially stretched sheet packed with porous material is studied numerically. Thermal radiation, Chemical reaction, the flow regulatingequations of boundary layer incorporates the impacts of thermophoresis and Brownian motion.SuitableTransformations of similarity are employed. for the main differential equations in partial to become differential equations inordinary that are nonlinear. The resultant governing equations are resolved by the application of a well utilized method in numericallycalled as the Runge-Kutta method and the shooting technique.Figures and numerical tables are used to show the representation of certain physical parameters on the flow model.. The pace of convergence for nonlinear differential systems is astonishing, according to an excellent comparison with the body of existing research, which also confirms a high degree of correctness. These tests are pertinent to the fields of plastic films, crystal growth, paper manufacture, and metal sheet cooling.
Classifying Face Features For Better Recognition And Detection
Authors: Avishek Dey, Dr. Satish Chander
Published: December 2023

Recognition of facial features is in high demand currently because of its value in individual proof of identity, wherein a short input picture or a glance can assist in recognizing an individual using a database of images. This type of identity isn't commonly used in India, although it is necessary. Fingerprint identity systems ought to utilise facial recognition to render the system frictionless and more secure. As a result, these researchers attempted to assess the suitability of two separate face recognition computational methods, facet, which came, and facial features, in this work. The traits are retrieved and then prepared before being turned to a list and then compared with the area of interest to determine the individual in question [5]. The characteristics considered were the Euclidean gap between the pupils, the contours of the nostrils, plus the lip-to-lip length. This initiative will assist in guarding factories wherein trespassing is possible, identifying people in crowds, tracing those who have vanished, and keeping tabs upon some disruptive elements of the community. It's used for a wide range of applications, including legislative bodies and vaults at financial institutions. At the exact same period of time the identification and detection percentages with the three distinct criteria for lateral recognition of faces, opacity identification, and face dramatic emotion are contrasted using the contrast test, and the adequacy for both methods is improved [6]. These results demonstrate the fact that every instance can be assessed individually. Conventional detectors may "perceive" an object or message, turn it into an electrical signal, store it, and then utilise a conversion circuitry to turn the electricity into an amount or another observable screen format.
Evaluation Of The Effectiveness Of Use Of A Complex Preparation Containing Glycine, Zinc And Curcumin As A Stress Protective Agent
Authors: Kosimova Dilnoza, Saidov Saidamir, Tursunkhodjaeva Feruza
Published: December 2023

We have developed a substance for pharmacocorrection of stress, conventionally called Kurglycin, with the ratio of components, %: zinc - 12.04, curcumin - 67.68, glycine - 13.65, water - 6.63. 14-day and 21-day oral administration of Kurglycin in the form of a 3% suspension at a dose of 5 ml/kg orally had a positive effect on the course of the stress reaction in the adrenal cortex, stabilizing the secretory activity of glucocorticoid-producing cells and reducing cortisol levels to levels close to control, in both studied groups of animals. The study of changes in the biochemical parameters of rats with low level of emotion (LLE) and high level of emotion (HLE) under the influence of Kurglycine showed a certain difference in the susceptibility of these groups both to the action of the drug.
Predicting Groundwater Level using Temporal Attention Enhanced Graph Neural Network
Authors: Sowndharya M, Duraisamy S, Pavithra M
Published: December 2023

A revolutionary approach for groundwater management is essential. To predicting the water levels using for integrating IoT sensors, cloud computing, and advanced data analysis methods. IoT sensors are employed for real-time measurement of groundwater levels, creating a robust dataset. The paper focuses on predicting future groundwater levels, a crucial aspect of sustainable resource management. To enhance predictive accuracy, a preprocessing algorithm, such as Min-Max normalization, is introduced to clean and normalize the collected data, ensuring its reliability. Additionally, a feature extraction algorithm, such as Principal Component Analysis (PCA), is implemented to identify relevant patterns and trends within the dataset, enhancing the efficiency of subsequent analysis. A novel classification algorithm, Spatial-Temporal Graph Convolutional Network, is introduced, enabling the identification of potential groundwater recharge areas. This classification algorithm leverages historical data and extracted features to categorize regions based on their suitability for groundwater renewal. Finally, this research uses a Temporal attention-enhanced graph Neural Network machine learning algorithm to predict groundwater levels in the next few years. This algorithm utilizes the preprocessed data and extracted features, identifying intricate patterns and trends in historical data to generate precise predictions for groundwater levels in the upcoming years.
A Comprehensive Investigation into the Material Removal Capability of Powder Mixed Wire Electric Discharge Machining
Authors: Prajapati Niravkumar Jayantibhai, Dr. Bharat Khatri
Published: December 2023

The powder mixed electric discharge machining (PMEDM) has demonstrated promising results in the machining of difficult-to-cut materials including metal matrix composites (MMCs). Adding powder particles to the dielectric fluid used in electric discharge machining (EDM) improves material removal rate (MRR) and surface quality of the machined item. This study presents a thorough overview of research works on PMEDM for the machining of MMCs, concentrating on the impacts of different process parameters on the MRR and surface quality, such as powder concentration, pulse on-time, and Pulse off-time. The study also notes the process's obstacles and limits, as well as opportunities for further research.

Previous research has shown that PMEDM can greatly increase the MRR and surface quality of machined MMCs when compared to regular EDM. The addition of powder particles to the dielectric fluid has been found to improve machining zone cleansing and cooling, decreasing tool wear and enhancing machining precision.
The Existential Self: A Journey through the Fictional World of Peter Handke’s The Afternoon of a Writer
Authors: Neethu .A. Nair, Dr. N.U. Lekshmi
Published: December 2023

The paper The Existential Self: A Journey through the Fictional World of Peter Handke’s The Afternoon of a Writer attempts to identify the existential precariousness of a writer and his search of self identity. The feeling of bereftness and barrenness in existence makes man alienated from himself, fellow men, nature, cosmos and God. Though human beings are free to choose and take decisions in their day-to-day lives, they are chained not only by the society but also by the abrupt situations of freedom, fear, dread, despair, anxiety, indecisiveness and alienation. The paper attempts to reveal the disconnection of the protagonist from the external world through many fleeting moments that reflect his existential struggle.
A Study of E-Authentication Systems Based on QR Codes Through Analysis
Authors: Priya Pankaj Kumar, Om Prakash, Sudhir Kumar
Published: December 2023

In order to improve security and user experience, this article suggests an authentication system that combines Quick Response (QR) code technology with One-Time Password (OTP) technology. Each time an authentication attempt is made, the system generates a unique QR code and an OTP that must be scanned with a mobile device in order to complete the authentication process. Encrypted data, validated by the server, including the user's identity and the OTP included in the QR code. An effective, user-friendly, and secure way of user identification is provided by the suggested system, which is essential in the modern digital world. An effective and safe way to authenticate consumers in online transactions is through an e-authentication system that makes use of OTP and QR code technologies. This method offers a straightforward two-factor authentication technique that effectively blocks illegal access by combining the advantages of OTP and QR code technology.