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Violence Prevention Climate and Turnover Intention: Mediating Role of Spirit at Work and Emotional Exhaustion
Workplace violence is a costly organizational problem. Violence prevention, incorporating employee perspectives on safe working policies, is crucial. A safe environment can enhance spirit at work, reducing burnout and turnover intention. This study investigates the relationship between violence prevention climate, emotional exhaustion, spirit at work, and turnover intention. Standardized tools were administered to 146 IT professionals aged 30-40 years. Results showed violence prevention climate positively correlated with spirit at work (r = 0.39; p < 0.001) and negatively with emotional exhaustion (r = -0.40; p < 0.001) and turnover intention (r = -0.35; p < 0.001). Emotional exhaustion mediated the relationship between violence prevention climate and turnover intention (b = -0.23; p < 0.001), while spirit at work did not show mediation. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
Symbolism, Ritual, and Continuity: A Socio-Cultural Interpretation of Kumaoni Folk Art Aipan
The chapter aims to explore Aipan, a traditional floor art form of the Kumaoni people in Uttarakhand. It examines Aipan as a cultural practice that encompasses symbolism, rituals, gender roles, and oral traditions. The chapter uses insights from folklore studies, anthropology, and cultural theory to study the visual language and social-religious importance of Aipan motifs. It will also examine how these motifs, which are rooted in Brahmanical tradition but maintained through folk practices, represent personal and community identities. Aipan is not just an art form; it is a culturally meaningful practice that conveys cosmological, spiritual, and social stories. In a time when tradition is both being revived and diluted in the modern market, the chapter will also discuss how Aipan is changing to fit todays consumer culture thus addressing important questions about preservation, change, and authenticity. 2026, IGI Global Scientific Publishing. -
Violence Prevention Climate and Turnover Intention: Mediating Role of Spirit at Work and Emotional Exhaustion
Workplace violence is a costly organizational problem. Violence prevention, incorporating employee perspectives on safe working policies, is crucial. A safe environment can enhance spirit at work, reducing burnout and turnover intention. This study investigates the relationship between violence prevention climate, emotional exhaustion, spirit at work, and turnover intention. Standardized tools were administered to 146 IT professionals aged 30-40 years. Results showed violence prevention climate positively correlated with spirit at work (r = 0.39; p < 0.001) and negatively with emotional exhaustion (r = -0.40; p < 0.001) and turnover intention (r = -0.35; p < 0.001). Emotional exhaustion mediated the relationship between violence prevention climate and turnover intention (b = -0.23; p < 0.001), while spirit at work did not show mediation. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
IoT-Integrated CNN Deep Learning for Automated Breast Cancer Detection and Diagnosis
Breast cancer continues to be a primary cause of death in women, requiring prompt and accurate diagnosis to enhance treatment results. Traditional diagnostic techniques depend on manual assessment, which leads to possible misclassification, significant inter-observer variability, and delays in decision-making. Current deep learning models, including CNNs, frequently experience feature loss, gradient declining and restricted adaptability to real-time data. To overcome these restrictions, we present a hybrid framework combining CNN and ResNet that merges deep learning-based feature extraction with real-time data collecting from IoT devices. The proposed approach utilises CNNs for preliminary feature extraction, ResNet for hierarchical learning with residual connections, and IoT for real-time patient monitoring and automatic notifications. The dataset undergoes preprocessing through normalisation, augmentation, and histogram equalisation to improve image quality and learning efficacy. The model is trained with cross-entropy loss and the Adam optimiser, guaranteeing stability and excellent performance. The evaluation results indicate a substantial enhancement compared to baseline models, with an accuracy of 97, an F1-score of 95.3, and a recall rate of 96.4%, exceeding traditional deep learning (90 accuracy) and CNN-based models (80% accuracy). The suggested model similarly minimises mistakes, with RMSE and MSE values declining to 1.2 and 1.6, respectively, signifying reduced misclassification rates. The inclusion of IoT facilitates instantaneous data transmission with little latency, hence improving clinical decision-making and minimising diagnostic delays. This advanced system facilitates automated and precise breast cancer detection, providing an innovative method for early diagnosis, optimised treatment planning, and improved patient outcomes, while ensuring data privacy and security through encryption and commitment to healthcare regulations. 2026 Yamini Kalva, R. Ganesh Babu, Sindhu V, S. Gokul Pran, Garaga Srilakshmi, Kavitha C T, Sathish Kumar Shanmugam and V. Bhoopathy. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Stakeholders' expressions of tech layoffs: A text mining analysis on the "Balance of Arguments"
The volume and nature of work undertaken by the tech employees is humongous, tech employees are known to manage those high tides because of attractive salary packages, perks, and other incentive options, which turn out to be a catastrophic collapse when such layoffs are levied, giving little or no room for a quick transfer to another job from the viewpoint of the affected employees. User-generated unstructured data content in the forms of either tweets, reviews, or comments from around seven major social media platforms were collected to understand the various expressions and discussions linked to layoffs. The collected data is further segregated into employee expressions, social media reviewers, news critiques views, etc., and their perspectives were further analysed. The overall analysis of sentiments on various stakeholders are formulated using Python (Juypter Notebook) package. The authors attempt to model out the viewpoints of various expressors and suggests various measures to be taken by the tech majors to better handle the phenomenon of layoffs. 2023, IGI Global. All rights reserved. -
Enhancement of Agriculture Feeder Performance by Optimal Sizing and Placing of Solar PV Tree through AEO-Based Optimization Technique
Electrical demand, which makes up a large share of the overall power market, agriculture at the top of the list of priorities. To provide end users with a dependable and high-quality supply via various feeders and renewable energy sources, distribution generations are now being developed. In recent years, solar PV systems have been used to meet the demands of numerous applications, including boosting the efficiency of distribution networks. This paper presents the system with effect ive optimization method like Artificial Eco-System based Optimization Technique for identification of the best location to install distribution generation and the optimum size to minimize feeder losses. To meet service expectations, the integration of a solar PV system is swapped out for a solar tree in this suggested work. A 28-bus Indian agriculture feeder is considered for better understanding the proposed algorithm. MATLAB software is used for implementing the proposed optimization technique and CREO-2.0 is used for designing the 3-dimensional solar PV tree. 2023 by the Kamal Kumar U and Varaprasad Janamala. -
Artificial Ecosystem-Based Optimization for Optimal Location and Sizing of Solar Photovoltaic Distribution Generation in Agriculture Feeders
In this paper, an efficient nature-inspired meta-heuristic algorithm called artificial ecosystem-based optimization (AEO) is proposed for solving optimal locations and sizes of solar photovoltaic (SPV) systems problem in radial distribution system (RDS) towards minimization of grid dependency and greenhouse gas (GHG) emission. Considering loss minimization as main objective function, the location and size of solar photovoltaic systems (SPV) are optimized using AEO algorithm. The results on Indian practical 22-bus agriculture feeder and 28-bus rural feeders are highlighted the need of optimally distributed SPV systems for maintaining minimal grid dependency and reduced GHG emission from conventional energy (CE) sources. Moreover, the results of AEO have been compared with different heuristic approaches and highlighted its superiority in terms of convergence characteristics and redundancy features in solving the complex, nonlinear, multi-variable optimization problems in real time. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Solar PV Tree Designed Smart Irrigation to Survive the Agriculture in Effective Methodology
The global economy benefits significantly from agriculture. However, there are significant issues and difficulties in the irrigation sector as a result of a significant regional imbalance in power supply, water availability, rainfall, and adoption of technology. The most economical approach to supporting agriculture in the modern day is through irrigation powered by renewable energy. Productivity is impacted by environmental issues, defective irrigation systems, and unknowable soil moisture content in agricultural fields. Traditional watering systems might lose up to 50% of the water used due to ineffective irrigation, evaporation, and overwatering. As a result, the proposed study will modify solar tree-based smart irrigation systems that use the most recent sensors for real-Time or old data to influence watering flows and change watering schedules to enhance the system efficiency. One application of a wireless sensor network is proposed for low-cost wireless controlled irrigation and real-Time monitoring of soil water levels using Arduino controllers. Data is gathered for drip irrigation control using wireless acquisition stations powered by renewable energy, which lowers the risk of electrocution and boosts output. 2022 IEEE. -
A Multi Objective Artificial Eco-System Based Optimization Technique Integrating Solar Photovoltaic System In Distribution Network
Agricultural sector contributes 6.4% of total economic generation across the world. Notably, the utilization of technology to improve the yield and economy is rapidly increasing. To provide continuous supply to the residential customers, the agricultural feeder grid-dependency has to be integrated with Solar Photo Voltaic (SPV) systems. In this paper, an Artificial Eco-System based Optimization (AEO) algorithm is proposed for simultaneously identifying the locations and quantifying the sizes of SPV systems. A practical distribution system feeder 'Racheruvu 11kV agricultural feeder' Andhra Pradesh, India is considered for simulation purpose and the performance is compared with the standard IEEE-33 radial distribution system. 2022 IEEE. -
Comparative Analysis of Maize Leaf Disease Detection using Convolutional Neural Networks
Worldwide, maize is a significant cereal crop for crop productivity, identifying diseases in the plant's leaves is essential to raise a good crop. Deep learning methods that have been used in recent years to precisely identify and categorize these serious diseases, offering a non-destructive and effective way to find maize leaf ailments. In order to detect maize leaf disease, this paper suggests using three well-liked deep learning models: VGG16, Inception V3, and EfficientNet. The models were trained and assessed using a datasets of 4000 images of three distinct maize leaf diseases and a healthy class. All three models had high accuracy rates, according to the results, though EfficientNet outperformed the other two models. The suggested method can detect and track diseases in maize crops with high accuracy and can be applied practically. It can accurately classify various diseases. The study also demonstrates that deep learning models can offer a trustworthy and effective solution for detecting crop diseases, which can aid in lowering crop losses, raising crop yields, and enhancing food security. 2023 IEEE. -
TSM: A Cloud Computing Task Scheduling Model
Cloud offers online-based runtime computing services through virtualized resources, ensuring scalability and efficient resource utilization on demand. Resource allocation in the dynamic cloud environment poses challenges for providers due to fluctuating user demand and resource availability. Cloud service providers must dynamically and economically allocate substantial resources among dispersed users worldwide. Users, in turn, expect reliable and cost-effective computing services, requiring the establishment of Service Level Agreements (SLAs). Resource distribution uncertainty arises in view of the dynamicity of the cloud, where VMs, memory capacity requirement, processing power, and networking are allocated to user applications using virtualization technology. Resource allocation strategies must address issues such as insufficient provisioning, scarcity, competition, resources fragmentation. CPU scheduler plays a crucial role in task completion, by selecting job from queue considering specific requirements. The Task Scheduling Model (TSM) algorithm improves scheduling by considering expected execution time, standard deviation, and resource completion time, aiming to address resource imbalances and task waiting times. The research discusses previous work, presents experimental findings, describes the experimental setup and results, and concludes with future research directions. 2023 IEEE. -
Evaluating the usability of mhealth applications on type 2 diabetes mellitus using various mcdm models
The recent developments in the IT world have brought several changes in the medical industry. This research work focuses on few mHealth applications that work on the management of type 2 diabetes mellitus (T2DM) by the patients on their own. Looking into the present doctor-to?patient ratio in our country (1:1700 as per a Times of India report in 2021), it is very essential to develop self?management mHealth applications. Thus, there is a need to ensure simple and user-friendly mHealth applications to improve customer satisfaction. The goal of this study is to assess and appraise the usability and effectiveness of existing T2DM?focused mHealth applications. TOP? SIS, VIKOR, and PROMETHEE II are three multi?criteria decision?making (MCDM) approaches considered in the proposed work for the evaluation of the usability of five existing T2DM mHealth applications, which include Glucose Buddy, mySugr, Diabetes: M, Blood Glucose Tracker, and OneTouch Reveal. The methodology used in the research work is a questionnaire?based evaluation that focuses on certain attributes and sub?attributes, identified based on the features of mHealth applications. CRITIC methodology is used for obtaining the attribute weights, which give the pri-ority of the attributes. The resulting analysis signifies our proposed research by ranking the mHealth applications based on usability and customer satisfaction. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Sentiment Analysis on Amazon Product Review
Users throughout the world may now access massive amounts of data thanks to the internet and social media platforms. [5] In every facet of human existence, electronic commerce (e-commerce) plays a crucial role. E-commerce is a marketing approach that enables businesses and consumers to buy and sell things via the internet. When buyers look for product information and compare alternatives online, they generally have access to dozens or hundreds of product reviews from alternative shoppers. Machine learning is the most appropriate approach to training a neural network in today's age of practical artificial intelligence. So implementing a model to polarize those reviews and learn from them would make passing hundreds of comments a lot easier. [24] The interpretation will be a very basic product with positive, neutral, and negative polarization. The product is checked. This study suggests a sentiment evaluation model for shopper reviews based on the object and emotive word mining for emotional level analysis using machine learning approaches. 2022 IEEE. -
Detection of Lung Cancer with a Deep Learning Hybrid Classifier
This article presents a deep learning framework combining a convolutional neural network (CNN) and a support vector machine (SVM) for lung cancer diagnosis. The model uses data divided into six groups: 250 images in the training set and 150 images in the test set. The work includes preliminary data and development using the Keras image data generator, VGG-16 architecture, high-level rules, and SVM classifier training with labels and vectors. The model achieves 90% accuracy with 85% selection impact and 75% cross-validation flexibility using VGG-16 and SVM hybrid classifier. This study finally revealed the classification of the model by multi-class ROC curve analysis and confusion matrix. 2024 IEEE. -
Quasi Z-Source Inverter with Simple Boost and Maximum Boost Pulse Width Modulation Techniques for PV Grid Connection
The voltage-fed quasi Z-source inverter (qZSI) is emerged as a promising solution for photovoltaic (PV) applications. This paper proposes a novel high-gain partition input union output dual impedance quasi Z-source inverter (PUDL-qZSI) for PV grid-connected system. This advanced inverter design achieves exceptionally low shoot-through duty ratios and high modulation index, resulting in a superior output current with reduced total harmonic distortion (THD). To modulate three-phase qZSIs and other equivalent topologies, a variety of modulation schemes may be used, some of which involve two extra reference signals to generate shoot-through state. The simulation is carried out on the MATLAB/Simulink environment with PV-based grid-connected PUDL-qZSI to measure the harmonic distortion and power measurement. The proposed inverter is subjected to two different pulse width modulation (PWM) analysis are simulated and compared to validate the proposed system. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Attitude of Parents Towards Various Behavior Management Techniques Utilized in Pediatric Dental Treatments
Dental experts are trusted to apply the knowledge and abilities they have acquired during their dental education to the diagnosis and effective treatment of any dental illness. When it comes to pediatric patients, however, the dentist's responsibility is different. However, without the right behavior management method (BMT), therapy outcomes would not be effective. Sometimes young children behave disruptively during dental visits, which makes it easier or harder for the dentist to perform dental work. Nonetheless, before being applied to children, behavior management strategies need the parents' acceptance and consent. This review's objective is to evaluate the dentists' use of effective behavior modification techniques (BMT) as well as the parents' attitudes regarding these techniques. RJPT All right reserved. -
Synthesis and Characterization of WO3 Nanostructures by the Solvothermal Method for Electrochromic Applications
In this study, a tungsten trioxide (WO3) thin film was deposited by direct current (DC) sputtering onto a fluorine-doped tin oxide (FTO) substrate as the seed layer at an oxygen partial pressure of 8 10?4mbar. A simple solvothermal method involving tungsten hexacarbonyl (W(CO)6), ethanol (C2H5OH), and hydrochloric acid (HCl) was used to synthesize vertically stacked nanoscale WO3 hierarchical structures on WO3 seed-layered FTO. After the deposition process, the FTO samples with nanostructures were subjected to annealing in air at 400C for 4 h. After annealing, the surface morphology, structural characteristics, and optical and electrochromic properties of the grown nanostructures were investigated using scanning electron microscopy (SEM), x-ray diffraction (XRD), Raman spectroscopy, UVvisible spectroscopy, and electrochemical analysis. From the XRD analysis, all the diffraction patterns were ascribed to a monoclinic phase. The SEM analysis showed that films grown with 5?L HCl had a nanoflower structure compared to the films grown with 0?L HCl and 20?L HCl. The nanoflower-structured films showed a higher cathodic peak current (?2.22mA), diffusion coefficient (5.43 10?9 cm2/s), and coloration efficiency (23.6 cm2/C). The increased electrochromic characteristics were attributed to the nanostructured films, which enhanced the diffusion of H+ ions by providing a large surface area during the charge transfer process. The Minerals, Metals & Materials Society 2024. -
Exploring the Impact of Disengagement on the Burnout Among ICU Nurses of Indian Private Hospitals: The Influence of Perceived Organization Support
Among healthcare workers, the nursing workforce has a high intensity of burnout which occurs as a response to the pressure they face at their workplace. Burnout in nursing professionalsis a widespread phenomenon characterized by a reduction in nurses energy that manifests in emotional exhaustion, lack of encouragement, and feelings of frustration and may lead to a decrease in work efficacy. This is a significant issue in human services that needs to be addressed.Several studies have been done on nurses; however, burnout in Indian ICU nurses has not received much attention. Intensive Care Units are specialized hospital areas requiring constant attention and vigorous patient care. Studies state that ICU nurses have to be more alert due to the severity of the illness in patients. Nurses in ICUs face many challenges at work which could lead to burnout. Disengagement is one such factor that might lead to burnout in nurses. When people experience disengagement, they feel detached from their workplace. Previous studies indicate that due to pandemic experiences, nurses are disengaging at a rate twice that of other healthcare staff. The challenging demands of their work have significantly affected the nurse's psychological wellness and overall health. Limited studies have been done on ICU nurse disengagement and its effect on burnout in Indian settings. Therefore, the study aims to examine how nurse disengagement affects the burnout they experience. Healthcare organizations must prioritize the support to be extended and care for the nurses well-being to prevent further disengagement. Nurses may become more committed to the organization when they feel supported by hospital administrators and have lighter workloads. This paper focuses on exploring the consequences of disengagement on burnout experienced by ICU nurses and the influence of Perceived Organizational Support (POS) between them. The sample used for the study was 449 nurses working in the ICU divisions of private hospitals in India. This study employed a simple random sampling technique. A survey was utilized and distributed to the nursing professionals working in the ICU divisions. The commonly recognized aspects of burnout are emotional exhaustion, depersonalization, and reduced personal achievement. The study results indicated that disengagement has no impact on the emotional exhaustion experienced by ICU nurses. However, it influences depersonalization and nurses achievement. The study results state that POS moderated the relationship between disengagement and the two burnout aspects: depersonalization and personal achievement. From the findings, it may be concluded that perceived organizational support is crucial in encouraging greater personal accomplishment, reduced depersonalization, and bringing about positive change for disengaged ICU nurses. Better organizational support for ICU nurses becomes a critical responsibility of nursing administration. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Modeling and analysis of the bioconvective flow of nanofluid over a stretching sheet with ThompsonTroian slip condition
In the present study, the flow, heat, and mass transfer characteristics of a bioconvective nanofluid over a stretching plate subjected to an external magnetic field are analyzed. The nonlinear slip at the surface is modeled using the ThompsonTroian velocity slip condition, while convective boundary conditions are applied to account for heat and mass transfer in the thermal and concentration fields. To ensure uniform nanoparticle distribution, motile microorganisms are incorporated into the fluid. These microorganisms help counteract particle aggregation and prevent solidification within the medium. Their motion gives rise to the bioconvection phenomenon, enhancing overall fluid transport. The governing equations for momentum, energy, and species concentration are formulated as partial differential equations (PDEs), incorporating key effects such as viscous dissipation, magnetic field influence, and heat sources. Using similarity transformations, the PDEs are reduced to a system of ordinary differential equations (ODEs). This system is then numerically solved via Python solve_bvp function, which employs a collocation method for boundary value problems. The computed solutions are validated against existing literature, and residual analysis is conducted to ensure accuracy. The results reveal that an increase in magnetic field strength suppresses fluid velocity while simultaneously raising the nanofluid temperature. Additionally, higher critical shear stress associated with the ThompsonTroian slip model further reduces the flow velocity near the surface. Akadiai KiadZrt 2025. -
Analysis of the Viscous Dissipation and Nonlinear Velocity Slip Effect on the Thin Film Nanofluid Flow
Abstract: In the contemporary study, the dynamics of the nanofluid thin film is investigated by considering the viscous dissipation and chemical reaction effects. Additionally, the surface is assumed to have a nonlinear slip rather than the conventional no-slip conditions. This helps in better flow and heat transfer characteristics. This nonlinear velocity slip at the boundary is modelled using the idea proposed by Thompson and Troian. Also, the presence of viscous dissipation in the energy equation, depicts the loss of energy due to the internal friction. Hence, the viscous dissipation turns out to be a critical factor in determining the thermal properties of the nanofluid thin film. The chemical reactions take place within the system because of the presence of nanoparticles, that in turn will have a significant impact on the mass transfer characteristics of the thin film nanofluid. The incorporation of the similarity transformation helps in converting the partial differential equations (PDEs) that govern the fluid flow into a system of nonlinear ordinary differential equations (ODEs). This resulting system is then solved using the BVP package in python whose accuracy is assessed through residual analysis. By this error analysis, convergence of residues was confirmed. Thus validating the method and the results obtained. The outcomes of the study are interpreted through the graphs which highlighted the intensification of heat transfer for the increase in the Eckert number while the magnetic field confirmed its flow controlling feature. Also, the streamlines and contours were plotted to understand and visulaise the flow, all these contours showed the significance of the presence of nonlinear velocity slip at the boundary. Pleiades Publishing, Ltd. 2025.