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Effectiveness of performance appraisal systems in relation to teacher dedication in public and private secondary schools in zimbabwe
Performance appraisal systems need to be effective in improving or sustaining employee performance; otherwise, they are a sheer waste of time and money spent on their development and implementation. This study was an evaluation of the effectiveness of the current teacher performance appraisal system, in relation to teacher dedication to work, newlineas practised in Zimbabwean Secondary Schools. Since the introduction of the current teacher appraisal scheme in Zimbabwe in 2011, no research was carried out to determine whether it serves the purposes for which it was designed. Evaluating the effectiveness of the system encompasses a wide scope, including the perceptions of those appraised. The question that comes to the fore is,and#8214; What are teachers perceptions of the effectiveness of the current system of teacher appraisal as practised in public and private secondary schools in Zimbabwe?and#8214; Both quantitative newlineand qualitative methods of research were used to address the question. The study sought to establish the strength of the relationship that exist between the current teacher performance appraisal system and day to day newlineduties of the teacher, the extent to which it leads to improvements in the teaching and students learning process. It also seeks to establish how it addresses teacher development needs and whether the mechanisms and procedures for the management and implementation of the appraisal system in the schools are adequate. The current Performance Appraisal System, Result-Based Management and is output oriented. The main objective of this study was to assess the effectiveness of the current performance appraisal system on the performance of teachers in public and private secondary schools in Zimbabwe, by studying its implementation in five of the ten provinces. The overall purpose of the newlinestudy is to contribute to current policy and practice debate on how to improve and strengthen teacher performance appraisal and management system in Zimbabwe. -
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IoT-based traffic prediction and traffic signal control system for smart city
Because of the population increasing so high, and traffic density remaining the same, traffic prediction has become a great challenge today. Creating a higher degree of communication in automobiles results in the time wastage, fuel wastage, environmental damage, and even death caused by citizens being trapped in the middle of traffic. Only a few researchers work in traffic congestion prediction and control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT-based traffic prediction using OWENN algorithm and traffic signal control system using Intel 80,286 microprocessor for a smart city. The proposed system consists of 5 phases, namely IoT data collection, feature extraction, classification, optimized traffic IoT values, and traffic signal control system. Initially, the IoT traffic data are collected from the dataset. After that, traffic, weather, and direction information are extracted, and these extracted features are given as input to the OWENN classifier, which classifies which place has more traffic. Suppose one direction of the place has more traffic, it optimizes the IoT values by using IBSO, and finally, the traffic is controlled by using Intel 80,286 microprocessor. An efficient OWENN algorithm for traffic prediction and traffic signal control using a Intel 80,286 microprocessor for a smart city. After extracting the features, the classification is performed in this step. Hereabout, the classification is done by using the optimized weight Elman neural network (OWENN) algorithm that classifies which places have more traffic. OWENN attains 98.23% accuracy than existing model also its achieved 96.69% F-score than existing model. The experimental results show that the proposed system outperforms state-of-the-art methods. 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Optimal Stacked Sparse Autoencoder Based Traffic Flow Prediction in Intelligent Transportation Systems
Recently, intelligent transportations system (ITS) has gained significant internet due to the higher needs for road safety and competence in interconnected road network. As a vital portion of the ITS, traffic flow prediction (TFP) is offer support in several dimensions like routing, traffic congestion, and so on. To accomplish effective TFP outcomes, several predictive approaches have been devised namely statistics, machine learning (ML), and deep learning (DL). This study designs an optimal stacked sparse autoencoder based traffic flow prediction (OSSAE-TFP) model for ITS. The goal of the OSSAE-TFP technique is to determine the level of traffic flow in ITS. In addition, the presented OSSAE-TFP technique involves the traffic and weather data for TFP. Moreover, the SSAE based prediction model is designed for forecasting the traffic flow and the optimal hyperparameters of the SSAE model can be adjusted by the use of water wave optimization (WWO) technique. To showcase the enhanced predictive outcome of the OSSAE-TFP technique, a wide range of simulations was carried out on benchmark datasets and the results portrayed the supremacy of the OSSAE-TFP technique over the recent state of art methods. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Blockchain with deep learning-enabled secure healthcare data transmission and diagnostic model
At these times, internet of things (IoT) technologies have become ubiquitous in the healthcare sector. Because of the increasing needs of IoT, massive quantity of patient data is being gathered and is utilized for diagnostic purposes. The recent developments of artificial intelligence (AI) and deep learning (DL) models are commonly employed to accurately identify the diseases in real-time scenarios. Despite the benefits, security, energy constraining, insufficient training data are the major issues which need to be resolved in the IoT enabled medical field. To accomplish the security, blockchain technology is recently developed which is a decentralized architecture that is widely utilized. With this motivation, this paper introduces a new blockchain with DL enabled secure medical data transmission and diagnosis (BDL-SMDTD) model. The goal of the BDL-SMDTD model is to securely transmit the medical images and diagnose the disease with maximum detection rate. The BDL-SMDTD model incorporates different stages of operations such as image acquisition, encryption, blockchain, and diagnostic process. Primarily, moth flame optimization (MFO) with elliptic curve cryptography (ECC), called MFO-ECC technique is used for the image encryption process where the optimal keys of ECC are generated using MFO algorithm. Besides, blockchain technology is utilized to store the encrypted images. Then, the diagnostic process involves histogram-based segmentation, Inception with ResNet-v2-based feature extraction, and support vector machine (SVM)-based classification. The experimental performance of the presented BDL-SMDTD technique has been validated using benchmark medical images and the resultant values highlighted the improved performance of the BDL-SMDTD technique. The proposed BDL-SMDTD model accomplished maximum classification performance with sensitivity of 96.94%, specificity of 98.36%, and accuracy of 95.29%, whereas the feature extraction is performed based on ResNet-v2 World Scientific Publishing Company. -
Steering through the pandemic: narrative analysis of school leader experiences in India
The COVID ?19 pandemic has disrupted the regular functioning of schools. Transitioning to online learning posed significant challenges to all stakeholders in the educational system. The continued changes and challenges due to the pandemic require school leaders to make intuitive decisions. School leaders vision and leadership styles can considerably impact successfully managing crises and challenges. The current study looks at the lived experiences of eight school leaders working in India. The data collected using an interview guide was subjected to narrative thematic analysis. The interviews were designed primarily in an open-ended manner to captivate the story of their experiences. The results yielded an understanding of how school leaders navigated through multiple challenges such as transitioning online, attending to student needs, financial challenges adopting crisis and collaborative leadership. The results highlight various personal feelings and experiences that helped the school leaders to hold up during the crisis. School leaders lack training in crisis management, and their mental health needs are neglected. The paper calls for professional support for school leaders in managing professional and personal challenges. The article gives direction for school professionals on focus areas and requirements in Indian schools. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Accident prevention system using real time embedded technology
Two different aspects are presented in the proposed system: a transmitter and a recipient. The velocity boundary is controlled immediately after entering the emitter area by receiving a signal from the RF transmitter. A few meters even before the area, the significantly impacted might be put for this purpose. The surveillance program contains an alcoholic detector, an eye detector, and a smoke detector. GPS and GSM for the detection of incidents on mobile phones. The electromechanical device monitors the information as a consequence of the impact by transmitting it to the microprocessor ATmega330Q. GPS of your smart telephone will then communicate with both the satellite to acquire latitude and longitudinal data as well as the incident names will be transmitted to the families, fire departments, etc. which are already defined. 2021, SciTechnol, All Rights Reserved. -
Factors Influencing Data Utilization and Performance of Health Management Information Systems: A Case Study
The Healthcare Management Information System (HCMIS) is a comprehensive collection of data systematically gathered at healthcare institutions to fulfill the requirements for statistical information on medical services. This research aimed to assess the use of HCMIS information and identify the elements that impact the efficiency of the medical system at the district and primary medical institution levels in Tanzania as a case study. This research was conducted in 11 districts in Tanzania and included 115 healthcare institutions. It was cross-sectional research. The data were gathered via a semi-structured survey given to healthcare professionals at the institution and district stages. The information was then recorded utilizing an observational checklist. The researchers used an analytical technique for thematic content to combine and validate the replies and findings and gather essential data. 93 healthcare institution personnel and 13 district authorities were surveyed. Approximately 61% of the facility participants said they utilized the HCMIS information, but only 39% of the district participants acknowledged consistently analyzing HCMIS information. Out of the participants from nine districts, 68% said that they regularly get feedback on the quality of their work from authority figures monthly and quarterly. The patient workload was often shown to significantly impact the efficiency of staff members in data collection and administration. Insufficient analysis and subpar use of information were prevalent in most districts and healthcare institutions in Tanzania. Inadequate human and financial resources, absence of rewards and monitoring, and lack of standard processes for data handling significantly hindered the HCMIS efficiency in Tanzania. The Research Publication, www.trp.org.in. -
Pertaining analysis of fracture risk in Osteoporotic patients using Machine Learning Techniques
Bone fractures in the spine or hip are the most severe complications of Osteoporosis. Older subjects with Osteoporosis are vulnerable to falls. This paper aims to review the breakthrough in machine learning methods over the past four years in assessing fracture risk in osteoporotic patients. Machine learning is applied in the healthcare and medical field. Machine learning professionals can accurately predict disease onset by analyzing a large amount of data. Osteoporosis is one of the healthcare domains in which new Machine learning and Artificial Intelligence techniques can be implemented. The objective of this research is to give an overview of the recent advancements in machine learning methods in finding out the risk factors for fractures or predicting the onset of disease. A systematic search was conducted in PubMed to get research papers published on Machine learning methods to detect, classify, or predict osteoporosis-related fracture risk. The articles belonging to Fracture prediction and risks (n=14), Osteoporosis classification(n=3), Diagnosis of fracture(n=3), and Predicting length of stay (n=1) were identified. The quality of the articles is assessed. Most articles described the efforts to create the model and showcased excellent results in predicting the risks. Significant limitations were in the form of inadequate data splitting and data validations. More validation studies are needed in various large groups to improve the model. Most of the participants in significant studies were in their initial stage of the disease, and the reproducibility analysis was done with major disease issues. 2023 IEEE. -
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Corporate Insolvency Resolution Process Under Insolvency and Bankruptcy : A Critical Study
A robust legal system is essential for corporations to carry on business smoothly. Previously in India, winding up and corporate rescue were dealt in multiple legislations for different entities. Provisions relating to winding up of companies were found in The Indian Companies Act 1913, Indian Companies Act, 1956 and further in the Companies Act, 2013. These provisions did not ensure expeditious winding up procedures and the same affected the interest of stakeholders. newlineThe increase in NPA compelled the need to bring an efficient framework to protect the rights of creditors and debtors. As a solution to this IBC was enacted in 2016, to facilitate timely resolution of insolvency and bankruptcy. This research aims to critically analyze the provisions of corporate insolvency resolution process, to examine whether IBC is facilitating newlinerehabilitation of insolvent corporations and protecting the interest of creditors, so as to balance their interests. It further aims to outline a draft policy for a better insolvency resolution process in India. Primary data for the study was collected through a structured interview of stakeholders and conclusion was drawn through a qualitative thematic analysis using NVivo software. The findings showed that, through CIRP there is debt recovery for financial creditors, but it is not expeditious. There are multiple reasons for the delay. The operational creditors are not able to newlinerecover from the process and as a result, many of them are turning insolvent. The rights of the corporate debtor are protected under the Code but they are not adequately protected under the Code as there is no value maximization under the Code. There are many delays in the process, resulting in more companies going into liquidation. IBC is a debtor friendly legislation. Both resolution and liquidation benefit the corporate debtor as it helps the company to resolve its newlineinsolvency. -
Biomass Derived Fluorescent Nanocarbon Sensor for Effective Sensing of Toxic Cadmium Metal Ions
Cadmium ion (Cd2+) is common in our surroundings and may readily bioaccumulate into the organism following passage through the respiratory and digestive systems. Chronic exposure to Cd2+ can lead to considerable bioaccumulation in an organism because of its longer biological high life (1030 years), which permanently harms the health of humans and animals. Considering this hazardous effect of toxic Cd2+ metal ions, there is a need to develop a toxic-free and simple sensor synthesized from easily available and biocompatible biomass or natural precursor. Herein we report the effective synthesis and development of a fluorescence sensor from Indigofera tinctoria (L.), a well-known medicinal plant via one step green, hydrothermal synthesis method. The remarkable fluorescence and larger stokes shift make it ideal for fluorescence sensing strategy. This sensor detects potentially toxic Cd2+ assisting fluorescence sensing strategy in the metal ion concentration range from 1 nM to 1 M. The SternVolmer plot exhibits a remarkable linear detection range exhibiting limit of detection (LOD) as 14.74 nM. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Intelligent Safety Life Jacket Using Wsn Technology
The body loses heat in hypothermia because it cannot maintain its internal temperature owing to a freezing environment. As a result, the body temperature will decrease rapidly. The person will lose consciousness or faint when the body temperature falls below 35C. This study targets detecting climbers' hypothermia and transmitting their health status to the climber's group. It is difficult for mountain climbers to check their health and hypothermia symptoms for themselves and their climbing companions. To address this issue, we created a life jacket with an integrated hardware kit with a Peltier, temperature, and pulse sensor. LoRa Network is used to communicate with the climber's group. Alert messages are delivered to mountaineers via the Android app and suitable protocols, which helps save the climbers if any discrepancies occur. 2023 IEEE. -
Propensity Score Matching and a Difference in Difference Approach to Assess ESGs Influence on Indian Acquirer Performance
This research involves an in-depth analysis of the intricate relationship between Environmental, Social, and Governance scores and the financial and operational performance of Indian acquirers. The research methodology employed herein entails a meticulously crafted design, incorporating a blend of the Propensity Score Matching and Difference-in-Differences model. This strategic amalgamation serves to rigorously assess the impact of ESG factors on the performance outcomes of Indian acquirers involved in M&As. The empirical findings of this study reveal a robust and statistically significant correlation between M&A endeavours and ESG considerations. Notably, the research discerns that M&A activities tend to exert an adverse influence on ESG performance metrics within the Indian corporate landscape. This nuanced insight underscores the multifaceted interplay between strategic corporate actions and the broader sustainability and governance landscape, thereby offering valuable implications for scholars and practitioners in finance and corporate strategy. 2024, University of Wollongong. All rights reserved. -
The Intersectionality of Social Exclusion and International Comparison: Rethinking the Methodological Nuances
This chapter foregrounds the methodological considerations involved in a cross-national comparison of qualitative data on social exclusion and marginalization. Drawing on the possibilities of intersectional multilevel analysis in Indian and Swiss contexts, the chapter further discusses the nuances of juxtaposing these data. It follows attempts by a Swiss-Indian research team to study how marginalization involves multiple intersectionalities that operate simultaneously. It aims to capture the experiences of migrant workers in India and people without legal residency status in Switzerland during the COVID-19 lockdown, placing them against the cultural histories, social structures, and state interventions. Using intersectionality, the study explores how structures of exclusion operate at multiple levels of subjectivity and, through epistemic violence, naturalize social inequalities. The project attempts to identify how they are still being rendered invisible and their miseries normalized, invoking the impression that it is perpetual. Cross-border research becomes significant in the context of internationalization and globalization, and the chapter discusses the issues and the promises of such a comparison. Beyond the cultural boundaries and differences and the conceptual-methodological incongruities, we argue that the comparison can illuminate how power relations operate at multiple levels in different contexts, reproducing and normalizing discriminatory mechanisms. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024, corrected publication 2024. -
Fast Fashion Brands: Sustainable Marketing Practices and Consumer Purchase Behaviour; [Blagovne znamke hitre mode: trajnostne trne prakse in nakupovalno vedenje potronikov]
The fast fashion boom is faced with economic, environmental and social justice objections. Sustainable marketing initiatives have become a new style statement, and brands are shifting to environment-friendly manufacturing. This study explores how fashion apparel brands adopt sustainable marketing practices to promote sustainable purchase behaviour. A cross-sectional survey using a quantitative research design was followed to collect responses from fashion brand consumers. Variance-based partial least squares-structural equation modelling (PLS-SEM) was used to assess the hypothesized model. Two-step bootstrapping was conducted to explore the mediating role of brand perception in the relationship between sustainable marketing activity and brand loyalty. The study suggests that firms can support sustainable marketing practices by creating a brand image and building trust. This can influence consumers' perceptions of sustainability and promote brand loyalty. The study also emphasizes the significance of brand loyalty in developing sustainable purchase behaviour that endures over time. The study provides insights into sustainable marketing strategies and policies in indigenous markets. 2024, University of Ljubljana Press. All rights reserved. -
Sustainable Marketing Initiatives and Consumer Perception of Fast Fashion Brands
The fast fashion industry has been criticized for negatively impacting the economy, environment, and social justice. Consequently, many brands adopt sustainable marketing practices to promote eco-friendly manufacturing and encourage sustainable purchase behaviour. To explore this trend, a cross-sectional survey was conducted among fashion brand consumers using a quantitative research design. The survey data was analyzed using variance-based partial least squares-structural equation modelling (PLS-SEM). The results showed that sustainable marketing practices can be supported by creating a positive brand image and building trust. Such practices can positively influence consumers' perception of sustainability and promote brand loyalty, which can lead to sustainable purchasing behaviour. The study provides valuable insights into sustainable marketing strategies that fashion brands can adopt to promote sustainable practices and policies in local markets. 2024, idd3. All rights reserved. -
Twitter Sentiment Analysis and Emotion Detection Using NLTK and TextBlob
On an average, approximately 7000 tweets are communicated each second and in total it piles up to around 300 billion tweets every year. Society are free to contribute their opinions on public platform and hence it acts as a reliable interface to assess society ongoing viewpoint and attitude over any matter or event. Consumers very often make use of social media to exchange their views about anything. Business may get domain for enhancement and smooth interpretation of the behavior of people regarding various facts through opinion mining. Thus to carry out this mining of opinions on social media interface, textual categorization with language analysis is of great help. With the help of NLP token tool, phrases can be divided into various word series after dropping stop phrases. Larger tweets tokenizing and classifying into distinct labels is a concern. Thus, the main objective of this framework is to process the tweets based on specific keywords given by user, categorize these phrases into negative, positive and neutral ones. TextBlob module assists users and developers to interpret user sentiments about a news. This research tries to give suggestion a textual opinion assessment on social media samples utilizing the NLTK and TextBlob modules. 2023 IEEE. -
Does fdi intensify economic growth? Evidence from china and India
[No abstract available]