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PBIB Designs and association schemes arising from some connected dominating sets.
Vol.4 ,225-232, ISSN 2251-1512 -
Modified Ceria as a Substitute for Sulfuric Acid in the Liquid Phase Nitration of Toluene
Reaction, Kinetics, Mechanisms and Catalysis, ISSN NO. 1878-5204 -
Fractional p()-Kirchhoff Type Problems Involving Variable Exponent Logarithmic Nonlinearity
In this paper, we investigate a fractional p()-Kirchhoff type problem involving variable exponent logarithmic nonlinearity. With the help of the Nehari manifold approach, the existence and multiplicity of nontrivial weak solutions for the above problem are obtained. The main aspect and challenges of this paper are the presence of double non-local terms and logarithmic nonlinearity. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Assessing Player Interaction for a Social Networking Cooperative Educational Game
Cooperative interaction in educational games, designed to stimulate teamwork, joint creativity and knowledge sharing, also carries potential security threats. One of the key dangers is data leakage. Player interaction involves the exchange of information, and in case of insufficient protection of the system, confidential data, such as personal information, game progress results or individual strategies, may become available to unauthorized persons. This may result in misuse of information, damage to reputation and violation of player privacy. The impact on the game space is also a threat. By interacting, players can change the game world, for example, by entering incorrect data, moving objects to an inappropriate location, or modifying the rules of the game. This can lead to a violation of the balance of the game, incorrect results and a deterioration in the learning effect. Substitution or falsification of game elements is no less dangerous. Attackers can introduce fake elements into the game space, for example, incorrect reviews, changed rules or incorrect data. This can lead to incorrect conclusions, distort learning outcomes, and undermine confidence in the game. In addition, the use of interaction tools can become an object of attack. Attackers can hack and modify tools, such as communication platforms or data storage systems. This can lead to data theft, incorrect operation of tools and malfunction during the game. It is shown that formal descriptions of the choice of a game strategy can exist in a game. Indicators that are essential for cooperative interaction are determined, and examples of their calculation for the case with remote interaction through a social network are given. The article contains information about collaborations, which can be used to assess and choose the direction of development in projects that use game cooperative strategies to implement tasks other than training. The project highlights aspects of cooperative interaction that affect the formation of game strategies in an educational project. Of particular interest are projects in which a social network is the tool and medium of interaction. The objectives of the project are to identify easy-to-use indicators that show the features of cooperative interaction within an educational game. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Support Vector Machine Performance Improvements by Using Sine Cosine Algorithm
The optimization of parameters has a crucial influence on the solution efficacy and the accuracy of the support vector machine (SVM) in the machine learning domain. Some of the typical approaches for determining the parameters of the SVM consider the grid search approach (GS) and some of the representative swarm intelligence metaheuristics. On the other side, most of those SVM implementations take into the consideration only the margin, while ignoring the radius. In this paper, a novel radiusmargin SVM approach is implemented that incorporates the enhanced sine cosine algorithm (eSCA). The proposed eSCA-SVM method takes into the account both maximizing the margin and minimizing the radius. The eSCA has been used to optimize the penalty and RBF parameter in SVM. The proposed eSCA-SVM method has been evaluated against four binary UCI datasets and compared to seven other algorithms. The experimental results suggest that the proposed eSCA-SVM approach has superior performances in terms of the average classification accuracy than other methods included in the comparative analysis. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Chaotic Binary Ant Lion Optimizer Approach for Feature Selection on Medical Datasets with COVID-19 Case Study
Binary version of the ant lion optimizer (ALO) are suggested and utilized in wrapper-mode to pick the best feature subset for classification. ALO is a recently developed bio-inspired optimization approach that mimics ant lion hunting behavior. Furthermore, ALO balances exploration and exploitation utilizing a unique operator to explore the space of solutions adaptively for the best solution. The difficulties of a plethora of noisy, irrelevant, and misleading features, as well as the capacity to deal with incorrect and inconsistent data in real-world subjects, provide rationale for feature selection to become one of the most important requirements. A difficult machine learning problem is to choose a subset of important characteristics from a vast number of features that characterize a dataset. Choosing the most informative markers and conducting a high-accuracy classification across the data may be a difficult process, especially if the data is complex. The feature selection task is usually expressed as a bio-objective optimization challenge, with the goal of enhancing the performance of the prediction model (data training fitting quality) while decreasing the number of features used. Various evaluation criteria are employed to determine the success of the suggested approach. The findings show that the suggested chaotic binary algorithm can explore the feature space for optimum feature set efficiently. 2022 IEEE. -
Firefly Algorithm andDeep Neural Network Approach forIntrusion Detection
Metaheuristic optimization has grown in popularity as a way for solving complex issues that are difficult to solve using traditional methods. With fast growth of the available storage space and processing capabilities of the modern computers, the machine learning domain, that can be succinctly formulated as the process of enabling the computers to make successful forecasts based on the previous experiences, has recently been under spectacular growth. This paper presents intrusion detection approach by utilizing hybrid method between firefly algorithm and deep neural network. The basic firefly algorithm, as a frequently employed swarm intelligence method, has several known deficiencies, and to overcome them, an enhanced firefly algorithm was proposed and used in this manuscript. For experimental purposes, KDD Cup 99 and NSL-KDD datasets from Kaggle and UCL repositories were taken and comparison with other frameworks that have been validated for the same datasets was executed. Based on simulation data, proposed method was able to establish better values for accuracy, precision, recall, F-score, sensitivity and specificity metrics than other approaches. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
The NGO research culture in zimbabwe: Its anatomy, architecture and typology /
International Journal of Science and Research, Vol-2 (12), pp. 50-61. ISSN 2319-7064. -
Corporate governance practices and shareholder protection in India
The present study aims to study the corporate governance practices and shareholder protection among Indian companies. For this purpose a sample of 100 companies are selected. The selected companies listed in BSE 100 Index. The required data are collected from various secondary sources like company website, annual report, notices and annual general meeting. Data were collected using a structured schedule adapted from G20/OECD principles of corporate governance. The finding of the study indicates that out of the selected companies, the level of practicing the corporate governance are not the same. The result of this study will help investors in identifying the companies for their investment, based on their priorities by keeping corporate governance scorecard as a benchmark. It will also be helpful for companies to see their scorecard and check the parameters for improvement and to attract and safeguard the investors and other stakeholders. This study will also add value to the existing literature in their relevant field. Indian Institute of Finance. -
Effects of green energy and productivity on environmental sustainability in BRICS economies: The role of natural resources rents
By developing a theoretical framework, this paper constructs two models for environmental sustainability, each with an ecological footprint and CO2 emissions. The empirical study considers panel data from Brazil, Russia, India, China, and South Africa (BRICS) based on the need for consistent economic growth with minimum environmental cost in these five major emerging countries. The data period ranges from 1994 to 2018. According to the findings from several estimation techniques, total factor productivity and renewable energy consumption improve environmental quality. However, natural resources rents and economic growth are detrimental to environmental sustainability. Therefore, it is concluded that the mere blind use of natural resources to achieve economic growth without raising productivity and green energy is ecologically unsustainable. Appropriate policies are discussed to promote the productivity of governments via emerging technologies and digitalisation. 2024 Elsevier Ltd -
Low temperature performance evaluation of asphalt binders and mastics based on relaxation characteristics
Low temperature cracking is one of the main distresses of asphalt pavement in cold regions. Stress relaxation characteristics is critical for cracking resistance of asphalt materials, especially at low temperatures, but there are few studies on the relaxation characteristic of asphalt mastics. To evaluate the effects of relaxation characteristics of asphalt binders and mastics on its low temperature performance, beam bending relaxation test was carried out through dynamic thermomechanical analyzer at low temperatures. Relaxation rate and relaxation time were proposed to illustrate the relaxation characteristics of asphalt binders and mastics. Then, the low-temperature performance of asphalt binders and mastics was evaluated by bending beam rheometer (BBR), glass transition temperature (Tg), and single edge notch beam bending test. Finally, the correlation of relaxation characteristics with low-temperature properties was analyzed based on Pearsons correlation coefficient and Spearman rank correlation coefficient. The results show that the elasticity of asphalt mastics increases with incorporation of mineral fillers and thus the viscous deformation potential is reduced, which affects the stress relaxation capability. The low-temperature cracking performance of asphalt mastics is indeed compromised as compared with asphalt binders, and the asphalt mastics prepared with fly ash performs the worst since it presents a stronger hardening effect. Fracture energy is determined not to be suitable for evaluating the low-temperature performance of asphalt mastics since its results contradict the BBR and Tg tests. The maximum displacement at fracture can better characterize the brittleness of asphalt materials at low temperatures. The relaxation characteristic index has the strongest correlation with Tg of asphalt binders and mastics, followed by maximum displacement at fracture and comprehensive compliance parameter (Jc). The correlation coefficients are almost larger than 0.5, suggesting that relaxation time and relaxation rate can characterize the low-temperature properties of asphalt binders and mastics. 2022, RILEM. -
Effects of Financial and Trade Globalization on Total Factor Productivity Growth in Emerging Economies
This article considers the annual sample from 1984 to 2019 in a panel dataset of 20 emerging economies (i.e. Brazil, Chile, China, Colombia, the Czech Republic, Egypt, Greece, Hungary, India, Indonesia, Kuwait, Malaysia, Mexico, Peru, the Philippines, Poland, Saudi Arabia, South Africa, Thailand, and Turkey) given by Morgan Stanley Capital International (MSCI), to explore the effects of trade and financial globalization on total factor productivity (TFP) growth. It considers domestic credit to the private sector by banks as a percentage of gross domestic product (GDP), labor force, and total gross fixed capital formation as a percentage of GDP as control variables in the total factor productivity function. The article considers the direct effects of trade and financial globalization. It also checks the moderating impact of domestic credit on TFP. The long-run estimation shows that domestic credit, labor force, and financial globalization reduce TFP growth, whereas investments and trade globalization enhance it. Interestingly, their moderating effect enhances TFP in the long run. The policy implications are also discussed. 2023 Taylor & Francis Group, LLC. -
Financial Development Convergence: Evidence from Top and Bottom Globalised Developing Economies
This paper investigates the pattern of the financial development convergence for the top (Europe and Central Asia) and the bottom (South Asia) globalized developing regions from 1984 to 2016. We employ the Philips-Sul club convergence approach to measure the financial development convergences speed. The results validate the convergence of financial development in all countries, including the top and bottom of globalized developing regions. Interestingly, the speed of financial development convergence is less in the bottom globalized developing region than in the top globalized developing region. However, these results vary across developing regions in the case of financial institutions and financial markets. Therefore, solid financial market governance can provide a productive and efficient financial system, particularly in the bottom globalized economies. 2023 The Author(s). Published with license by Taylor & Francis Group, LLC. -
The Consortium on Vulnerability to Externalizing Disorders and Addictions (c-VEDA): an accelerated longitudinal cohort of children and adolescents in India
The global burden of disease attributable to externalizing disorders such as alcohol misuse calls urgently for effective prevention and intervention. As our current knowledge is mainly derived from high-income countries such in Europe and North-America, it is difficult to address the wider socio-cultural, psychosocial context, and genetic factors in which risk and resilience are embedded in low- and medium-income countries. c-VEDA was established as the first and largest India-based multi-site cohort investigating the vulnerabilities for the development of externalizing disorders, addictions, and other mental health problems. Using a harmonised data collection plan coordinated with multiple cohorts in China, USA, and Europe, baseline data were collected from seven study sites between November 2016 and May 2019. Nine thousand and ten participants between the ages of 6 and 23 were assessed during this time, amongst which 1278 participants underwent more intensive assessments including MRI scans. Both waves of follow-ups have started according to the accelerated cohort structure with planned missingness design. Here, we present descriptive statistics on several key domains of assessments, and the full baseline dataset will be made accessible for researchers outside the consortium in September 2019. More details can be found on our website [cveda.org]. 2020, Springer Nature Limited. -
Applications of bioconvection for tiny particles due to two concentric cylinders when role of Lorentz force is significant
The bioconvection flow of tiny fluid conveying the nanoparticles has been investigated between two concentric cylinders. The contribution of Lorenz force is also focused to inspect the bioconvection thermal transport of tiny particles. The tiny particles are assumed to flow between two concentric cylinders of different radii. The first cylinder remains at rest while flow is induced due to second cylinder which rotates with uniform velocity. Furthermore, the movement of tiny particles follows the principle of thermophoresis and Brownian motion as a part of thermal and mass gradient. Similarly, the gyro-tactic microorganisms swim in the nanofluid as a response to the density gradient and constitute bio-convection. The problem is modeled by using the certain laws. The numerical outcomes are computed by using RKF-45 method. The graphical simulations are performed for flow parameters with specific range like 1?Re?5, 1?Ha?5, 0.5?Nt?2.5, 1?Nb?3, 0.2?Sc?1.8, 0.2?Pe?1.0 and 0.2???1.0. It is observed that the flow velocity decreases with the increase in the Hartmann number that signifies the magnetic field. This outcome indicates that the flow velocity can be controlled externally through the magnetic field. Also, the increase in the Schmidt numbers increases the nanoparticle concentration and the motile density. 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. -
Robust Bidirectional Long Short-Term Memory-Based Class Imbalance Handling in Dyslexia Prediction at its Early Stage
Dyslexia is a neurological condition that presents difficulties and obstacles in learning, particularly in reading. Early diagnosis of dyslexia is crucial for children, as it allows the implementation of appropriate resources and specialized software to enhance their skills. However, the evaluation process can be expensive, time-consuming, and emotionally challenging. In recent years, researchers have turned to machine learning and deep learning techniques to detect dyslexia using datasets obtained from educational and healthcare institutions. Despite the existence of several deep learning models for dyslexia prediction, the problem of handling class imbalance significantly impacts the accuracy of detection. Therefore, this study proposes a robust deep learning model based on a variant of long short-term memory (LSTM) to address this issue. The advantage of Bidirectional LSTM, which has the ability to traverse both forward and backward, improves the pattern of understanding very effectively. Still, the problem of assigning values to the hyper-parameters in BLSTM is the toughest challenge which has to be assigned in a random manner. To overcome this difficulty, the proposed model induced a behavioral model known as Red Fox Optimization algorithm (RFO). Based on the inspiration of red fox searching behavior, this proposed work utilized the local and the global search in assigning and fine-tuning the values of hyper-parameters to handle the class imbalance in dyslexia dataset. The performance evaluation is conducted using two different dyslexia datasets (i.e., dyslexia 12_14 & real-time dataset). The simulation results explore that the proposed robust Bidirectional Long Short-Term Memory accomplishes the highest detection rate with reduced error rate compared to other deep learning models. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Effectiveness of interventions to address obesity and health risk behaviours among people with severe mental illness in low- and middle-income countries (LMICs): A systematic review and meta analysis
Introduction People with severe mental illness (SMI) are more likely to have obesity and engage in health risk behaviours than the general population. The aims of this study are (1) evaluate the effectiveness of interventions that focus on body weight, smoking cessation, improving sleeping patterns, and alcohol and illicit substance abuse; (2) Compare the number of interventions addressing body weight and health risk behaviours in low- and middle-income countries (LMICs) v. those reported in published systematic reviews focusing on high-income countries (HICs). Methods Intervention studies published up to December 2020 were identified through a structured search in the following database; OVID MEDLINE (1946-December 2020), EMBASE (1974-December 2020), CINAHL (1975-2020), APA PsychoINFO (1806-2020). Two authors independently selected studies, extracted study characteristics and data and assessed the risk of bias. and risk of bias was assessed using the Cochrane risk of bias tool V2. We conducted a narrative synthesis and, in the studies evaluating the effectiveness of interventions to address body weight, we conducted random-effects meta-analysis of mean differences in weight gain. We did a systematic search of systematic reviews looking at cardiometabolic and health risk behaviours in people with SMI. We compared the number of available studies of LMICs with those of HICs. Results We assessed 15 657 records, of which 9 met the study inclusion criteria. Six focused on healthy weight management, one on sleeping patterns and two tested a physical activity intervention to improve quality of life. Interventions to reduce weight in people with SMI are effective, with a pooled mean difference of -4.2 kg (95% CI -6.25 to -2.18, 9 studies, 459 participants, I2 = 37.8%). The quality and sample size of the studies was not optimal, most were small studies, with inadequate power to evaluate the primary outcome. Only two were assessed as high quality (i.e. scored 'low' in the overall risk of bias assessment). We found 5 reviews assessing the effectiveness of interventions to reduce weight, perform physical activity and address smoking in people with SMI. From the five systematic reviews, we identified 84 unique studies, of which only 6 were performed in LMICs. Conclusion Pharmacological and activity-based interventions are effective to maintain and reduce body weight in people with SMI. There was a very limited number of interventions addressing sleep and physical activity and no interventions addressing smoking, alcohol or harmful drug use. There is a need to test the feasibility and cost-effectiveness of context-appropriate interventions to address health risk behaviours that might help reduce the mortality gap in people with SMI in LMICs. The Author(s), 2022. Published by Cambridge University Press. -
Developing a democratic constitutional framework through a people-driven constitution making process for zimbabwe /
International Journal of Science and Research, Vol.2, Issue 8, pp. 10-16. ISSN-2319-7064.
This research paper is on a study of how Zimbabwe can produce a democratic people-driven constitution as a permanent solution to the country's problems of poor governance, violent political conflicts, economic collapse, social disintegration, and international isolation. The purpose of the study was to explore a people-driven democratic constitution-making process that Zimbabweans want. Two groups of research units comprised of 1 120 individuals and 67 institutions were used. The inquiry discovered contextual meaning of six phenomena associated with a people-driven democratic constitution-making process. The study recommends a constitution-making process model that Zimbabwe should adopt to produce a people-driven constitution democratically. -
Investigate the distinctive link between a balanced scorecard and organizational performance in ITand non-IT sectors
Purpose: The purpose of this research is to examine how the implementation of a balanced scorecard (BSC) affects business outcomes in both information technology (IT) and non-IT sectors. Design/methodology/approach: Partial least squares structural equation modeling (PLS-SEM) was used to test the hypothesis. A random sample was used to collect 170 responses from the IT companies and 166 from non-IT companies by using the questionnaire method. The questionnaire was distributed to the top- and middle-level managers in Bangalore city, and we used SmartPLS software to explore the relationship between our research constructs. Findings: The results of this study indicate that a BSC has a significant and positive impact on organizational performance in IT and non-IT sectors. The main distinction in this study is that all BSC perspectives [learning and growth perspective, internal business process (IBP) perspective, customer perspective (CP) and financial perspective (FP)] have a significant, direct and indirect impact on IT companies. On the other hand, solely three BSC perspectives (IBP perspective, CP and FP) have a significant impact on non-IT companies, while learning and growth perspective has an insignificant impact on the FP. Originality/value: This study provides a critical theoretical and practical contribution of a BSC on business performance in IT and non-IT industries. 2024, Emerald Publishing Limited. -
Galerkin finite element analysis of magneto-hydrodynamic natural convection of Cu-water nanoliquid in a baffled U-shaped enclosure
In this paper, single-phase homogeneous nanofluid model is proposed to investigate the natural convection of magneto-hydrodynamic (MHD) flow of Newtonian CuH2O nanoliquid in a baffled U-shaped enclosure. The Brinkman model and Wasp model are considered to measure the effective dynamic viscosity and effective thermal conductivity of the nanoliquid correspondingly. Nanoliquid's effective properties such as specific heat, density and thermal expansion coefficient are modeled using mixture theory. The complicated PDS (partial differential system) is treated for numeric solutions via the Galerkin ?nite element method. The pertinent parameters Hartmann number (1 ? Ha ? 60), Rayleigh number (103 ? Ra ? 106) and nanoparticles volume fraction (0% ? ? ? 4%) are taken for the parametric analysis, and it is conducted via streamlines and isotherms. Excellent agreement between numerical results and open literature. It is ascertained that heat transfer rate enhances with Rayleigh number Ra and volume fraction ?, however it is diminished for larger Hartmann number Ha. 2020 Beihang University