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Causal relationship between leverage and performance: Exploring Dhaka Stock Exchange
To magnify shareholders' returns, managers employ the use of debt in the firms' capital structure. However, excessive debt financing can often cause financial distress for the firms. In fact, various debt equity ratio levels may lead to different financial performance when compared for high levered and low levered firms. Thus, the aim of this paper is to examine the cause and effect relationship between financial leverage and financial performance of firms. To pursue the purpose, a purposive sample of 163 non-financial firms listed on the Dhaka Stock Exchange (DSE) was selected to conduct this study. Findings indicate that there was no significant difference in the financial performance between high levered and low levered firms, neither in terms of their size nor growth rates. A negative relationship therefore persists between leverage and performance of such firms. Implications of these findings can provide policy guidelines for managers and directions for any further work in this context. Copyright 2018 Inderscience Enterprises Ltd. -
Causal relationship between leverage and performance: Exploring Dhaka Stock Exchange
To magnify shareholders' returns, managers employ the use of debt in the firms' capital structure. However, excessive debt financing can often cause financial distress for the firms. In fact, various debt equity ratio levels may lead to different financial performance when compared for high levered and low levered firms. Thus, the aim of this paper is to examine the cause and effect relationship between financial leverage and financial performance of firms. To pursue the purpose, a purposive sample of 163 non-financial firms listed on the Dhaka Stock Exchange (DSE) was selected to conduct this study. Findings indicate that there was no significant difference in the financial performance between high levered and low levered firms, neither in terms of their size nor growth rates. A negative relationship therefore persists between leverage and performance of such firms. Implications of these findings can provide policy guidelines for managers and directions for any further work in this context. Copyright 2018 Inderscience Enterprises Ltd. -
Predictive Analysis of Academic Performance Among Students using A-CNN-BiLSTM Approach
The number of possibilities to analyze educational data using data mining techniques is expanding, with the goal of improving learning outcomes. There is an explosion in data produced by online and virtual education, e-learning platforms, and institutional IT. Using these statistics, teachers could gain valuable insights into their students' learning habits. Academic performance of students and other useful information can be analyzed with the help of educational data mining. Model training consists of three primary steps: data preprocessing, feature selection, and training the model. To eliminate unwanted problems like noise and redundant attributes, data preparation is necessary. By prioritizing which features to calculate, the mRMR algorithm lowers calculation costs. Feature selection plays a crucial role in training A-CNN-BiLSTM models. The suggested approach routinely outperforms BiLSTM and CNN, two state-of-the-art algorithms. With a data accuracy percentage of 96.57%, it's clear that there was a significant improvement. 2024 IEEE. -
SUSTAINABLE CLOUD COMPUTING THROUGH GREEN NETWORK FUNCTION VIRTUALISATION (NFV)
Modern information technology has made cloud computing a cornerstone by providing scalable and flexible services to fulfill the ever-increasing demands of businesses and individuals. However, since data centres use enormous quantities of energy and contribute to rising carbon emissions, the exponential rise of cloud infrastructure has caused serious environmental concerns. This research addresses the environmental issues that traditional cloud computing poses and presents a way forward by incorporating Green Network Function Virtualisation (NFV). A paradigm change towards sustainable alternatives is required due to the traditional cloud data centres increasing energy consumption and carbon impact. The suggested Green NFV strategy utilises the virtualisation technologies to optimise and combine network services, which lowers energy consumption and improves resource efficiency. The goal of this research is to reduce the environmental impact of data centres and increase the ecological sustainability of cloud services by incorporating NFV principles into cloud computing in a seamless manner. This work investigates the effectiveness of Green NFV in reducing the environmental impact of cloud computing through an in-depth analysis and empirical analysis. It assesses the energy efficiency benefits of NFV adoption, taking into account operational sustainability overall, server consolidation, and dynamic resource allocation. The results highlight that Green NFV can help with the environmental issues regarding cloud computing and provide a viable route forward for a more ecologically conscious and sustainable future for digital infrastructure. This research offers significant aspects to experts, policymakers, and industry practitioners who are looking for practical methods to balance the need for environmental sustainability with the rapid expansion of cloud computing. 2024, Scibulcom Ltd.. All rights reserved. -
Ultra-low loss compact active TM mode pass polarizer using phase change material in silicon waveguide
An active low-loss transverse magnetic (TM) pass polarizer, based on the phase change material (Ge2Sb2Te5), is proposed. The proposed polarizer is based on silicon-on-insulator technology that consists of a silicon waveguide that incorporates a thin layer of Si3N4 placed in-between GST. Enhancing the interaction between light and GST is achieved by strategically placing a double-layer GST adjacent to the slot waveguide. The polarizers tunability, on the other hand, depends on the shift in the refractive index (RI) of GST as it transitions between its crystalline and amorphous phases. By optimizing the structure, the polarizer exhibits negligible loss for both modes in the amorphous phase, and with the change of phase to crystalline, the loss of TE mode is more than 8 dB. In contrast, the loss of TM is less than 0.05 dB with a high ER of 21.82 dB, propagation length of 79.89 m and Figure of merit reaches up to 108 at 1550 nm. Due to the combination of these performance parameters, the suggested active TM pass polarizer is an appealing and effective device for various photonic applications. In addition, the fabrication technique of the proposed active TM pass polarizer is explained. 2024 IOP Publishing Ltd. -
Exploring the integration of human resource management and organizational culture in achieving environmental sustainability
This book explores the urgent need for organizational transformation in the face of impending environmental crises, highlighting the intrinsic link between environmental well-being and economic progress. Advocating a shift away from profit-centric models, it champions organizations actively contributing to the ecological system by harnessing the synergy between organizational culture and human resource management (HRM). In a changing world demanding genuine environmental commitment, the book positions sustainability as a strategic imperative. Departing from traditional HRM, the book proposes an integrated approach embedding sustainability in every facet of employee engagement. Concepts like sustainable recruitment, purpose-driven performance, and engagement for change are explored. The book provides insights, tactics, and real-world examples for individuals and organizations to embrace environmental responsibilities through HRM and organizational culture, fostering a sustainable corporate ethos. 2024 by IGI Global. All rights reserved. -
Environmentally responsible behaviour among the teachers: role of gratitude and perceived social responsibility
Purpose: Based upon the broaden-and-build theory of positive emotions, this study aims to assess the role of perceived social responsibility (PSR) in mediating the relationship between gratitude and environmentally responsible behaviour (ERB) among teachers. Design/methodology/approach: Data were collected, following a correlational design, from a total of 292 school teachers in Kerala state, India. In total, 256 data were taken for final analysis. Out of the total participants, 63.3% were female and the remaining 36.7% were male. Confirmatory factor analysis was carried out to verify the factor structure and discriminant as well as convergent validity of the study variables. The relationship between gratitude and ERB with mediating role of PSR was tested. Findings: The mediation analysis output revealed that PSR fully mediates the effect of gratitude on ERB, and it is concluded from the findings of the study that ERB can be enhanced by humanizing the citizens to integrate social responsibility in their acts and promoting the significance of having positive emotions like gratitude to widen their thoughtaction repertoires. Research limitations/implications: In line with the broaden-and-build theory, a positive state of mental faculty can be a prime facilitator to increase concern for green environments as an outcome of an expanded thoughtaction repertoire. The findings imply the importance of inculcating enduring personal resources like the sense of gratefulness as it weighs the effect of producing altruistic acts like ERB along with many other benefits associated with having a positive emotion which is obviously considered to be a fair contribution to serve social resources in the community. Social implications: The study findings can be an inspiration for the formation of policies to encourage pro-environmental behaviour and to further expansion of policies like national education policy of India. As teachers being the facilitators of knowledge and wisdom, they are potential sources to inspire students to practice healthy behaviours, they can be better models by practicing ERB. Originality/value: The authors have verified the application of broaden-and-build theory of positive emotion in the context of ERB along with identifying its relationship with gratitude and PSR. 2023, Emerald Publishing Limited. -
Partial domination in prisms of graphs
For any graph G = (V,E) and proportion p ? (0,1], a set S ? V is a p-dominating set if |N|V[S|]| ? p. The p-domination number ?p(G) equals the minimum cardinality of a p-dominating set in G. For a permutation ? of the vertex set of G, the graph ?G is obtained from two disjoint copies G1 and G2 of G by joining each v in G1 to ?(v) in G2. i.e., V (?G) = V (G1) ? V (G2) and E(G) = E(G1) ? E(G2) ? {(v, ?(v)): v ? V (G1), ?(v) ? V (G2)}. The graph ?G is called the prism of G with respect to ?. In this paper, we find some relations between the domination and the p-domination numbers in the context of graph and its prism graph for particular values of p. 2022 Forum-Editrice Universitaria Udinese SRL. All rights reserved. -
On some properties of partial dominating sets
A subset of the vertex set of a graph is a dominating set of the graph if that subset and all the adjacent vertices of that subset form the whole of the vertex set. In case, if a subset and all the adjacent vertices of that subset form part of the whole set, say, for 0 < p < 1, ptimes of the whole vertex set, we say it is a partial domination. In this paper, we explore some of the properties of partial dominating sets with respect to particular values of p. 2020 Author(s). -
Balancing cerebrovascular disease data with integrated ensemble learning and SVM-SMOTE
The paper addresses the challenge of imbalanced classification in the context of cerebrovascular diseases, including stroke, transient ischemic attack (TIA), and vascular dementia. The imbalanced nature of cerebrovascular disease datasets poses significant challenges to conventional machine learning algorithms, making precise diagnosis and effective management difficult. The aim of the paper is to propose a novel approach, the INTEL_SS algorithm, which combines ensemble learning techniques with Support Vector Machine-Synthetic Minority Over-sampling Technique (SVM-SMOTE) to effectively handle the imbalanced nature of cerebrovascular disease datasets. The goal is to improve the accuracy of diagnosis and management of cerebrovascular diseases through advanced machine learning techniques. The proposed methodology involves several key steps, including preprocessing, SVM-SMOTE, and ensemble learning. Preprocessing techniques are used to improve the quality of the dataset, SVM-SMOTE is employed to address class imbalance, and ensemble learning methods such as bagging, boosting, and stacking are utilized to improve overall classification performance. The experimental results demonstrate that the INTEL_SS algorithm outperforms existing methods in terms of accuracy, precision, recall, F1-score, and AUC-ROC. Performance metrics are used to assess the effectiveness of the proposed approach, and the results consistently show the superiority of INTEL_SS compared to state-of-the-art imbalanced classification algorithms. The paper concludes that the INTEL_SS algorithm has the potential to enhance the diagnosis and management of cerebrovascular diseases, offering new opportunities to apply machine learning techniques to improve healthcare outcomes. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. -
Cerebral Stroke Classification Using Over Sampling Technique and Machine Learning Models
In recent years, cerebral stroke has ascended as a paramount concern in global public health. Proactive strategies emphasizing metabolic control over salient risk factors present a superior approach compared to relying solely on physiological indicators, which may not delineate clear preventive directives. In this research, we present the SPX-CerebroPredict modela novel machine learning framework designed to classify imbalanced cerebral stroke data for clinical diagnostics. The study delves into feature selection methodologies, employing both information gain and principal component analysis (PCA). To address the class imbalance dilemma, the Synthetic Minority Over-sampling Technique (SMOTE) was harnessed. The empirical evaluation, conducted on the cerebral stroke prediction dataset from Kagglecomprising 43,400 medical records with 783 stroke instancespitted well-established algorithms such as support vector machine, logistic regression, decision tree, random forest, XGBoost, and K-nearest neighbor against one another. The results evince that our SPX-CerebroPredict model, integrating SMOTE, PCA, and XGBoost, surpasses its contemporaries, achieving an impressive accuracy rate of 95%. This discovery underscores the models potential for clinical applicability in cerebral stroke diagnostics. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Microwave-assisted extraction of phytochemicals
Microwave-assisted extraction (MAE) has emerged as a promising technique for the extraction of phytochemicals and has received substantial scientific attention in recent years. MAE involves the utilization of microwaves to heat the sample, which facilitates the release of bioactive compounds from the plant matrix. MAE offers several advantages over traditional extraction methods, including faster extraction times, higher extraction yields, and reduced solvent consumption. To improve the efficiency of the extraction process, research has concentrated on optimizing various parameters, including the extraction temperature, extraction time, and solvent type. Additional studies have investigated the effect of MAE on the chemistry and bioactivity of the extracted phytochemicals. Several classes of phytochemicals, including phenolic compounds, flavonoids, and alkaloids, have been successfully extracted using MAE. These compounds possess various biological activities, such as antioxidant, antimicrobial, and anticancer properties. Essential oils from aromatic plants have also been extracted using MAE, which is widely employed in the food, cosmetic, and pharmaceutical industries. Despite its many advantages, the major challenge in the application of MAE is the potential degradation of the extracted compounds due to the high-temperature and high-pressure conditions during extraction. Additionally, the cost of microwave equipment and the need for specialized expertise may stunt its widespread adoption. In diverse omics disciplines, MAE shows promise, notably for the development of analytical platforms for research in genomics, proteomics, metabolomics, and related subdisciplines. Nonetheless, more investigation is required to optimize the extraction conditions and guarantee that the chemical makeup and biological activity of the isolated phytochemicals are preserved. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023, Corrected Publication 2023. All rights reserved. -
IOT based prediction of rainfall forecast in coastal regions using deep reinforcement model
This research proposes an IoT based technique for predicting rainfall forecast in coastal regions using a deep reinforcement learning model. The proposed technique utilizes Long Short-Term Memory (LSTM) networks to capture the temporal dependencies between the rainfall data collected from the coastal regions and the prediction model parameters. The proposed technique is evaluated on a dataset of rainfall data collected from the coastal regions of India and compared to traditional methods of rainfall forecasting. The accuracy and reliability of these models are evaluated by comparing them to prior models. Precipitation in coastal locations may be predicted with an average accuracy of 89% using the suggested model, as shown by the results. The suggested framework is computationally efficient and can be trained with little input. The results of this research give strong evidence that the proposed model is an effective tool for coastal precipitation forecasting. 2023 The Authors -
Secure Data Processing System Using Decision Tree Architecture
[No abstract available] -
Magnetic iron oxide nanoparticles immobilized on microporous molecular sieves as efficient porous catalyst for photodegradation, transesterification and esterification reactions
Magnetic iron oxide nanoparticles were immobilized on microporous molecular sieves (13X) via a plant extract mediated green synthesis method. The prepared material was then characterized using XRD, FTIR, TGA, FESEM, and TEM techniques. The synthesized iron oxide nanoparticles-molecular sieves (Fe2O3/MS) composite showed excellent photodegradation of methylene blue (MB) at 99% efficiency. Enhanced photocatalytic properties were observed in comparison with the pure iron oxide (Fe2O3) nanoparticles synthesized. Catalytic conversion of triglycerides to fatty-acid ethyl esters (FAEE) was carried out using sunflower oil, and the reaction showed very good catalytic activity in the transesterification of sunflower oil, converting 84% of the sunflower oil to FAEE. The catalyst was also used in the esterification reaction and found to have excellent applicability. The catalyst showed excellent reusability, and easy separation from the reaction mixture using an external magnet. This enables the synthesized material to act as a promising photocatalyst in degradation and organic synthesis. Very few reports are available on the synthesis of magnetic iron oxide coated on molecular sieves and used for photodegradation, transesterification, and esterification catalysis. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Single activity recognition system: A review
Human Activity Recognition (HAR) plays an important role in smart home assisted living system which is one among the growing research area in smart computing. In this modern era, Smart home assisted living is highly recommended for elderly people to monitor and assist in taking care of themselves. HAR is applied in various ambiences to recognize single activity and group activity as well. This chapter focuses on single activity recognition system with respect to variety of sensors used in smart homes, activity recognition methods and wide range of communication systems that helps to ease the living style of elderly people in healthy environment which can be linked to the advancement of IoT technology in smart building. This chapter reviews many applications with variety of sensors, real time smart home projects, and smart home assisted living systems including activity recognition methods and communication systems. Springer Nature Switzerland AG 2020. -
Effect of Coriolis force on Rayleigh-Bard convection with internal heat generation
The objective of this paper is to analyze the influence of the Coriolis force and internal heat source on Rayleigh-Bard convection in a Boussinesquian fluid of depth d. A linear theory which is oriented towards the normal mode analysis technique is used for this mono-diffusive convection in order to find the criteria for the onset of Rayleigh-Bard convection. The eigenvalue of the said problem was obtained by the use of the Galerkin method in the cases of rigid-rigid, rigid-free, and free-free velocity boundary combinations considering the isothermal and adiabatic temperature boundaries that determine the stability of the system. The effects of various parameters, Taylor number and the internal Rayleigh number are put under consideration only for stationary convection. Treating Taylor number as a critical parameter, shown that it plays a major role in stabilization of the system in case of any particular infinitesimal disturbance. The destabilization of the system has been possible with rotation by treating internal Rayleigh numbers as a critical parameter since the increase in values of the internal Rayleigh number advances the onset of convection. Oscillatory convection seems highly improbable as the scaled frequency of oscillation remains less than 0 for all combinations of Prandtl number, internal Rayleigh number, and Taylor number. 2019, Accent Social and Welfare Society. All rights reserved. -
Heat and mass transfer of triple diffusive convection in viscoelastic liquids under internal heat source modulations
The influence of sinusoidal (trigonometric cosine [TC]) and nonsinusoidal waveforms (square, sawtooth, and triangular) of internal heat source modulation on triple diffusive convection in viscoelastic liquids is investigated. An Oldroyd-B type model is taken into account for viscoelastic liquids. Nonlinear analysis is carried out using a truncated representation of the Fourier series. To analyze the heat and mass transfer over a triply diffusive liquid layer, expressions for average Nusselt and average Sherwood numbers are derived using 8-mode generalized Lorenz equations. The transient behavior of Nusselt and Sherwood numbers is analyzed on different parameters of the problem. From the results, it is found that the internal heat source enhances the heat transfer and diminishes the mass transfer while the heat sink diminishes the heat transfer and enhances the mass transfer. The results for respective waveforms are obtained for each parameter and it is found that the maximum heat and mass transfer occurs due to TC waveform. The limiting cases of viscoelastic liquids (Newtonian, Oldroyd-B, Maxwell, and RivlinEricksen) have been tabulated and corresponding results for each of the waveforms onheat and mass transfer have been shown. 2021 Wiley Periodicals LLC -
Effect of internal heat source modulations on the onset of triple diffusive convection in viscoelastic liquids
The paper aims to study the dynamic behavior of a triple diffusive system subjected to sinusoidal (trigonometric cosine) and non-sinusoidal wave forms (square, sawtooth and triangular) of internal heat source modulation. The configuration of the system is such that a layer of viscoelastic liquid is heated and salted with two solutes from below. An Oldroyd-B type model is made use for viscoelastic liquids. In order to regulate the convection onset, internal heat source modulation is applied. This investigation is modelled using a linear stability analysis where a stationary convection is preferred. Venezian approach facilitates a solution by finding the eigen values of the problem. The influence of pertinent parameters which are varied for a wide range of values have been reported. It is captured via graphs that for small values of frequency of modulation, square wave form is more stable while sawtooth wave form is more stable for an increment in the values of frequency of modulation. Further, liquids such as Newtonian, Maxwell and Rivlin-Ericksen are analysed as the limiting cases of the problem. It seems worthwhile to discuss the results of the present study as it is the first work on linear theory of different wave forms of internal heat source modulation and thus paves a way for new theoretical and experimental endeavors. 2021, National Institute of Science Communication and Information Resources. All rights reserved. -
Elastic circuit de-constructor: a pattern to enhance resiliency in microservices
Cloud-based workloads have proliferated with the deep penetration of the internet. Microservices based handling of high volume transactions and data have become extremely popular owing to their scalability and elasticity. The major challenge that cloud-based microservice patterns face is predicting dynamic load and failure patterns, which affect resiliency and uptime. Existing Circuit breaker patterns are biased toward denying incoming requests to maintain acceptable latency values, at the cost of availability. This paper proposes the Elastic Circuit De-Constructor (ECD) pattern to address these gaps. The proposed ECD pattern addresses this challenge by dynamically adapting to changing workloads and adjusting circuit-breaking thresholds based on real-time performance metrics. The proposed ECD pattern introduces a novel De-constructed state, that allows the ECD to identify alternate paths pre-defined by the application, ensuring user requests continue to be routed to the microservice. By leveraging Availability, Latency and Error rate as performance metrics, the ECD pattern is able to balance the fault tolerance and resiliency imperatives in the cloud-based microservices environment. The performance of the proposed ECD pattern has been verified against both no Circuit Breaker and a default Circuit Breaker setting. 2024 Informa UK Limited, trading as Taylor & Francis Group.