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Exploring the Dream Pattern among the Nightshift Workers
Globalisation led to the increase in technology and development of multinational companies in the developing countries. This development has caused the increased need for working round the clock and the only option for such a need is different shifts in the companies. Nightshift workers are increasing day by day, but many times, people forget the health and sleep effects caused by the nightshift. One such impact is the altered circadian rhythm, which is very important for proper functioning of the body and a good sleep. Freud put forward that dreams occurring during sleep serve as the guardian of sleep. Dreams are the reflections of the waking life. This altered circadian rhythm can have an impact on waking and sleep life of the nightshift workers. This qualitative study is to find the dream pattern among the nightshift workers and to find the frequency in dreaming among this group. This study is conducted with nine nightshift and nine dayshift workers, dream journal was used to collect the dreams from the participants. Also, semistructured interview was done among the nightshift workers for in-depth understanding on their sleep habits and dream pattern. The dream patterns among both the groups are similar but there are dreams that make the nightshift group different from the dayshift. The frequency of dreaming is seen more among the nightshift workers. The study shows that sexual dreams are seen majorly among the nightshift group. This finding can be further used to conduct researches on the impact of nightshift on the sexual health and overall well being of nightshift workers and the reflection of the same in their dreams. -
Gravity modulation effect on ferromagnetic convection in a Darcy-Brinkman layer of porous medium /
International Conference On Applied And Computational Mathematics, Vol.1139, pp.1-10 -
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. -
Effect Of Cooperative Learning Strategies on Self-Directed Learning and Reflective Thinking of Pre-Service Teachers
Cooperative learning (CL) research demonstrates its robustness. While acknowledging the empirical benefits, there is room for improvement in implementing CL in teacher education classrooms. Teacher educators often resist adopting CL, favouring the frontal teaching method. The cultivation of self-directed learning and reflective newlinethinking is crucial for pre-service teachers (PSTs) to evolve into lifelong learners, newlinemeeting the demands of 21st-century classrooms. Online cooperative learning (OCL) newlineplays a vital role in enhancing essential skill sets such as collaboration, digital newlineproficiency, communication, and interaction among pre-service teachers. This study newlineunfolded in two phases. The initial pilot study, utilizing a concurrent triangulation newlinemixed-method research design, delved into perceived challenges faced by teacher newlineeducators in India regarding cooperative learning implementation. The subsequent newlineexperimental stage employed a quasi-experimental non-equivalent control group newlinedesign to investigate the impact of OCL strategies on self-directed learning (SDL) and reflective thinking (RT) among Indian pre-service teachers. Following the newlineintervention with OCL modules, the researcher also assessed pre-service teachers newlinesatisfaction and perceptions towards OCL, utilizing a mixed-method research approach with concurrent triangulation. The sample for experimental stage encompassed 130 pre-service teachers from two teacher education colleges affiliated with Mangalore University, Karnataka, India. The researcher constructed OCL intervention modules for the study and experts validated it. The researcher adopted standardized instrument for measuring SDL by Acar et al. (2016), and standardised instrument for measuring RT by Kember et al. (2000). The pilot study revealed that teacher educators perceived challenges at an average rate of 63% due to teacher challenges, learner challenges, curriculum syllabus, and administrative challenges. -
A novel mobile sink placement in wireless sensor network using deep maxout network based energy prediction with adjacent cell score
The majority of Wireless Sensor Networks (WSNs) are made up of energy- and cost-efficient detecting nodes. Traditional wireless sensor networks encounter serious problems, including latency, network failure, and congestion, since they rely on individual base stations (BSs) to gather data from the whole network. Sensor nodes adjacent to the base station will use more energy because of excessive energy consumption and energy-hole constraints, affecting the network's life. Understanding the best place for mobile sink nodes can help alleviate this issue by lowering energy usage and extending the network's lifespan. In this paper, utilizing a deep learning-based energy prediction and neighbour cell score model, we build and construct an efficient method to locate mobile receivers using distance, expected energy, and fairness variables. Furthermore, a Deep Maximum Output Network (DMN) calculates the desired power. However, the minimum length, maximum residual energy, complete normalized right, maximum network lifespan, and maximum normalized throughput for our suggested neighbor-based cell scoring with Deep Maxout Network are 137.364, 30.903, 64.426, and 60.613, respectively. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Optimizing Car Recommendations: Power Analysis of Machine Learning Algorithms
The growing demand for efficient automobile recommendation systems has called for the need of algorithms that can proficiently assess and predict user preferences. This research focuses on the assessment of various machine learning algorithms, K-Nearest Neighbors (KNN), Decision Trees, Linear Regression, Weighted Scoring, and Content-Based Filtering. One of the main concerns of this study is to identify which recommendation algorithm is best suited for vehicle suggestions from an application perspective based on cost, mileage, engine size, fuel category, and user reviews. A dataset of 100 records was utilized to perform preliminary analyses so that algorithms were tested. Preprocessing procedures involved missing data handling, normalization of numerical features, and categorical variables encoding so that full precision predictions were obtained. Performances of algorithms were tested in terms of accuracy, scalability, and computational efficiency. Based on results, the highest accuracy was realized by Decision Trees with 85%, followed by Weighted Scoring at 82% and Linear Regression at 78%. Although KNN has an excellent accuracy of 74%, it is less scalable for very large datasets that are needed for an automobile recommendation system. The experimental results of this paper add to the evolving knowledge on the application of machine learning in the automobile world, again reinforcing the adequacy of Decision Trees as a valid technique for car recommendation systems. Recommendations for future studies include enhancing the database and exploring contemporary approaches to improve the accuracy of recommendations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
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. -
50 years of statehood in Sikkim: a comprehensive study of the healthcare system
Sikkim is one of the smallest states located in the Eastern Himalayas of India. This paper is framed within the context of Sikkim commemorating 50 years of democracy on 16 May 2025, following its integration into the Indian Union on 16 May 1975. Over the past five decades, Sikkim has claimed several accolades; the state was declared Indias first fully organic state in 2016 and acclaimed as the best performing small state in cleanliness and most improved small state in governance in 2020. However, there are some paradoxes in Sikkims developmental paradigm. This paper focuses solely on the healthcare system in Sikkim, exploring various aspects such as the allocation of budget to the health sector, the condition of health institutions, the execution of different health programmes and schemes, the accessibility of health facilities, and the availability of human resources. An additional significant factor is the reliance of the states population on other states for healthcare services. Furthermore, it examines the factors that contribute to the increasing disparity between planning and implementation. 2026 The Round Table Ltd. -
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. -
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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. -
A Study on Partial Domination in Graphs
The theory of domination is one of the most studied fields in graph theory. Many new domination parameters have been defined and studied so far. One such parame- ter that was introduced in 2017 is partial domination number. For a graph G = (V, E) and for a p and#8712; (0, 1], a subset S of V (G) is said to partially dominate or p-dominate G if |N[S]| and#8805; p|V (G)|. The cardinality of a smallest p-dominating set is called the p-domination number and it is denoted by and#947;p(G). In scenarios wherein domination con-cepts are applied, partial domination concepts can also be applied with the added ad-vantage of being able to dominate the underlying graph partially, when the need arises. This advantage makes this parameter appear unique amongst most other domination parameters. We present some basic properties of partial dominating sets, some prop- erties related to particular values of p, some properties related to the eccentricity of a p-dominating set, some results in the line of classical domination and characterization of minimal and minimum p-dominating sets. Then we study partial domination in the con-text of prisms of graphs. We give some bounds for partial domination numbers of prisms of graphs G in terms of partial domination numbers of G for particular values of p. We define universal and#947;p-fixers and universal and#947;p-doublers and we characterize paths, cycles and complete bipartite graphs which are universal and#947;1 2 - fixers and universal and#947;1 2 - dou- blers. Then we concentrate on establishing a domination chain in the context of partial domination, which we call as partial domination chain . For this, we defined indepen-dent partial domination number (IPD-number), found exact values of IPD-numbers for some classes of graphs, found bounds for IPD-numbers in terms of independent domi-nation number and some relations between the independent partial dominating sets and the independent dominating sets.