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Effects of supply chain integration on firm's performance: A study on micro, small and medium enterprises in India /
Uncertain Supply Chain Management, Vol.8, Issue 1, pp.231-240, ISSN No: 2291-6830. -
Effects of supply chain integration on firms performance: a study on micro, small and medium enterprises in India
The cooperation in the supply chain assumes an adequate job for enhancing an organisation's performance and increasing competitive advantage. Supply Chain Integration (SCI) affects organisational performance. This paper studies the impact of the integration of supply chain procedures and practices on organisational performance and explores the effect of SCI on organisational performance at Micro, Small and Medium Enterprises (MSMEs) in Madurai District, Tamilnadu, India. A questionnaire is developed with validated measurement scales from previous studies and empirical data are collected through a survey questionnaire from 250 randomly selected MSMEs. This research provides sound recommendations to MSMEs in Madurai District, Tamilnadu, India, and maybe used for different industries and decision making policies. Finally, the study will contribute to the scientific field by providing some future studies. 2020 by the authors; licensee Growing Science, Canada. -
Effects of variable thermal properties on thermoelastic waves induced by sinusoidal heat source in half space medium
Aim of the present study is to characterize the effects of changing thermal conductivity on the propagation of thermoelastic waves in the half space medium when it is exposed to a periodic heat source. Closed form solutions of all significant physical fields such as conductive temperature, stress and displacement are evaluated in their dimensionless form in the Laplace transform domain. Impact of changing thermal conductivity parameter is exhibited on all field variables with the help of quantitative outcomes in time-domain. Following this pattern, the effects of time parameter is also observed on the field quantities. 2022 -
Effects of Variable Viscosity and Internal Heat Generation on RayleighBard Convection in Newtonian Dielectric Liquid
The onset of RayleighBard convection of variable-viscosity Newtonian dielectric liquid confined between two parallel plates is subject to free-free isothermal boundary condition. The combined and individual effects of temperature-dependent and electric-field-dependent variable-viscosity along with the internal heat generation are studied using the higher order Galerkin technique. This theoretical study shows that even a mild temperature-dependent variable-viscosity destabilizes the system and the electric-field-dependent variable-viscosity stabilizes the system both in the absence/presence of heat source/sink. 2021, The Author(s), under exclusive licence to Springer Nature India Private Limited. -
Effects of variable viscosity and rotation modulation on ferroconvection
We theoretically explore the dynamics of a ferrofluid with temperature and magnetic field-dependent viscosity, which is in a RayleighBard situation and is subjected to rotation. The problem considers both sinusoidal and non-sinusoidal time-periodic variations of rotation to study the onset and post-onset regimes of RayleighBard ferroconvection. We perform a weakly nonlinear stability analysis using a truncated Fourier series representation and arrive at the third-order Lorenz system for ferrofluid convection with variable viscosity. By using the linearized form of the Lorenz system for ferrofluid convection with variable viscosity, we arrive at the critical Rayleigh number to study the onset of rotating ferroconvection. The heat transport is quantified in terms of the time-averaged Nusselt number and the effects of various parameters on it are studied. The effect of modulated rotation is found to have a stabilizing effect on the onset of ferroconvection while that of variable viscosity has a destabilizing effect. The effects of magnetorheological and thermorheological effects are antagonistic in nature. It is found that the square waveform modulation facilitates maximum heat transport in the system due to advanced onset of ferroconvection. 2021, Akadiai Kiad Budapest, Hungary. -
EFFECTS OF VIRTUAL PRIVATE SOCIAL NETWORKING IN ACADEMIC PERFORMANCE OF STUDENTS
A virtual private social network (VPSN) is generated automatically amongst peers using a social media app to build ties. One of the most significant repercussions of students' excessive usage of social networking sites is a decline in their academic performance. In a study of medical students, social media and the internet were shown to harm students' academic performance and classroom attentiveness. An increasing number of studies link the use of social media to poorer academic performance, such as fewer students doing their assignments and lower test scores. Students who receive specialised training in deep learning will have the superior cognitive abilities needed to succeed in today's more cognitively demanding workplaces. It teaches children to be critical thinkers, productive members of society, and active participants in a democratic society. As a perceptron used in image recognition and processing, a convolutional neural network (CNN) processes pixel data from social networks. A CNN uses multiplayer perception to lessen the processing needs of pupils. Humans and neurons make up the VPSN-CNN network, which the article explains. Neurons generate dendrites and axons to receive and transmit signals, while humans engage with long-reaching telecommunication equipment or biological communication systems. These will help remember, learn, unlearn, and relearn what has already been learned. In courses where social networking sites were utilised in addition to traditional teaching methods, most students reported feeling more socially engaged and more positive about their educational experiences. Students' and instructors' concerns regarding the educational usage of social media are addressed with recommendations for further study and practice in better performance and accuracy for student's data secure and comparison with existing methods. 2023 Little Lion Scientific. All rights reserved. -
Effects of Yoga and Combined Yoga with Neuro-Linguistic Programming on Psychological Management in Mothers of Adolescents: A Randomized Controlled Trial
Adolescent parenting presents significant challenges for mothers, often leading to elevated levels of stress and anxiety that can adversely affect their well-being and parenting effectiveness. This study aims to evaluate the efficacy of yoga alone and in combination with Neuro-Linguistic Programming (NLP) in managing stress and anxiety among mothers of adolescent children. In this randomized controlled trial, 90 participants aged 35-55 years (mean age 44.564.58 years), each with at least one child aged 13-19 years, were randomly assigned to one of three groups: control, yoga, or yoga with NLP. Interventions were conducted over 12 weeks, with outcome measures assessed pre- and post-intervention by trained research assistants blinded to group allocation. The Depression, Anxiety, and Stress Scale (DASS-21), and Pittsburgh Sleep Quality Index (PSQI), were utilized to evaluate outcomes. Both intervention groups demonstrated significant reductions in depression, anxiety, and stress levels compared to the control group. The yoga with NLP group exhibited superior improvements across all primary outcomes, with statistically significant differences noted in depression (mean difference =7.1, p<0.001), anxiety (mean difference =5.1, p<0.001) and stress levels (mean difference =5.5, p<0.001). Additionally, sleep quality improved significantly in both intervention groups, with the yoga with NLP group showing greater benefits. This study provides evidence that yoga, particularly in combination with NLP, is an effective non-pharmacological approach for reducing stress and anxiety and improving sleep quality among mothers of adolescents. These findings support the integration of mind-body practices into mental health care, highlighting the potential synergistic benefits of combining physical and cognitive interventions. Future research should explore long-term effects and the mechanisms underlying these improvements. 2024 Montenegrin Sports Academy. All rights reserved. -
Effectual Energy Optimization Stratagems for Wireless Sensor Network Collections Through Fuzzy-Based Inadequate Clustering
Wireless Sensor Networks (WSNs) are crucial in the burgeoning Internet of Things (IoT) landscape, serving as a backbone technology that enables myriad applications across various industries. Originating as a simple methodology, WSNs have evolved significantly, propelled by rapid advancements in sensor technology and hardware capabilities. These networks play a pivotal role in collecting and transmitting data, which is essential for the infrastructure of most IoT systems. WSNs operate by deploying sensor nodes across diverse locations to gather environmental data. This scalability and adaptability of WSNs were demonstrated in studies where network coverage was expanded to include 100 and 200 nodes. Notably, the implementation of the innovative FLECH (Fuzzy Logic Energy-efficient Clustering Hierarchy) protocol significantly enhanced energy efficiency, reducing consumption by 12.69% in networks with 100 nodes and by 36.85% in those with 200 nodes, compared to the traditional LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol. This work innovatively combines fuzzy logic and Particle Swarm Optimization (PSO) for efficient Cluster Head selection in Wireless Sensor Networks. The evaluation of these protocols involved numerous simulations and communication tests to ascertain the First Node Die (FND) pointindicative of when a network begins to lose efficacy due to energy depletion. Results indicated that the LEACH protocol reached the FND point faster than FLECH, suggesting that FLECH may offer better longevity and durability for IoT applications, aligning with the needs for sustainable and efficient operation in expanding technological ecosystems. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. -
Efficacy of digital MBCT-PD in preventing postpartum depression and enhancing work motivation: A study protocol
Background: Postpartum depression (PPD) is a significant challenge for women transitioning back to work. While preventive measures are essential, the effectiveness of Mindfulness-Based Cognitive Therapy (MBCT) in this context remains underexplored. This study will assess the efficacy of a digital MBCT program (MBCT-PD) in preventing PPD, enhancing well-being, and motivating work resumption after childbirth. Methods: A randomized controlled trial (RCT) with repeated measures will evaluate MBCT-PD, a digitally delivered intervention designed to promote mindfulness and emotional resilience. Eighty consenting pregnant women aged 18+, between 16 and 32 weeks gestation, residing in urban India will be recruited and randomized to either the MBCT-PD group or an enhanced treatment-as-usual (TAU) control group, which includes additional prenatal wellness resources. The intervention will span eight weeks, with assessments at baseline, post-intervention (T1), and six weeks postpartum (T2). Primary outcomes are depression (Edinburgh Postnatal Depression Scale), well-being (Pregnancy Experience Scale-Brief), and work motivation (Multidimensional Work Motivation Scale). Secondary outcome is mindfulness level (Three Facet Mindfulness Questionnaire-Short Form). Descriptive statistics, repeated measures ANOVA, and regression analyses will determine the effect of MBCT-PD on these outcomes. Expected Results: We anticipate that the MBCT-PD group will show reduced PPD symptoms, improved well-being, and greater motivation to resume work than the control group, consistent with previous findings on mindfulness-based interventions. Conclusion: The findings from this study are expected to support the efficacy of MBCT-PD as a cost-effective, scalable intervention for enhancing postpartum mental health and work reintegration, with potential applications in maternal mental health practices and policies worldwide. Trial Registration: Clinical Trial Registry of India. CTRI/2024/03/064,831 2025 -
Efficacy of in-person versus digital mental health interventions for postpartum depression: meta-analysis of randomized controlled trials
Aim: This meta-analysis aimed to compare the efficacy of in-person and digital mental health interventions in addressing Postpartum Depression. Methods: Following PRISMA guidelines, the protocol for this meta-analysis was registered at the Open Science Framework (Retrieved from osf.io/wy3s4). This meta analysis included Randomized Controlled Trials (RCTs) conducted between 2013 and 2023. A comprehensive literature search identified 35 eligible RCTs from various electronic databases. Inclusion criteria focused on pregnant women over 18 years old, encompassing antenatal depression and up to two years postpartum. Diagnostic interviews or Edinburgh Postnatal Depression Scale (EPDS) were used to establish PPD. Digital interventions included telephonic, app-based, or internet-based approaches, while in-person interventions involved face-to-face sessions. Results: The meta-analysis revealed a moderate overall effect size of ?0.69, indicating that psychological interventions are effective for PPD. Digital interventions (g = ?0.86) exhibited a higher mean effect size than in-person interventions (g = ?0.55). Both types of interventions displayed substantial heterogeneity (digital: I2 = 99%, in-person: I2 = 92%), suggesting variability in intervention content, delivery methods, and participant characteristics. Conclusion: Digital mental health interventions show promise in addressing PPD symptoms, with a potentially greater effect size compared to in-person interventions. However, the high heterogeneity observed in both modalities underscores the need for further research to identify key drivers of success and tailor interventions to diverse populations. Additionally, the choice between digital and in-person interventions should consider individual needs and preferences. Ongoing research should further investigate and optimise intervention modalities to better serve pregnant women at risk of PPD. 2024 Society for Reproductive & Infant Psychology. -
Efficacy of Natural Zeolite and Metakaolin as Partial Alternatives to Cement in Fresh and Hardened High Strength Concrete
Urbanization and industrialization have dramatically increased the manufacture of cement causing substantial pollution of the environment. The primary global concern related to cement manufacture has been the management of the large carbon footprints. The usages of environmentally friendly cementitious materials in the construction of structures have proved to be a viable option to deal with this environmental concern. Therefore, it is necessary to further explore the usage of cementitious materials which can replace cement albeit partially. In this direction of research, two such cementitious materials, namely, natural zeolite and metakaolin have been investigated in this study. High-strength concrete M60 with natural zeolite and metakaolin as the partial replacements for the cement has been prepared in this work. Polycarboxylic ether-based superplasticizer solution has been used to enhance workability. The test specimen cast and cured for 3, 7, 28, 60, and 90 days at ambient room temperature has been tested for compressive strength, split tensile strength, and flexural strength as per the Indian standards. The optimum mix of high-strength concrete thus manufactured has met the Indian standards, and the combination of cement +5% natural zeolite +10% metakaolin has exhibited the highest compressive, split tensile, and flexural strengths at 90 days of curing. Natural zeolite and metakaolin when used in smaller proportions have increased the concrete strength, and these materials are recommended for partial replacement of cement. 2021 Iswarya Gowram et al. -
Efficacy of Psychosocial Care Training Programme for the Staff Working in Old Age Homes
Background: Training the old-age home staff is essential in raising geriatric mental health care standards in India. Inadequate knowledge on ageing and psychosocial interventions is a significant issue in old-age homes. Old-age home staff must know how to provide individualized psychosocial care and support for older adults. Hence this study aimed to test the feasibility of the psychosocial care training program for the staff working in old-age homes. Methods: A quasi-experimental research design (pre-post without a control group) was used. Forty-two staff members participated. Mary Starke Harper Aging Knowledge Exam (MSHAKE) and structured checklist to measure the staffs knowledge on ageing, psychosocial interventions, welfare legislations, schemes, and support services were administered before, immediately after, and two months after the program and the self-efficacy checklist was administered immediately and two months after the program, to examine the efficacy of the program. Results: Significant improvement was found in the ageing knowledge and the knowledge of psychosocial intervention and psychosocial care. These improvements continued for two months (p <.001). Similarly, their self-efficacy in managing such problems was also sustained across two post-measurements (p =.045). Conclusions: Face-to-face training programs would enhance the knowledge of the old age home staff. This Psychosocial Care Training module can be used for training old age home staff to address various psychosocial needs, concerns and other psychosocial problems of the residents. 2023 The Author(s). -
Efficiency evaluation of total manufacturing sectors of India DEA approach
Efficiency, Productivity and Competitiveness are some of the performance indicators of any manufacturing firm. In Indian soil manufacturing industry plays predominant role in the countrys economy. Industrialization of manufacturing sectors generates employment, income and promotes GNP. To institute suitable policy measures it is desirable to divide the total manufacturing sectors of Indian states into efficient and inefficient. This study treats the total manufacturing sectors of a state as a decision making unit. 14 states account for more than 80% of total value added. Data Envelopment Analysis models are used to assess the efficiency of total manufacturing sectors of 14 Indian states. Research India Publications. -
Efficiency Wage and Productivity in the Indian Microfinance Industry: A Panel Evidence
Enhanced productivity remains a crucial agenda for firms to attain cost and competitive advantages in the market. Hence, the main purpose of this study is to investigate the effects of efficiency wage (EW) on the productivity of microfinance institutions (MFIs) with respect to their dual objectives, namely, outreach (depth and breadth) and financial sustainability. Unbalanced panel data of 179 Indian MFIs were collected over the period 20102018 from the Microfinance Information Exchange (MIX) market platform (now obtainable from the World Bank catalogue). Under a static model setting (fixed effects model), the observed relationship between EW and MFIs productivity is mixed. On the one hand, EW exhibits a strong and statistically significant positive relationship with the breadth of outreach, even after considering various control variables and alternative proxies of EW. On the other hand, EW shows no positive influence on the MFIs depth of outreach; rather, it results in a mission drift of MFIs, with the poorest of the poor being neglected (weak and insignificant for proxy of EW). Concerning the financial sustainability of MFIs, EW exhibits a positive and statistically significant effect, except for the profitability dimension when an alternative proxy of EW is used. A two-step system generalized method of moments (GMM) performed to limit endogeneity problems also validates most of our findings. The outcomes of this study could help MFIs managers in designing appropriate financial packages to enhance MFIs productivity and subsequently attain the dual objective of outreach and sustainability. 2022 SAGE Publications. -
Efficient Brain Tumor Identification Based on Optimal Support Scaling Vector Feature Selection (OSSCV) Using Stochastic Spin-Glass Model Classification
Brain tumor detection is a developing defect finding task in medical imaging, as premature and early identification is a critical once for recommending early treatment. The tumor are identified by the laboratory through MRI images by finding the tumor regions. The Artificial intelligence play a vital role for finding, analyzing, the image data to attain the target results in medical image using various learning methodologies. Most of the existing system failed to find the find the feature dimension leads poor accuracy for identifying tumor regions due to low precision, recall rate, lower intensity in image coverage region. To resolve this problem, to propose an Optimal Support Scaling Vector Based Feature Selection (OSSCV) brain tumor identification using Stochastic Spin-Glass Model Classification (SSGM). Initially the preprocessing is done by bilateral filter and segmentation is applied by suing Active Region Slice Window Segmentation (ARSWS). To separate the tumor entity feature projection using Histogram color quantization and the features process are carried by Optimal Support Scaling Vector Based Feature Selection (OSSCV). The selected features get trained using Stochastic Spin-Glass Model Classification (SSGM) to find the tumor region. The proposed system outperforms traditional machine learning methods in brain tumor detection. Finally proposed system of Stochastic Spin-Glass Model (SSGM) performance of recall is 95.5%, the performance of F1-score is 96.1% and the performance of the 96.5%. The proposed approach has the potential to assist radiologists in diagnosing brain tumors more accurately and efficiently, leading to improved patient outcomes. 2024, Ismail Saritas. All rights reserved. -
Efficient cationic dye removal from water through Arachis hypogaea skin-derived carbon nanospheres: a rapid and sustainable approach
The present study investigates the potential of Arachis hypogaea skin-derived carbon nanospheres (CNSs) as an efficient adsorbent for the rapid removal of cationic dyes from aqueous solutions. The CNSs were synthesized through a facile, cost-effective, catalyst-free and environmentally friendly process, utilizing Arachis hypogaea skin waste as a precursor. This is the first reported study on the synthesis of mesoporous carbon nanospheres from Arachis hypogaea skin. The structural and morphological characteristics of the CNSs were confirmed by different nano-characterization techniques. The adsorption performance of the carbon nanospheres was evaluated through batch adsorption experiments using two cationic dyes-methylene blue (MB) and malachite green (MG). The effects of the initial dye concentration, contact time, adsorbent dosage, and pH were investigated to determine the optimal conditions for dye removal. The results revealed that the obtained CNSs exhibited remarkable adsorption capacity and rapid adsorption kinetics. Up to ?98% removal efficiency was noted for both dyes in as little as 2 min for a 5 mg L?1 dye concentration, and the CNSs maintained their structural morphology even after adsorption. The adsorption data were fitted to various kinetic and isotherm models to gain insights into the adsorption mechanism and behaviour. The pseudo-second-order kinetic model and Redlich-Peterson model best described the experimental data, indicating multi-layer adsorption and chemisorption as the predominant adsorption mechanism. The maximum adsorption capacity was determined to be 1128.46 mg g?1 for MB and 387.6 mg g?1 for MG, highlighting the high affinity of the carbon nanospheres towards cationic dyes. Moreover, CNS reusability and stability were examined through desorption and regeneration experiments, which revealed sustained efficiency over 7 cycles. CNSs were immobilised in a membrane matrix and examined for adsorption, which demonstrated acceptable efficiency values and opened the door for further improvement. 2024 RSC. -
Efficient chemical fixation of CO2from direct air under environment-friendly co-catalyst and solvent-free ambient conditions
The capture and conversion of CO2from direct air into value-added products under mild conditions represents a promising step towards environmental remediation and energy sustainability. Consequently, herein, we report the first example of a Mg(ii)-based MOF exhibiting highly efficient fixation of CO2from direct air into value-added cyclic carbonates under eco-friendly co-catalyst and solvent-free mild conditions. The bifunctional MOF catalyst was rationally constructed by utilizing an eco-friendly Lewis acidic metal ion, Mg(ii), and a nitrogen-rich tripodal linker, TATAB. The MOF possesses a high BET surface area of 2606.13 m2g?1and highly polar 1D channels decorated with a high density of CO2-philic sites which promote a remarkably high CO2uptake of 50.2 wt% at 273 K with a high heat of adsorption value of 55.13 kJ mol?1. The high CO2-affinity combined with the presence of a high density of nucleophilic and Lewis acidic sites conferred efficient catalytic properties to the Mg-MOF for chemical fixation of CO2from direct air under environment-friendly mild conditions. The remarkable performance of the Mg-MOF for the fixation of CO2from direct air was further supported by in-depth theoretical calculations. Moreover, the computational studies provided an insight into the mechanistic details of the catalytic process in the absence of any co-catalyst and solvent. Overall, this work represents a rare demonstration of carbon capture and utilization (CCU) from direct air under eco-friendly mild conditions. The Royal Society of Chemistry 2021. -
Efficient detection of faults and false data injection attacks in smart grid using a reconfigurable Kalman filter
The distribution denial of service (DDoS) attack, fault data injection attack (FDIA) and random attack is reduced. The monitoring and security of smart grid systems are improved using reconfigurable Kalman filter. Methods: A sinusoidal voltage signal with random Gaussian noise is applied to the Reconfigurable Euclidean detector (RED) evaluator. The MATLAB function randn() has been used to produce sequence distribution channel noise with mean value zero to analysed the amplitude variation with respect to evolution state variable. The detector noise rate is analysed with respect to threshold. The detection rate of various attacks such as DDOS, Random and false data injection attacks is also analysed. The proposed mathematical model is effectively reconstructed to frame the original sinusoidal signal from the evaluator state variable using reconfigurable Euclidean detectors. 2022, Institute of Advanced Engineering and Science. All rights reserved. -
Efficient discrimination by MIRU-VNTRs of Mycobacterium tuberculosis clinical isolates belonging to the predominant SIT11/EAI3-IND ancestral genotypic lineage in Kerala, India
The present study evaluated the ability of MIRU-VNTRs to discriminate Mycobacterium tuberculosis (MTB) clinical isolates belonging to the SIT11/EAI3-IND ancestral genotypic lineage, which is highly prevalent in Kerala, India. Starting from 168 MTB clinical isolates, spoligotyping (discriminatory index of 0.9113) differentiated the strains into 68 distinct patterns, the biggest cluster being SIT11/48 SIT11 ( n= 48). The present study shows that 12-loci MIRUs and 3 ETRs allowed an efficient discrimination of these isolates (discriminatory indexes of 0.7819 and 0.5523, respectively). 2013 Asian-African Society for Mycobacteriology. -
Efficient feature fusion model withmodified bidirectional LSTM for automatic Parkinson's disease classification
The majority of people affected by Parkinsons disease (PD) are middle-aged and older. The condition causes a variety of severe symptoms, including tremors, limited flexibility, and slow movements. As Parkinsons disease develops with changing symptoms and growing severity, the importance of computer-aided diagnosis based on algorithms cannot be highlighted. Gait recognition technology appears to be a potential path for Parkinson's disease identification since it captures unique properties of a persons walking pattern without requiring active participation, providing stability and non-intrusiveness. To begin,the median filter is used to remove noise from the input images received during data collection. This paper describes a new method for finding local and global features in gait images to assess the severity of Parkinsons disease.Local features are extracted using a stacked autoencoder, and global features are obtained using an Improved Convolutional Neural Network (ICNN). The Enhanced Sunflower Optimisation (ESO) technique is used to improve the CNN model's performance by optimizing hyperparameters such as batch size, learning rate, and number of convolutional layers. To classify PD severity, a modified bidirectional LSTM (MBi-LSTM) classifier receives input in the form of a combination of local and global features. The proposed model's performance is completely evaluated with the GAIT-IT and GAIT-IST datasets, which include key measures such as accuracy, precision, recall, and the F-measure. This study improves the diagnosis of Parkinsons disease by introducing a non-intrusive real-time monitoring system capable of early detection and prevention. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.