Journal of Research Administration

Salahedin Abdu Ebrahim, Prof. Jaladi Ravi PDF

This paper investigates green intellectual Capital for Sustainable Development within the Midroc Investment Group in Ethiopia. The main objective of the studies is to identify the challenges, Opportunities, and implementation factors of Intellectual Capital for Sustainable Development within the MIDROC Investment Group in Ethiopia.The researcher used a descriptive research design and cluster sampling, then selected a random sample from the resulting clusters. All units within the selected clusters are then surveyed. The number of people in the population who filled out and returned the form was 347, 316. The data were collected using a five-point Likert-scale questionnaire from the respondents and analysed in SPSS 27 to describe the data in a table and to test reliability. Based on the studies' findings, the researchers concluded that significant underappreciation of the importance of intellectual capital, insufficient costs for training and development, and insufficient R&D funding were major problemsMIGfaced. Insufficient investment in training, elevated turnover rates, Lack of involvement, ineffective management of intellectual capital, and obsolete technology are significant obstacles to advancing intellectual capital.Research indicates that leveraging intellectual capital can provide a competitive advantage and foster sustained innovation. A proposed framework utilising both quantitative and qualitative indicators is suggested for measuring intellectual capital. MIGis advised to manage knowledge and enhance technology to boost its intellectual capital and sustain competitiveness.  

Mobi.V.Babu, Ms.Suby Iype PDF

Forgetfulness can be a normal part of ageing. As people grow older, changes occur in all parts of the body, including the brain. Neuroplasticity or brain plasticity is the ability of the brain to change throughout an individual’s life. One of the ways to improve brain plasticity is by exercising the brain. The present study was undertaken to assess the effect of brain boosting exercises on age related memory loss among the elderly. The objectives of the study were to identify the prevalence of memory loss among the elderly, to determine the effectiveness of brain boosting exercise on age related memory loss among the  elderly and to examine the association between age related memory loss scores and selected baseline variables. The research design used for the study was a quasi experimental prê-test post-test control group design and the sampling technique was purposive sampling. The investigator selected 60 elderly individual between 65 and 85 years of age from a selected old age home in Pathanamthitta district of these 30 samples belongs to the experimental group and 30 samples belongs to the control group. The base line data were collected using socio demographic questionnaire. A memory scale was used to assess the memory deficiency of the subjects. The experimental group received the interventions for three weeks. The Independent sample t - test was used to assess the effect of brain boosting exercises and to determine the association between pre interventional score and baseline variables. The result showed that there was a statistically significant difference between pre and post memory score (p<0.01) in the experimental group. The study also study established a significant association between age and memory related problem with pre interventional score (p<0.01, 0.008).The study concluded that brain boosting exercise were effective in improving memory among the elderly.  

Srinivas Parashivamurthy; Ananda. H. T; Nagaraja. N; Ramachandra. S. R PDF

This study computes the elastic stiffness constants of crystals α-Al2O3 and ZnO belongs to group of oxides, -Si3N4 and h-BN belongs to group ofnitridesandSiC-4H, SiC-6Hbelongs to group of Carbides and its comparison with experimental results. Further, elastic moduli, eigenvalues of the stiffness matrix, the velocity of primary waves and shear waves in sample crystals along c-axis are computed using two different computational processes, viz., General Utility Lattice Program (GULF) and the ELATE Program. For the computation, the   reported experimental data of cell parameters of sample crystals are used. The computed elastic stiffness constants of sample crystals are  in close agreement with experimentally reported values. A high P- wave velocity of 13.52 Km/s is observed in -Si3N4 indicating high stiffness, high density with low porosity. A high S- wave velocity of 7.22 Km/s is observed in SiC-6H, indicating SiC-6H is more rigid, dense and posses strong elastic properties. All the sample crystals, except ZnO have significantly high elastic moduli. This indicates excluding ZnO, the remaining crystals are highly thermodynamically and mechanically stable, stiffer with hardness and toughness nature. Poisson’s ratio of  -Al2O3,ZnO, -Si3N4 and h-BN are in the range of 0.2 to 0.35 where as it is low in the range of 0.15 to 0.17for SiC-4H and SiC-6H. Spatial variation of elastic moduli and Poisson’s ratio of sample crystals are figured. The eigenvalues of stiffness matrix λ1, λ2, λ3, λ4 and λ5 are high for SiC-6H compared to that of other sample crystals. Interestingly, λ6 is high for h-BN compared to that of other sample crystals. Higher value of λ6 of h-BN compared to other samples indicates that, h-BN is adequately constrained and therefore stable.  

Mohammed Riad; Hicham El Bouanani PDF

In a context of accelerating digitalization of logistics and supply chains, organizations expect information systems (ERP, WMS, TMS) to deliver tangible efficiency gains (shorter lead times, higher productivity, fewer errors). However, such benefits depend less on the mere presence of tools than on perceived system quality, perceived information quality, and the level of support/training provided to users. Drawing on the information systems success literature (DeLone & McLean, 2003; Petter et al., 2008), this study examines the effects of system quality (QS), information quality (QI), and support/training (SF) on process performance (EP). Based on a 5-point Likert survey administered to N = 227 professionals, we estimate hierarchical multiple regression models with HC3 robust standard errors (MacKinnon & White, 1985). Results show positive and significant effects of QS (B = 0.339, p < 0.001), QI (B = 0.402, p < 0.001), and SF (B = 0.204, p = 0.002) on EP in the full model (R2 = 0.506), while tested moderation effects are not significant. Common method bias checks and diagnostic tests (VIF, Breusch-Pagan, Shapiro-Wilk, influential observations) complement the analysis and support the robustness of conclusions.  

Gausiya Yasneem, Tasneem Ahmed PDF

Monitoring of crops is considered a crucial component of today's agriculture, which allows for early identification of pests, diseases, and stress, as well as resource optimization and the promotion of sustainable practices.  Digital image processing (DIP) techniques, particularly those applied to satellite and drone photos, play an important role in soil moisture retrieval, crop health assessment, yield prediction, and insect identification.  These technologies enable the deployment of precision agriculture, which leads to increased production, lower costs, and more ecologically friendly agricultural practices.  Traditional manual approaches, on the other hand, are inefficient, time-consuming, and susceptible to inconsistencies, whereas DIP-based crop monitoring systems provide a more accurate, efficient, and scalable alternative. In this paper, an exhaustive review of DIP-based crop monitoring techniques using satellite and drone images, along with a bibliometric study of articles published between 2015 and 2024 and the growing impact of precision agriculture, internet of things (LOT), and machine learning on global agricultural practices, revealing key trends and influential research contributions from various regions, has been conducted. It is observed that in the future, precision agriculture systems using explainable artificial intelligence (AI) can enhance crop management by accurately identifying plant stress and infections through visualization maps, leading to smarter, data-driven farming decisions.