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This research focused on Walton, Bangladesh's largest manufacturer in the electrical, electronics, automobile, and home appliance sectors. Walton mainly focused on its innovative and sustainable business practices. Its strategic emphasis on advanced R&D and Advanced manufacturing system has positioned it in leading position locally and helps it to compete global market. The company’s commitment to quality, affordability, and sustainability has made it competitive internationally. This research highlights Walton's technological, organizational, environmental approach to demonstrating how these elements contribute to its economic success and social impact. By exploring Walton's role in job creation, skill development, and export growth, the study offers insights into how emerging market firms can achieve global competitiveness. Walton's practices provide insights for integrating technological advancement and environmental responsibility, setting new standards in the industry and offering a model for other corporations in developing economies.

This exploratory research of data lakes in big data times is a prominent topic for both academia and industry. One of the main motivations behind is that companies need to cope with more data than ever before, and the problems of how to analyze even how to store data are becoming more and more challenging in many industries. The occurrence of the concept of a data lake to meet such big data problems is enlightening and will most likely be considered in any relevant big data strategy. This review paper presents a summary of some popular data lake concepts at present, followed by its advantages, potential risks and criticism from some professionals as well. Additionally, a general process in a data lake is described.

Internet of Things (IoT) is supposed to revolutionize the future extended smart applications and also helped to create the smart environments such as smart cities, smart buildings, smart kitchen, smart vehicles and also smart hospitals for every moment monitoring of the MAPE 15.15, VAF 96.16% and R2 0.92) is better than normal IoT based kitchen. Therefore, our proposed model is Patients. Artificial Intelligence (AI) plays a spreading role in IoT development and applications. Till date, most of the major companies integrated their IoT platforms with AI capabilities such as analytics based on Deep and Machine learning based. Synergy of IoT and AI is the key to unlock the potential of IoT. This paper proposed a smart kitchen system in this regard. An approach of prediction and tracing has been adopted with various prediction-based algorithms while using the devices (components of kitchen). Predictions are calculated to achieve subsequent next acknowledgment focusing on specific occasions for mechanization. Experimental results show that our proposed model of Smart kitchen (RMSE 0.0561, better and promising way for research and development engineers in the task of implementing smart kitchen.

The global business landscape is undergoing a significant transformation driven by the rapid growth of electronic business (e-business). This study investigates the e-business evolution stages in the Philippines and South Korea through a quantitative research design, employing a survey questionnaire based on the e-business evolution model by Bradley et al. (2015). The findings indicate that the Philippines is predominantly in the Growth stage, whereas South Korea is in the Transformation stage, reflecting varying levels of e-business maturity. Both countries demonstrate positive perceptions of social, economic, and technical factors, although concerns remain regarding perceived risks and transaction costs. This comparative analysis provides valuable strategic insights for policymakers, industry leaders, and stakeholders aiming to navigate and leverage emerging e-business opportunities across Asia.

Late blight, caused by Phytophthora infestans, threatens tomato and potato crops in Nepal. This study explores developing and deploying a mobile application powered by a graph neural network (GNN) to predict late blight risk for Nepali farmers. The GNN trained on 43 years of NASA satellite weather data can generate 5-days forecast for 320 weather stations in Sudurpashim and Karnali Province, Nepal. The mobile application offers user-friendly forecasts and visualizes late blight risk through clear graphical interfaces. In the visited sites, 30% of tomato and potato crops were found infected with late blight, which the app had identified as high-risk. Samples infected with late blight were collected and analyzed in a wet lab setting. All infected samples tested positive for P. infestans, confirming the app's ability to predict real-world late blight outbreaks. This study showcases the successful development and deployment of a GNN-powered mobile application for assessing late blight risk in Nepal. The application disseminates critical weather information and localized risk assessments, potentially enhancing late blight management in tomato and potato crops. Further research, including extensive field trials comparing with farmers' practices, could increase the application's usability in Nepali fields.

The global population is increasing rapidly because of increasing urbanization and such increasing urbanization directs the up-growing need of urban safety and preventions. This urbanization is also responsible for two things that is increased job opportunities and increased the crime rates. In this era technology has gone far more forward in a positive way. By making use of these technologies such as machine learning, artificial intelligence and big data we presented an approach through which crime pattern analysis is done. We have used apache spark (scala-programming) and machine learning algorithm for predictive crime pattern analysis. The data that we have used is a real-world data set based on Chicago city of United State of America. Our main goal of work is to define a predictive crime analysis which shows top crime patterns related to the top community areas of Chicago city.

Network analysis is one of the hottest areas of research in biotechnology and biomedical research. It is a straight forward method of representing local and global characteristics of biological nodes representing the various biological elements. In network analysis, we use mathematical graph theory to show the interaction among the genes and its product. This represents the various biological activities in the cell. In order to predict the certain activity and interaction among the biological elements, it is necessary to find the optimal path in the network. This optimal path is usually the shortest path, which clearly depicts the key elements involved for the particular reaction. It is also necessary to count the number of shortest paths between the given pairs of genes in the network. This paper describes the need for finding shortest path in the biological network and illustrate the usage of Bellman-Ford algorithm to find the shortest path in Hutchinson-Gilford Progeria Syndrome (HGPS) data sets.

In this paper, a microstrip bandstop filter (BSF) is designed and simulated for X-band wireless communication system. It consists of open stubs (parallel line with T-shaped) coupled with high impedance of stepped impedance resonator (SIR) at 7.5 GHz for X-band system applications. All the stubs are optimized to have the stopband characteristics. The layout pattern is simulated using Sonnet EM tool on Teflon substrate with a dielectric constant of 2.54 and thickness of 0.54 mm. The main features of this work are smaller size, low cost, and controllable bandwidth. The bandwidth can be controlled by adjusting the parallel line open stubs connected with open T-stub. The 3dB bandwidth is achieved as 0.3 GHz at 7.5 GHz with good frequency responses. The overall physical dimension of the designed BSF is 23.62 mm by 8.0 mm excluding reference lines.

This paper presents the design and implementation of a microwave based cavity based structure to determine the real-time glucose level based on the frequency shift. The glucose meter, which has an attenuation level greater than -30 dB using a single transmission pole, resulted in better selectivity and can be implemented for the measurement glucose concentration in an aqueous glucose solution. The noticeable variation in the resonance frequency as well as complex permittivity was achieved using the various glucose that validates the possibility of the devices as a glucose meter. The obtained parameters supports the development of resonance-based dielectric sensors using permittivity characterization with a resolution of 3.11 pF/mgdl-1 and a minimum detectable glucose level of 8.60 mgdl-1. This unique approach, which has long-term reliability for mediator-free glucose sensing, is the incremental step in the filed non-invasive glucose monitoring.