Genome data, characterized by its high dimensionality and complexity, presents significant challenges for computational analysis and biological interpretation. Feature selection plays a crucial role in reducing dimensionality, improving model interpretability, and enhancing predictive performance by identifying the most informative genomic attributes. In this study, we construct a robust, generalisable ensemble framework for the feature selection and ML classification of metagenomic data. The framework incorporates six different feature selection algorithms of different types working in an ensemble. We comprehensively assess four ML classifiers to pair with them and three aggregation methods to combine their results, testing numerous configurations to find which ones perform best. Our result shows that Random Forest is a general and reliable algorithm for metagenomic datasests and consistent with the literature, we found that feature selection universally improves classification performance, though this improvement varies per dataset and, on non-wrapper methods, depends on choosing the right subset size. When looking at their best scores, the six FS algorithms performed broadly similarly across the data, with the largest differences being on the hardest-to-classify datasets, where mRMR and Boruta edged out the others.
The dam repair process entails the usage of several vital equipment and techniques. There are many problems that dam structures can encounter. Hence, repair is necessary. The paper looks into how different materials and substances are utilized in fixing processes by examining scientific literature and real-world examples. The main aim of this research is to understand how these parts work together to maintain the integrity and functionality of dams and also prolong their lifespan and safety. The study of the properties of materials and their practical applications is vital in the field of dam engineering and maintenance. The data presented here can aid in enhancing methods of construction and maintenance of dams.
In the history of digital ecosystems, security has always been and will continue to be a primary issue. Newer fields of study, such as blockchain and wireless networks, continue to confront the difficulties that are exclusive to them despite technological breakthroughs. This article provides an assessment of these two important domains, diving into their relevance and fundamental security methodologies, primary research, unsolved challenges, and prospective future paths. This article covers the key components of blockchain security and emphasizes the importance of wireless network security. A comparative study of the security of blockchain technology and wireless networks elucidates the distinctive qualities, difficulties, and solutions that each one presents. This paper also explores the synergistic integration of AI and blockchain to address the multifaceted security challenges in modern wireless networks.
Utilizing available renewable energy resources has been characterized as a reliable indicator to mitigate energy dependency in countries as well as securing the supplying of energy-based needs in the future. This research explores the impact of renewable energy as trustworthy resources in mitigating energy imports and how accurately predicting the energy consumption can lead to better examination of energy dependency. A system dynamics model with special aim on the role of renewable energy resources on decreasing energy dependency has been constructed. By analyzing the dynamics of the model, different scenarios of renewable energy policies are employed as interventions to be implemented and assessed in the model while investigating the applicability of renewable energies to manage national energy supply sustainably. To illustrate the benefits of renewable energy utilization, the proposed model is applied to a case study to analyze the decrease in imported energy resources from external sources. The results indicate that the system dynamics approach outperforms in predicting energy demand compared to the most commonly used techniques in energy forecasting studies and under which policies the desired level of energy dependency will be sustainably achieved.
This study critically assesses the current state, global positioning, and future prospects of Bangladesh's shipbuilding industry. Using a mixed-methods approach that combines global benchmarking, policy analysis, focus group discussions, and statistical forecasting (ARIMA and Holt-Winters models), the paper identifies key structural deficiencies and emerging opportunities. Although Bangladesh offers one of the world’s lowest labor costs, it significantly lags in productivity, technological integration, and coordinated policy support compared to global leaders such as Japan, South Korea, and China. The analysis finds that Bangladesh is well-positioned to target niche markets for small- and medium-sized vessels (1,000–10,000 DWT), which remain underserved globally. Forecast models suggest that Bangladesh could achieve a 2% global market share by 2033, translating to over USD 4 billion in annual export earnings. To realize this potential, the study recommends a comprehensive national industrial policy, strategic investment in automation and R&D, and dedicated export financing mechanisms. Limitations include outdated productivity data and limited access to primary financial records from local shipyards. Future research should address green shipbuilding, institutional capacity building, and innovation ecosystems.
