Green University of Bangladesh

The conventional reactive dyeing method of cotton fabric uses a large amount of Glauber salt for exhaustion. The large quantity of salt is an indirect threat to the environment and aquatic life as it produces toxic effluents and increases the salinity of discharged water, and also creates osmotic imbalance by increasing total dissolved solids. Elevated salinity also has significant adverse effects on plant growth and agricultural production. The present work focuses on salt-free dyeing of cotton fabric with reactive Remazol Red 198 (RR198) dye in the presence of alkali. The proposed method was evaluated by the color strength and fastness properties of the dyed fabrics. Various fastness properties (color fastness to wash, water, perspiration, rubbing and light) presented satisfactory results, mostly in the range of 4-5 (on a scale of 5). The maximum color strength value (K/S) obtained was 4.8, which is significant for optimum depth of shade. The CIE DE value was 0.29 for 1% shade, which is within the permitted limit. Moreover, there was no evidence of any deterioration in the fabric's strength. Wastewater produced in the salt-free dyeing approaches had better water quality than waste-water produced using the conventional dyeing method. Therefore, from an eco-friendly dyeing perspective, the suggested dyeing procedure is attractive.

In Bangladesh, dairy industry plays a pivotal role in country's economic growth. Which is why integrating Biogas with Solar Photovoltaic (PV) systems offers a reliable and sustainable energy solution for dairy farms. By supplementing solar energy in a hybridized setup, biogas ensures a consistent power supply, while also contributing to waste management and environmental sustainability. In this paper, area, load demand and biogas-solar generation potential in a conventional Bangladeshi dairy farm is studied. Then four renowned nature-inspired algorithms, Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Artificial Hummingbird Algorithm (AHA), and Polar Fox Optimization Algorithm (PFOA) are applied to optimize the design parameters of a Hybrid Biogas-Solar System for the farm. The primary objectives of optimization are to minimize costs, maximize energy output, and promote self-sustainability in dairy farming. The simulation results revealed that the system provided by the Artificial Hummingbird Algorithm (AHA) achieved a levelized cost of energy (LCOE) of $0.045/kWh, a grid independent design with 0% loss of power supply probability (LPSP), and a payback period of 10 years and 4 months, making it the best-performing solution in terms of system reliability, self-sufficiency and energy efficiency compared to the GWO, WOA and PFOA.

Skin cancer is a potentially life-threatening disease resulting from DNA damage, where early detection is critical to improving survival rates. This study introduces an advanced hybrid deep learning framework designed to enhance the accuracy of skin cancer classification, distinguishing between benign and malignant lesions. Our approach begins with data pre-processing to improve input quality, followed by training two high-performing pre-trained deep learning models, InceptionV3 and DenseNet121. We then apply a weighted sum rule for fusion of the model predictions, leading to high accuracy and generalizability across datasets. On the primary dataset, our hybrid model achieved 92.27% accuracy, 90.80% precision, 92.33% sensitivity, 92.22% specificity, and a 91.57% F1 score. Notably, the model also demonstrated robustness on an external dataset, achieving 93.45% accuracy, 91.87% precision, 93.04% sensitivity, 93.18% specificity, and a 92.30% F1 score, outperforming existing models. This study offers a reliable, highly generalizable solution for automated skin cancer diagnosis and represents a valuable contribution to early detection methods, with the potential to improve patient outcomes significantly.

In the new era of adopting and managing new and robust technologies in banking, the use of blockchain technology has significantly transformed overall banking systems. To add new insights to the body of existing knowledge, the authors conducted a systematic review with bibliographic network mapping to identify and analyse the factors contributing to adopting blockchain in the banking industry. Following the latest protocols of the PRISMA flowchart, this study acknowledged 16 relevant publications from 2590 papers in the databases, namely Scopus, ScienceDirect, Web of Science, and IEEE Xplore. The bibliographic data were grouped and analysed using VOSviewer to create network visualization maps that included citation and co‐citation, bibliographic coupling, co‐authorship, and co‐occurrence of terms. Subsequently, significant terms were identified through the analyses and compared with those found in the 16 relevant papers. The aggregate findings suggest that multiple influencing factors have been recognized and later categorized into three thematic drivers: transparency‐driven security, collaborative interoperability, and organizational infrastructure. The current research provides valuable insights for policymakers, technologists, researchers, consultants, and practitioners of information systems by proposing a technological framework, which will aid in developing tailored strategies to facilitate the sustainable practice of blockchain in the banking industry to a wider extent.

The performance of wireless networks has continuously improved using Directional Wireless Sensor Networks (DSNs). DSN achieves this by enhancing data sensing accuracy, maximizing spectrum use, decreasing interference and collisions, and lengthening the network’s operating lifetime.Within the domain of Directional Sensor Networks (DSNs), the primary difficulties are coordinating transmission and reception possibilities for a variety of sender-receiver pairs and offering traffic priority-aware channel access. To improve the quality and efficiency of multi-channel communication in DSNs, this research presents a quality-aware Directional MAC, Q-DMAC a novel medium access control protocol. The novelty of our protocol lies in its ability to leverage multi-channel resources effectively, enabling reuse through nodes equipped with directional antennas while ensuring the Quality of Service (QoS). Proposed Q-DMAC assigns a higher number of slots inside a data window to nodes carrying more packets waiting to be sent and prioritizes medium access to nodes carrying a significant amount of data packets. Also by employing regret matching of game theory Q-DMAC selects channels resulting in reduced channel switching cost as well as computation cost. Simulation results demonstrate that Q-DMAC outperforms existing protocols such as CAR MAC, 2D-CMDMAC, and DCDS-MAC, achieving up to 20% higher throughput, 15% lower latency, 12% better packet delivery ratio, 25% longer network lifetime, and 15% reduced overhead.

To stabilize frequency in a power system, this research study suggests a novel modified Gorilla Troops Optimizer (mGTO) technique, which builds on the original technique, and offers notable gains in effectiveness and efficiency when solving real-world optimization problems. A thorough comparative analysis reveals that the mGTO algorithm is the best option, outperforming its counterparts in terms of stability and overall performance. Interestingly, mGTO performs better than any of its competitors in terms of stability, making it the best option. The mGTO algorithm significantly reduces implementation time and enhances solution quality compared to the conventional GTO algorithm. A new linearized Phillips–Heffron model with an IPFC was developed to investigate power systems’ stability. To effectively dampen low-frequency oscillations, an auxiliary controller for modeling the IPFC is proposed. It provides four options for damping controllers, and the recommended mGTO algorithm is used to adjust the controller parameters. This method is superior to traditional controllers in stability control and has undergone extensive validation. It is a crucial instrument for controlling the frequency of an SMIB power system based on IPFC. Based on the simulation results, the updated strategy that has been suggested is the most effective way to define the mentioned damping controller by considering the percentage improvement in the goal function value.

Computational approaches can speed up the drug discovery process by predicting drug-target affinity, otherwise it is time-consuming. In this study, we developed a convolutional neural network (CNN)-based model named SMILES2DTA (Simplified Molecular Input Line Entry System to Drug-Target Affinity) for predicting the binding affinity between a drug and a target protein. The model utilizes CNNs to process sequences of both drug SMILES and target proteins. SMILES2DTA generates multiple sequences from a single drug SMILES sequence, validates them based on Lipinski’s rule of five, and assesses their binding affinity against a target protein sequence. We evaluated our model using publicly available datasets and compared its performance to state-of-the-art methods. The results showed that SMILES2DTA outperformed the existing methods and demonstrated improved accuracy, mean squared error, and area under the precision-recall curve. SMILES2DTA has the potential to speed up the drug discovery process by reducing the time and cost complexity of identifying effective drugs.

Many areas in Asia may lack the necessary infrastructure, such as sensor networks and data analytics platforms, to support artificial intelligence (AI) applications effectively. Additionally, there may be barriers to technology adoption, including cost constraints and technical expertise. AI offers significant potential for improving water management in Asia; addressing these challenges will be crucial for realizing the full benefits of AI-driven solutions and achieving water sustainability goals in the region. Thus, this study aims to investigate how AI can contribute to sustainable water management systems in Asia. This research used a three-phase systematic review approach involving planning, conducting, and reporting. The approach aims to ensure a rigorous and unbiased examination of extant literature, enhancing the study’s credibility. Using appropriate inclusion–exclusion criteria and preset filters, 41 papers were selected from a total of 245 documents across both Web of Science (WoS) and Scopus databases. The review highlights AI’s potential to revolutionize water management practices by facilitating informed decision-making, optimizing resource allocation, and enhancing infrastructure maintenance. AI technologies, including machine learning and deep learning, offer advanced capabilities for predicting water availability, managing water footprints, and optimizing infrastructure operations. AI can be applied across various water management aspects, such as distribution, quality monitoring, disaster management, and infrastructure development. Despite its benefits, AI adoption faces challenges, such as data availability, computational complexity, and regional adoption variability. Ensuring reliable, secure, and ethical AI use is crucial. While promising, further empirical evidence and case studies are needed to validate AI’s effectiveness in real-world settings. Collaboration among researchers, utilities, policymakers, and communities is vital for developing AI-driven approaches aligned with Sustainable Development Goals. While AI holds promise for improving water management sustainability and efficiency, addressing data, adoption, and ethical challenges is essential for realizing its full potential in ensuring equitable access to clean water and achieving long-term water security in Asian nations and beyond.

The chapter highlights a value-driven initiative of co-creation in teaching and learning in higher education institutions to address the needs that emerged among local communities. The study presents three cases from three universities in Bangladesh. Findings demonstrate that staff-student co-creation can be applied as an inclusive tactic for facilitating teaching and learning in higher education. It can also build awareness among learners as active well-being agents. The pedagogical approaches endorsed several sustainable development goals (SDGs) and accrued cumulative social, economic and environmental values for the local community. The analysis of the cases offers significant implications for educators, researchers, and policymakers in academia and beyond.

This work introduces an efficient operational strategy for electric vehicles (EVs) to optimize economic outcomes in a wind‐integrated hybrid power system. The proposed method enhances the profitability of a combined wind‐thermal‐EV‐fuel cell system while maintaining grid frequency stability and managing the energy states of EV storage. Accurate wind speed forecasts are crucial, as wind farms must provide projected generation data to the market controller for coordinated scheduling with thermal units. Due to wind speed variability, discrepancies between actual and predicted values can lead to mismatches in wind power output, causing financial penalties from divergence prices. To address this, the optimal deployment of the EV storage system is designed to mitigate these financial impacts. By coordinating EV, wind, and thermal operations, the approach effectively reduces wind power unpredictability and ensures economic efficiency, a necessity in competitive power markets. Four distinct energy states of the EV battery‐ maximum, optimal, low, and minimum, are proposed to enhance cost efficiency. The EV storage mode is dynamically adjusted based on real‐time grid frequency and wind speed data. Additionally, a fuel cell is incorporated to boost economic returns further. The effectiveness of the strategy is validated using an IEEE 30‐bus test system, employing sequential quadratic programming and demonstrating notable improvements over existing methods.

This paper improves the ill-condition of bone-conducted (BC) speech signal by reducing the eigenvalue expansion. BC speech commonly contains a large spectral dynamic range that causes ill-condition for the classical linear prediction (LP) methods. In the field of numerical analysis, we often face the situation where an ill-conditioned case occurs in finding the solution. Principally, eigenvalue expansion causes ill-condition in numerical analysis. To mitigate this problem, the regularized least squares (RLS) technique is commonly used. Motivated by the RLS concept, we derive the regularized modified covariance (RMC) method for BC speech analysis in this study. The RMC method reduces eigenvalue expansion by compressing the spectral dynamic range of the speech signal. Thus, the RMC method resolves the ill-conditioned problem of LP. In experiments, we show that the RMC method provides compressed eigenvalue expansion than the conventional methods for BC speech where synthetic and real BC speeches are considered. The performance of the RMC method is affected by the setting of the regularization parameter. In this paper, the regularization parameter in practice is iteratively and rule-based derived. The RMC method with such a setting provides the best performance for BC speech analysis.

Recently, there has been a push to integrate renewable energy system (RES) into grid‐connected load system in enhancing reliability and reducing losses. However, integrating these systems introduces power quality (PQ) issues, especially with non‐linear, critical, and imbalanced loads. Addressing this, a hybrid mantis search‐reptile search algorithm (HMS‐RSA) combined with a unified power quality conditioner (UPQC) to mitigate PQ problems related to current and voltages in RES systems. In other words, the UPQC, enhanced by fractional order proportional integral derivative controller parameters tuned using the proposed HMS‐RSA assists in enhancing the power quality. The approach has been validated by connecting a non‐linear load to the system, which typically creates PQ issues. The proposed method is implemented in MATLAB/Simulink and their performance is analysed in three scenarios, such as sag, swell, and disturbance, and the total harmonic distortion is evaluated to quantify improvements in PQ. Finally, the proposed method is compared with existing approaches, such as ant colony optimization (ACO), artificial bee colony optimization (ABC), and bacterial foraging optimization (BFO). The method also outperforms ACO, ABC, and BFO in terms of convergence speed and effectiveness in mitigating PQ issues.