Bangladesh University of Engineering and Technology

Bangladesh University of Engineering and Technology
Short name
BUET
Country, city
Bangladesh, Dhaka
Publications
5 567
Citations
102 157
h-index
119
Top-3 journals
Procedia Engineering
Procedia Engineering (179 publications)
AIP Conference Proceedings
AIP Conference Proceedings (155 publications)
Top-3 organizations
Top-3 foreign organizations
University of Malaya
University of Malaya (84 publications)
University of Alberta
University of Alberta (66 publications)

Most cited in 5 years

Saadi M.A., Maguire A., Pottackal N.T., Thakur M.S., Ikram M.M., Hart A.J., Ajayan P.M., Rahman M.M.
2022-04-28 citations by CoLab: 628 Abstract
Additive manufacturing (AM) has gained significant attention due to its ability to drive technological development as a sustainable, flexible, and customizable manufacturing scheme. Among the various AM techniques, direct ink writing (DIW) has emerged as the most versatile three-dimensional (3D) printing technique for the broadest range of materials. DIW allows printing of practically any material, as long as the precursor ink can be engineered to demonstrate appropriate rheological behavior. This technique acts as a unique pathway to introduce design freedom, multifunctionality, and stability simultaneously into its printed structures. Here, we present a comprehensive review of DIW of complex 3D structures from various materials, including polymers, ceramics, glass, cement, graphene, metals, and their combinations through multimaterial printing. The review begins with an overview of the fundamentals of ink rheology, followed by an in-depth discussion of the various methods to tailor the ink for DIW of different classes of materials. Then, the diverse applications of DIW ranging from electronics to food to biomedical industries have been discussed. Finally, we highlight the current challenges and limitations of this technique, followed by its prospects as a guideline towards possible futuristic innovations. This article is protected by copyright. All rights reserved
Rahman C.R., Arko P.S., Ali M.E., Iqbal Khan M.A., Apon S.H., Nowrin F., Wasif A.
2020-06-01 citations by CoLab: 318 Abstract
An accurate and timely detection of diseases and pests in rice plants can help farmers in applying timely treatment on the plants and thereby can reduce the economic losses substantially. Recent developments in deep learning based convolutional neural networks (CNN) have greatly improved the image classification accuracy. Being motivated by the success of CNNs in image classification, deep learning based approaches have been developed in this paper for detecting diseases and pests from rice plant images. The contribution of this paper is two fold: (i) State-of-the-art large scale architectures such as VGG16 and InceptionV3 have been adopted and fine tuned for detecting and recognizing rice diseases and pests. Experimental results show the effectiveness of these models with real datasets. (ii) Since large scale architectures are not suitable for mobile devices, a two-stage small CNN architecture has been proposed, and compared with the state-of-the-art memory efficient CNN architectures such as MobileNet, NasNet Mobile and SqueezeNet. Experimental results show that the proposed architecture can achieve the desired accuracy of 93.3\% with a significantly reduced model size (e.g., 99\% less size compared to that of VGG16).
Karmaker C.L., Ahmed T., Ahmed S., Ali S.M., Moktadir M.A., Kabir G.
2021-04-01 citations by CoLab: 279 Abstract
Motivated by the COVID-19 pandemic and the challenges it poses to supply chain sustainability (SCS), this research aims to investigate the drivers of sustainable supply chain (SSC) to tackle supply chain disruptions in such a pandemic in the context of a particular emerging economy: Bangladesh. To achieve this aim, a methodology is proposed based on the Pareto analysis, fuzzy theory, total interpretive structural modelling (TISM), and Matriced Impacts Cruoses Multiplication Applique a un Classement techniques (MICMAC). The proposed methodology is tested using experienced supply chain practitioners as well as academic experts' inputs from the emerging economy. This study reveals the influential relationships and indispensable links between the drivers using fuzzy TISM to improve the SCS in the context of COVID-19. Findings also reveal that financial support from the government as well as from the supply chain partners is required to tackle the immediate shock on SCS due to COVID-19. Also, policy development considering health protocols and automation is essential for long-term sustainability in supply chains (SCs). Additionally, MICMAC analysis has clustered the associated drivers to capture the insights on the SCS. These findings are expected to aid industrial managers, supply chain partners, and government policymakers to take initiatives on SSC issues in the context of the COVID-19 pandemic.
Rafi S.H., Nahid-Al-Masood, Deeba S.R., Hossain E.
IEEE Access Q1 Q2 Open Access
2021-02-20 citations by CoLab: 258 Abstract
In this study, a new technique is proposed to forecast short-term electrical load. Load forecasting is an integral part of power system planning and operation. Precise forecasting of load is essential for unit commitment, capacity planning, network augmentation and demand side management. Load forecasting can be generally categorized into three classes such as short-term, midterm and long-term. Short-term forecasting is usually done to predict load for next few hours to few weeks. In the literature, various methodologies such as regression analysis, machine learning approaches, deep learning methods and artificial intelligence systems have been used for short-term load forecasting. However, existing techniques may not always provide higher accuracy in short-term load forecasting. To overcome this challenge, a new approach is proposed in this paper for short-term load forecasting. The developed method is based on the integration of convolutional neural network (CNN) and long short-term memory (LSTM) network. The method is applied to Bangladesh power system to provide short-term forecasting of electrical load. Also, the effectiveness of the proposed technique is validated by comparing the forecasting errors with that of some existing approaches such as long short-term memory network, radial basis function network and extreme gradient boosting algorithm. It is found that the proposed strategy results in higher precision and accuracy in short-term load forecasting.
Bharati S., Podder P., Mondal M.R.
2020-07-04 citations by CoLab: 256 Abstract
Lung disease is common throughout the world. These include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, fibrosis, etc. Timely diagnosis of lung disease is essential. Many image processing and machine learning models have been developed for this purpose. Different forms of existing deep learning techniques including convolutional neural network (CNN), vanilla neural network, visual geometry group based neural network (VGG), and capsule network are applied for lung disease prediction.The basic CNN has poor performance for rotated, tilted, or other abnormal image orientation. Therefore, we propose a new hybrid deep learning framework by combining VGG, data augmentation and spatial transformer network (STN) with CNN. This new hybrid method is termed here as VGG Data STN with CNN (VDSNet). As implementation tools, Jupyter Notebook, Tensorflow, and Keras are used. The new model is applied to NIH chest X-ray image dataset collected from Kaggle repository. Full and sample versions of the dataset are considered. For both full and sample datasets, VDSNet outperforms existing methods in terms of a number of metrics including precision, recall, F0.5 score and validation accuracy. For the case of full dataset, VDSNet exhibits a validation accuracy of 73%, while vanilla gray, vanilla RGB, hybrid CNN and VGG, and modified capsule network have accuracy values of 67.8%, 69%, 69.5%, 60.5% and 63.8%, respectively. When sample dataset rather than full dataset is used, VDSNet requires much lower training time at the expense of a slightly lower validation accuracy. Hence, the proposed VDSNet framework will simplify the detection of lung disease for experts as well as for doctors.
Hasan M.M., Kubra K.T., Hasan M.N., Awual M.E., Salman M.S., Sheikh M.C., Rehan A.I., Rasee A.I., Waliullah R.M., Islam M.S., Khandaker S., Islam A., Hossain M.S., Alsukaibi A.K., Alshammari H.M., et. al.
2023-02-01 citations by CoLab: 244 Abstract
The chemical ligand of N,N–bis(salicylidene)1,2–bis(2–aminophenylthio)ethane (BSBAE) was synthesized and then embedded indirectly on the mesoporous silica for the fabrication of optical composite materials (OCM) for toxic cadmium (Cd(II)) ion detection and removal from wastewater solutions. The variable parameters were measured including solution acidity, contact time, initial concentration, selectivity, and sensitivity, on the detection and removal of Cd(II) by the OCM. The solution pH played an important role in detection and removal but the present OCM worked well in the acidic pH region at 3.50. The data clarified that OCM formed distinguished color upon the addition of trace level of Cd(II) ions. The results also disclosed that the OCM was not affected by the existing diverse metal ions and the signal intensity was observed only toward the Cd(II) ion. The OCM was able to detect the low-level Cd(II) ion as the detection limit was 0.32 µg/L and the adsorption of the highest removal capacity was 179.65 mg/g. In addition, the diverse foreign ions were not reduced the Cd(II) ion adsorption significantly, and the OCM has approximately no adsorption capacity for other ions at this pH. The elution of Cd(II) ions from the saturated OCM was desorbed successfully with 0.25 M HCl. The regenerated OCM that remained maintained the high selectivity to Cd(II) ions and exhibited almost the same adsorption capacity as that of the original OCM. However, the adsorption efficiency slightly decreased after several cycles according to the experimental data observation. Therefore, the proposed OCM offered a cost-effective material and may be considered a viable alternative for effectively toxic Cd(II) ion detection and removal from wastewater as potential materials.
Awual M.R., Hasan M.N., Hasan M.M., Salman M.S., Sheikh M.C., Kubra K.T., Islam M.S., Marwani H.M., Islam A., Khaleque M.A., Waliullah R.M., Hossain M.S., Rasee A.I., Rehan A.I., Awual M.E.
2023-08-01 citations by CoLab: 232 Abstract
The hybrid donor chemical ligand of 5-tert-butyl-2-hydroxybenzaldehyde thiosemicarbazone (THTB) was prepared and then embedded onto inorganic porous silica as hybrid conjugate materials (HCM). The Europium (Eu(III)) ion was selected from the lanthanides (Ln(III)) series for green and robust adsorption and recovery based on the adsorption, complexation, and selectivity tendency from the standpoint of the pH-dependent factor. The chemical compound of THTB consisted of O-, N-, and S-donor atoms and was able to make stable complexation with Ln(III) ions in optimum conditions due to the open functionality of the HCM. A surface complexation with a good complexation fitting to the experimentally collected data was used to describe the adsorption mechanism. The Eu(III) ion adsorption performance was measured with batch equilibrium methods. The affecting experimental protocols including solution pH, contact time, initial Eu(III) ion concentration, foreign ions effect, and recovery were carried out and evaluated consistently. The Eu(III) ion adsorption by the HCM was at pH 5.0 and this pH was selected to avoid the precipitation problem to ensure the adsorption mechanism. The co-existing several metal ions were not interfered with Eu(III) ion adsorption by the HCM due to the high affinity between Eu(III) ion and the functional groups of HCM. The bonding mechanism suggested that O-, N-, and S-donor atoms of THTB were strongly coordinated to Eu(III) with 2:1 ratio complexation. The Langmuir adsorption isotherm model was plotted due to the HCM morphology and applied to validate the adsorption isotherms according to the homogeneous ordered frameworks. The Eu(III) ion adsorption capacity by the HCM was 176.31 mg/g as expected because of the high surface area of the HCM. The adsorbed Eu(III) ion was completely eluted from HCM with the eluent of 0.20 M HNO3 and simultaneously regenerated into its initial form without significant deterioration. This study could be of great applicative utility for Eu(III) ions from waste aqueous solutions as green technology.
Tushar W., Saha T.K., Yuen C., Morstyn T., Nahid-Al-Masood, Poor H.V., Bean R.
2020-03-01 citations by CoLab: 223 Abstract
This paper proposes a peer-to-peer (P2P) energy trading scheme that can help a centralized power system to reduce the total electricity demand of its customers at the peak hour. To do so, a cooperative Stackelberg game is formulated, in which the centralized power system acts as the leader that needs to decide on a price at the peak demand period to incentivize prosumers to not seek any energy from it. The prosumers, on the other hand, act as followers and respond to the leader's decision by forming suitable coalitions with neighboring prosumers in order to participate in P2P energy trading to meet their energy demand. The properties of the proposed Stackelberg game are studied. It is shown that the game has a unique and stable Stackelberg equilibrium, as a result of the stability of prosumers' coalitions. At the equilibrium, the leader chooses its strategy using a derived closed-form expression, while the prosumers choose their equilibrium coalition structure. An algorithm is proposed that enables the centralized power system and the prosumers to reach the equilibrium solution. Numerical case studies demonstrate the beneficial properties of the proposed scheme.
Yeamin M.B., Islam M.M., Chowdhury A., Awual M.R.
2021-04-01 citations by CoLab: 187 Abstract
The removal of organic dyes from wastewater by innovative effluent treatment plant, which can truly clean the wastewater without leaving any fragments of dye species without generating secondary waste, is one of the prime challenges to the present world. The natural polymers were wheat flour, turmeric powder, pure starch, starch nanoparticles and some other forms of rice or wheat grains, while the synthetic polymeric adsorbents were polyaniline (PAni) and PAni/starch composites systematically studied for dyes adsorption. The cationic dye, methylene blue (MB), and an anionic dye, orange green (OG), from aqueous solutions through the adsorption using ten adsorbents of three categories; natural and synthetic polymers, and their composites, were widely investigated. The adsorbents either prepared or pre-treated were characterized using Fourier transform infra-red (FTIR) spectroscopy, scanning electron microscopy (SEM), X-ray diffraction spectroscopy (XRD) and differential thermal analyses techniques. The formation of PAni/starch composites was confirmed by the results of FTIR and thermal analyses. The SEM and XRD measurements were employed to determine the surface morphology and particle/crystallite size of the adsorbents. The degrees of adsorption of MB and OG on all adsorbents studied were evaluated by UV–visible spectroscopic technique. The cost effectiveness of the adsorbents studied was evaluated where the starch-based adsorbents are explored to be promising from economic and environmental viewpoints. The mechanism of adsorption of MB and OG dyes on starch-based adsorbents was also discussed. • Exploring the potential of starch-based biodegradable adsorbents for the removal of toxic dyes. • Synthesis of starch nanoparticles and polyaniline/starch nanocomposite materials. • Determination of encapsulation efficiency and suitability of the adsorbents. • Estimation of cost-effectiveness of the adsorbents was measured.
Islam M.A., Ali I., Karim S.M., Hossain Firoz M.S., Chowdhury A., Morton D.W., Angove M.J.
2019-12-01 citations by CoLab: 181 Abstract
Dyes are priority pollutants, commonly found at significant concentrations in textile effluents. The presence of dyes stuffs in wastewater can cause severe problems to aquatic life and human beings. Therefore, the removal of dyes from wastewater is important in order to minimize their hazardous effects on the environment. One way of removing dyes is to use nanosized manganese oxides (MnOs). To date, there has been much work reported on the use of nanosized MnOs as sorbents for dyestuffs. They are promising sorbents for commercial use due to their amorphous nature, high specific surface areas (SSA), mesoporous structure, and low to the moderate point of zero charge (pHPZC). This review summarizes the toxicity and recent advances for removing dyes from wastewater using nanosized MnO sorbents. The article also describes the various experimental parameters necessary for adsorption optimization, such as adsorption time, pH, initial dye concentration, amount of sorbent and temperature. Adsorption mechanisms investigated by various modeling approaches are also discussed. In particular, it was observed that much work has been reported on the use of birnessite and its composites for dye removal. There are many papers reporting on the use of MnO in batch mode dye removal, but very few that report on the use of MnO in continuous column removal systems. Therefore, there is still a considerable need for further research to develop effective and economical large scale MnO column systems for commercial use.
from 3 chars
Publications found: 5567
Assessing vulnerability of fishermen communities in coastal Bangladesh: A “climate vulnerability index”- based study in Assasuni Upazila, Satkhira, Bangladesh
Ahmed I., Chowdhury M.A., Zzaman R.U., Ul Islam S.L., Nahar S., Roy S.K.
Elsevier
Natural Hazards Research 2024 citations by CoLab: 1
Open Access
Open access
 |  Abstract
Climate Vulnerability Index (CVI) is developed to measure the susceptibility of communities to climate change using a case study. The index includes factors for each of the three aspects of vulnerability, including ‘Exposure’, ‘Sensitivity’, and ‘Adaptive Capability’. Sensitivity is determined by “Health”, “Food”, and “Water”, Adaptive Capability is characterized by “Socio-demographic profile,” “Livelihood strategies,” and “Social networks”, and Exposure is identified by “Natural Disaster” and “Climate Variability”. A study was conducted to investigate the vulnerability of fishermen in Assasuni Upazila, Satkhira, Bangladesh. The study involved individual surveys of randomly identified 100 fishermen from three groups: Gher-based, Ocean-based, and River-based. The findings indicate that the Gher-based fishing community exhibits higher levels of adaptive capacity (0.39), sensitivity (0.57), and exposure (0.74) in comparison to the other two communities. The sub-indicator about the migration of individuals for Gher-based livelihoods exhibits a relatively higher value of 0.85, in contrast to the relatively lower values of 0.23 and 0.11 for river and ocean-based livelihoods, respectively. The utilization of index-based output observations may aid policymakers from national to local levels in identifying and implementing the appropriate adaptation practices that prioritize the welfare of fishing communities residing in the coastal regions of Bangladesh.
Impact of Process Variation in Spin–Orbit Torque-Based Magnetic Tunnel Junctions on the Performance of Spiking Neural Networks
Hamid S.B., Baten M.Z.
Q1
Institute of Electrical and Electronics Engineers (IEEE)
IEEE Transactions on Electron Devices 2024 citations by CoLab: 0
AI‐assisted Field Plate Design of GaN HEMT Device
Xiang X., Palash R.H., Yagyu E., Dunham S.T., Teo K.H., Chowdhury N.
Q1
Wiley
Advanced Theory and Simulations 2024 citations by CoLab: 0  |  Abstract
AbstractGaN High Electron Mobility Transistors (HEMTs) plays a vital role in high‐power and high‐frequency electronics. Meeting the demanding performance requirements of these devices without compromising reliability is a challenging endeavor. Field Plates are employed to redistribute the electric field, minimizing the risk of device failure, especially in high‐voltage operations. While machine learning is applied to GaN device design, its application to field plate structures, known for their geometric complexity, is limited. This study introduces a novel approach to streamlining the field plate design process. It transforms complex 2D field plate structures into a concise feature space, reducing data requirements. A machine learning‐assisted design framework is proposed to optimize field plate structures and perform inverse design. This approach is not exclusive to the design of GaN HEMTs and can be extended to various semiconductor devices with field plate structures. The framework combines technology computer‐aided design (TCAD), machine learning, and optimization, streamlining the design process.
Eco-Friendly Route Planning Algorithms: Taxonomies, Literature Review and Future Directions
Fahmin A., Cheema M.A., Eunus Ali M., Nadjaran Toosi A., Lu H., Li H., Taniar D., A. Rakha H., Shen B.
Q1
Association for Computing Machinery (ACM)
ACM Computing Surveys 2024 citations by CoLab: 1  |  Abstract
Eco-friendly navigation (a.k.a. eco-routing) finds a route from A to B in a road network that minimizes the greenhouse gas (GHG) emission or fuel/energy consumption of the traveling vehicle. As road transport is a major contributor to GHG emissions, eco-routing has received considerable research attention in the past decade, mainly on two research themes: (1) developing models to estimate emissions or fuel/energy consumption of vehicles; and (2) developing algorithms to find eco-friendly routes for a vehicle. There are some excellent literature reviews that cover the existing estimation models. However, there is no literature review on eco-friendly route-planning algorithms. This article fills this gap and provides a systematic literature review in this area. From mainstream online databases, we obtained 2,494 articles and shortlisted 76 articles using our exclusion criteria. Accordingly, we establish a holistic view of eco-routing systems and define five taxonomies of estimation models, eco-routing problems and algorithms, vehicle types, traffic, and road network characteristics. Concerning the taxonomies, we categorize and review the shortlisted articles. Finally, we highlight research challenges and outline future directions in this important area.
Modal Shift Potential of Different Mode Users Due to Introduction of the First MRT in Dhaka: A Prelaunch Study
Aktar S.L., Islam M., Anzum N., Tahsin S., Waliullah M., Hasan M.M.
Q2
Springer Nature
Urban Rail Transit 2024 citations by CoLab: 0
Open Access
Open access
PDF  |  Abstract
AbstractDhaka city is experiencing tremendous growth in traffic. Until recently, the city’s demand for traffic was entirely served by public buses, a mix of motorized and non-motorized paratransit, and private personalized transport. The first ever rail-based metro, mass rapid transit (MRT), namely MRT Line 6, was partially inaugurated on 28 December 2022. Authority expects that there will be visible modal shift. However, MRT systems in many Asian and European countries are attracting much lower private motorists than what is expected. Moreover, in Dhaka, a unique mix in road-based transport with public transit and varieties of paratransit and private personalized vehicles intensifies the uncertainties involved in modal shift. Therefore, based on a field survey done before four (04) months of the partial inauguration of MRT Line 6, this study intends to explore the modal shift potentials of different mode users to a completely new mode and the modal choice factors. The analysis of the results finds overall, 75% of all mode users are willing to shift, while para and public transit users are comparatively more willing than private personalized vehicle users. However, such willingness comes up with one or more conditions: comfort, reasonable fare, reduced total travel time, less crowd, etc. The study finds that affordability, availability, and accessibility factors have an influence on their modal shift behavior. Also, results from binary logistic model identify significant impact of several sociodemographic, trip- and accessibility-related factors influencing modal shift choice. Findings from this study explain the optimism regarding MRT by different mode users and inform the decision-makers about their course of actions including different interventions, strict and carrot approaches to hold on to the potential shifters and attract more.
Efficient Alternative Route Planning in Road Networks
Fahmin A., Shen B., Cheema M.A., Toosi A.N., Ali M.E.
Q1
Institute of Electrical and Electronics Engineers (IEEE)
IEEE Transactions on Intelligent Transportation Systems 2024 citations by CoLab: 0
Low-Energy Spiking Neural Network using Ge4Sb6Te7 Phase Change Memory Synapses
Hamid S.B., Khan A.I., Zhang H., Davydov A.V., Pop E.
Q1
Institute of Electrical and Electronics Engineers (IEEE)
IEEE Electron Device Letters 2024 citations by CoLab: 0
Acoustic scattering by smooth and rough elastic cylinders insonified by directional sonars: Bistatic experiments
Mursaline M.A., Stanton T.K., Lavery A.C.
Q1
Acoustical Society of America (ASA)
Journal of the Acoustical Society of America 2024 citations by CoLab: 0  |  Abstract
Bistatic laboratory measurements are presented for acoustic scattering from both smooth and rough elastic cylinders insonified by directional spherical waves. A scattering model, accounting for incident directional spherical waves while assuming negligible end effects, was derived in a previous article [Mursaline, Stanton, Lavery, and Fischell, J. Acoust. Soc. Am. 154, 307–322 (2023)] but only evaluated for monostatic scattering by smooth cylinders. The evaluation is extended here to bistatic geometries for both smooth and rough cylinders. The effect of axi-symmetric Gaussian roughness (axi-symmetric random variations in cylinder radius) on the cylinder on overall scattering levels and resonances is investigated. Particular emphasis is given to the influence of roughness on the excitation of axially propagating guided wave resonances associated with oblique incident angles. Bistatic laboratory observations presented herein further substantiate the effects on scattering due to the properties of the incident field from practical sonars, such as spherical spreading, as observed in the above-mentioned article. For smooth cylinders, axially propagating guided wave resonances are seen to become more prominent during bistatic in-plane scattering compared to bistatic orthogonal-plane scattering and previously published monostatic data. For rough cylinders, both overall scattering levels and resonances are found to be diminished compared to the smooth case.
Stress Detection and Audio-Visual Stimuli Classification from Electroencephalogram
Troyee T.G., Chowdhury M.H., Khondakar M.F., Hasan M., Hossain M.A., Hossain Q.D., Ali Akber Dewan M.
Q1
Institute of Electrical and Electronics Engineers (IEEE)
IEEE Access 2024 citations by CoLab: 1
Open Access
Open access
Pair-EGRET: Enhancing the prediction of protein-protein interaction sites through graph attention networks and protein language models
Alam R., Mahbub S., Bayzid M.S.
Q1
Oxford University Press
Bioinformatics 2024 citations by CoLab: 0
Open Access
Open access
PDF  |  Abstract
Abstract Motivation Proteins are responsible for most biological functions, many of which require the interaction of more than one protein molecule. However, accurately predicting protein-protein interaction (PPI) sites (the interfacial residues of a protein that interact with other protein molecules) remains a challenge. The growing demand and cost associated with the reliable identification of PPI sites using conventional experimental methods call for computational tools for automated prediction and understanding of PPIs. Results We present Pair-EGRET, an edge-aggregated graph attention network that leverages the features extracted from pre-trained transformer-like models to accurately predict PPI sites. Pair-EGRET works on a k-nearest neighbor graph, representing the three-dimensional structure of a protein, and utilizes the cross-attention mechanism for accurate identification of interfacial residues of a pair of proteins. Through an extensive evaluation study using a diverse array of experimental data, evaluation metrics, and case studies on representative protein sequences, we demonstrate that Pair-EGRET can achieve remarkable performance in predicting PPI sites. Moreover, Pair-EGRET can provide interpretable insights from the learned cross-attention matrix. Availability and Implementation Pair-EGRET is freely available in open source form at the GitHub Repository https://github.com/1705004/Pair-EGRET. Supplementary Information Supplementary data are available at Bioinformatics online.
Effect of pH, acid catalyst, and aging time on pore characteristics of dried silica xerogel
Hasanuzzaman M., Tanha T.
Q2
Taylor & Francis
Advances in Materials and Processing Technologies 2024 citations by CoLab: 0
Diverse Applications of Graphene-Based Photocatalysts
Foisal M.R., Imran A.B.
Springer Nature
Advanced Structured Materials 2024 citations by CoLab: 1  |  Abstract
Graphene-based photocatalysts (GBPs) offer versatile and sustainable applications across various disciplines. Unlike commonly used photocatalysts, GBPs, thanks to their exceptional structural and chemical properties, enhance photocatalysis efficiency and address the separation issue after degradation. This book chapter will delve into the expanding applications of GBPs, beyond the well-discussed areas of pollutant degradation, hydrogen generation, water splitting, and disinfection. It will highlight their pivotal role in converting CO2 into valuable fuels for sustainable energy solutions and improving photocatalyst stability in nanocomposites, notably in wastewater treatment and advanced energy storage. GBPs also excel in photocatalytic sensing and detection due to their conductivity and sensitivity to environmental changes. These applications will be discussed, showcasing their utility in real-time environmental monitoring and early disease diagnosis through selective analyte detection. In biomedical applications, GBPs offer precise drug delivery control, enable cancer cell destruction via photothermal therapy, and enhance early disease detection through advanced bioimaging. These topics will also be explored in this chapter. Challenges, including scalability and cost-effectiveness, especially in environmental remediation, are acknowledged. Ensuring safety in biomedical applications remains a priority. The chapter will conclude by underscoring the importance of continued research for enhanced GBP performance and cost-efficiency, novel applications, and deeper insights. It will highlight GBP's transformative potential across diverse industries, shaping a sustainable and innovative future.
Evolution of Traditional Resources and Their Uses in the Field of Drug Discovery: Its Present-Day Impact in Society
Saha T., Hoque M.E.
Springer Nature
Interdisciplinary Biotechnological Advances 2024 citations by CoLab: 0  |  Abstract
Traditional resources, enriched with time-honored knowledge, have served as potent remedies for diverse diseases throughout history, igniting renewed interest in their integration into modern drug discovery. This chapter explores the evolution of traditional resources in drug development and their profound impact on society. Beginning with a global perspective on traditional medicine, it unearths healing practices across cultures. Scientific insights into the active compounds and mechanisms underpinning traditional remedies are examined, accompanied by evidence from preclinical studies and clinical research. The chapter investigates the opportunities and roadblocks presented by utilizing traditional resources in contemporary drug research, considering standardization, sustainability, and regulatory factors. Emphasizing their contemporary societal impact, it showcases traditional resource utilization in modern healthcare practices and their potential contribution to novel drug development. By embracing the confluence of ancient traditions and cutting-edge science, traditional resources offer a promising avenue for advancing integrative health care through the interdisciplinary approach of ethnopharmacology and sustainable sourcing practices.
Evolution of user behaviour on social media during 2018 road safety movement in Bangladesh
Sharmin S.
Q1
Springer Nature
Social Network Analysis and Mining 2024 citations by CoLab: 0  |  Abstract
The widespread use of social media networks has significantly influenced protest-related activities globally, as seen in movements like the Arab Spring and Occupy Wall Street. This paper presents an in-depth case study of the 2018 student road safety movement in Bangladesh, analyzed through Facebook and Twitter. We explore how these platforms enabled the movement to spread rapidly across the country and internationally. By examining the spatio-temporal characteristics of the protest, we illustrate how real-world events influenced online activities and vice versa. Our analysis delves into the role of social media users in disseminating information, organizing activities, and sustaining momentum. The study reveals that online and offline actions are intricately intertwined, with social media conversations often spurred by real-world developments, leading to shifts in individual behaviors. This research highlights the vital role of digital media in modern activism, emphasizing its capacity to transcend traditional boundaries, facilitate coordination, and foster collective action. Our findings contribute to a deeper understanding of how social media can drive social movements, particularly in under-researched regions. Our findings reveal that social media networks not only mobilized participants but also amplified the movement’s reach and coordination. The study underscores the intricate relationship between online and offline spheres, highlighting how real-world events trigger significant online discourse and behavioral changes. This research contributes to the broader understanding of digital media’s role in contemporary activism, especially in less-studied regions.
Effects of Crumpling Stage and Porosity of Graphene Electrode on the Performance of Electrochemical Supercapacitor
Khan A.A., Rabi S.N., Jamee T., Galib M., Elahi F., Rahman M.A.
Q1
American Chemical Society (ACS)
Journal of Physical Chemistry B 2024 citations by CoLab: 0

Since 1973

Total publications
5567
Total citations
102157
Citations per publication
18.35
Average publications per year
107.06
Average authors per publication
4.16
h-index
119
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1200
1400
1600
1800
2000
200
400
600
800
1000
1200
1400
1600
1800
2000

With other organizations

20
40
60
80
100
120
140
160
180
200
20
40
60
80
100
120
140
160
180
200

With foreign organizations

10
20
30
40
50
60
70
80
90
10
20
30
40
50
60
70
80
90

With other countries

100
200
300
400
500
600
700
800
900
USA, 889, 15.97%
Japan, 349, 6.27%
Canada, 340, 6.11%
Australia, 317, 5.69%
United Kingdom, 303, 5.44%
India, 201, 3.61%
Malaysia, 186, 3.34%
Saudi Arabia, 169, 3.04%
China, 133, 2.39%
Republic of Korea, 93, 1.67%
Singapore, 48, 0.86%
Germany, 46, 0.83%
Netherlands, 45, 0.81%
Italy, 43, 0.77%
Pakistan, 40, 0.72%
Turkey, 40, 0.72%
France, 39, 0.7%
Denmark, 36, 0.65%
Ireland, 26, 0.47%
Egypt, 23, 0.41%
UAE, 23, 0.41%
Sweden, 22, 0.4%
Vietnam, 21, 0.38%
Finland, 19, 0.34%
Belgium, 18, 0.32%
Brazil, 18, 0.32%
Spain, 17, 0.31%
Thailand, 17, 0.31%
Brunei, 16, 0.29%
100
200
300
400
500
600
700
800
900
  • We do not take into account publications without a DOI.
  • Statistics recalculated daily.
  • Publications published earlier than 1973 are ignored in the statistics.
  • The horizontal charts show the 30 top positions.
  • Journals quartiles values are relevant at the moment.