Recent publications
The integration of carbon nanomaterials in supramolecular architectures has demonstrated potential as a viable approach for drug delivery, and this chapter presents a concise overview of this prospect. Because of their one-of-a-kind physicochemical features, carbon nanostructures are being eyed as potential medication delivery devices. To enhance pharmaceuticals’ solubility, stability, and bioavailability, supramolecular chemistry enables synthesizing complex structures with distinctive characteristics and activities. The chapter examines the fundamental principles underlying supramolecular chemistry, an exploration of the characteristics and implementation of carbon nanomaterials, and a comprehensive analysis of the benefits and constraints associated with their use in drug administration. Examples of carbon nanomaterial-incorporated supramolecular drug delivery systems are presented, and the future directions and potential applications of this approach are discussed. In conclusion, this chapter emphasizes the promise of carbon nanomaterial as an efficient and effective supramolecular drug delivery technology with various therapeutic applications.
Implementing graphene quantum dots (GQDs) as zero-dimensional nanomaterials for drug delivery is obvious due to exceptional physicochemical, optical (photoluminescence and electrochemiluminescence), and biological properties such as good photostability, emission of multicolor, biocompatibility, less toxicity, and chemical inertness. Moreover, surface adhesion to organic molecules and planner structure increases surface area compared to volume, quantum confinement, and edge effects, facilitating drug delivery applications. Specific properties related to drug delivery of GQDs are emphasized at the beginning of this chapter. Several synthesis procedures of GQDs, including bottom-up and top-down methods with newly developed preparation of GQDs for drug delivery applications, are highlighted. Finally, this chapter will summarize novel applications in medication administration, stating limitations, challenges, and future scope of research.
In this study, natural radioactivity levels in sediments of the Padma River and concomitant radiological risks were assessed. Sediment samples were collected from the Padma River near the under-construction Rooppur Nuclear Power Plant (RNPP) of Bangladesh and analyzed for ²²⁶ Ra, ²³² Th, and ⁴⁰ K radioactivity levels using a gamma-ray spectrometry system. The activity concentrations (Bq kg ⁻¹ ) of ²²⁶ Ra, ²³² Th, and ⁴⁰ K in sediments of the Padma River varied from 45.6 ± 6.7 to 119 ± 11 with average 73.2 ± 17.4; 49.8 ± 6.9 to 137 ± 11 with average 86.6 ± 20.3, and 540 ± 23 to 1,032 ± 32 with average 782 ± 146, respectively. This study indicates that activity concentrations of these radionuclides in the Padma River sediments are relatively higher than the world average values. Among the seven radiological hazard indices determined, four of them: radium equivalent activity, annual effective dose rate, and external and internal hazard indices are within their international guideline values. However, values of absorbed dose rate, gamma representative level index, and excess lifetime cancer risk are considerably higher at all sampling points, suggesting radiological risks for the river environment. The statistical analyses revealed a strong correlation between ²³⁸ U and ²³² Th radionuclides. The spatial distribution of activity concentrations and radiological hazard indices for the studied area will serve as a documented radiological reference for the Padma River near RNPP. This study recommends routine monitoring of the radionuclides in the surrounding regions of RNPP to assess any post-operational environmental impact due to radionuclide contamination.
This study investigates the potential of Yttria-Stabilized Zirconia (YSZ) and Partially Stabilized Zirconia (PSZ) thermal barrier coatings (TBCs) in improving gas turbine performance through a theoretical simulation. Using the finite element method, we compared temperature and thermal flow distributions within YSZ and PSZ coated combustion chambers against an uncoated Ni substrate liner wall. Our results demonstrate significant reductions in outer wall temperature (2.61%–3.54%) and heat flux (0.44%–0.6%) achieved through TBC application. The findings were validated through similar studies, with results aligning closely with established data. These reductions translate to cooler and more efficient engine operation. This comparative analysis not only sheds light on TBC effectiveness but also provides valuable insights for optimizing combustion chamber design. These findings pave the way for the development of next-generation TBC materials and engineering strategies, ultimately leading to enhanced gas turbine performance.
The integration of Artificial Intelligence (AI) has significantly advanced oral and maxillofacial cancer (OMC) care. This paper explores the transformative potential of AI in OMC diagnosis, staging, treatment, and prognosis. AI-driven applications, including computervision and machine learning, are discussed, emphasizing their impact on early detection,accurate diagnosis, and personalized treatment planning. The paper also explores the role of AI in OMC education, research, and practice, outlining future directions. In OMC staging, AI automates the process by analyzing medical records and imaging data, enhancing accuracy. The paper also discusses AI's role in tailoring treatment plans, optimizing radiation therapy, and facilitating robotic surgery. Furthermore, the integration of ChatGPT in OMC education, research, and practice is explored. The paper outlines future directions, including the integration of multi-omics data and real-time patient monitoring, emphasizing collaboration, clinical trials, and validation as essential steps in realizing AI's potential in routine clinical practice. In conclusion, AI has the potential to transform OMC management by enhancing diagnosis accuracy, staging precision, personalized treatment planning, and prognosis estimation. Addressing ethical concerns and fostering interdisciplinary collaboration are crucial in harnessing AI's capabilities. By embracing AI advancements, OMC care can be significantly improved, leading to better patient outcomes and contributing to the fight against oral and maxillofacial cancer.
Sustainable aviation is currently a major concern since people are constantly relying on aircraft for transport over long distances in a short amount of time. Newer technologies are constantly being developed to enhance the aerodynamic efficiency of existing aircraft and improve fuel conservation. Winglets are one such technology. As such, a strong understanding of aerodynamics is required for the design of winglets. Our research question centers on how different design features in a winglet change the aerodynamic performance of a blended wind body (BWB) aircraft model. This study addresses the question through the application of both CFD Simulation and Wind Tunnel Testing on a BWB model designed with specific airfoils as a starting point. This approach allows for further exploration of various airfoils for enhancing
the performance of BWB aircraft. Models with different winglet designs were tested at a range of different velocities and angles of attack to evaluate their aerodynamic performance. Throughout the duration of this project, the group tested how the different winglet designs affected the Lift and Drag experienced by the Blended Wing Body Aircraft model. The results show how the different winglet designs change the aerodynamic performance of the designed BWB model. The findings from this research can contribute to optimizing winglet designs for BWB aircraft, potentially leading to significant improvements in fuel efficiency and reductions in carbon emissions within the aerospace industry.
This research investigates the application of Ladle Furnace Slag in stabilizing expansive soil, particularly in pavement construction. Laboratory-prepared expansive soils have been added with different proportions of Ladle Furnace Slag-based geopolymer. The study also considered two moisture content levels: the optimum moisture content (OMC) and a moisture level 4% lower than OMC to examine the effect of water on strength development. A series of experiments have been conducted to estimate the Unconfined Compressive Strength (UCS) and California Bearing Ratio (CBR) of treated soils at two curing ages, 14 days and 28 days, followed by an exploration of the soaking effect after 24 h of soaking. The results demonstrated a positive correlation between geopolymer content and increased unconfined compressive strength and CBR values. Notably, samples with moisture content below 4% of OMC exhibited approximately 60% higher UCS than their OMC counterparts. The swelling potential was substantially reduced by 93% for both types of expansive soil. In addition, the Mechanistic-Empirical approach of pavement analysis has been conducted to evaluate the structural benefits of stabilization. The pavement analysis showed that soil with 15% geopolymer content could substitute the conventional subbase layer requirement, leading to an economical design.
The global spread of SARS-CoV-2 has prompted a crucial need for accurate medical diagnosis, particularly in the respiratory system. Current diagnostic methods heavily rely on imaging techniques like CT scans and X-rays, but identifying SARS-CoV-2 in these images proves to be challenging and time-consuming. In this context, artificial intelligence (AI) models, specifically deep learning (DL) networks, emerge as a promising solution in medical image analysis. This article provides a meticulous and comprehensive review of imaging-based SARS-CoV-2 diagnosis using deep learning techniques up to May 2024. This article starts with an overview of imaging-based SARS-CoV-2 diagnosis, covering the basic steps of deep learning-based SARS-CoV-2 diagnosis, SARS-CoV-2 data sources, data pre-processing methods, the taxonomy of deep learning techniques, findings, research gaps and performance evaluation. We also focus on addressing current privacy issues, limitations, and challenges in the realm of SARS-CoV-2 diagnosis. According to the taxonomy, each deep learning model is discussed, encompassing its core functionality and a critical assessment of its suitability for imaging-based SARS-CoV-2 detection. A comparative analysis is included by summarizing all relevant studies to provide an overall visualization. Considering the challenges of identifying the best deep-learning model for imaging-based SARS-CoV-2 detection, the article conducts an experiment with twelve contemporary deep-learning techniques. The experimental result shows that the MobileNetV3 model outperforms other deep learning models with an accuracy of 98.11%. Finally, the article elaborates on the current challenges in deep How to cite this article Islam MS
Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions.
This study explores a potential technique for predicting the load bearing capacity of piles without extensive field testing. In this study, the Chin-Konder method has been utilized in numerical models to make experimental results in useable form. Primarily, the pile capacities derived from actual pile static load tests have been compared with their respective numerical analyses across 15 distinct locations of Bangladesh. Consequently, this comparison offers a clear insight into the accuracy of numerical simulations in approximating pile behavior. In this study, a series of finite element-based numerical analysis has been conducted to determine the bearing capacity of piles using the Plaxis 3D foundation program using actual soil investigation data. The numerical model considers Mohr–Coulomb soil model, which has a good reputation for yielding accurate results though it operates in linear states only. This entire analysis involves a comprehensive numerical model and analysis of fifteen bored piles with varying loading conditions and different lengths ranging from 18 m to 35.125 m, with diameters of 0.5 m and 0.6 m. The test outcomes indicated remarkable consistency between field and numerical simulations particularly in terms of piles bearing capacity. Afterward, the Chin-Konder approach was utilized to generate ideal load-settlement responses, aiding in estimating the allowable load bearing capacity of cast-in situ bored piles. The numerically estimated pile capacity was found approximately 45% larger than that of the design capacity prior to any load test. The numerically predicted result shows a convergence of 94%, with a good reliability of the field test. Therefore, the numerical simulations for the bearing capacity of pile could be an effective strategy for foundation design, establishing it as a cost-effective approach.
In this study, heat transfer performance within a rectangular lid-driven containing an elliptical shaped block has been investigated for different boundary conditions. A wavy wall of cosine function at the bottom wall was added to the rectangle to enhance the heat transfer rate. Using base fluid inside the enclosure as the working fluid, the study also investigated single-walled carbon nanotubes (SWCNT) and multiple-walled carbon nanotubes (MWCNT)-incorporated nanoparticles. The finite element method (FEM) is used to solve governing equations that are based on the Boussinesq approximation. The findings are presented as different 2D graphs, isothermal contours, and a thorough computation of the Nusselt number based on the examination of several relevant non-dimensional values. The achieved results indicate that with the increase in Reynolds number and volume fraction, Nusselt number also increases. Also, SWCNT nanofluid provided greater Nusselt number in most of the cases. The usage of a wavy wall at the enclosure's bottom wall, which featured ellipses with varying aspect ratios (0.5, 1, 2, 4), was one of the novel aspects. The maximum achieved Nusselt number was 18.19 for an aspect ratio of 2. A maximum 157.57% enhancement of Nu was observed compared to recent relevant literature with the addition of CNT nanofluid and wavy wall in that particular geometry.
The human skin, which is the biggest organ and the outermost layer of the body, has seven layers that serve to shield interior organs. Because of its broad role in the integumentary system, maintaining the health of the skin is essential. Skin problems present substantial classification challenges for medical professionals since they include a wide spectrum of diseases, including dermatoses. Consequently, they are depending more and more on machine learning (ML) technologies to help them predict and categorize these diseases. In the field of imaging, convolutional neural networks (CNNs) have demonstrated performance that is comparable to, and in some cases surpasses, human capabilities. Within this research, we propose a novel CNN architecture designed to classify two specific skin diseases: Eczema (symptoms on legs and hands) and Seborrheic Keratoses (symptoms on ears and skin). Additionally, we compare the performance of six ML algorithms to determine the most accurate model. We trained and tested our proposed technique on the Dermnet 2021 DATASET, which consists of 2,332 pictures and is publically available on Kaggle. Our findings show that the suggested CNN model, which achieves an accuracy of 91.1% and an F1-score of 92.3%, surpasses other cutting-edge techniques. With an F1-score of 79.12% and an accuracy of 78.41%, Linear Regression (LR) was the most successful ML model that was examined.
A comprehensive analysis of coastline changes was conducted over a span of two decades for Swarnadwip, an offshore island of Bangladesh in the Bay of Bengal. Located near the Meghna estuary, the island is influenced by the vast sedimentary contributions of the Ganges-Brahmaputra-Meghna (GBM) river system. This makes Swarnadwip a focal point for research aimed at deepening our understanding of coastal morpho-dynamics. In this research, three different shoreline detection strategies were explored, and the suitability of each method was assessed to identify the most optimum method for this geographically complex region. Changes in the island’s area were calculated for each time interval, and further shoreline change statistics (NSM, EPR and LRR) were estimated using the Digital Shoreline Analysis System (DSAS) on the GIS platform. The study revealed that from 2003 to 2022, shoreline length extended from 50.6 to 74.15 km (1.24 km/yr), and net land accretion was estimated 10,230.20 ha (538.43 ha/yr). Between 2006 and 2010, the island witnessed a substantial land expansion. However, much of this newly acquired land mass was lost again, as erosional forces governed from 2010 to 2022, especially in the northern and western sections. These findings provide critical insights for future coastal management strategies and highlight the need for continuous monitoring to safeguard Bangladesh’s dynamic coastlines.
Recent statistics reveal a rapid decline in green spaces due to urbanization, impacting both urban and forested areas. Neglecting environmental concerns in urban planning has led to the need to restore greenery in communities. Hence, the objective of this research is to develop a predictive system using deep learning models to forecast changes in green spaces of a specific location over time, contributing to offering insights into urban planning and environmental conservation. Five time series model were explored to predict the change in green space of a location in future, with Long Short-Term Memory (LSTM) demonstrating superior performance. Furthermore, a two-module Siamese-LSTM framework was proposed to forecast future Green View Index (GVI) differences between consecutive years with an i-year gap. The proposed two-module Siamese-LSTM framework predicts the change in a location’s future green view index consisting of individual Siamese Models and an LSTM Model, achieving promising results with an MAE of 0.271, MSE of 0.150 and MAPE of 0.1989.
Rheumatoid arthritis (RA) affects an estimated 0.1% to 2.0% of the world’s population, leading to a substantial impact on global health. The adverse effects and toxicity associated with conventional RA treatment pathways underscore the critical need to seek potential new therapeutic candidates, particularly those of natural sources that can treat the condition with minimal side effects. To address this challenge, this study employed a deep-learning (DL) based approach to conduct a virtual assessment of natural compounds against the Tumor Necrosis Factor-alpha (TNF-α) protein. TNF-α stands out as the primary pro-inflammatory cytokine, crucial in the development of RA. Our predictive model demonstrated appreciable performance, achieving MSE of 0.6, MAPE of 10%, and MAE of 0.5. The model was then deployed to screen a comprehensive set of 2563 natural compounds obtained from the Selleckchem database. Utilizing their predicted bioactivity (pIC50), the top 128 compounds were identified. Among them, 68 compounds were taken for further analysis based on drug-likeness analysis. Subsequently, selected compounds underwent additional evaluation using molecular docking (< − 8.7 kcal/mol) and ADMET resulting in four compounds posing nominal toxicity, which were finally subjected to MD simulation for 200 ns. Later on, the stability of complexes was assessed via analysis encompassing RMSD, RMSF, Rg, H-Bonds, SASA, and Essential Dynamics. Ultimately, based on the total binding free energy estimated using the MM/GBSA method, Imperialine, Veratramine, and Gelsemine are proven to be potential natural inhibitors of TNF-α.
The LipBengal dataset represents a significant advancement in Bengali lip-reading and visual speech recognition research, poised to drive future applications and technological progress. Despite Bengali's global status as the seventh most spoken language with approximately 265 million speakers, linguistically rich and widely spoken languages like Bengali have been largely overlooked by the research community. LipBengal fills this gap by offering a pioneering dataset tailored for Bengali lip-reading, comprising visual data from 150 speakers across 54 classes, encompassing Bengali phonemes, alphabets, and symbols. Captured under diverse and uncontrolled conditions, LipBengal stands as the most extensive Bengali lip-reading dataset to date, designed to facilitate robust benchmarking and validation of novel deep learning architectures. Detailed annotations extend from phoneme- level classifications to full sentence constructions, providing a granular and comprehensive dataset. The primary potential of LipBengal lies in its thorough coverage of Bengali phonemes, capturing diverse lip movements linked to distinct sounds. This rich dataset holds promise for training accurate lip-reading models, with implications for improved accessibility, enhanced speech recognition, silent speech interfaces, and linguistic research. The dataset's diversity in speaker backgrounds enhances its utility, ensuring broader representation of Bengali pronunciation patterns. Meticulous annotation and curation further bolster its quality and reliability, making LipBengal a valuable asset for researchers and developers in the field.
Rice is a major crop and staple food for more than half of the world’s population and plays a vital role in ensuring food security as well as the global economy pests and diseases pose a threat to the production of rice and have a substantial impact on the yield and quality of the crop. In recent times, deep learning methods have gained prominence in predicting rice leaf diseases. Despite the increasing use of these methods, there are notable limitations in existing approaches. These include a scarcity of extensive and diverse collections of leaf disease images, lower accuracy rates, higher time complexity, and challenges in real-time leaf disease detection. To address the limitations, we explicitly investigate various data augmentation approaches using different generative adversarial networks (GANs) for rice leaf disease detection. Along with the GAN model, advanced CNN-based classifiers have been applied to classify the images with improving data augmentation. Our approach involves employing various GANs to generate high-quality synthetic images. This strategy aims to tackle the challenges posed by limited and imbalanced datasets in the identification of leaf diseases. The key benefit of incorporating GANs in leaf disease detection lies in their ability to create synthetic images, effectively augmenting the dataset’s size, enhancing diversity, and reducing the risk of overfitting. For dataset augmentation, we used three distinct GAN architectures—namely simple GAN, CycleGAN, and DCGAN. Our experiments demonstrated that models utilizing the GAN-augmented dataset generally outperformed those relying on the non-augmented dataset. Notably, the CycleGAN architecture exhibited the most favorable outcomes, with the MobileNet model achieving an accuracy of 98.54%. These findings underscore the significant potential of GAN models in improving the performance of detection models for rice leaf diseases, suggesting their promising role in the future research within this domain.
The study of whether life exists, is extinct, or not depends on various sophisticated experimental studies, as many different signatures of life can be used. The experimental procedures that can be performed to identify life can be further restricted by time, resources, and mobility constraints. Therefore, any research analyzing the presence of extraterrestrial life must be precise and unambiguous. This research focuses on the objective of the extraterrestrial life detection domain and seeks to provide an efficient protocol that can produce life detection decisions based on empirical data obtained through chemical analysis under time and resource-constrained conditions. While the majority of existing frameworks in this field are designed to identify biomolecules, our goal is to accomplish the same with minimal operational expense and mission complexity. We argue that the thoughtful integration of multiple biomolecular detections with lesser complexity and a robust framework can improve overall mission performance by satisfying the necessary time and resource constraints. In this study, a rapid multiple biomolecules-based life detection protocol (MBLDP-R) from soil samples is developed from scratch and embedded in an operational scientific rover subsystem targeted for planetary analysis missions. The study uses artificial biomolecule samples and simulated extraterrestrial environments to illustrate the suggested protocol’s end-to-end process. First, we list a few significant biomolecules, including lipids, proteins, carbohydrates, nucleic acids, ammonia, and pigments. Then, a weighted qualitative test scoring is applied to sort out the best test method for the finally selected biomolecules which are used as operational analogue to showcase the protocol’s in-situ analysis and decision-making capabilities. Based on the suitable biomolecules, a scientific exploration subsystem is developed, and the implemented protocol is built to perform onboard sample analysis. Evaluation results show that: (1) the proposed MBLDP-R protocol could effectively predict the classes with an average f1-score of 98.65% (macro) and 90.00% (micro), (2) the area under the Receiver Operating Characteristics (AUC-ROC) curve shows the sample categories to be correctly predicted 92% of the time (97% for Extant, 88% for Extinct, and 92% in the case of NPL), and (3) the protocol is time-efficient with an average completion time of 17.60 min, demonstrating the protocol’s rapid nature in detecting biosignatures in soil samples. The research outcome yields useful additional data for related future studies, particularly in the design of scientific frameworks for mission-specific requirements with limited resources while also serving as a reference point for constraint evaluation methods for similar systems.
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