Bangladesh University of Engineering and Technology

High levels of arsenic in well water are causing widespread poisoning in Bangladesh. In a typical aquifer in southern Bangladesh, chemical data imply that arsenic mobilization is associated with recent inflow of carbon. High concentrations of radiocarbon-young methane indicate that young carbon has driven recent biogeochemical processes, and irrigation pumping is sufficient to have drawn water to the depth where dissolved arsenic is at a maximum. The results of field injection of molasses, nitrate, and low-arsenic water show that organic carbon or its degradation products may quickly mobilize arsenic, oxidants may lower arsenic concentrations, and sorption of arsenic is limited by saturation of aquifer materials.

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 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, 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 is presented. 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 are discussed. Finally, the current challenges and limitations of this technique are highlighted, followed by its prospects as a guideline toward possible futuristic innovations.

Imbalanced learning problems contain an unequal distribution of data samples among different classes and pose a challenge to any classifier as it becomes hard to learn the minority class samples. Synthetic oversampling methods address this problem by generating the synthetic minority class samples to balance the distribution between the samples of the majority and minority classes. This paper identifies that most of the existing oversampling methods may generate the wrong synthetic minority samples in some scenarios and make learning tasks harder. To this end, a new method, called Majority Weighted Minority Oversampling TEchnique (MWMOTE), is presented for efficiently handling imbalanced learning problems. MWMOTE first identifies the hard-to-learn informative minority class samples and assigns them weights according to their euclidean distance from the nearest majority class samples. It then generates the synthetic samples from the weighted informative minority class samples using a clustering approach. This is done in such a way that all the generated samples lie inside some minority class cluster. MWMOTE has been evaluated extensively on four artificial and 20 real-world data sets. The simulation results show that our method is better than or comparable with some other existing methods in terms of various assessment metrics, such as geometric mean (G-mean) and area under the receiver operating curve (ROC), usually known as area under curve (AUC).

Traditional chemical, physical and biological processes for treating wastewater containing textile dye have such disadvantages as high cost, high energy requirement and generation of secondary pollution during treatment process. The advanced oxidation processes technology has been attracting growing attention for the decomposition of organic dyes. Such processes are based on the light-enhanced generation of highly reactive hydroxyl radicals, which oxidize the organic matter in solution and convert it completely into water, CO2 and inorganic compounds. In this presentation, the photocatalytic degradation of dyes in aqueous solution using TiO2 as photocatalyst under solar and UV irradiation has been reviewed. It is observed that the degradation of dyes depends on several parameters such as pH, catalyst concentration, substrate concentration and the presence of oxidants. Reaction temperature and the intensity of light also affect the degradation of dyes. Particle size, BET-surface area and different mineral forms of TiO2 also have influence on the degradation rate.

Trend analysis is one of the most important measurements in studying time series data. Both parametric and non-parametric tests are commonly used in trend analysis. Parametric tests require data to be independent and normally distributed. On the other hand, non-parametric trend tests require only that the data be independent and can tolerate outliers in the data However, parametric tests are more powerful than nonparametric ones.

Graphene, a two-dimensional material of sp2 hybridization carbon atoms, has fascinated much attention in recent years owing to its extraordinary electronic, optical, magnetic, thermal, and mechanical properties as well as large specific surface area. For the tremendous application of graphene in nano-electronics, it is essential to fabricate high-quality graphene in large production. There are different methods of generating graphene. This review summarizes the exfoliation of graphene by mechanical, chemical and thermal reduction and chemical vapor deposition and mentions their advantages and disadvantages. This article also indicates recent advances in controllable synthesis of graphene, illuminates the problems, and prospects the future development in this field.

Large Language Models (LLMs) recently demonstrated extraordinary capability, including natural language processing (NLP), language translation, text generation, question answering, etc. Moreover, LLMs are a new and essential part of computerized language processing, having the ability to understand complex verbal patterns and generate coherent and appropriate replies for the situation. Though this success of LLMs has prompted a substantial increase in research contributions, rapid growth has made it difficult to understand the overall impact of these improvements. Since a lot of new research on LLMs is coming out quickly, it is getting tough to get an overview of all of them in a short note. Consequently, the research community would benefit from a short but thorough review of the recent changes in this area. This article thoroughly overviews LLMs, including their history, architectures, transformers, resources, training methods, applications, impacts, challenges, etc. This paper begins by discussing the fundamental concepts of LLMs with its traditional pipeline of the LLMs training phase. It then provides an overview of the existing works, the history of LLMs, their evolution over time, the architecture of transformers in LLMs, the different resources of LLMs, and the different training methods that have been used to train them. It also demonstrated the datasets utilized in the studies. After that, the paper discusses the wide range of applications of LLMs, including biomedical and healthcare, education, social, business, and agriculture. It also illustrates how LLMs create an impact on society and shape the future of AI and how they can be used to solve real-world problems. Then it also explores open issues and challenges to deploying LLMs in real-world scenario. Our review paper aims to help practitioners, researchers, and experts thoroughly understand the evolution of LLMs, pre-trained architectures, applications, challenges, and future goals.

Food preservation involves different food processing steps to maintain food quality at a desired level so that maximum benefits and nutrition values can be achieved. Food preservation methods include growing, harvesting, processing, packaging, and distribution of foods. The key objectives of food preservation are to overcome inappropriate planning in agriculture, to produce value-added products, and to provide variation in diet. Food spoilage could be caused by a wide range of chemical and biochemical reactions. To impede chemical and microbial deterioration of foods, conventional and primitive techniques of preserving foods like drying, chilling, freezing, and pasteurization have been fostered. In recent years, the techniques to combat these spoilages are becoming sophisticated and have gradually altered to a highly interdisciplinary science. Highly advanced technologies like irradiation, high-pressure technology, and hurdle technology are used to preserve food items. This review article presents and discusses the mechanisms, application conditions, and advantages and disadvantages of different food preservation techniques. This article also presents different food categories and elucidates different physical, chemical, and microbial factors responsible for food spoilage. Furthermore, the market economy of preserved and processed foods has been analyzed in this article.

This paper conducts a comprehensive study on the application of big data and machine learning in the electrical power grid introduced through the emergence of the next-generation power system-the smart grid (SG). Connectivity lies at the core of this new grid infrastructure, which is provided by the Internet of Things (IoT). This connectivity, and constant communication required in this system, also introduced a massive data volume that demands techniques far superior to conventional methods for proper analysis and decision-making. The IoT-integrated SG system can provide efficient load forecasting and data acquisition technique along with cost-effectiveness. Big data analysis and machine learning techniques are essential to reaping these benefits. In the complex connected system of SG, cyber security becomes a critical issue; IoT devices and their data turning into major targets of attacks. Such security concerns and their solutions are also included in this paper. Key information obtained through literature review is tabulated in the corresponding sections to provide a clear synopsis; and the findings of this rigorous review are listed to give a concise picture of this area of study and promising future fields of academic and industrial research, with current limitations with viable solutions along with their effectiveness.

Abstract A new method is proposed for determining in situ unsaturated hydraulic conductivities from unsaturated infiltration measurements made at several tensions on the same infiltration surface. Wooding's equation for steady‐state unconfined infiltration rates is used in calculating hydraulic conductivities. Hydraulic conductivities calculated with the new method are consistent with unit gradient laboratory measurements of saturated and unsaturated hydraulic conductivity. This simple field method is potentially valuable because it is faster than unit gradient laboratory methods, and it is less disruptive of pore continuity than other field infiltration techniques.

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.

Understanding the drivers of energy and material flows of cities is important for addressing global environmental challenges. Accessing, sharing, and managing energy and material resources is particularly critical for megacities, which face enormous social stresses because of their sheer size and complexity. Here we quantify the energy and material flows through the world's 27 megacities with populations greater than 10 million people as of 2010. Collectively the resource flows through megacities are largely consistent with scaling laws established in the emerging science of cities. Correlations are established for electricity consumption, heating and industrial fuel use, ground transportation energy use, water consumption, waste generation, and steel production in terms of heating-degree-days, urban form, economic activity, and population growth. The results help identify megacities exhibiting high and low levels of consumption and those making efficient use of resources. The correlation between per capita electricity use and urbanized area per capita is shown to be a consequence of gross building floor area per capita, which is found to increase for lower-density cities. Many of the megacities are growing rapidly in population but are growing even faster in terms of gross domestic product (GDP) and energy use. In the decade from 2001-2011, electricity use and ground transportation fuel use in megacities grew at approximately half the rate of GDP growth.

Although there have been a number of recent reviews on the use of traffic conflict techniques (TCTs), none have focused on the use of proximal surrogate indicators. This paper comprehensively reviews the development and application of proximal surrogate safety indicators to address this gap. There is a particular focus on more recent advancements in the application of such indicators. For each of themain indicators reviewed, the paper provides a synthesis of themain guiding principles, aswell as the most prominent features, including critical or threshold values used in the past. In addition, the main advantages and disadvantages of the reviewed indicators are highlighted. Finally, a number of research gaps are identified together with recommendations for potentially useful avenues of future research. (C) 2017 International Association of Traffic and Safety Sciences. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

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% 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.

This paper discusses the power quality issues for distributed generation systems based on renewable energy sources, such as solar and wind energy. A thorough discussion about the power quality issues is conducted here. This paper starts with the power quality issues, followed by discussions of basic standards. A comprehensive study of power quality in power systems, including the systems with dc and renewable sources is done in this paper. Power quality monitoring techniques and possible solutions of the power quality issues for the power systems are elaborately studied. Then, we analyze the methods of mitigation of these problems using custom power devices, such as D-STATCOM, UPQC, UPS, TVSS, DVR, etc., for micro grid systems. For renewable energy systems, STATCOM can be a potential choice due to its several advantages, whereas spinning reserve can enhance the power quality in traditional systems. At Last, we study the power quality in dc systems. Simpler arrangement and higher reliability are two main advantages of the dc systems though it faces other power quality issues, such as instability and poor detection of faults.

Despite research that has been conducted elsewhere, little is known, to-date, about land cover dynamics and their impacts on land surface temperature (LST) in fast growing mega cities of developing countries. Landsat satellite images of 1989, 1999, and 2009 of Dhaka Metropolitan (DMP) area were used for analysis. This study first identified patterns of land cover changes between the periods and investigated their impacts on LST; second, applied artificial neural network to simulate land cover changes for 2019 and 2029; and finally, estimated their impacts on LST in respective periods. Simulation results show that if the current trend continues, 56% and 87% of the DMP area will likely to experience temperatures in the range of greater than or equal to 30 °C in 2019 and 2029, respectively. The findings possess a major challenge for urban planners working in similar contexts. However, the technique presented in this paper would help them to quantify the impacts of different scenarios (e.g., vegetation loss to accommodate urban growth) on LST and consequently to devise appropriate policy measures.

Over the last decade, many power systems have significantly changed with the proliferation of renewable generation sources, such as wind and solar photovoltaic. Due to their variability and nonsynchronous nature, new challenges and complexities have emerged regarding operational security of modern power systems. The 2016 South Australia (SA) blackout was the first known blackout due to such a high renewable situation. An official report has recently been published to review the causes and provide the corresponding recommendations for improvement of network operation, control, and security. However, there are still a number of critical issues and debates which remain unsolved, such as network bottleneck identification, overvoltage explanation, pole slip concern, frequency dip mystery, and frequency/voltage instability debate. In this paper, based on the reconstruction of the event, these unsettled issues are prudently analyzed to unveil their root causes. In addition, an innovative scheme is proposed to prevent the blackout by identifying the network separation at an early stage. This research will not only further advance the understanding of the 2016 SA blackout, but also will provide valuable guidelines for the management of future renewable-rich networks.

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.

The Purpose of this work is to study about the microstructures, mechanical properties and wear characteristics of as cast silicon carbide (SiC) reinforced aluminum matrix composites (AMCs). AMCs of varying SiC content (0, 5, 10 and 20 wt. %) were prepared by stir casting process. Microstructures, Vickers hardness, tensile strength and wear performance of the prepared composites were analyzed. The results showed that introducing SiC reinforcements in aluminum (Al) matrix increased hardness and tensile strength and 20 wt. % SiC reinforced AMC showed maximum hardness and tensile strength. Microstructural observation revealed clustering and non-homogeneous distribution of SiC particles in the Al matrix. Porosities were observed in microstructures and increased with increasing wt. % of SiC reinforcements in AMCs. Pin-on-disc wear test indicated that reinforcing Al matrix with SiC particles increased wear resistance.

Food adulteration is a global concern and developing countries are at higher risk associated with it due to lack of monitoring and policies. However, this is one of the most common phenomena that has been overlooked in many countries. Unfortunately, in contrast to common belief, milk adulterants can pose serious health hazards leading to fatal diseases. This paper presents a detailed review of common milk adulterants as well as different methods to detect the adulterants both qualitatively and quantitatively. This study is organized to be an 'adulterant based' study instead of 'techniques based' one, where qualitative detection for most of the common adulterants are enlisted and quantitative detection methods are limited to few major adulterants of milk. Apart from regular techniques, recent development in these detection techniques have also been reported. Nowadays milk is being adulterated in more sophisticated ways that demands for cutting edge research for the detection of the adulterants. This review intends to contribute towards the common knowledge base regarding possible milk adulterants and their detection techniques.