AIUB Journal of Science and Engineering

In recent years, increasingly more soils are getting contaminated with organic and inorganic toxins globally due to waste emissions. Among inorganic pollutants, heavy metal like chromium (Cr) is alarming to our environment, even though its environmental management is also ignored. As a result, Cr accumulates in plant tissues at toxic concentrations end up in the food chain. Therefore, pot experiment was conducted to investigate the effects of biochar and modified biochar application on the properties of Cr polluted soils and interaction of Cr with other soil nutrients. Two different biochar viz. rice stubble and saw dust were slow pyrolyzed (450 ± 50ºC) and modified with1M KOH. All biochars were applied at a rate of 20 t ha-1 on soils artificially polluted with Cr at the levels of 0, 100, 200 and 300 µg g-1. The biochars and modified biochars had significant effects (P&lt;0.05) on available K, P, CEC, EC, and N of incubated soils. Therefore, it has convincing evidence that application of biochar and modified biochar is very imperative to improve soil health, ameliorate Cr polluted soils, reduce the amount of carbon produced due to biomass burning and thereby enhances plant growth.

Asha Siddika

AFM Masum Rabbani

Zakia Parveen

Md. Faruque Hossain

AIUB Journal of Science and Engineering (AJSE)

Effects of biochar and modified biochar on Chromium contaminated soil properties

生物炭及改性生物炭对铬污染土壤性质的影响

Bangladesh is highly affected by climate change scenarios notably floods due to its location on the world map in the South Asian region. Besides due to monsoon rains and high upstream rainfall in several areas eventually turn into floods. Thus, early flood forecasting might save human lives as well as agriculture crops. In this paper, we have applied different machine learning classifier models (Decision tree, Naive bayes, k-NN and Random forest) with a view to predicting the occurrence of flood. RapidMiner tool has been used extensively to perform data preparation, decision tree model generation, cross-validation, model selection and optimization of the model parameters. It is seen that the decision tree model has performed well by achieving an accuracy of 94.23% which is further optimized to reach 94.68%. Feature Selection using ‘correlation matrix’ is also a good aspect of this work by which we have achieved a good accuracy.

Md Sajid Hossain

Mohammad Zeyad

Prediction of Flood in Bangladesh Using Different Classifier Model

使用不同分类器模型预测孟加拉国的洪水

In this research paper, effects of fixing one barrier and varying another barrier have been analyzed and compared for a GaAs/Al0.3Ga0.7As based double barrier resonant tunnelling diode for two different models - Hartree Quantum Charge model and semi-classical Thomas Fermi model. VI characteristic graphs are studied to assess the overall performance of both models. The simulations are carried out in a nanoelectronics modelling tool suite – Nano electronic Modelling 5 (NEMO5) considering Non-Equilibrium Green’s Function (NEGF), at room temperature of 300K and biased voltage of 0 to 0.5 V. In this paper, it was demonstrated that a very larger amount of current is supplied by both models when the first barrier is varied and second barrier is fixed in comparison to the first barrier when kept fixed and second barrier is varied. But as quantum charge inside the quantum well is existed in the Hartree model, so overall Hartree model supplied a greater amount of current compared to the Thomas Fermi model. Quantum charge inside its quantum well is not present in the Thomas Fermi model. But a better NDR region is created by the Thomas Fermi model in both varied first barrier-fixed second barrier and fixed first barrier-varied second barrier cases compared to the Hartree model. This NDR region can be used for numerous digital applications. On the other hand, a vast range of analog applications can be used by the Hartree model that produced larger current per unit voltage.

None Tahseen Asma Meem

None SHAIRA TASHNUB TORSA

None Mehedi Hasan

None Mahfujur Rahman

A Comparative Study of Fixing One Barrier Varying Another Barrier for a Resonant Tunneling Diode

共振隧穿二极管中固定一个势垒变化另一个势垒的比较研究

Generally, human epidermal growth factor 2 (HER2) breast cancer is more aggressive than other kinds of breast cancer. Currently, HER2 breast cancer is detected using variety of medical test which are most expensive. Therefore, the aim of this study was to develop a computational model named convoHER2 for detecting HER2 breast cancer with image data using convolution neural network (CNN). Hematoxylin and eosin (H&amp;E) and immunohistochemical (IHC) stained images were used as raw data from the Bayesian information criterion (BIC) benchmark dataset. This dataset consists of 4873 images of H&amp;E and IHC. Among all images of the dataset, 3896 and 977 images were applied to train and test the convoHER2 model, respectively. All images of this dataset are resized due to high resolution of the image for forming better detection performance of convoHER2 model. Moreover, the dataset is classified into four different labels (0+, 1+, 2+, 3+) for identifying the grade of detected HER2 breast cancer. The convoHER2 model is able to detect HER2 cancer and its grade with accuracy 85% and 88% using H&amp;E images and IHC images, respectively. The outcomes of this study determined that the HER2 cancer detecting rates of the convoHER2 model are much enough to provide better diagnosis to the patient for recovering their HER2 breast cancer in future.

M. F. Mridha

Md. Kishor Morol

Md. Asraf Ali

MD SAKIB HOSSAIN SHOVON

convoHER2: A Deep Neural Network for Multi-Stage Classification of HER2 Breast Cancer

convoHER2：一种用于HER2乳腺癌多阶段分类的深度神经网络

An essential instrument for the operation of a power system is to monitor and analyze the data to find the fault and rectify it before the system collapses completely. This paper intents to utilize the idea to create a control system that will fulfill objectives like monitoring vital parameters controlling the power distribution, outage management by fault detection based on the variation of Voltage, Frequency, and Current &amp; protection of the Circuit against any significant incidents by isolating the load from utility and flagging the information through feedback to the utility authority. The Project implements the high-end technology for IoT applications that will connect with the Microcontroller to receive the data at regular intervals and post the timestamped data in a cloud platform for remote monitoring and archiving. This innovation can reduce human dependency and overcome the whole outage situation by building a two-way communication that means electricity and information are traded between consumers and utilities to capitalize on the efficiency of a grid system. The proposed model offers intelligent monitoring of the timestamped data with remote access &amp; historical data archiving for a better load profile. An outage management system is designed for the proposed system by intellectual fault detection through Voltage, Frequency &amp; current variation, and isolation of the faulty part from the entire system, maintaining an auto circuit recloser application before the permanent isolation for reducing the human interaction &amp; flagging the outage information &amp; supply emergency backup during the shutdown period, and in the process, helping the power grid from total power outage or blackout.

Ahmed Muntasir

Md. Rifat Hazari

Mohammad Abdul Mannan

Design and Analysis of Iot-Based Remote Load Monitoring and Outage Management System

基于物联网的远程负载监控与断电管理系统的设计与分析

The present paper focuses on pricing with European or American put option that has yet to be studied. In solving option pricing problems, numerical methods form an essential part. Five numerical methods: Black-Scholes-Merton, Monte Carlo, Binomial, Trinomial, and Finite Difference have been implemented in the present work. Here Computer Algebra System (CAS) Python is used for the simulation. A comparison of these methods for both European and American put options has been shown by a graphical representation. Results show that Crank Nicolson Finite Difference Methods (FDM) gives us more accurate results than the other methods.

Afroza Akter

A B M Shahadat Hossain

Salma Parvin

A Numerical Analysis of Comparing Several Practical Approaches for Pricing both American and European Put Options

比较美式和欧式看跌期权定价的几种实用方法的数值分析

In the context of earthquake and flood disasters, effective communication remains a pivotal concern due to the vulnerability of conventional networks. This study presents a comparative analysis of off-grid communication technologies—namely WiFi and LoRa—with the aim of ascertaining their efficacy in addressing this challenge and proposes a LoRa rescue model. Critical parameters such as communication range, SNR (Signal to noise ratio), RSSI (Received Signal Strength Indicator) rate, data transmission speed, bandwidth, frequency, and reliability are evaluated. The outcomes of this investigation highlight the importance of alternative communication solutions when centralized networks falter. Particularly, the intrinsic attributes of LoRa exhibit potential for sustaining connectivity amidst network disruptions. These findings not only enrich the discourse on disaster management strategies but also offer insights into crafting adaptive and resilient communication systems. Informed decision-making regarding communication technology deployment in emergency contexts in order to make the rescue process convenient and reliable is the direct implication of this research.

Danium Shahnowaz Syed

Shababa Binte Hossain

Anika Islam

Tamim-Al- Islam Jim

Saniat Rahman Zishan

Design and Feasibility Analysis of a LoRa Based Communication System for Disaster Management

基于LoRa的灾害管理通信系统设计与可行性分析

The semiconductor industry has been making life easier by providing loads of electronic devices. With the advancement of technology, electronic devices need to be updated. This gradual upgradation has led the industry towards nanotechnology. The study is to provide an analytical comparison of the impact of Si and GaAs as semiconductor materials in designing 3D density gradient nanowire MOSFETS. The model has been designed and evaluated on the basis of the characteristics curve and transconductance range. The drain current in the Id-Vg and Id-Vd curve for GaAs used model is higher than the Si used model by about 10 times which is useful for analog applications. The threshold voltage for both models is 0.7V. Besides that, the electron concentration forming a potential well of the depth of about 104 for GaAs used material than the model made using Si as material. In the case of AC analysis, the transconductance range for the model using GaAs is almost double of the model designed using Si. Later on, the extracted data from the curves have been utilized to generate model and library files. Finally, an inverter circuit and biasing circuit have been designed with models where the output of the inverter circuit perfectly works. The frequency response curve generated using the biasing circuit showed the cut-off frequency of the GaAs used model is 10MegaHz whereas it is 1MegaHz for Si used model.

None Syeda Fahima Nazreen

None M Tanseer Ali

Impact of Si and GaAs as Semiconductor Materials: Designing to Application-Level Comparison

硅和砷化镓作为半导体材料的冲击：从设计到应用层面的比较

Perturb and observe (P&amp;O) is a well-known maximum power point tracking (MPPT) algorithm that is used in solar photovoltaic (PV) systems to increase its efficiency. However, as the PV system uses solar irradiance and temperature for making electric power, the fast change of these two affects the performance of P&amp;O and the efficiency of the PV system. Thus, the P&amp;O algorithm fails to detect maximum power point (MPP) if temperature and irradiance change quickly. Therefore, this paper presents an adaptive neuro-fuzzy inference system (ANFIS) based P&amp;O algorithm of MPPT controller for a solar PV system to solve the issues mentioned earlier. The utilization of the proposed ANFIS in the P&amp;O algorithm can track the fast changes in solar irradiance and temperature to extract the maximum power from the solar PV panel. Comparative analysis has been done on MATLAB/Simulink software for both the traditional P&amp;O and the proposed ANFIS-based P&amp;O algorithm to show the effectiveness of the proposed MPPT controller.

Arnob Chandra Dafader

Shameem Ahmad

Design and Performance Analysis of Robust Adaptive Neuro-Fuzzy Inference System-Based Modified P&amp;O Algorithm of MPPT Controller for a Solar PV System

基于鲁棒自适应神经模糊推理系统的改进P&O算法在太阳能光伏系统MPPT控制器中的设计与性能分析

This study investigated the steam power plant performances using a response surface methodology (RSM). The thermodynamic simulations are conducted by the Engineering Equation Solver (EES) program for distinct parameters such as inlet temperature (350≤T3≤600°C), boiler pressure (5000≤P3≤15000kPa), and condenser pressure (5≤T1≤15 kPa). A centered composite design (CCD) with process parameters was used for statistical analysis. A second-order regression model was developed to correlate the process parameters with thermal efficiency (ηI), exergetic efficiency (ηII), vapor quality (x), and specific fuel consumption (SFC). A better determination coefficient (R2) was attained with the quadratic model, which showed 99.88%, 99.85%, 99.44%, and 99.80% for ηI, ηII, x, and SFC, respectively. Hence, numerical and graphical optimization was conducted operating the desirability function approach to get the suitable input variables to deliver the highest thermal and exergetic efficiencies with maximum vapor quality and minimal specific fuel consumption rate.

Mohamed Walid Azizi

Moumtez Bensouici

Fatima Zohra Bensouici

Thermoexergetic analysis and multi-objective optimization of steam power plant performances

蒸汽动力厂性能的热力学分析与多目标优化

Publication Information	Publisher: AIUB Office of Research and Publication，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2018，Proportion of original research papers: 0.00%，Self Citation Rate:N.A.， Gold OA Rate: 0.00%

AIUB Journal of Science and Engineering

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