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Groundwater arsenic poisoning in a primary educational institution:health risks to school-going children

Acta Geochimica 2022年第6期41卷 1069-1082页

作者： md.Sohel Rana md.Jahangir Alam mdAbu Musa Sazal Kumar md.Aminur rahman Zonal Laboratory Department of Public Health Engineering(DPHE)RangpurBangladesh Department of Chemistry Carmichael CollegeRangpurBangladesh Department of Applied Chemistry and Chemical Engineering Islamic UniversityKushtia7003Bangladesh School of Environmental and Life Sciences The University of NewcastleCallaghanNSW2308Australia Global Centre for Environmental Remediation(GCER) College of EngineeringScience and EnvironmentThe University of NewcastleCallaghanNSW2308Australia Zonal Laboratory Department of Public Health Engineering(DPHE)Jashore-7400Bangladesh

This study aimed to estimate arsenic(As)and iron(Fe)content in tubewell water(n=58)in primary educational institutions and subsequently assess the health risks to school-going *** described that the As concentration ranged between 0.002 and 0.994 mg L^(-1)with an average value of 0.044 mg L^(-1);which exceeded the World Health Organization(WHO)provisional guideline value of 0.01 mg L^(-1).Similarly,the Fe content varied from 0.05 to 10 mg L^(-1)averaging to 2.84 mg L^(-1).Samples of 55.17%contained a greater As concentration than0.01 mg L^(-1)and 18.97%greater than Bangladesh drinking water quality(BDWQ)standard of 0.05 mg L^(-1),***,75.86%of samples contained a higher Fe concentration than the maximum Bangladesh permissible limit of 1 mg L^(-1).Health risk assessment indicated that girls are more vulnerable than boys *** average hazard quotients(HQs)for As intake through drinking water were 6.01±17.85 and 7.41±22.03 for boys and girls,respectively,implying non-carcinogenic health risks to both *** HQs for Fe intake were less than threshold value of 1 indicating no health issues may arise from Fe intake ***,consumption of As and Fe may trigger health risks to students as indicated by the hazard index(HI),which was higher than *** average cancer risk(CR)values for both boys(0.0027±0.008)and girls(0.0033±0.0099)exceeded the threshold limit of 10-6-10-4,suggesting a possibility of lifetime cancer risks to the school-going ***,school authorities should find alternative ways to ensure safe drinking water for school-going children to avoid possible cancer and non-cancer health risks through consumption of As-poisoning water.

关键词： Arsenic Iron Human health risks Groundwater

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Efficient removal of heavy metals from electroplating wastewater using polymer ligands

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Frontiers of Environmental Science & Engineering 2016年第2期10卷 352-361页

作者： md. Lutfor rahman Shaheen M. SARKAR Mashitah Mohd YUSOFF Faculty of Industrial Sciences & Technology Universiti Malaysia Pahang 26300 Gambang Kuantan Malaysia

Poly（hydroxamic acid）-poly（amidoxime） che- lating ligands were synthesized from poly（methyl acrylate-co-acrylonitrile） grafted acacia cellulose for removing toxic metal ions from industrial wastewaters. These ligands showed higher adsorption capacity to copper （2.80 mmol. g-l） at pH 6. In addition, sorption capacities to other metal ions such as iron, zinc, chromium, and nickel were also found high at pH 6. The metal ions sorption rate （tl/2） was very fast. The rate of adsorption of copper, iron, zinc, chromium, nickel, cobalt, cadmium and lead were 4, 5, 7, 5, 5, 8, 9 and 11 min, respectively. Therefore, these ligands have an advantage to the metal ions removal using the column technique. We have successfully investigated the known concentration of metal ions using various parameters, which is essential for designing a fixed bed column with ligands. The wastewater from electroplating plants used in this study, having chromium, zinc, nickel, copper and iron, etc. For chromium wastewater, ICP analysis showed that the Cr removal was 99.8% and other metal ions such as Cu, Ni, Fe, Zn, Cd, Pb, Co and Mn removal were 94.7%, 99.2%, 99.9%, 99.9%, 99.5%, 99.9%, 95.6% and 97.6%, respectively. In case of cyanide wastewater, the metal removal, especially Ni and Zn removal were 96.5 and 95.2% at higher initial concentra- tion. For acid/alkali wastewater, metal ions removing for Cd, Cr and Fe were 99.2%, 99.5% and 99.9%, respectively. Overall, these ligands are useful for metal removal by column method from industrial wastewater especially plating wastewater.

关键词： heavy metals adsorption wastewater chelat-ing ligands plating industry

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Antioxidant,antibacterial and cytotoxic effects of the phytochemicals of whole Leucas aspera extract

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Asian Pacific Journal of Tropical Biomedicine 2013年第4期3卷 273-279页

作者： md Atiar rahman md Saiful Islam Department of Biochemistry and Molecular Biology University of ChittagongChittagong-4331Bangladesh Department of Biochemistry School of BiochemistryGenetics and MicrobiologyUniversity of KwaZulu-Natal.(Westville Campus)Durban 4000South Africa

Objective:To investigate the antioxidant,antibacterial and cytotoxic activity of whole Leucas aspera(Labiatae)(***)alcoholic ***:Whole *** powder was extracted by absolute ethanol(99.50%).The ethanolic extract was subjected to antioxidant,antibacterial and brine shrimp lethality ***:The extract showed potent radical scavenging effect(antioxidant)with IC_(50)value of(99.58±1.22)μg/mL which was significant(P<0.01)in comparison to ascorbic acid with IC_(50)value of(1.25±0.95)μg/*** case of antibacterial screening,the extract showed notable antibacterial effect against the tested microbial ***(P<0.05)zone of inhibitions against Gram positive Bacillus subtilis[(12.00±1.32)mm]and Bacillus megaterium[(13.00±1.50)mm],Staphylococcus aureus[(8.00±0.50)mm]and Gram negative Salmonella typhi[(6.00±0.50)mm],Salmonella paratyphi[(8.00±1.00)mm],Shigella dysenteriae[(9.00±1.32)mm]and Vibrio cholerae[(9.00±0.66)mm]was *** brine shrimp lethality bioassay,the extract showed the LC_(50)value as(181.68±2.15)μg/mL which was statistically significant(P<0.01)compared to positive control vincristine sulfate[LC_(50)=(0.76±0.04)μg/mL].Conclusions:The results demonstrate that the ethanolic extract of *** could be used as antibacterial,pesticidal and various pharmacologic actives.

关键词： Leucas aspera Radical scavenging Antibacterial Cytotoxic Probit

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An efficient hybrid system for anomaly detection in social networks

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Cybersecurity 2021年第1期4卷 136-146页

作者： md.Shafiur rahman Sajal Halder mdAshraf Uddin Uzzal Kumar Acharjee Department of Computer Science and Engineering Dhaka International UniversityDhakaBangladesh Department of Computer Science and Engineering Jagannath UniversityDhakaBangladesh Department Computer Science and Information Technology RMIT UniversityMelbourneAustralia Internet Commerce Security Laboratory Federation University AustraliaBallarat VIC 3350Australia

Anomaly detection has been an essential and dynamic research area in the data mining.A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and *** social network refers to a forum used by different groups of people to express their thoughts,communicate with each other,and share the content *** social networks also facilitate abnormal activities,spread fake news,rumours,misinformation,unsolicited messages,and propaganda post malicious ***,detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social *** this paper,we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree(C5.0),Support Vector Machine(SVM)and Naïve Bayesian classifier(NBC)for classifying normal and abnormal users in social *** have extracted a list of unique features derived from users’profile and *** two kinds of dataset with the selected features,the proposed machine learning model called DT-SVMNB is *** model classifies users as depressed one or suicidal one in the social *** have conducted an experiment of our model using synthetic and real datasets from social *** performance analysis demonstrates around 98%accuracy which proves the effectiveness and efficiency of our proposed system.

关键词： Anomaly Detection Machine Learning Hybrid Anomaly Detection Social Networks

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Industrial IoT Based Condition Monitoring for Wind Energy Conversion System

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CSEE Journal of Power and Energy Systems 2021年第3期7卷 654-664页

作者： md Liton Hossain Ahmed Abu-Siada S.M.Muyeen md Mubashwar Hasan Momtazur rahman Department of Electrical and Computer Engineering Curtin UniversityPerthAustralia School of Science Engineering and ITFederation UniversityAustralia Edith Cowan University Australia

Wind energy has been identified as the second dominating source in the world renewable energy generation after *** and distribution of wind energy has brought technology revolution by developing the advanced wind energy conversion system(WECS)including multilevel inverters(MLIs).The conventional rectifier produces ripples in their output waveforms while the MLI suffers from voltage balancing issues across the DC-link *** paper proposes a simplified proportional integral(PI)-based space vector pulse width modulation(SVPWM)to minimize the output waveform ripples,resolve the voltage balancing issue and produce better-quality output *** experiences various types of faults particularly in the DC-link capacitor and switching devices of the power *** faults,if not detected and rectified at an early stage,may lead to catastrophic failures to the WECS and continuity of the power *** paper proposes a new algorithm embedded in the proposed PI-based SVPWM controller to identify the fault location in the power converter in real *** most wind power plants are located in remote areas or offshore,WECS condition monitoring needs to be developed over the internet of things(IoT)to ensure system *** this paper,an industrial IoT algorithm with an associated hardware prototype is proposed to monitor the condition of WECS in the real-time environment.

关键词： Asset management condition monitoring fault diagnosis industrial internet of things(IoT) wind energy conversion system

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Ensemble learning of diffractive optical networks

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Light(Science & Applications) 2021年第1期10卷 123-135页

作者： md Sadman Sakib rahman Jingxi Li Deniz Mengu Yair Rivenson Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USA

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine ***,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation *** deep neural networks(D^(2)NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D^(2)NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and ***,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble *** independently training 1252 D^(2)NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D^(2)NNs that collectively improved the image classification *** this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D^(2)NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D^(2)NNs within each *** results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems.

关键词： networks collective successive

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基于三维UNet与混合焦点损失函数的脑肿瘤全自动分割算法

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中国医学物理学杂志 2023年第9期40卷 1114-1120页

作者：田恒屹肖洪兵计亚荣 rahman md.mostafizur 北京工商大学人工智能学院北京100048

针对脑肿瘤分割方法中由于正常脑组织、脑肿瘤等不同类别的数据量严重不平衡,导致分割精度受到极大影响的问题,提出一种结合混合焦点损失函数与三维UNet(3D UNet)的全自动脑肿瘤分割算法。在3D UNet模型框架中,使用包含焦点损失与改进... 详细信息

针对脑肿瘤分割方法中由于正常脑组织、脑肿瘤等不同类别的数据量严重不平衡,导致分割精度受到极大影响的问题,提出一种结合混合焦点损失函数与三维UNet(3D UNet)的全自动脑肿瘤分割算法。在3D UNet模型框架中,使用包含焦点损失与改进的焦点Tversky损失的混合损失函数,两种损失函数可以优势互补,分别缓解输入与输出数据类不平衡带来的不利影响,使分割模型聚焦在难以分类和学习的样本上。利用公开的脑肿瘤数据集进行相关实验,提出的混合焦点损失函数分割模型在完整肿瘤区域、核心肿瘤区域(TC)和增强肿瘤区域(ET)的Dice均值分别可达89.01%、88.67%与83.74%,豪斯多夫距离均值分别为14.29、5.01与3.84 mm,实验结果表明,基于混合损失函数的深度学习分割模型可以显著提升由于数据类不平衡导致的难以分类区域(TC和ET)的分割效果。

关键词：脑肿瘤深度学习 3D UNet 混合焦点损失函数

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Data Classification Using Combination of Five Machine Learning Techniques

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Journal of Computer and Communications 2021年第12期9卷 48-62页

作者： md. Habibur rahman Jesmin Akhter Abu Sayed md. mostafizur Rahaman md. Imdadul Islam Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Institute of Information Technology Jahangirnagar University Dhaka Bangladesh

Data clustering plays a vital role in object identification. In real life we mainly use the concept in biometric identification and object detection. In this paper we use Fuzzy Weighted Rules, Fuzzy Inference System (FIS), Fuzzy C-Mean clustering (FCM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) to distinguish three types of Iris data called Iris-Setosa, Iris-Versicolor and Iris-Virginica. Each class in the data table is identified by four-dimensional vector, where vectors are used as the input variable called: Sepal Length (SL), Sepal Width (SW), Petal Length (PL) and Petal Width (PW). The combination of five machine learning methods provides above 98% accuracy of class identification.

关键词： Co-Variance of Fuzzy Rule Objective Function Surface Plot Confusion Matrix Scatterplot and Accuracy of Detection

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A Critical Analysis of Machine Learning and Deep Learning Methods for Cervical Cancer Screening

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Journal of Computer and Communications 2023年第12期11卷 64-85页

作者： Muhtasim Mahmudur rahman Jakir Khan Abu Sale Mohammad mostafizur rahman Redoanul Haque md. Sumon Ali Department of Computer Science and Engineering Bangladesh Army University of Engineering & Technology Natore Bangladesh

Cervical cancer is a serious public health issue worldwide, and early identification is crucial for better patient outcomes. Recent study has investigated how ML and DL approaches may be used to increase the accuracy of vagina tests. In this piece, we conducted a thorough review of 50 research studies that applied these techniques. Our investigation compared the outcomes to well-known screening techniques and concentrated on the datasets used and performance measurements reported. According to the research, convolutional neural networks and other deep learning approaches have potential for lowering false positives and boosting screening precision. Although several research used small sample sizes or constrained datasets, this raises questions about how applicable the findings are. This paper discusses the advantages and disadvantages of the articles that were chosen, as well as prospective topics for future research, to further the application of ml and dl in cervical cancer screening. The development of cervical cancer screening technologies that are more precise, accessible, and can lead to better public health outcomes is significantly affected by these findings.

关键词： Cervical Cancer Neoplasms Screening Machine Learning Techniques Deep Learning Techniques

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Analysis and modeling of hysteresis of piezoelectric micro-actuator used in high precision dual-stage servo system

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Control Theory and Technology 2015年第2期13卷 184-203页

作者： md.Arifur rahman Abdullah Al MAMUN Kui YAO Department of Electrical and Computer Engineering National University of Singapore Institute of Materials Research and EngineeringA*STAR (Agency for Science Technology and Research)

A dual-stage servo system consists of a primary coarse actuator for facilitating large motion and a secondary micro-actuator for small but precise motion to improve tracking performance. Piezoelectric micro-actuator made from lead zirconate titanate(PZT)has been a popular choice for the secondary stage. However, the advantage gained by the resolution of the secondary PZT actuator is reduced by its inherent hysteresis nonlinearity. Model based hysteresis compensation techniques are preferred due to their simplicity and fast response. Identification and modeling are two substantial parts in such model-based techniques. This paper presents a rigorous analysis and modeling of the hysteresis of PZT micro-actuator. Modified Generalized Prandtl-Ishlinskii and Coleman-Hodgdon models are studied. Identification of the model through nonlinear least square and particle swarm optimization are examined and compared. Several analyses are done through tuning of the model parameters and identification *** analysis and simulation results underscore the effectiveness of this modeling approach. Finally as a design example,a dual-stage simulation analysis is done to show the effectiveness of systematic modeling on hysteresis compensation.

关键词： Hysteresis particle swarm optimization(PSO) modified generalized Prandtl-Ishlinskii(GPI) inverse GPI

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