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Journal of Environmental Sciences 2019年第8期31卷 179-191页

作者： Niloy Chandra Sarker Md Borhan Ann-Marie Fortuna shafiqur rahman Agricultural and Biosystems Engineering Department North Dakota State University USDA-ARS Grazinglands Research Laboratory

Manure is an impending source of carbon(C), sulfur(S) and water(H_2 O). Consequently,microbial populations utilize these constituents to produce methane(CH4), carbon dioxide(CO_2), greenhouse gases(GHGs), and hydrogen sulfide(H_2 S). Application of nanoparticles(NPs) to stored manure is an emerging GHG mitigation technique. In this study, two NPs: nano zinc oxide(nZnO) and nano silver(nAg) were tested in swine manure stored under anaerobic conditions to determine their effectiveness in mitigating gaseous emissions and total gas production. The biological sources of gas production, i.e., microbial populations were characterized via Quantitative Polymerase Chain Reaction(qPCR) analysis. Additionally, pH, redox, and VFAs were determined using standard methods. Each treatment of the experiment was replicated three times and NPs were applied at a dose of 3 g/L of manure. Also, headspace gas from all treatment replicates were analyzed for CH_4 and CO_2 gas concentrations using an SRI-8610 Gas Chromatograph and H_2 S concentrations were measured using a Jerome 631 X meter. Nanoparticles tested in this study reduced the cumulative gas volume by 16%–79% compared to the control. Among the NPs tested, only nZnO consistently reduced GHG concentrations by 37%–97%. Reductions in H_2 S concentrations ranged from 87% to97%. Gaseous reductions were likely due to decreases in the activity and numbers of specific gas producing methanogenic archaea and sulfate reducing bacterial(SRB)species.

关键词： Swine manure Nanoparticles Greenhouse gas Methanogen Sulfate reducing bacteria(SRB)

Comparative Evaluation of Machine Learning Models and Input Feature Space for Non-intrusive Load Monitoring

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Journal of Modern Power Systems and Clean Energy 2021年第5期9卷 1161-1171页

作者： Attique Ur Rehman Tek Tjing Lie Brice Valles shafiqur rahman Tito Department of Electrical and Electronic Engineering Auckland University of TechnologyAucklandNew Zealand Brice Valles Consulting AucklandNew Zealand School of Engineering and Trades Manukau Institute of TechnologyAucklandNew Zealand

Recent advancement in computational capabilities has accelerated the research and development of non-intrusive load ***-intrusive load monitoring(NILM)offers many promising applications in the context of energy efficiency and *** classification is a key component of NILM that relies on different artificial intelligence techniques,e.g.,machine *** study employs different machine learning models for load classification and presents a comprehensive performance evaluation of the employed models along with their comparative ***,this study also analyzes the role of input feature space dimensionality in the context of classification *** the above purposes,an event-based NILM methodology is presented and comprehensive digital simulation studies are carried out on a low sampling real-world electricity load acquired from four different *** on the presented analysis,it is concluded that the presented methodology yields promising results and the employed machine learning models generalize well for the invisible diverse testing *** multi-layer perceptron learning model based on the neural network approach emerges as the most promising ***,it is also noted that it significantly facilitates the classification performance by reducing the input feature space dimensionality.

关键词： Machine learning model load feature non-intrusive load monitoring(NILM) comparative evaluation

Deep reinforcement learning based computation offloading and resource allocation for low-latency fog radio access networks

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Intelligent and Converged Networks 2020年第3期1卷 243-257页

作者： G.M.shafiqur rahman Tian Dang Manzoor Ahmed Key Laboratory of Universal Wireless Communications(Ministry of Education) Beijing University of Posts and TelecommunicationsBeijing 100876China

Fog Radio Access Networks(F-RANs)have been considered a groundbreaking technique to support the services of Internet of Things by leveraging edge caching and edge ***,the current contributions in computation offloading and resource allocation are inefficient;moreover,they merely consider the static communication mode,and the increasing demand for low latency services and high throughput poses tremendous challenges in F-RANs.A joint problem of mode selection,resource allocation,and power allocation is formulated to minimize latency under various *** propose a Deep Reinforcement Learning(DRL)based joint computation offloading and resource allocation scheme that achieves a suboptimal solution in *** core idea of the proposal is that the DRL controller intelligently decides whether to process the generated computation task locally at the device level or offload the task to a fog access point or cloud server and allocates an optimal amount of computation and power resources on the basis of the serving *** results show that the proposed approach significantly minimizes latency and increases throughput in the system.

关键词： fog radio access networks computation offloading mode selection resource allocation distributed computation low latency deep reinforcement learning

Nanoparticles in mitigating gaseous emissions from liquid dairy manure stored under anaerobic condition

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Journal of Environmental Sciences 2019年第2期31卷 26-36页

作者： Niloy Chandra Sarker shafiqur rahman Md.Saidul Borhan Parthiban Rajasekaran Swadeshmukul Santra Ali Ozcan Agricultural and Biosystems Engineering North Dakota State University NanoScience Technology Center University of Central Florida Burnett School of Biomedical Sciences University of Central Florida Department of Chemistry University of Central Florida Department of Materials Science and Engineering University of Central Florida

A number of mitigation techniques exist to reduce the emissions of pollutant gases and greenhouse gases(GHGs) from anaerobic storage of livestock manure. Nanoparticle(NP)application is a promising mitigating treatment option for pollutant gases, but limited research is available on the mode of NP application and their effectiveness in gaseous emission reduction. In this study, zinc silica nanogel(ZnSNL), copper silica nanogel(CuSNL), and N-acetyl cysteine(NACL) coated zinc oxide quantum dot(Qdot) NPs were compared to a control lacking NPs. All three NPs tested significantly reduced gas production and concentrations compared to non-treated manure. Overall, cumulative gas volumes were reduced by 92.73%–95.83%, and concentrations reduced by 48.98%–99.75% for H_2S, and 20.24%–99.82% for GHGs. Thus, application of NPs is a potential treatment option for mitigating pollutant and GHG emissions from anaerobically stored manure.

关键词： Nanoparticles Greenhouse gas Hydrogen sulfide Anaerobic storage Gaseous reduction

Non-invasive load-shed authentication model for demand response applications assisted by event-based non-intrusive load monitoring

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Energy and AI 2021年第1期3卷 180-191页

作者： Attique Ur Rehman Tek Tjing Lie Brice Valls shafiqur rahman Tito School of Engineering Computerand Mathematical SciencesAuckland University of TechnologyNew Zealand Brice Valles Consulting New Zealand School of Professional Engineering Manukau Institute of TechnologyNew Zealand

With today’s growth of prosumers and renewable energy resources,it is inevitable to incorporate the demand-side approaches for reliable and sustainable grid *** this context,demand response is a promising technique facilitating the consumers to play a substantial role in the energy market by altering their energy consumption patterns in times of peak demand or other critical ***,effective demand response deployment faces numerous challenges including trust deficit among the concerned *** paper addresses the mentioned issue by proposing a non-invasive load-shed authentication model for demand response applications,assisted by an improved event-based non-intrusive load monitoring *** the said purposes,an improved event detection algorithm and machine learning model:support vector machine with a combination of genetic algorithm and GridSearchCV,is *** paper also presents a comprehensive real-world case study to validate the effectiveness of the proposed model in a real-life *** the given context,all the simulations are carried out on low sampling real-world load measurements:Pecan Street-Dataport,where electric vehicle and air conditioning are employed as potential load elements for evaluation *** on the presented case study and analysis of the results,it is established that the presented improved event-based non-intrusive load monitoring approach yields promising performance in the context of multi-class ***,it is also concluded that the proposed low sampling event-based non-intrusive load monitoring assisted non-invasive load-shed authentication model is a viable and promising solution for the effective implementation of demand response applications.

关键词： Non-Intrusive Load Monitoring Load-Shed Authentication Demand Response Machine Learning Model Genetic Algorithm Energy Efficiency

Latency Minimization for Internet of Things (IoT) in Device to Device (D2D) Communication with Intervention of Fog Computing

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Latency Minimization for Internet of Things (IoT) in Device ...

作者： G. M. shafiqur rahman 北京邮电大学

学位级别：硕士

Over the past decade,due to immense advancement of powerful mobile devices and internet-connected things,there is a tremendous growing trend for proximity-based applications,such as peer-to-peer file sharing,local multicasting and *** addition,we are on the verge of a transition into a state of fully connected fifth generation wireless technology where high capacity is ***,with current infrastructure deployments and radio resources,telecommunication operators will not be able to adapt with this upcoming rigorous ***,to satisfy the flourishing demand of high-data-rate,device to device(D2D)communication has been considered as one of the key techniques for next generation's wireless communications.D2D communication is a hot issue for future wireless communication system in both academia and *** D2D communications,proximity users in a cellular network can communicate directly to each other without traversing base station(BS).Furthermore,due to promising features of D2D communications and rapid growth of internet-connected things the emerging Internet of Things(IoT)concepts have evolved and become very popular in contemporary D2D based communication *** promising applications of IoT are increasing at an unprecedented scale around the ***,the IoT generate harmful delay into the existing cellular network and become a bottleneck for entire communication ***,the latency minimization is one of the challenging and important issues for IoT in D2D *** this dissertation,we mainly focus on minimizing latency for IoT in D2D communications with the intervention of newly proposed fog computing *** the research of latency minimization for IoT in D2D communications,an advanced system architecture and mathematical model has been presented,where the objective is to minimize the overall latency and enhance the performance of next generation IoT *** framework includes three part

关键词： internet of things latency device to device fog computing cloud computing

Frequency of Malignancy in Solitary Thyroid Nodule in a Tertiary Level Hospital of Bangladesh

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International Journal of Otolaryngology and Head & Neck Surgery 2019年第4期8卷 132-138页

作者： Md Shazibur Rashid Najnin Akhter Md shafiqur rahman Jahangir Alam Majumder A. H. M. Delwar Md Golam Mustafa Otolaryngology Comilla Medical College Cumilla Bangladesh Pediatrics Comilla Medical College Cumilla Bangladesh

Objectives: To determine the frequency of thyroid malignancy in clinically and sonographically solitary thyroid nodule in patients undergoing thyroidectomy in Comilla Medical College Hospital. Methods: A cross sectional study was carried out at the Department of Otolaryngology and Head Neck Surgery of Comilla Medical College Hospital during the period from January 2016 to January 2019. This study includes all patients admitted and undergoing surgery with solitary thyroid nodule. Results: Out of 188 patients 146 patients were female and 42 patients were male with female:male ratio of 3.4:1. Out of 188 patients 24 patients were found histologically thyroid malignancy. Among them 14 patients were male and 10 patients were female. Papillary carcinoma was found in all cases. Conclusions: Follicular carcinoma is rare in our country. Histopathological examiners may not efficiently be doing their job to differentiate carcinoma from adenoma.

关键词： Solitary Thyroid Nodule Papillary Carcinoma Follicular Carcinoma

A Synergistic Multi-Attribute Decision-Making Method for Educational Institutions Evaluation Using Similarity Measures of Possibility Pythagorean Fuzzy Hypersoft Sets

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 501-530页

作者： Khuram Ali Khan Saba Mubeen Ishfaq Atiqe Ur rahman Salwa El-Morsy Department of Mathematics University of SargodhaSargodha40100Pakistan Department of Mathematics University of Management and TechnologyLahore54000Pakistan Department of Mathematics College of ScienceQassim UniversityBuraydah51452Saudi Arabia

Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be *** concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such *** study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and *** results related to these operations are also modified for ***,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and ***,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested *** algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical ***,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.

关键词： Hypersoft set Pythagorean fuzzy hypersoft set computational complexity multi-attribute decision-making optimization similarity measures uncertainty

Solid Waste Management:A MADM Approach Using Fuzzy Parameterized Possibility Single-Valued Neutrosophic Hypersoft Expert Settings

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 531-553页

作者： Tmader Alballa Muhammad Ihsan Atiqe Ur rahman Noorah Ayed Alsorayea Hamiden Abd El-Wahed Khalifa Department of Mathematics College of SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia 2 Department of Mathematics University of Management and TechnologyLahore54000Pakistan Department of Mathematics College of ScienceQassim UniversityBuraydah51452Saudi Arabia

The dramatic rise in the number of people living in cities has made many environmental and social problems *** search for a productive method for disposing of solid waste is the most notable of these *** scholars have referred to it as a fuzzy multi-attribute or multi-criteria decision-making problem using various fuzzy set-like approaches because of the inclusion of criteria and anticipated *** goal of the current study is to use an innovative methodology to address the expected uncertainties in the problem of solid waste site *** characteristics(or sub-attributes)that decision-makers select and the degree of approximation they accept for various options can both be indicators of these *** tackle these problems,a novel mathematical structure known as the fuzzy parameterized possibility single valued neutrosophic hypersoft expert set(ρˆ-set),which is initially described,is integrated with a modified version of Sanchez’s *** this,an intelligent algorithm is *** steps of the suggested algorithm are explained with an example that explains *** compatibility of solid waste management sites and systems is discussed,and rankings are established along with detailed justifications for their *** study’s strengths lie in its application of fuzzy parameterization and possibility grading to effectively handle the uncertainties embodied in the parameters’nature and alternative approximations,*** uses specific mathematical formulations to compute the fuzzy parameterized degrees and possibility grades that are missing from the prior *** is simpler for the decisionmakers to look at each option separately because the decision is *** the computed results,it is discovered that they are consistent and dependable because of their preferred properties.

关键词： Hypersoft expert set Sanchez’s method decision making optimization solid waste management possibility grade fuzzy parameterization

A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika Jain Saxena Amit Pundir Sanjeev Singh Akshat Gaurav Savi Bansal Razaz Waheeb Attar Mosiur rahman Brij B.Gupta Institute of Informatics and Communication University of DelhiDelhi110021India Maharaja Agrasen College University of DelhiDelhi110096India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour