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Microbial stabilisation and kinetic enhancement of marine methane hydrates in both deionised-and sea-water

Petroleum 2021年第4期7卷 402-406页

作者： Mohammad Reza Ghaani Jonathan M.Young prithwish k.nandi Shamsudeen Dandare Christopher C.RAllen Niall J.English School of Chemical and Bioprocess Engineering University College DublinBelfieldDublin 4Ireland School of Biological Sciences Queen's University BelfastUniversity RoadBelfastBT71NNNorthern IrelandUK

The large quantity of marine methane hydrates has driven substantial interest in methane-gas-fuel potential,especially with the qualified success of Shensu(2017)and Nankai-Trough(2014&17)production trials via depressurisation(blighted ultimately by sanding out),building on an earlier Malik-2008 trial for permafrost-bound *** particular,obviating deep-water-drilling approaches,such as the MeBO production rig(without such a drill bit),together with blowout preventers,constitutes a tantalising cost-saving *** means of addressing sand production by customised gravel packs,wellbore screens and slotted liners with from-seafloor drilling will be expected to lead to future production-trial ***,despite these exciting engineering advances and a few marinemimicking laboratory studies of methane-hydrate kinetics and stabilisation from microbial perspectives,relatively little is known about the thermogenic or microbial origin of marine hydrates,nor their possible formation kinetics or potential stabilisation by microbial sources as an exponent of Gaia's hypothesis,or within the context of“Gaia's breath”as regards global methane‘exhalations’.Here,for the first time,we elucidate the methylotrophic-microbial basis for kinetic enhancement and stabilisation of marine-hydrate formation in both deionised-and sea-water,identifying the key protein at play,which has some similarity to porins in other methylotrophic *** so doing,we suggest such phenomena in marine hydrates as evidence of Gaia's hypothesis.

关键词： Gas hydrate Microbes Methylotrophs

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Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 932-945页

作者： Bin Yang Yaguo Lei Xiang Li Naipeng Li Asoke k.nandi the key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System the School of Mechanical EngineeringXi’an Jiaotong UniveristyXi’an 710049 the Hunan Provincial key Labortory of Health Maintenance for Mechanical Equipment Hunan Univeristy of Science and TechnologyXiangtan 411201China the key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong UniveristyXi’an 710049China the Department of Electronic and Electrical Engineering Brunel University LondonUxbridge UB83PHUK

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source ***,in engineering scenarios,achieving such high-quality label annotation is difficult and *** incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled *** overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three ***,a residual network with dual classifiers is to learn features from cross-domain ***,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source *** the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised ***,the adaptation trajectories are designed for sample distributions across *** ensures that the target domain samples are only adapted with the pure-labeled *** LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related *** results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

关键词： Deep transfer learning domain adaptation incorrect label annotation intelligent fault diagnosis rotating machines

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Uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1317-1330页

作者： Jiaxin Ren Jingcheng Wen Zhibin Zhao Ruqiang Yan Xuefeng Chen Asoke k.nandi School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China Department of Electronic and Electrical Engineering Brunel University LondonKingston LaneUxbridgeUB83PHUK IEEE

Recently,intelligent fault diagnosis based on deep learning has been extensively investigated,exhibiting state-of-the-art ***,the deep learning model is often not truly trusted by users due to the lack of interpretability of“black box”,which limits its deployment in safety-critical applications.A trusted fault diagnosis system requires that the faults can be accurately diagnosed in most cases,and the human in the deci-sion-making loop can be found to deal with the abnormal situa-tion when the models *** this paper,we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks,called *** SAEU,Multivariate Gaussian distribution is employed in the deep architecture to compensate for the shortcomings of complexity and applicability of Bayesian neural *** on the SAEU,we propose a unified uncertainty-aware deep learning framework(UU-DLF)to realize the grand vision of trustworthy fault ***,our UU-DLF effectively embodies the idea of“humans in the loop”,which not only allows for manual intervention in abnor-mal situations of diagnostic models,but also makes correspond-ing improvements on existing models based on traceability ***,two experiments conducted on the gearbox and aero-engine bevel gears are used to demonstrate the effectiveness of UU-DLF and explore the effective reasons behind.

关键词： Out-of-distribution detection traceability analysis trustworthy fault diagnosis uncertainty quantification.

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Research on Indoor Positioning Technology of WSN based on T-RL Partition Path Model

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Journal of Communications and Information Networks 2024年第3期9卷 219-232页

作者： Wei Wang Xinlin Wang Yutong Liu Yulin Ren Maozhen Li Asoke k.nandi School of Information and Communication Engineering North University of ChinaTaiyuan 030051China Department of Electronic and Computer Engineering Brunel University LondonUxbridge UB83PHUK

To address the issues of unstable received signal strength indicator(RSSI)and low indoor positioning accuracy caused by walls and obstacles,the propagation conditions of the wireless communication system are categorized into two distinct environments:line-of-sight(LOS)and non-line-of-sight(NLOS).In the LOS environment,the traditional logarithmic path loss model is *** the NLOS environment,the impact of walls on signal transmission is considered,leading to the development of a multi-wall path loss model based on the T-RL method,with improvements made to the key parameter,the Fresnel coefficient *** breakpoint value d=2.3 m in the partitioned model is determined,and the positional coordinates of the unknown nodes are calculated using the trilateration *** results indicate that the T-RL based multi-wall model improves localization accuracy by 47%in NLOS environments compared to the traditional logarithmic path loss *** average localization error using the T-RL partitioned path loss model is 0.7021 m,representing a 55.9%improvement over the logarithmic path loss model and a 16.8%enhancement over the T-RL attenuation multi-wall model,thereby providing better environmental adaptability.

关键词： WSN indoor positioning loss model reflection transmission RSSI

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Investigation on FBG based optical sensor for pressure and temperature measurement in civil application

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Optoelectronics Letters 2024年第9期20卷 531-536页

作者： Somesh nandi Chethana k T Srinivas Preeta Sharan Department of Artificial Intelligence and Machine Learning Rashtreeya Vidyalaya College of EngineeringBangalore560059India Department of Electronics and Communications Jyothy Institute of TechnologyBangalore 560082India Department of Electrical Communication Engineering Indian Institute of ScienceBangalore 560012India Department of Electronics and Communications the Oxford College of EngineeringBangaloreIndia

Optical fiber Bragg grating(FBG)sensors have advanced significantly in the last several *** use of innovative FBG in temperature and pressure measurement is examined in this *** benefits of FBGs,such as their compact size,low weight,resilience to corrosion,immunity to electromagnetic interference,distributed sensing,and remote monitoring,have brought attention to the growing research in this field of structural health monitoring of civil infrastructures.

关键词： immunity civil application

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke k.nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐kD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐kD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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Polymeric biomaterials-based tissue engineering for wound healing: a systemic review

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Burns & Trauma 2023年第1期11卷 122-145页

作者： Pratik Das Suvendu Manna Shivam Roy Samit k.nandi Piyali Basak School of Bioscience and Engineering Jadavpur University188Raja Subodh Chandra Mallick RdJadavpurKolkata 700032West BengalIndia Sustainability Cluster University of Petroleum and Energy StudiesDehradun 248007UttarakhandIndia Department of Veterinary Surgery and Radiology West Bengal University of Animal and Fishery SciencesBelgachiaKolkata 700037West BengalIndia

Background:Biomaterials are vital products used in clinical sectors as alternatives to several biological macromolecules for tissue engineering techniques owing to their numerous beneficial properties,including wound *** healing pattern generally depends upon the type of wounds,and restoration of the skin on damaged areas is greatly dependent on the depth and severity of the *** rate of wound healing relies on the type of biomaterials being incorporated for the fabrication of skin substitutes and their stability in in vivo *** this review,a systematic literature search was performed on several databases to identify the most frequently used biomaterials for the development of successful wound healing agents against skin damage,along with their mechanisms of ***:The relevant research articles of the last 5 years were identified,analysed and reviewed in this *** meta-analysis was carried out using PRISMA and the search was conducted in major scientific *** research of the most recent 5 years,from 2017-2021 was taken into *** collected research papers were inspected thoroughly for further *** advances in the utilization of natural and synthetic biomaterials(alone/in combination)to speed up the regeneration rate of injured cells in skin wounds were ***,23 papers were critically reviewed and ***:In total,2022 scholarly articles were retrieved from databases utilizing the aforementioned input *** eliminating duplicates and articles published before 2017,∼520 articles remained that were relevant to the topic at hand(biomaterials for wound healing)and could be evaluated for *** different procedures,23 publications were selected as best fitting for data *** Reporting Items for Systematic Reviews and Meta-Analyses for this review illustrates the selection criteria,such as exclusion and inclusion *** 23 recent publications

关键词： Wound healing Biomaterials Tissue engineering Skin regeneration Polymers

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Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

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Chinese Journal of Mechanical Engineering 2021年第3期34卷 73-93页

作者： Hosameldin O.A.Ahmed Asoke k.nandi Department of Electronic and Electrical Engineering Brunel University LondonUxbridge UB83PHUK School of Mechanical Engineering Xi’an Jiaotong UniversityXi’anChina

Roller bearing failure is one of the most common faults in rotating *** techniques for bearing fault diagnosis based on faults feature extraction have been *** feature extraction from fault signals requires expert prior information and human ***,deep learning algorithms have been applied extensively in the condition monitoring of rotating machines to learn features automatically from the input *** its robust performance in image recognition,the convolutional neural network(CNN)architecture has been widely used to learn automatically discriminative features from vibration images and classify health *** paper proposes and evaluates a two-stage method RGBVI-CNN for roller bearings fault *** first stage in the proposed method is to generate the RGB vibration images(RGBVIs)from the input vibration *** begin this process,first,the 1-D vibration signals were converted to 2-D grayscale vibration *** the conversion was completed,the regions of interest(ROI)were found in the converted 2-D grayscale vibration ***,to produce vibration images with more discriminative characteristics,an algorithm was applied to the 2-D grayscale vibration images to produce connected components-based RGB vibration images(RGBVIs)with sets of colours and texture *** the second stage,with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and to classify bearing health *** cases of fault classification of rolling element bearings are used to validate the proposed *** results of this investigation demonstrate that RGBVI-CNN can generate advantageous health condition features from bearing vibration signals and classify the health conditions under different working loads with high ***,several classification models trained using RGBVI-CNN offered high performance in the testing results of the overall classification accura

关键词： Bearing fault diagnosis Image representation of vibrations Deep learning Convolutional neural networks

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Pre-treatment of rocks prior to comminution--A critical review of present practices

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International Journal of Mining Science and Technology 2017年第2期27卷 339-348页

作者： Somani Aditya nandi Tapas k. Pal Samir k. Majumder Arun k. Department of Mining Engineering Indian Institute of TechnologyKharagpur 721302India Cryogenic Engineering Centre Indian Institute of TechnologyKharagpur 721302India

It has been an established fact that comminution processes, crushing and grinding, are most energy intensive processes which account for more than half of the total energy consumed in mineral *** alternative pre-treatment methods have been tried by experts around the globe. Although these methods yielded positive results in terms of reduction in energy consumption in crushing and particularly, in grinding operations at laboratory scale, their industrial application still remains an unresolved issue and challenge. Present review paper describes each one of these methods along with outcome of earlier studies and issues that need to be addressed through further rigorous experimental *** also suggests the direction in which future studies can be carried out to meet the primary objective of making comminution processes more energy efficient than today they are.

关键词： Comminution Rock breakage Microwave Thermal shock

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基于地理探测器的元阳梯田地质灾害影响因素及滑坡成因分析

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中国地质调查 2024年第3期11卷 92-100页

作者：苏航卢楠迪伏小双李奇云南省有色地质局三O八队云南昆明650200

元阳梯田受其特殊的地理位置、地表植被和农耕文化影响,表生地质灾害频发,对其生态价值和文化价值造成极大损害,梯田区域的地质灾害防治显得尤为关键。在详细野外调查基础上,结合元阳梯田区15 m分辨率遥感影像数据,利用空间叠加分析和... 详细信息

元阳梯田受其特殊的地理位置、地表植被和农耕文化影响,表生地质灾害频发,对其生态价值和文化价值造成极大损害,梯田区域的地质灾害防治显得尤为关键。在详细野外调查基础上,结合元阳梯田区15 m分辨率遥感影像数据,利用空间叠加分析和地理探测器模型对研究区地质灾害影响因素开展了详细研究。结果表明:元阳梯田区地质灾害(隐患)点中滑坡灾害(隐患)点数量占比为93.39%,小型规模灾害(隐患)点数量占比为87.69%,总体表现为以滑坡性质的小型灾害为主;地质灾害分布的影响因素主要为地形坡度,其次为地质构造,受地貌单元影响最弱;滑坡灾害形成的影响因素具有复合性特点,其中强烈的河流侵蚀作用、地壳抬升作用和活跃的断裂构造活动是其成灾的主要地质环境背景,丰富的降雨量和人类工程活动是其成灾的主要触发因素。通过研究可进一步认识元阳梯田地质灾害影响因素及形成条件,可更好地助力于地方防灾减灾体系提升,也可为区域地质灾害风险分析提供一定的参考。

关键词：元阳梯田地质灾害地理探测器影响因素成因机制

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