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Effect of process parameter on the flowability of nanocrystalline CoNiCrAlY powder synthesized by mechanical milling

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Particuology 2024年第9期92卷 210-223页

作者： Annadaa Shankara Dash Indran.l Manna Deepa Devapal Sharad Chandra sharma Jyotsna Dutta Majumdar School of Nanoscience and Technology IIT KharagpurWest BengalIndia BIT Mesra RanchiJharkhandIndia Department of Metallurgical and Materials Engineering IIT KharagpurWest BengalIndia Vikram Sarabhai Space Centre ThiruvanthapuramKeralaIndia

The present study concerns understanding the effect of process parameters on the characteristics and flowability of nanocrystalline CoNiCrAlY powder synthesized by mechanical *** milling has been conducted in a planetary ball mill with tungsten carbide(WC)ball,with ball to powder ratio of 10:1 at 300 rpm speed,using 1%stearic acid and toluene as process control agent(PCA)with time varying from 10 h to 36 *** synthesized nanocrystalline powder were characterized by Scanning Electron Microscopy,X-ray Diffraction technique,X-ray Photoelectron Spectroscopy,and Differential Scanning ***,flowability in terms of Hausner ratio was assessed by Tap Density *** particle size of the powder was found to decrease from 33μm to 22μm after 10 h of milling and further increases to 43μm and 38μm after 25 and 36 h of milling,respectively,in stearic acid ***,in toluene medium particle size continuously decreases from 33μm to 9.7μm with increasing milling *** particle morphology changes from spherical to platelet shape at low milling hours in both of the *** 25 h of milling,the shape of the particles is nearly spherical for stearic acid and irregular for toluene used as a *** size was found to decrease with increasing milling time from 147 nm to 7.7 nm and to 6.4 nm in stearic acid and toluene media,*** was presence ofγ,γʹ,β,hcp-Co,Al_(2)O_(3)and AlOOH phases on the powder particles milled in both the *** measured Hausner ratio of the powders was found to vary from 1.18 to 1.32 in stearic acid medium,and was found to increase with increasing milling *** the other hand,in toluene media flowability decreases(Hausner ratio increases from 1.33 to 1.44)with increasing milling time.

关键词： Mechanical milling Microstructure Process control agent(PCA) CoNiCrAIY Toluene Stearic acid Flowability

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Numerical modelling of post-failure behaviors of coal specimens

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第2期16卷 514-531页

作者： Ajeet Yadav Bhaskara Behera Gauri Shankar Prasad Singh Sanjay Kumar sharma Department of Mining Engineering Indian Institute of Technology(Banaras Hindu University)VaranasiUttar Pradesh221005India

A modelling approach consisting of best-fit relations to estimate the post-yield strength parameters is presented for simulating post-peak behavior beyond the point of residual strength of coal pillars having different w/h *** model was developed based on back-an.lysis of the complete stress-strain behavior of specimens belonging to six different Indian coal seams with different w/h ratios of 0.5 *** was found that the simultaneous degradation of the cohesion and friction an.le of the Mohr-Coulomb rock material characterizes the post-peak strength behavior of the *** resulting expressions are simplistic as they require parameters that can be easily determined using uniaxial and triaxial compression ***,the developed model was validated by simulating the triaxial tests of coal specimens with different sizes under varying confining stresses and comparing its findings with the published test *** study showed that its implementation in the numerical model could reproduce laboratory-observed mechanical response,deformation behavior,and failure mechanism very closely.

关键词： Strain softening Dilatancy Interface Shape effect Residual strength

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Effectiveness of hybrid ensemble machine learning models for landslide susceptibility an.lysis:Evidence from Shimla district of North-west Indian Himalayan region

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Journal of Mountain Science 2024年第7期21卷 2368-2393页

作者： sharma Aastha SAJJAD Haroon RAHAMAN Md Hibjur SAHA Tamal Kanti BHUYAN Nirsobha Department of Geography Faculty of SciencesJamia Millia IslamiaNew Delhi-110025India

The Indian Himalayan region is frequently experiencing climate change-induced ***,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide *** paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan *** examined the effectiveness of random forest(RF),multilayer perceptron(MlP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MlP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)*** site-specific influencing factors were selected by employing a multicollinearity *** relationship between past landslide occurrences and influencing factors was established using the frequency ratio *** effectiveness of machine learning models was verified through performance *** landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and *** key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide *** results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility *** factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide *** texture,lineament density and elevation have been attributed to high and moderate ***,the study calls for devising suitable landslide mitig

关键词： landslide susceptibility Site-specific factors Machine learning models Hybrid ensemble learning Geospatial techniques Himalayan region

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A review of torsional vibration mitigation techniques using active control and machine learning strategies

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PETROlEUM 2024年第3期10卷 411-426页

作者： sharma, Aditya Abid, Khizar Srivastava, Saket Velasquez, Andres Felipe Baena Teodoriu, Catalin Univ Oklahoma Norman OK 73019 USA

Drilling is one of the most challenging and expensive processes in hydrocarbon extraction and geothermal well development. Dysfunctions faced during drilling can increase the non-productive time (NPT) greatly, resulting in inflating the drilling cost and also pose a safety concern. One of the main problems faced during drilling that limits the life of drilling equipment and tools and decreases the overall productivity of the system is drilling vibrations. These vibrations can be categorized into three modes: axial, lateral, and torsional. Stick-slip vibrations are a type of torsional vibration in which the bottom hole assembly (BHA) periodically stops to rotate followed by a spike in the bottom hole RPM. This paper provides a comprehensive review of techniques used to control and mitigate torsional vibration with an emphasis on stick-slip. A brief introduction to drillstring and friction modeling is presented followed by a concise summary of passive control techniques to control stick-slip. Then the focus is shifted to an up-to-date review of active control and machine learning for stick-slip control and mitigation. The paper ultimately highlights the importance of adapting novel control and mitigation concepts to improve stick slip detection and improve the overall drilling process. A unique solution is insufficient to control a complex process such as drilling, but integration of various techniques has been found promising. (c) 2023 Southwest Petroleum University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. ltd. This is an open access article under the CC BY-NC-ND license (http:// ***/licenses/by-nc-nd/4.0/).

关键词： Drilling vibrations Controllers Mitigations strategies Stick slip Torsional vibrations Machine learning STICK-SlIP OSCIllATIONS DRIllSTRING VIBRATION BENDING VIBRATIONS OUTPUT-FEEDBACK DRAG BITS DESIGN SUPPRESSION STABIlITY SYSTEM MODEl

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High-precision inverse potentials for neutron-proton scattering using piece-wise smooth Morse functions

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Chinese Physics C 2024年第10期48卷 201-213页

作者： Ayushi Awasthi Arushi sharma Ishwar Kant O.S.K.S.Sastri Department of Physics and Astronomical Science Central University of Himachal PradeshDharmshala176215India

The aim of this study is to construct inverse potentials for various ℓ-channels of neutron-proton scattering using a piece-wise smooth Morse function as a *** phase equations for single-channel states and the coupled equations of multi-channel scattering are solved numerically using the 5^(th) order Runge-kutta *** employ a piece-wise smooth reference potential comprising three Morse functions as the initial *** a machine learning-based genetic algorithm,we optimize the model parameters to minimize the mean-squared error between simulated and anticipated phase *** approach yields inverse potentials for both single and multichannel scattering,achieving convergence to a mean-squared error≤10^(-3).The resulting scattering lengths"a_(0)"and effective ranges"r"for ^(3)S_(1) and ^(1)S_(0) states,expressed as[a_(0),r],are found to be[5.445(5.424),1.770(1.760)]and[–23.741(–23.749),2.63(2.81)],respectively;these values are in excellent agreement with experimental ***,the calculated total scattering cross-sections are highly consistent with their experimental counterparts,having a percentage error of less than 1%.This computational approach can be easily extended to obtain interaction potentials for charged particle scattering.

关键词： inverse potentials neutron-proton scattering piece-wise smooth Morse function phase function method reference potential approach genetic algorithm

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Prediction of rock fragmentation in a fiery seam of an open-pit coal mine in India

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第8期16卷 2879-2893页

作者： Mukul sharma Bhanwar Singh Choudhary Autar K.Raina Manoj Khandelwal Saurav Rukhiyar Department of Mining Engineering Indian Institute of Technology(ISM)Dhanbad826004India CSIR-Central Institute of Mining and Fuel Research Nagpur Research Center(Mining Technology)Nagpur440006India Institute of Innovation Science and SustainabilityFederation University AustraliaBallaratVIC3350Australia

Spontaneous combustion of coal increases the temperature in adjoining overburden strata of coal seams and poses a challenge when loading *** condition,known as hot-hole blasting,is dangerous due to the increased possibility of premature explosions in loaded ***,it is crucial to load the blastholes with an appropriate amount of explosives within a short period to avoid premature detonation caused by high temperatures of ***,it will help achieve the desired fragment *** study tried to ascertain the most influencial variables of mean fragment size and their optimum values adopted for blasting in a fiery *** on blast design,rock mass,and fragmentation of 100 blasts in fiery seams of a coal mine were collected and used to develop mean fragmentation prediction models using soft computational *** coefficient of determination(R^(2)),root mean square error(RMSE),mean absolute error(MAE),mean square error(MSE),variance account for(VAF)and coefficient of efficiency in percentage(CE)were calculated to validate the *** indicates that the random forest algorithm(RFA)outperforms the artificial neural network(ANN),response surface method(RSM),and decision tree(DT).The values of R^(2),RMSE,MAE,MSE,VAF,and CE for RFA are 0.94,0.034,0.027,0.001,93.58,and 93.01,*** parametric sensitivity an.lyses(MPSAs)of the input variables showed that the Schmidt hammer rebound number and spacing-to-burden ratio are the most influencial variables for the blast fragment *** an.lysis was finally used to define the best blast design variables to achieve optimum fragment size from *** optimum factor values for RFA of S/B,ld/B and ls/ld are 1.03,1.85 and 0.7,respectively.

关键词： Fiery seam Rock fragmentation Response Surface Method(RSM) Artificial Neural Network(ANN) Random Forest Algorithm(RFA) Multiple Parametric Sensitivity an.lysis (MPSA)

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lighting up plants with near-infrared fluorescence probes

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Science China Chemistry 2024年第3期67卷 774-787页

作者： Yida Pang Mengjiao lu Hyeonji Rha Wenchao Yang Amit sharma Yao Sun Jong Seung Kim National Key laboratory of Green Pesticide Key Laboratory of Green Pesticide and Agricultural BioengineeringMinistry of EducationCenter for R&D of Fine Chemicals of Guizhou UniversityGuiyang 550025China National Key laboratory of Green Pesticide College of ChemistryCentral China Normal UniversityWuhan 430079China Department of Chemistry Korea UniversitySeoul 02841Korea CSIR-Central Scientific Instruments Organisation Sector-30CChandigarh160030India TheranoChem Incorporation Seongbuk-guSeoul02856Korea

Fluorescence imaging is a non-invasive and highly sensitive bioimaging technique that has shown remarkable strides in plant science. It enables real-time monitoring and an.lysis of biological and pathological processes in plants by labeling specific molecular or cellular structures with fluorescent probes. However, tissue scattering and phytochrome interference have been obstacles for conventional fluorescence imaging of plants in the ultraviolet and visible spectrum, resulting in unsatisfactory imaging quality. Fortunately, advances in near-infrared(NIR) fluorescence imaging technology(650-900 nm) offer superior spatial-temporal resolution and reduced tissue scattering, which is sure to improve plant imaging quality. In this review, we summarize recent progress in the development of NIR fluorescence imaging probes and their applications for in vivo plant imaging and the identification of plant-related biomolecules. We hope this review provides a new perspective for plant science research and highlights NIR fluorescence imaging as a powerful tool for an.lyzing plant physiology, adaptive mechanisms, and coping with environmental stress in the near future.

关键词： NIR fluorescent probe plant imaging biologic application

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Machine learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection

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Computers, Materials & Continua 2024年第2期78卷 2441-2468页

作者： Vaishnawi Priyadarshni Sanjay Kumar sharma Mohammad Khalid Imam Rahmani Baijnath Kaushik Rania Almajalid Department of Computer Science Gautam Buddha UniversityGreater NoidaUttar PradeshIndia College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia School of Computer Science Shri Vaishno Devi UniversityKatraIndia

Breast cancer(BC)is one of the leading causes of death among women worldwide,as it has emerged as the most commonly diagnosed malignancy in *** detection and effective treatment of BC can help save women’s *** an efficient technology-based detection system can.lead to non-destructive and preliminary cancer detection *** paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography(CBIS-DDSM)data *** novelty of the proposed framework lies in the integration of various techniques,where the fusion of deep learning(Dl),traditional machine learning(Ml)techniques,and enhanced classification models have been deployed using the curated *** an.lysis outcome proves that the proposed enhanced RF(ERF),enhanced DT(EDT)and enhanced lR(ElR)models for BC detection outperformed most of the existing models with impressive results.

关键词： Autoencoder breast cancer deep neural network convolutional neural network image processing machine learning deep learning

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An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2029-2059页

作者： Sheetal sharma Kamali Gupta DeepaliGupta Shalli Rani Gaurav Dhiman Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India PG Department of Information Technology Goswami Ganesh Dutta Sanatan Dharma CollegeChandigarh160030India Department of Computer Science Government Bikram College of CommercePatialaIndia Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon University Centre for Research and Development Department of Computer Science and EngineeringChandigarh UniversityMohali140413India Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India Division of Research and Development Lovely Professional UniversityPunjab144001India

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to han.lefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart *** address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed *** an.lysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

关键词： Error fault detection techniques sensor faults outliers Internet of Things

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Pharmacological potential of Man.lkara zapota(l.)***(Sapodilla):a narrative review

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Journal of Traditional Chinese Medicine 2024年第2期44卷 403-407页

作者： Priyanka sharma Aakash Deep Harish Kumar Nitin Bansal Sanjiv Kumar Arun Davinder Kumar Department of Pharmaceutical Sciences Chaudhary Bansi Lal UniversityBhiwani 127021India Department of Pharmaceutical Sciences Maharishi Dayanand UniversityRohtak 124001India

Herbal medicine is safe and effective in treating various *** herbal medicine plays a tremendous role in treatment of various diseases and accompanying complications,hence herbal medicine requires remarkable attention in further research for the development of numerous active formulations for treatment of health *** plant needs special consideration for development and research of unidentified compound and characterization of novel active molecules that overcome multiple pathological *** genus Man.lkara contains 135 plants around the *** overview discusses all the virtues of most important and commonly used plant Man.lkara zapota(l.)***(***),also known as *** has various traditional beneficial effects in treatment of various diseases and disorders dating back to prehistoric times and used in ancient traditional system of herbal medicine.

关键词： herbal medicine Man.lkara zapota phytoconstituents pharmacological activity review

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