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Bayes-Q-Learning Algorithm in Edge Computing for Waste Tracking

Intelligent Automation & Soft Computing 2023年第5期36卷 2425-2440页

作者： d.Palanikkumar R.Ramesh Kumar Mehedi Masud Mrim M.Alnfiai Mohamed Abouhawwash department of Computer Science and Engineering Dr.NGP Institute of TechnologyCoimbatore641048India department of Information Technology Sri Krishna College of TechnologyCoimbatore641042India department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The major environmental hazard in this pandemic is the unhygienic dis-posal of medical *** wastage is not properly managed it will become a hazard to the environment and *** medical wastage is a major issue in the city,municipalities in the aspects of the environment,and *** efﬁcient supply chain with edge computing technology is used in managing medical *** supply chain operations include processing of waste collec-tion,transportation,and disposal of *** research works have been applied to improve the management of *** main issues in the existing techniques are ineffective and expensive and centralized edge computing which leads to failure in providing security,trustworthiness,and *** over-come these issues,in this paper we implement an efﬁcient Naive Bayes classiﬁer algorithm and Q-Learning algorithm in decentralized edge computing technology with a binary bat optimization algorithm(NBQ-BBOA).This proposed work is used to track,detect,and manage medical *** minimize the transferring cost of medical wastage from various nodes,the Q-Learning algorithm is *** accuracy obtained for the Naïve Bayes algorithm is 88%,the Q-Learning algo-rithm is 82%and NBQ-BBOA is 98%.The error rate of root Mean Square Error(RMSE)and Mean Error(MAE)for the proposed work NBQ-BBOA are 0.012 and 0.045.

关键词： Binary bat algorithm naïve bayes supply chain edge medical wastage

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Estimation of aboveground biomass of arboreal species in the semi-arid region of Brazil using SAR(synthetic aperture radar)images

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Journal of Arid Land 2023年第6期15卷 695-709页

作者： Janisson B de JESUS Tatiana M KUPLICH Íkaro d de C BARRETO Fernando L HILLEBRANd Cristiano N da ROSA Postgraduate Program in Remote Sensing Federal University of Rio Grande do SulCampus ValePorto Alegre 91501970Brazil National Institute for Space Research(INPE) COESU(Southern Spatial Coordination)Santa Maria 97105970Brazil Postgraduate Program Biometry and Applied Statistics Rural Federal University of PernambucoRecife 52171900Brazil Federal Institute of Education Science and Technology of Rio Grande do Sul(IFRS)Campus RolanteRolante 95690000Brazil Postgraduate Program in Remote Sensing Polar and Climate CenterFederal University of Rio Grande do SulPorto Alegre 91501970Brazil

The Caatinga biome is an important ecosystem in the semi-arid region of *** has significantly degraded due to human activities and is currently a region undergoing ***,monitoring the variation in the Caatinga biome has become essential for its sustainable ***,traditional methods for estimating aboveground biomass(AGB)are time-consuming and *** sensing,such as optical and radar imaging,can estimate and correlate with ***,radar imaging is still a novelty to be applied in estimating the AGB of this biome,which is an area with little ***,this study aimed to use Sentinel-1 images to estimate the AGB of the Caatinga biome in Sergipe State(northeastern Brazil)and to verify its influencing *** sample plots(30 m×30 m)were selected,and the stems of individuals with a circumference at breast height(1.3 m above the ground)equal to or greater than 6.0 cm were measured,and the AGB through an allometric equation was *** Sentinel-1 images from 3 different periods(green,intermediate,and dry periods)were used to consider the phenological conditions of the Caatinga *** the pre-processing and extraction of attributes(co-polarized VV(vertical transmit and vertical receive),cross-polarized VH(vertical transmit and horizontal receive),and band ratio VH/VV backscatter,radar vegetation index,dual polarization synthetic aperture radar(SAR)vegetation index(dPSVI),entropy(H),and alpha angle(α))were performed with Sentinel’s Application *** attributes were used to estimate the AGB through simple and multiple linear regressions and evaluated by the coefficients of determination(R2),correlation(r),and.root mean squared error(RMSE).The results showed that the attributes individually had little ability to estimate the AGB of the Caatinga biome in the three *** with multiple regression,we found that the intermediate period presented the equation with the best

关键词： Caatinga tropical dry forest coherent and incoherent attributes C-band Sentinel-1

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Continental velocity through Precambrian times:The link to magmatism,crustal accretion and episodes of global cooling

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Geoscience Frontiers 2013年第1期4卷 7-36页

作者： J.d.A.Piper Geomagnetism Laboratory Geology and GeophysicsSchool of Environmental SciencesUniversity of Liverpool.Liverpool L69 7ZEUK

Quasi-integrity of continental crust between Mid-Archaean and Ediacaran times is demonstrated by conformity of palaeomagnetic poles to near-static positions between -2.7-2.2 Ga, -1.5-1.2 Ga and -0.75-0.6 Ga. Intervening data accord to coherent APW loops turning at ＂hairpins＂ focused near a continental-centric location. Although peripheral adjustments occurred during Early Proterozoic （-2.2 Ga） and Grenville （- 1.1 Ga） times, the crust retained a low order symmetrical crescent-shaped form constrained to a single global hemisphere until break-up in Ediacaran times. Conformity of palaeomagnetic data to specific Eulerian parameters enables definition of a master Precambrian APW path used to estimate the root mean square velocity （VRMS） of continental crust between 2.8 and 0.6 Ga. A long interval of little polar movement between -2.7 and 2.2 Ga correlates with global magmatic shutdown between -2.45 and 2.2 Ga, whilst this interval and later slowdown at -0.75-0.6 Ga to velocities of 〈2 cm/year correlate with episodes of widespread glaciation implying that these prolonged climatic anomalies had an internal origin; the reduced input of volcanically-derived atmospheric greenhouse gases is inferred to have permitted freeze-over conditions with active ice sheets extending into equatorial latitudes as established by low magnetic inclinations in glaciogenic deposits. VRMS vari- ations through Precambrian times correspond to the distribution of U-Pb ages in orogenic granitoids and detrital zircons and demonstrate that mobility of continental crust has been closely related to crustal tectonism and incrementation. Both periods of near-stillstand were followed by rapid VRMS recording massive heat release from beneath the continental lid at -2.2 and 0.6 Ga. The first coincided with the Lomagundi-Jatuli isotopic event and led to prolonged orogenesis accompanied by continental flooding and reconfiguration of the crust on the Earth＇s surface; the second led to continental break-up and i

关键词： Continental velocity Precambrian Palaeopangaea Palaeomagnetism Magmatism Tectonics

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Transcatheter Ventricular Septal defect Closure with Nit-Occlud LêVSd device—Five Years’Experience and Literature Review

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Congenital Heart disease 2023年第3期18卷 361-371页

作者： Ivana B.Cerović Vladislav A.Vukomanović Jovan Lj.Košutić Mila S.Stajević Sanja S.Ninić Saša S.Popović Ivan d.dizdarević Staša d.Krasić Sergej M.Prijić Cardiology department Mother and Child Health Institute of Serbia“Dr VukanČupić”Belgrade11070Serbia Faculty of Medicine University of BelgradeBelgrade11070Serbia

Introduction:Transcatheter closure is an alternative to ventricular septal defect(VSd)occlusion ***-Occlud LêVSd coil is a new device yet to be *** study aimed to evaluate immediate and midterm results after transcatheter closure with the Nit-Occlud LêVSd ***:The retrospective analysis included 30 patients with VSd referred for closure during the period from October 2015 to december ***:At the time of intervention,the patients’mean age and body weights were 7.5±5.6 years and 29.3±19.1 *** majority of the defects had perimembranous location(24/30),four defects had muscular and two outlet subaortic *** mean effective right-side diameter of the VSds was 3.6±1.3 *** ventricular fibrillation,device embolization,and hemolysis developed in different patients and were successfully *** of the patients had a complete atrioventricular *** coil was successfully placed in 25/30(83.3%)*** majority of the devices were 10 mm×6 mm(11/25)and 12 mm×8 mm(8/25)in *** patients required the implantation of a second *** follow-up period was 2.1±1.4 *** VSd closure was achieved in 48%of cases immediately after the intervention,74%during 2.1±1.6 months after the procedure,and 81%over *** remaining patients had a trivial residual *** the follow-up,approximately one-third of patients developed trivial aortic and mitral valve regurgitation,and half of the patients acquired trace/mild tricuspid ***(z-score)left ventricular end-diastolic diameter(0.15±0.37 vs.0.92±0.82,p=0.005)and left atrium dimension(0.47±0.58 vs.1.89±1.11,p=0.005),as well as the left atrium to aortic root ratio(1.2±0.1 vs.1.4±0.2,p=0.005)showed a significant decrease over follow-up related to the period before ***:Intervention with Nit-OccludLêVSd coil showed appropriate results regarding VSd closure rate,complications,and chamber *** introduction of t

关键词： Ventricular septal defect transcatheter device children

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Industrial Centric Node Localization and Pollution Prediction Using Hybrid Swarm Techniques

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Computer Systems Science & Engineering 2022年第8期42卷 545-560页

作者： R.Saravana Ram M.Vinoth Kumar N.Krishnamoorthy A.Baseera d.Mansoor Hussain N.Susila department of Electronics and Communication Engineering Anna UniversityUniversity College of Engineering DindigulDindigul624622India department of Computer Science and Engineering Anna UniversityUniversity College of Engineering DindigulDindigul624622India department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India department of Information Technology Sri Krishna College of Engineering and TechnologyCoimbatore641008India

Major fields such as military applications,medical fields,weather forecasting,and environmental applications use wireless sensor networks for major computing *** play a vital role in emerging technologies of the 20th *** of sensors in needed locations is a very serious *** environment is home to every living being in the *** growth of industries after the industrial revolution increased pollution across the *** to recent uncontrolled growth and development,sensors to measure pollution levels across industries and surroundings are *** interesting and challenging task is choosing the place to fit the *** meta-heuristic techniques have been introduced in node *** intelligent algorithms have proven their efficiency in many studies on localization *** this article,we introduce an industrial-centric approach to solve the problem of node localization in the sensor ***,our work aims at selecting industrial areas in the sensed *** use random forest regression methodology to select the polluted ***,the elephant herding algorithm is used in sensor node *** two algorithms are combined to produce the best standard result in localizing the sensor *** check the proposed performance,experiments are conducted with data from the Kdd Cup 2018,which contain the name of 35 stations with concentrations of air pollutants such as PM,SO_(2),CO,NO_(2),and O_(3).These data are normalized and tested with *** results are comparatively analyzed with other swarm intelligence algorithms such as the elephant herding algorithm,particle swarm optimization,and machine learning algorithms such as decision tree regression and multi-layer *** can indicate our proposed algorithm can suggest more meaningful locations for localizing the sensors in the *** proposed method achieves a lower root mean square value with 0.06 to 0.08 for lo

关键词： Wireless sensor networks node localization industrial-centric approach random forest regression elephant herding optimization swarm intelligence pollution

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Self-Supervised Plant Phenotyping by Combining domain Adaptation with 3d Plant Model Simulations: Application to Wheat Leaf Counting at Seedling Stage

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Plant Phenomics 2023年第2期5卷 226-238页

作者： Yinglun Li Xiaohai Zhan Shouyang Liu Hao Lu Ruibo Jiang Wei Guo Scott Chapman Yufeng Ge Benoit de Solan Yanfeng ding Frédéric Baret Plant Phenomics Research Centre Academy for Advanced Interdisciplinary StudiesJiangsu Collaborative Innovation Center for Modern Crop ProductionNanjing Agricultural UniversityNanjingChina Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina Graduate School of Agricultural and Life Sciences The University of Tokyo1-1-1 Midori-choNishitokyo CityTokyoJapan School of Agriculture and Food Sciences The University of QueenslandSt.LuciaQueensland 4072Australia department of Biological Systems Engineering University of Nebraska-LincolnLincolnNebraska 68583United States INRAE Avignon UniversitéUMR EMMAHUMT CAPTE228route de l’aérodrome-CS 4050984914 Avignon Cedex 9France ARVALIS Institut du Végétal 3 Rue Joseph et Marie Hackin75116 ParisFrance

The number of leaves at a given time is important to characterize plant growth and *** this work,we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB *** digital plant phenotyping platform was used to simulate a large and diverse dataset of RGB images and corresponding leaf tip labels of wheat plants at seedling stages(150,000 images with over 2 million labels).The realism of the images was then improved using domain adaptation methods before training deep learning *** results demonstrate the efficiency of the proposed method evaluated on a diverse test dataset,collecting measurements from 5 countries obtained under different environments,growth stages,and lighting conditions with different cameras(450 images with over 2,162 labels).Among the 6 combinations of deep learning models and domain adaptation techniques,the Faster-RCNN model with cycle-consistent generative adversarial network adaptation technique provided the best performance(R^(2)=0.94,root mean square error=8.7).Complementary studies show that it is essential to simulate images with sufficient realism(background,leaf texture,and lighting conditions)before applying domain adaptation ***,the spatial resolution should be better than 0.6 mm per pixel to identify leaf *** method is claimed to be self-supervised since no manual labeling is required for model *** self-supervised phenotyping approach developed here offers great potential for addressing a wide range of plant phenotyping *** trained networks are available at https://***/YinglunLi/Wheat-leaf-tip-detection.

关键词： Wheat seedling Plant

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Arbuscular Mycorrhiza Prevents Suppression of Actual Nitrification Rates in the (Myco-)Rhizosphere of Plantago lanceolata

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Pedosphere 2012年第2期22卷 225-229页

作者： S.d.VERESOGLOU Faculty of Agriculture Laboratory of Ecology and Environmental ProtectionAristotle University of Thessaloniki

The vast majority of herbaceous plants engage into arbuscular mycorrhizal (AM) symbioses and consideration of their mycorrhizal status should be embodied in studies of plant-microbe interactions. To establish reliable AM contrasts, however, a sterilized re-inoculation procedure is commonly adopted. It was questioned whether the specific approach is sufficient for the studies targeting the bacterial domain, specifically nitrifiers, a group of autotrophic, slow growing microbes. In a controlled experiment mycorrhizal and non-mycorrhizal Plantago lanceolata were grown up in compartmentalized pots to study the AM effect on nitrification rates in the plant rhizosphere. Nitrification rates were assayed following an extensive 3-week bacterial equilibration step of the re-inoculated soil and a 13-week plant growth period in a controlled environment. Under these specific conditions, the nitrification potential levels at harvest were exceptionally low, and actual nitrification rates of the root compartment of non-mycorrhizal P. lanceolata were significantly lower than those of any other compartment. It is then argued that the specific effects should be attributed to the alleged higher growth rates of non-mycorrhizal plants that are known to occur early in the AM experiment. It is concluded that the specific experimental approach is not suitable for the study of microbes with slow growth rates.

关键词：丛枝菌根硝化速率杉木生长根际效应结核分枝杆菌车前子草本植物灭菌接种

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Parametric optimization of friction stir welding process of age hardenable aluminum alloys-ANFIS modeling

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Journal of Central South University 2016年第8期23卷 1847-1857页

作者： d.Vijayan V.Seshagiri Rao department of Mechanical Engineering Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya Enathur Kanchipuram- 631561 Tamilnadu India St. Joseph's College of Engineering Chennai - 600025 Tamilnadu India

A comparative approach was performed between the response surface method(RSM) and the adaptive neuro-fuzzy inference system(ANFIS) to enhance the tensile properties, including the ultimate tensile strength and the tensile elongation, of friction stir welded age hardenable AA6061 and AA2024 aluminum alloys. The effects of the welding parameters, namely the tool rotational speed, welding speed, axial load and pin profile, on the ultimate tensile strength and the tensile elongation were analyzed using a three-level, four-factor Box-Behnken experimental design. The developed design was utilized to train the ANFIS models. The predictive capabilities of RSM and ANFIS were compared based on the root mean square error, the mean absolute error, and the correlation coefficient based on the obtained data set. The results demonstrate that the developed ANFIS models are more effective than the RSM model.

关键词： ANFIS模型 2024铝合金搅拌摩擦焊接时效硬化工艺参数优化自适应神经模糊推理系统 RSM模型断裂伸长率

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An integrated machine learning model for accurate and robust prediction of superconducting critical temperature

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Journal of Energy Chemistry 2023年第3期78卷 232-239,I0007页

作者： Jingzi Zhang Ke Zhang Shaomeng Xu Yi Li Chengquan Zhong Mengkun Zhao Hua-Jun Qiu Mingyang Qin X.-d.Xiang Kailong Hu Xi Lin School of Materials Science and Engineering Harbin Institute of TechnologyShenzhen 518055GuangdongChina Blockchain development and Research Institute Harbin Institute of TechnologyShenzhen 518055GuangdongChina State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001HeilongjiangChina School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001HeilongjiangChina department of Materials Science and Engineering&department of Physics Southern University of Science and TechnologyShenzhen 518055GuangdongChina

discovering new superconductors via traditional trial-and-error experimental approaches is apparently a time-consuming process,and the correlations between the critical temperature(Tc) and material features are still *** rise of machine learning(ML) technology provides new opportunities to speed up inefficient exploration processes,and could potentially uncover new hints on the unclear *** this work,we utilize open-source materials data,ML models,and data mining methods to explore the correlation between the chemical features and Tcvalues of superconducting *** further improve the prediction accuracy,a new model is created by integrating three basic algorithms,showing an enhanced accuracy with the coefficient of determination(R2) score of 95.9 % and.root mean square error(RMSE) of 6.3 *** average marginal contributions of material features towards Tcvalues are estimated to determine the importance of various features during prediction *** results suggest that the range thermal conductivity plays a critical role in Tcprediction among all element ***,the integrated ML model is utilized to screen out potential twenty superconducting materials with Tcvalues beyond 50.0 *** study provides insights towards Tcprediction to accelerate the exploration of potential high-Tcsuperconductors.

关键词： Superconductors Integrated machine learning Superconducting critical temperature

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Evaluation of thermal imaging system and thermal radiation detector for real-time condition monitoring of high power frequency converters

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Advances in Manufacturing 2014年第1期2卷 88-94页

作者： Anders Eriksen d. Osinski d. R. Hjelme department of Electrical and Computer Engineering Faculty ofTechnology SOr-TrCndelag University College TrondheimNorway

We have carried out at laboratory test to study the feasibility of using thermal radiation detectors for online thermal monitoring of electrical systems in wind turbines. A 25 kW frequency converter is instrumented with a thermal camera, operating in the 8-14 μm wave- length range, and a single-pixel thermopile sensor, operating in the 4-8 μm wavelength range, to monitor the temperature development of the power electronics under various load sequences. Both systems performed satisfactorily with insignificant temperature deviations when compared to data from calibrated point contact sensor. With spatial averaging over a 7 mm × 7 mm for the camera and temporal averaging over 60 s for the thermo- pile sensor, we reduce the root mean square noise to 45 mK and 68 mK respectively. The low cost and simple operation of the thermopile sensor make it very attractive for condition monitoring applications, whereas the attractive feature of the camera is the possibility of multi-point or distributed temperature measurements.

关键词： Temperature measurement Conditionmonitoring Frequency converter Wind turbine

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