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Author Correction: Enhancing untargeted metabolomics using metadata-based source annotation

Nature biotechnology 2023年第11期41卷 1656页

作者： Julia M Gauglitz Kiana A West Wout Bittremieux Candace L Williams Kelly C Weldon Morgan Panitchpakdi Francesca Di Ottavio Christine M Aceves Elizabeth Brown Nicole C Sikora Alan K Jarmusch Cameron Martino Anupriya Tripathi Michael J Meehan Kathleen Dorrestein Justin P Shaffer Roxana Coras Fernando Vargas Lindsay DeRight Goldasich Tara Schwartz MacKenzie Bryant Gregory Humphrey Abigail J Johnson Katharina Spengler Pedro Belda-Ferre Edgar Diaz Daniel McDonald Qiyun Zhu Emmanuel O Elijah Mingxun Wang Clarisse Marotz Kate E Sprecher Daniela Vargas-Robles Dana Withrow Gail Ackermann Lourdes Herrera Barry J Bradford Lucas Maciel Mauriz Marques Juliano Geraldo Amaral Rodrigo Moreira Silva Flavio Protasio Veras Thiago Mattar Cunha Rene Donizeti Ribeiro Oliveira Paulo Louzada-Junior Robert H Mills Paulina K Piotrowski Stephanie L Servetas Sandra M Da Silva Christina M Jones Nancy J Lin Katrice A Lippa Scott A Jackson Rima Kaddurah Daouk Douglas Galasko Parambir S Dulai Tatyana I Kalashnikova Curt Wittenberg Robert Terkeltaub Megan M Doty Jae H Kim Kyung E Rhee Julia Beauchamp-Walters Kenneth P Wright Maria Gloria Dominguez-Bello Mark Manary Michelli F Oliveira Brigid S Boland Norberto Peporine Lopes Monica Guma Austin D Swafford Rachel J Dutton Rob Knight Pieter C Dorrestein Collaborative Mass Spectrometry Innovation Center University of California San Diego La Jolla CA USA. Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA USA. Beckman Center for Conservation Research San Diego Zoo Wildlife Alliance Escondido CA USA. Center for Microbiome Innovation Joan and Irwin Jacobs School of Engineering University of California San Diego La Jolla CA USA. Division of Biological Sciences University of California San Diego La Jolla CA USA. Department of Pediatrics School of Medicine University of California San Diego La Jolla CA USA. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Division of Rheumatology Allergy & Immunology Department of Medicine University of California San Diego La Jolla CA USA. Division of Epidemiology and Community Health School of Public Health University of Minnesota Minneapolis MN USA. Department of Integrative Physiology University of Colorado Boulder Boulder CO USA. Department of Population Health Sciences University of Wisconsin-Madison Madison WI USA. Servicio Autónomo Centro Amazónico de Investigación y Control de Enfermedades Tropicales Simón Bolívar Puerto Ayacucho Amazonas Venezuela. Department of Pediatrics Billings Clinic Billings MT USA. Department of Animal Science Michigan State University East Lansing MI USA. Department of Pharmacology Ribeirão Preto Medicinal School Center of Research in Inflammatory Diseases University of São Paulo Ribeirão Preto Sao Paolo Brazil. Multidisciplinary Health Institute Federal University of Bahia Vitória da Conquista Bahia Brazil. NPPNS Department of Biomolecular Sciences School of Pharmaceutical Sciences of Ribeirão Preto University of São Paulo Ribeirão Preto Sao Paolo Brazil. Department of Internal Medicine Ribeirão Preto Medical School Center of Research in Inflammatory Diseases University of São Paulo Ribeirão Preto Sao Paolo Brazil. Department

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Traffic parameters prediction using a three-channel convolutional neural network 2nd

Traffic parameters prediction using a three-channel convolut...

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2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017

作者： Zang, Di Wang, Dehai Cheng, Jiujun Tang, Keshuang Li, Xin Department of Computer Science and Technology Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Transportation Information and Control Engineering Tongji University Shanghai China Shanghai Lujie Electronic Technology Co. Ltd. PudongShanghai China

ISBN: (纸本)9783319681207

Traffic three elements consisting of flow, speed and occupancy are very important parameters representing the traffic information. Prediction of them is a fundamental problem of Intelligent Transportation systems (ITS). Convolutional Neural Network (CNN) has been proved to be an effective deep learning method for extracting hierarchical features from data with local correlations such as image, video. In this paper, in consideration of the spatiotemporal correlations of traffic data, we propose a CNN-based method to forecast flow, speed and occupancy simultaneously by converting raw flow, speed and occupancy (FSO) data to FSO color images. We evaluate the performance of this method and compare it with other prevailing methods for traffic prediction. Experimental results show that our method has superior performance. © IFIP International Federation for Information Processing 2017.

关键词： Forecasting

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Using convolutional neural network with asymmetrical kernels to predict speed of elevated highway 2nd

Using convolutional neural network with asymmetrical kernels...

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2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017

作者： Zang, Di Ling, Jiawei Cheng, Jiujun Tang, Keshuang Li, Xin Department of Computer Science and Technology Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Transportation Information and Control Engineering Tongji University Shanghai China Shanghai Lujie Electronic Technology Co. Ltd. Pudong Shanghai China

ISBN: (纸本)9783319681207

In this paper, we present a deep learning based approach to performing the whole-day prediction of the traffic speed for the elevated highway. In order to learn the temporal features of traffic speed data in a hierarchical way, an improved convolutional neural network (CNN) with asymmetric kernels is proposed. Speed data are collected from loop detectors of Yan’an elevated highway of Shanghai. To test the performance of the presented method, we compare it with some conventional approaches of traffic speed estimation. Experimental results demonstrate that our method outperforms all of them. © IFIP International Federation for Information Processing 2017.

关键词： Speed

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Modular Fault Diagnosis in Fixed-Block Railway Signaling systems

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IFAC-PapersOnLine 2016年第3期49卷 459-464页

作者： Durmus, Mustafa S. Ustoglu, lker Tsarev, Roman Y. Schwarz, Michael Electrical and Electronics Engineering Department Pamukkale University Denizli Turkey Control and Automation Engineering Department Yildiz Technical University Istanbul Turkey Institute of Space and Information Technology Siberian Federal University Krasnoyarsk Russia Computer Architecture and System Programming Kassel University Kassel Germany

The diagnosis of possible faults in railway signaling systems is an important issue to provide safe travel and transportation in railways. Signaling system designers have to consider the possible faults which may occur in railway field components both on the requirements preparation phase and on the development phase of the signaling system software or namely, the interlocking system. Although the diagnosis of different unobservable faults is relatively hard, especially for large scale railway fields, this complexity can be overcome by using the Discrete Event system (DES) based modular diagnosis approach which is explained in this paper. The main advantage of using such modular approach for fault diagnosis in fixed-block signaling systems is the inspection of the diagnosability of the whole system with respect to its subsystems (railway field components). In this study, the diagnosability of the railway field equipment and the whole system is also explained with a case study. © 2016

关键词： Fault detection Discrete event simulation Failure analysis Railroad signal systems Railroads Signaling Development phase Field components Field equipment Fixed block signaling Interlocking systems Modular approach Railway signaling systems Signaling systems

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Variance-Constrained H∞ Filtering for Stochastic Parameter systems Under Event-Triggered Protocol

Variance-Constrained H∞ Filtering for Stochastic Parameter ...

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第36届中国控制会议

作者： Licheng Wang Zidong Wang Guoliang Wei Shanghai Key Lab of Modern Optical System Department of Control Science and EngineeringUniversity of Shanghai for Science and Technology Department of Computer Science Brunel University London

In this paper,the variance-constrained H filtering problem is investigated for a class of discrete-time stochastic parameter *** event-triggered communication protocol is adopted in order to reduce the communication burden where the measurement information required by the filter is updated only when a certain prescribed event is *** purpose of the addressed filtering problem is to design a recursive filter such that,in the presence of random parameter perturbations and external noise disturbances,both the prespecified H performance index and the variance constraints on the filtering errors are satisfied over a *** stochastic analysis is carried out and recursive matrix inequality technique is developed to obtain the filter *** a filter design scheme is based on the solution to a set of forward recursive matrix inequalities and therefore facilitates the on-line ***,the usefulness of the developed event-triggered recursive filter design scheme is demonstrated by a numerical simulation example.

关键词： Variance-constrained filtering H∞ performance event-triggered communication protocol recursive matrix inequality stochastic parameter system

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An Incremental Learning-Based Mechanism for Object Recognition in Cloud Robotic system

An Incremental Learning-Based Mechanism for Object Recogniti...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Wenyu Li Zihao An Shurui Jiang Jeffrey Too Chuan Tan Feng Duan Chi Zhu Haoyong Yu College of Computer and Control Engineering Nankai University Tianjin China Department of System Life Engineering Maebashi Institute of Technology Maebashi Gunma Japan Department of Biomedical Engineering National University of Singapore Singapore

ISBN: (纸本)9781538604915

Cloud robotic system needs to learn real time data stream in order to adapt to the new environment for better performance. Incremental learning as a kind of dynamics classification method can avoid sample repeating learning, and develop model for updating itself. In this paper, we proposed an incremental learning-based cloud robot object recognition mechanism. It can learn new objects through human-robot interaction by speech recognition. The proposed incremental learning method was tested with small scale data in domestic indoor environment application, and the cloud robotic system was validated by basic conversation with different testing groups. The experimental results verify the feasibility of the incrementally learning new information algorithm and the proposed cloud robotic system.

关键词： Cloud computing Servers Speech recognition Object recognition Service robots Training

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Robust ${\mathscr {H}}_{\infty }$ Filtering for a Class of Two-Dimensional Uncertain Fuzzy systems With Randomly Occurring Mixed Delays

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IEEE Transactions on Fuzzy systems 2017年第1期25卷 70-83页

作者： Yuqiang Luo Zidong Wang Guoliang Wei Fuad E. Alsaadi Shanghai Key Laboratory of Modern Optical System Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai China Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Brunel University London Uxbridge U.K

This paper is concerned with the robust H ∞ filtering problem for a class of 2-D uncertain fuzzy systems with randomly occurring mixed delays (ROMDs). The underlying 2-D systems are described by the Fornasini-Marchesini model, and the uncertainty is expressed in a linear fraction form. An improved Takagi-Sugeno (T-S) fuzzy model corresponding to the spatial promise variables is adopted to represent the complicated 2-D nonlinear system. The mixed delays consisting of both discrete and distributed delays are allowed to appear in a random manner governed by two sets of Bernoulli distributed white sequences with known probability. A full-order fuzzy filter is constructed to estimate the output signal such that, in the presence of parameter uncertainties and ROMDs, the dynamics of the estimation errors is asymptotically stable with a prescribed H ∞ disturbance attenuation level. Based on the stochastic analysis technique and the Lyapunov-like functional, sufficient conditions are established to ensure the existence of the desired filters, and the explicit expressions of such filters are derived by means of the solution to a class of convex optimization problems that can be solved via standard software packages. A numerical example is provided to demonstrate the effectiveness of the developed filter design algorithms, and the filter performances with and without fuzzy rules are also compared.

关键词： Delays Fuzzy systems Uncertainty Symmetric matrices Robustness Uncertain systems Stochastic processes

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Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM_2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

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The Lancet Planetary Health 2022年第7期6卷 e586-e600页

作者： Burkart, Katrin Causey, Kate Cohen, Aaron J Wozniak, Sarah S Salvi, Devashri Digvijay Abbafati, Cristiana Adekanmbi, Victor Adsuar, Jose C Ahmadi, Keivan Alahdab, Fares Al-Aly, Ziyad Alipour, Vahid Alvis-Guzman, Nelson Amegah, Adeladza Kofi Andrei, Catalina Liliana Andrei, Tudorel Ansari, Fereshteh Arabloo, Jalal Aremu, Olatunde Aripov, Timur Babaee, Ebrahim Banach, MacIej Barnett, Anthony Bärnighausen, Till Winfried Bedi, Neeraj Behzadifar, Masoud Béjot, Yannick Bennett, Derrick A Bensenor, Isabela M Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Biondi, Antonio Bohlouli, Somayeh Breitner, Susanne Brenner, Hermann Butt, Zahid A Cámera, Luis Alberto Cantu-Brito, Carlos Carvalho, Felix Cerin, Ester Chattu, Vijay Kumar Chauhan, Bal Govind Choi, Jee-Young Jasmine Chu, Dinh-Toi Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Daryani, Ahmad Davletov, Kairat De Courten, Barbora Demeke, Feleke Mekonnen Denova-Gutiérrez, Edgar Dharmaratne, Samath Dhamminda Dhimal, Meghnath Diaz, Daniel Djalalinia, Shirin Duncan, Bruce B El Sayed Zaki, Maysaa Eskandarieh, Sharareh Fareed, Mohammad Farzadfar, Farshad Fattahi, Nazir Fazlzadeh, Mehdi Fernandes, Eduarda Filip, Irina Fischer, Florian Foigt, Nataliya A Freitas, Marisa Ghashghaee, Ahmad Gill, Paramjit Singh Ginawi, Ibrahim Abdelmageed Gopalani, Sameer Vali Guo, Yuming Gupta, Rajat Das Habtewold, Tesfa Dejenie Hamadeh, Randah R Hamidi, Samer Hankey, Graeme J Hasanpoor, Edris Hassen, Hamid Yimam Hay, Simon I Heibati, Behzad Hole, Michael K Hossain, Naznin Househ, Mowafa Irvani, Seyed Sina Naghibi Jaafari, Jalil Jakovljevic, Mihajlo Jha, Ravi Prakash Jonas, Jost B Jozwiak, Jacek Jerzy Kasaeian, Amir Kaydi, Neda Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khan, Md Nuruzzaman Khatab, Khaled Khater, Amir M Kim, Yun Jin Kimokoti, Ruth W Kisa, Adnan Kivimäki, Mika Knibbs, Luke D Kosen, Soewarta Koul, Parvaiz A Koyanagi, Ai Defo, Barthelemy Kuate Kugbey, Nuworza Lauriola, Paolo Lee, Paul H Leili, Mostafa Lewycka, Sonia Li, Shanshan Lim, Lee-Ling Li Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Plastic and Reconstructive Surgery University of Washington Seattle WA United States Research Insights and Innovation Seattle WA United States Health Effects Institute Boston MA United States School of Public Health Boston University Boston MA United States Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Population Medicine Cardiff University Cardiff United Kingdom Sport Science Department University of Extremadura Badajoz Spain School of Public Health Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center US Department of Veterans Affairs (VA) St Louis MO United States Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Health Economics Iran University of Medical Sciences Tehran Iran Preventive Medicine and Public Health Research Center Iran University of Medical Sciences Tehran Iran Pars Advanced and Minimally Invasive Medical Manners Research Center Iran University of Medical Sciences Tehran Iran Department of Community and Family Medicine Iran University of Medical Sciences Tehran Iran Research Group in Hospital Management and Health Policies Universidad de la Costa (University of the Coast) Barranquilla Colombia Research Group in Health Economics University of Cartagena Cartagena Colombia Department of Biomedical Science University of Cape Coast Cape Coast Ghana Cardiology Department Ca

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM_2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM_2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM_2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM_2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM_2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South Am

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Expression of Concern: Application and economic viability of wind turbine installation in Lutak, Iran

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Environmental Earth Sciences 2018年第1期78卷 1-1页

作者： Kasra Mohammadi Ali Mostafaeipour Ahmad Sedaghat Shahaboddin Shamshirband Dalibor Petković Department of Mechanical and Industrial Engineering University of Massachusetts Amherst USA Industrial Engineering Department Yazd University Yazd Iran Department of Mechanical Engineering Isfahan University of Technology Isfahan Iran Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department for Mechatronics and Control Faculty of Mechanical Engineering University of Niš Nis Serbia

The Editors-in-Chief of Environmental Earth Sciences are issuing an editorial expression of concern to alert readers that this article [1] shows substantial indication of irregularities in authorship during the submis...

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