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Frontiers of Structural and Civil Engineering 2017年第1期11卷 90-99页

作者： Faezehossadat KHADEMI Mahmoud AKBARI sayed mohammadmehdi jamal Mehdi NIKOO Department of Civil Architectural and Environmental Engineering Illinois Institute of Technology Chicago IL 60616 USA Civil Engineering Department University of Kashan Kashan *** Iran Department of Civil Engineering University of Hormozgan Bandar Abbas 3995 IRAN Ahvaz Branch Islamic Azad University Ahvaz *** IRAN

Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model （MLR）, artificial neural network （ANN）, and adaptive neuro-fuzzy inference system （ANFIS） are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis （SA） for two different sets of parameters on the concrete compressive strength prediction are carried out.

关键词： concrete 28 days compressive strength multiple linear regression artificial neural network ANFIS sensitivity analysis (SA)

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

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Computers, Materials & Continua 2022年第4期71卷 1189-1203页

作者： Wafaa Alsaggaf Felwa Abukhodair Amani Tariq jamal sayed Abdel-Khalek Romany F.Mansour Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics and Statistics College of ScienceTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

In recent days,advancements in the Internet of Things(IoT)and cloud computing(CC)technologies have emerged in different application areas,particularly *** use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud ***,energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT *** the same time,the advent of deep learning(DL)models helps to analyze the healthcare data in the cloud server for decision *** this motivation,this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems,called IDDM-EAC *** proposed IDDM-EAC technique involves a 3-stage process namely data acquisition,clustering,and disease *** addition,the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering(CSOEAC)technique to group the IoT devices into clusters and select cluster heads(CHs).Moreover,a new coyote optimization algorithm(COA)with deep belief network(DBN),called COA-DBN technique is employed for the disease diagnostic *** COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN *** order to inspect the betterment of the IDDM-EAC technique,a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI *** experimental results demonstrate the promising performance with the minimal total energy consumption of 63%whereas the EEPSOC,ABC,GWO,and ACO algorithms have showcased a higher total energy consumption of 69%,78%,83%,and 84%correspondingly.

关键词： Intelligent models healthcare systems disease diagnosis internet of things cloud computing clustering deep learning

Efficiency of combining pomegranate juice with low-doses of cisplatin and taxotere on A549 human lung adenocarcinoma cells

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Asian Pacific Journal of Tropical Biomedicine 2018年第1期8卷 19-24页

作者： Nasser Mohamad Hijazi Akram sayed Ahmad Bouchra jamal Eddine Zeinab Ibrahim Sajida Rammal Hassan Al Rekaby Abd-El-Ameer Nasser Mouhamad Doctoral School of Science and Technology Research Platform for Environmental Science (PRASE) Lebanese University Université Fédérale de Toulouse Midi-Pyrénées INP-ENSIACET Laboratoire de Chimie Agro-industrielle Al Mustansiriya University College of Science Department of Biology American University of Beirut

Objective: To test the coalescence effect of two chemotherapy drugs at low effective dose(cisplatin and taxotere) combined with pomegranate juice on A549 cancer cells. Methods: Infrared spectroscopy method is a qualitative test that was performed to ensure the existence of the phytochemicals providing the antioxidant activity through the presence of the hydroxyl group(-OH). The viability of A549 cell line and normal MCs was tested using the neutral red uptake, Clonogenic survival, XTT and Cell migration assays. Results: Our results showed that this combination firstly led to a greater decrease in the viability of cells comparing to those treated with chemotherapy drugs alone, and secondly led to a significant reduction in cell migration. Conclusions: These data suggest a synergistic effect between the pomegranate and cisplatin which makes probably this combination a powerful option for treating lung adenocarcinoma and in parallel minimizing the systemic side effects.

关键词： Lung cancer Chemotherapy Cisplatin Taxotere Pomegranate A549 cells

Nicotine Content in Electronic Cigarette Refill Solutions and Its Release in Aerosols

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Journal of Environmental Protection 2019年第10期10卷 1317-1332页

作者： Sharifah Mazrah sayed Mohamed Zain Sara Khalida jamal Khair Nadia Mohamad Mohd Fairulnizal Md. Noh Dinash Aravind Radhakrishnan Environmental Health Research Centre Institute for Medical Research Ministry of Health Malaysia Selangor Malaysia National Pharmaceutical Regulatory Agency Ministry of Health Malaysia Selangor Malaysia Nutrition Metabolic and Cardiovascular Research Centre Institute for Medical Research Ministry of Health Malaysia Selangor Malaysia.

Electronic cigarette (EC) is a device that imitates conventional cigarettes, which vaporizes a solution, with or without nicotine. This study evaluates the nicotine levels in EC refill solutions, its release in aerosols and comparison to the amount stated on the label. Seventy-two (72) different EC refill solutions were obtained from local shops in Selangor, Malaysia consisting of 40 nicotine-containing (3 - 12 mg) and 32 nicotine-free (0 mg) solutions. Aerosols were obtained by using an air tight syringe connected to an EC device to draw aerosol through an XAD-4 sorbent tube that trapped the nicotine. Nicotine in solution and aerosol samples are analyzed using gas chromatography-mass spectrometry equipment. Quantified nicotine in 40 nicotine-containing solutions ranged from 0.224 - 17.306 mg/mL. Nicotine is detected in 3 of the 32 nicotine-free solutions. Percentage of nicotine released into aerosols from refill solutions varied from 0.54% - 28.2%. Out of the 40 samples, thirty-two (32) had nicotine content that violated by more than ±10% from the value on the label. In comparing the labels, 19 samples have 0.3% - 77% higher nicotine level and 13 samples have 0.2% - 96.3% lower nicotine content. The inconsistency between the labelled and true levels of nicotine content indicates that commercial information may be misleading. The presence of nicotine in the nicotine-free refill solutions and higher nicotine content in aerosols may be addictive and may have negative health effects on users.

关键词： Electronic Nicotine Delivery Systems Tobacco Products Tobacco Industry Vapers