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| 1 | Particle shape consideration in numerical simulation of assemblies of irregularly shaped particles显示文摘The mechanical behavior of granular materials depends much on the shape of the constituent particles. Therefore appropriate modeling of particle, or grain, shape is quite important. This study employed the method of direct modeling of grain shape (Matsushima & Saomto, 2002), in which, the real shape of a grain is modeled by combining arbitrary number of overlapping circular elements which are connected to each other in a rigid way. Then, accordingly, a discrete-element program is used to simulate the assembly of grains. In order to measure the effects of grain shape on mechanical properties of assembly of grains, three types of grains-high angular grains, medium angular grains and round grains are considered where several biaxial tests are conducted on assemblies with different grain types. The results show that the angularity of grains greatly affects the behavior of granular soil. | Saba Abedi Ali Asghar Mirghasemi | 2011 | Particuology2011，9，4： | 8 |
| 2 | Effects of TiO_2 nanoparticles on the aquatic plant Spirodela polyrrhiza: Evaluation of growth parameters, pigment contents and antioxidant enzyme activities显示文摘Plants are essential components of all ecosystems and play a critical role in environmental fate of nanoparticles. However, the toxicological impacts of nanoparticles on plants are not well documented. Titanium dioxide nanoparticles(TiO_2-NPs) are produced worldwide in large quantities for a wide range of purposes. In the present study, the uptake of TiO_2-NPs by the aquatic plant Spirodela polyrrhiza and the consequent effects on the plant were evaluated.Initially, structural and morphological characteristics of the used TiO_2-NPs were determined using XRD, SEM, TEM and BET techniques. As a result, an anatase structure with the average crystalline size of 8 nm was confirmed for the synthesized TiO_2-NPs. Subsequently, entrance of TiO_2-NPSto plant roots was verified by fluorescence microscopic images. Activity of a number of antioxidant enzymes, as well as, changes in growth parameters and photosynthetic pigment contents as physiological indices were assessed to investigate the effects of TiO_2-NPs on S. polyrrhiza. The increasing concentration of TiO_2-NPs led to the significant decrease in all of the growth parameters and changes in antioxidant enzyme activities. The activity of superoxide dismutase enhanced significantly by the increasing concentration of TiO_2-NPs. Enhancement of superoxide dismutase activity could be explained as promoting antioxidant system to scavenging the reactive oxygen species. In contrast, the activity of peroxidase was notably decreased in the treated plants. Reduced peroxidase activity could be attributed to either direct effect of these particles on the molecular structure of the enzyme or plant defense system damage due to reactive oxygen species. | Ali Movafeghi Alireza Khataee Mahboubeh Abedi Roshanak Tarrahi Mohammadreza Dadpour Fatemeh Vafaei | 2018 | Journal of Environmental Sciences2018，30，2： | 6 |
| 3 | Rational Design and Application of an Indolium-Derived Heptamethine Cyanine with Record-Long Second Near-Infrared Emission显示文摘Heptamethine cyanine dyes,typified by indocyanine green,have been extensively employed as bioimaging indicators and theranostic agents.Significant efforts have been made to develop functional heptamethine cyanine dyes with outstanding bioimaging and theranostic utilities.In this work,we rationally designed and successfully developed a novel indolium-like heptamethine cyanine dye by installing indolium-derived polycyclic aromatic hydrocarbons on the terminal ends of a conjugated polyene backbone.This dye showed excellent photostability and showed bright fluorescent emission in the second near-infrared(NIR-Ⅱ)window with a peak at approximately 1120 nm.Such long wavelength emission prompted a superior bioimaging resolution in vivo.In particular,this NIR-Ⅱ dye had the remarkable capability of marking the blood vessels of the hindlimbs,abdomens,and brains of mice.More significantly,this dye involved a typical indolium-like heptamethine skeleton and exhibited two strong absorption bands in the 700–1300 nm NIR range,which endowed it with an intrinsic tumor-targeting capability and a high photothermal conversion efficiency(up to 68.2%),serving for the photothermal therapy of tumors under the guidance of NIR-Ⅱ fluorescence imaging.This work provides an efficient design strategy for achieving indolium-like heptamethine cyanine dyes with further NIR-Ⅱ emission. | Xiaoxie Ma Yurou Huang Syed Ali Abbas Abedi Heejeong Kim Tan Teck Boon Davin Xiaogang Liu Wen-Chao Yang Yao Sun Sheng Hua Liu Jun Yin Juyoung Yoon Guang-Fu Yang | 2022 | CCS Chemistry2022，4，6： | 2 |
| 4 | Effects of Bulbous Bow on Cross-Flow Vortex Structures Around a Streamlined Submersible Body at Intermediate Pitch Maneuver: A Numerical Investigation显示文摘在在一个中间的入射角的优化身体附近的一块流动地被跨流动的分离和旋涡的流动地统治。分开的流动在身体的 leeside 上导致一双旋涡;因此,精确地决定这对并且估计它的尺寸和地点是必要的。这研究基于 RANS 方程利用基于元素的有限体积方法在 SUBOFF 赤裸的 submarined 壳附近计算 3D axisymmetric 流动。跨流动的旋涡结构然后数字地与 SUBOFF 和 DRDC STR 弓为一艘潜水艇被模仿并且比较。计算结果迫使并且砍旋涡结构的壳表面和力量和地点上的压力分发在 20 的一个中间的发生角度被介绍?? | Saeed Abedi Ali Akbar Dehghan Ali Saeidinezhad Mojtaba Dehghan Manshadi | 2016 | Journal of Marine Science and Application2016，15，1： | 1 |
| 5 | Sensorless Direct Torque Control of Hybrid Stepper Motor Based on MRAS显示文摘 | Mojtaba Khalilian Ali Abedi Adel Deris Zadeh | 2012 | Energy Procedia2012，，： | 1 |
| 6 | Development and validation of a HPLC method for the determination of buprenorphine hydrochloride, naloxone hydrochloride and noroxymorphone in a tablet formulation显示文摘 | Ali Mostafavi Ghazaleh Abedi Ahmad Jamshidi Daryoush Afzali Mohammad Talebi | 2008 | Talanta2008，，4： | 1 |
| 7 | Sulfur release from a model Pt/Al203 diesel oxidation catalyst: Temperature - pro-grammed and step -response techniques characterization显示文摘 | Luo Jinyong Darren Kisinger Ali Abedi | 2010 | Ap- plied Catalysis A : General2010，383，12： | 1 |
| 8 | Comparison of liquid-liquid extraction-thin layer chromatography with solid-phase extraction-high-performance thin layer chromatography in detection of urinary morphine显示文摘Liquid-liquid extraction-thin layer chromatography (LLE-TLC) has been a common and routine combined method for detection of drugs in biological materials.Solid-phase extraction (SPE) is gradually replacing the traditional LLE method.High performance thin layer chromatography (HPTLC) has several advantages over TLC.The present work studied the higher efficiency of a new SPE-HPTLC method over that of a routine LLE-TLC method,in extraction and detection of urinary morphine.Fifty-eight urine samples,primarily identified as morphine-positive samples by a strip test,were re-screened by LLE-TLC and SPE-HPTLC.The results of LLE-TLC and SPE-HPTLC were then compared with each other.The results showed that the SPE-HPTLC detected 74% of total samples as morphine-positive samples whereas the LLE-TLC detected 48% of the same samples.We further discussed the effect of codeine abuse on TLC analysis of urinary morphine.Regarding the importance of morphine detection in urine,the present combined SPE-HPTLC method is suggested as a replacement method for detection of urinary morphine by many reference laboratories. | Ali Ahadi Alireza Partoazar Mohammad Hassan Abedi Khorasgani Seyed Vahid Shetab Boushehri | 2011 | The Journal of Biomedical Research2011，25，5： | 1 |
| 9 | Severity Based Light-Weight Encryption Model for Secure Medical Information System显示文摘As the amount of medical images transmitted over networks and kept on online servers continues to rise,the need to protect those images digitally is becoming increasingly important.However,due to the massive amounts of multimedia and medical pictures being exchanged,low computational complexity techniques have been developed.Most commonly used algorithms offer very little security and require a great deal of communication,all of which add to the high processing costs associated with using them.First,a deep learning classifier is used to classify records according to the degree of concealment they require.Medical images that aren’t needed can be saved by using this method,which cuts down on security costs.Encryption is one of the most effective methods for protecting medical images after this step.Confusion and dispersion are two fundamental encryption processes.A new encryption algorithm for very sensitive data is developed in this study.Picture splitting with image blocks is nowdeveloped by using Zigzag patterns,rotation of the image blocks,and random permutation for scrambling the blocks.After that,this research suggests a Region of Interest(ROI)technique based on selective picture encryption.For the first step,we use an active contour picture segmentation to separate the ROI from the Region of Background(ROB).Permutation and diffusion are then carried out using a Hilbert curve and a Skew Tent map.Once all of the blocks have been encrypted,they are combined to create encrypted images.The investigational analysis is carried out to test the competence of the projected ideal with existing techniques. | Firas Abedi Subhi R.M.Zeebaree Zainab Salih Ageed Hayder M.A.Ghanimi Ahmed Alkhayyat Mohammed A.M.Sadeeq Sarmad Nozad Mahmood Ali S.Abosinnee Zahraa H.Kareem Ali Hashim Abbas Waleed Khaild Al-Azzawi Mustafa Musa Jaber Mohammed Dauwed | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 10 | Application of 2,2'-dithiobis(4-methylthiazole) as sensing material for construction of Lu^(3+) PVC-membrane sensor显示文摘在这个工作,一个新 Lu3+PVC 膜传感器基于 2,2-dithiobis (4-methylthiazole )(TMT ) 被制作了。传感器展出 19.6 潮慰瑲捩敬 ? ? 的一个 Nernstian 斜坡 ??? 柳言吗?? | Mohammad Reza Abedi Hassan Ali Zamani | 2011 | Chinese Chemical Letters2011，22，8： | 0 |
| 11 | Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification显示文摘Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and databases.Association rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within datasets.In the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information rapidly.This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)processes.Initially,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant attributes.Inspired by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for food.Subsequently,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute relationships.We propose a COA-based feature selection approach for medical data classification using machine learning techniques.This approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature set.We evaluate the performance of our approach on various medical datasets employing diverse machine learning classifiers.Experimental results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification tasks.The study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various diseases.To provide further validation,we conduct detailed experiments on a benchmark medical dataset,revealing the superiority of the MLARMCHDMS model over other methods,with a maximum accuracy of 99.75%.Therefore,this research contributes to the advancement of feature selection techniques in medical data classification and highlights the potential for improving healthcare outcomes through accurate and efficient data analysis.The presented MLARMC-HDMS framework and COA-based feature selection approach offer valuable insights for researchers and practitioners working in the field of healthcare data mining and machine learning. | Firas Abedi Hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat | 2023 | Computer Systems Science & Engineering2023，47，12： | 0 |
| 12 | Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment显示文摘Computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life problems.The CI is useful in the UAV domain as it produces efficient,precise,and rapid solutions.Besides,unmanned aerial vehicles(UAV)developed a hot research topic in the smart city environment.Despite the benefits of UAVs,security remains a major challenging issue.In addition,deep learning(DL)enabled image classification is useful for several applications such as land cover classification,smart buildings,etc.This paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification(MDLS-UAVIC)model in a smart city environment.Themajor purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class labels.The proposedMDLS-UAVIC model follows a two-stage process:encryption and image classification.The encryption technique for image encryption effectively encrypts the UAV images.Next,the image classification process involves anXception-based deep convolutional neural network for the feature extraction process.Finally,shuffled shepherd optimization(SSO)with a recurrent neural network(RNN)model is applied for UAV image classification,showing the novelty of the work.The experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset,and the outcomes are examined in various measures.It achieved a high accuracy of 98%. | Firas Abedi Hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat | 2023 | Computer Systems Science & Engineering2023，47，12： | 0 |
| 13 | Hybrid Deep Learning Enabled Load Prediction for Energy Storage Systems显示文摘Recent economic growth and development have considerably raised energy consumption over the globe.Electric load prediction approaches become essential for effective planning,decision-making,and contract evaluation of the power systems.In order to achieve effective forecasting outcomes with minimumcomputation time,this study develops an improved whale optimization with deep learning enabled load prediction(IWO-DLELP)scheme for energy storage systems(ESS)in smart grid platform.The major intention of the IWO-DLELP technique is to effectually forecast the electric load in SG environment for designing proficient ESS.The proposed IWO-DLELP model initially undergoes pre-processing in two stages namely min-max normalization and feature selection.Besides,partition clustering approach is applied for the decomposition of data into distinct clusters with respect to distance and objective functions.Moreover,IWO with bidirectional gated recurrent unit(BiGRU)model is applied for the prediction of load and the hyperparameters are tuned by the use of IWO algorithm.The experiment analysis reported the enhanced results of the IWO-DLELP model over the recent methods interms of distinct evaluation measures. | Firas Abedi Hayder M.A.Ghanimi Mohammed A.M.Sadeeq Ahmed Alkhayyat Zahraa H.Kareem Sarmad Nozad Mahmood Ali Hashim Abbas Ali S.Abosinnee Waleed Khaild Al-Azzawi Mustafa Musa Jaber Mohammed Dauwed | 2023 | Computers, Materials & Continua2023，，5： | 0 |
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