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| 1 | 用基于树的Bagging和Boosting集成技术预测硬岩矿山岩爆显示文摘岩爆预测对地下硬岩矿山的设计和施工至关重要。使用三种基于树的集成方法,对由102个历史案例(即1998—2011年期间14个硬岩矿山数据)组成的岩爆数据库进行了检查,以用于有岩爆倾向矿井的岩爆预测。该岩爆数据集包含六个广泛接受的倾向性指标,即:开挖边界周围的最大切向应力(MTS)、完整岩石的单轴抗压强度(UCS)和单轴抗拉强度(UTS)、应力集中系数(SCF)、岩石脆性指数(BI)和应变能储存指数(EEI)。以分类树作为基准分类器的两种Boosting算法(AdaBoost.M1,SAMME)和Bagging算法进行了评估,评估了它们学习岩爆的能力。将可用数据集随机分为训练集(整个数据集的2/3)和测试集(其余数据集)。采用重复10倍交叉验证(CV)作为调整模型超参数的验证方法,并利用边际分析和变量相对重要性分析了各集成学习模型特征。根据重复10倍交叉验证结果,对岩爆数据集的精度分析表明,与AdaBoost.M1、SAMME算法和岩爆经验判据相比,Bagging方法是预测硬岩矿山岩爆的最佳方法。 | 王世鸣 周健 李传奇 Danial Jahed ARMAGHANI 李夕兵 Hani SMITRI | 2021 | Journal of Central South University2021，28，2： | 19 |
| 2 | Application of several optimization techniques for estimating TBM advance rate in granitic rocks显示文摘This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R^2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior. | Danial Jahed Armaghani Mohammadreza Koopialipoor Aminaton Marto Saffet Yagiz | 2019 | Journal of Rock Mechanics and Geotechnical Engineering2019，11，4： | 15 |
| 3 | Predicting TBM penetration rate in hard rock condition:A comparative study among six XGB-based metaheuristic techniques显示文摘A reliable and accurate prediction of the tunnel boring machine(TBM)performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB)which are optimized by gray wolf optimization(GWO),particle swarm optimization(PSO),social spider optimization(SSO),sine cosine algorithm(SCA),multi verse optimization(MVO)and moth flame optimization(MFO),for estimation of the TBM penetration rate(PR).To do this,a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation,the rock mass rating,Brazilian tensile strength(BTS),rock mass weathering,the uniaxial compressive strength(UCS),revolution per minute and trust force per cutter(TFC),were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models,four single models i.e.,artificial neural network,random forest regression,XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then,their performance capacities were assessed through the use of root mean square error,coefficient of determination,mean absolute percentage error,and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453,and 0.1325),R^(2) of(0.951,and 0.951),mean absolute percentage error(4.0689,and 3.8115),and a10-index of(0.9348,and 0.9496)in training and testing phases,respectively.The developed hybrid PSO-XGB can be introduced as an accurate,powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis,it was found that UCS,BTS and TFC have the deepest impacts on the TBM PR. | Jian Zhou Yingui Qiu Danial Jahed Armaghani Wengang Zhang Chuanqi Li Shuangli Zhu Reza Tarinejad | 2021 | Geoscience Frontiers2021，12，3： | 10 |
| 4 | Long term performance of warm mix asphalt versus hot mix asphalt显示文摘The fatigue behavior, indirect tensile strength (ITS) and resilient modulus test results for warm mix asphalt (WMA) as well as hot mix asphalt (HMA) at different ageing levels were evaluated. Laboratory-prepared samples were aged artificially in the oven to simulate short-term and long term ageing in accordance with AASHTO R30 and then compared with unaged specimens. Beam fatigue testing was performed using beam specimens at 25℃ based on AASHTO T321 standard. Fatigue life, bending stiffness and dissipated energy for both unaged and aged mixtures were calculated using four-point beam fatigue test results. Three-point bending tests were performed using semi-circular bend (SCB) specimens at -10℃ and the critical mode I stress intensity factor K I was then calculated using the peak load obtained from the load-displacement curve. It is observed that Sasobit and Rheofalt warm mix asphalt additives have a significant effect on indirect tensile strength, resilient modulus, fatigue behavior and stress intensity factor of aged and unaged mixtures. | Ziari Hasan Behbahani Hamid Izadi Amir Nasr Danial | 2013 | Journal of Central South University2013，20，1： | 7 |
| 5 | Advances in fungal-assisted phytoremediation of heavy metals:A review显示文摘Trace metals such as manganese(Mn),copper(Cu),zinc(Zn),and iron(Fe)are essential for many biological processes in plant life cycles.However,in excess,they can be toxic and disrupt plant growth processes,which is economically undesirable for crop production.For this reason,processes such as homeostasis and transport control of these trace metals are of constant interest to scientists studying heavily contaminated habitats.Phytoremediation is a promising cleanup technology for soils polluted with heavy metals.However,this technique has some disadvantages,such as the slow growth rate of metal-accumulating plant species,low bioavailability of heavy metals,and long duration of remediation.Microbial-assisted phytoremediation is a promising strategy for hyperaccumulating,detoxifying,or remediating soil contaminants.Arbuscular mycorrhizal fungi(AMF)are found in association with almost all plants,contributing to their healthy performance and providing resistance against environmental stresses.They colonize plant roots and extend their hyphae to the rhizosphere region,assisting in mineral nutrient uptake and regulation of heavy metal acquisition.Endophytic fungi exist in every healthy plant tissue and provide enormous services to their host plants,including growth enhancement by nutrient acquisition,detoxification of heavy metals,secondary metabolite regulation,and enhancement of abiotic/biotic stress tolerance.The aim of the present work is to review the recent literature regarding the role of AMF and endophytic fungi in plant heavy metal tolerance in terms of its regulation in highly contaminated conditions. | Muhammad KHALID Saeed UR-RAHMAN Danial HASSANI Kashif HAYAT Pei ZHOU Nan HUI | 2021 | Pedosphere2021，31，3： | 6 |
| 6 | Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network显示文摘In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models. | Bhatawdekar Ramesh Murlidhar Hoang Nguyen Jamal Rostami XuanNam Bui Danial Jahed Armaghani Prashanth Ragam Edy Tonnizam Mohamad | 2021 | Journal of Rock Mechanics and Geotechnical Engineering2021，13，6： | 6 |
| 7 | Predicting crest settlement in concrete face rockfill dams using adaptive neuro-fuzzy inference system and gene expression programming intelligent methods显示文摘This paper deals with the estimation of crest settlement in a concrete face rockfill dam (CFRD), utilizing intelligent methods. Following completion of dam construction, considerable movements of the crest and the body of the dam can develop during the first impoundment of the reservoir. Although there is vast experience worldwide in CFRD design and construction, few accurate experimental relationships are available to predict the settlement in CFRD. The goal is to advance the development of intelligent methods to estimate the subsidence of dams at the design stage. Due to dam zonification and uncertainties in material properties, these methods appear to be the appropriate choice. In this study, the crest settlement behavior of CFRDs is analyzed based on compiled data of 24 CFRDs constructed during recent years around the world, along with the utilization of gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS) methods. In addition, dam height (H), shape factor (S f ), and time (t, time after first operation) are also assessed, being considered major factors in predicting the settlement behavior. From the relationships proposed, the values of R 2 for both equations of GEP (with and without constant) were 0.9603 and 0.9734, and for the three approaches of ANFIS (grid partitioning (GP), subtractive clustering method (SCM), and fuzzy c-means clustering (FCM)) were 0.9693, 0.8657, and 0.8848, respectively. The obtained results indicate that the overall behavior evaluated by this approach is consistent with the measured data of other CFRDs. | Danial BEHNIA Kaveh AHANGARI Ali NOORZAD Sayed Rahim MOEINOSSADAT | 2013 | Journal of Zhejiang University-Science A(Applied Physics & Engineering)2013，14，8： | 6 |
| 8 | Material and regenerative properties of an osteon-mimetic cortical bone-like scaffold显示文摘The objective of this work was to fabricate a rigid,resorbable and osteoconductive scaffold by mimicking the hierarchical structure of the cortical bone.Aligned peptide-functionalize nanofiber microsheets were generated with calcium phosphate(CaP)content similar to that of the natural cortical bone.Next,the CaP-rich fibrous microsheets were wrapped around a microneedle to form a laminated microtube mimicking the structure of an osteon.Then,a set of the osteon-mimetic microtubes were assembled around a solid rod and the assembly was annealed to fuse the microtubes and form a shell.Next,an array of circular microholes were drilled on the outer surface of the shell to generate a cortical bone-like scaffold with an interconnected network of Haversian-and Volkmann-like microcanals.The CaP content,porosity and density of the bone-mimetic microsheets were 240 wt%,8%and 1.9 g/ml,respectively,which were close to that of natural cortical bone.The interconnected network of microcanals in the fused microtubes increased permeability of a model protein in the scaffold.The cortical scaffold induced osteogenesis and vasculogenesis in the absence of bone morphogenetic proteins upon seeding with human mesenchymal stem cells and endothelial colony-forming cells.The localized and timed-release of morphogenetic factors significantly increased the extent of osteogenic and vasculogenic differentiation of human mesenchymal stem cells and endothelial colony-forming cells in the cortical scaffold.The cortical bonemimetic nature of the cellular construct provided balanced rigidity,resorption rate,osteoconductivity and nutrient diffusivity to support vascularization and osteogenesis. | Danial Barati Ozan Karaman Seyedsina Moeinzadeh Safaa Kader Esmaiel Jabbari | 2019 | Regenerative Biomaterials2019，6，2： | 5 |
| 9 | Cell Death显示文摘 | Nika N Danial Stanley J Korsmeyer | 2004 | Cell2004，，2： | 4 |
| 10 | Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization显示文摘The advance rate(AR)of a tunnel boring machine(TBM)under hard rock conditions is a key parameter in the successful implementation of tunneling engineering.In this study,we improved the accuracy of prediction models by employing a hybrid model of extreme gradient boosting(XGBoost)with Bayesian optimization(BO)to model the TBM AR.To develop the proposed models,1286 sets of data were collected from the Peng Selangor Raw Water Transfer tunnel project in Malaysia.The database consists of rock mass and intact rock features,including rock mass rating,rock quality designation,weathered zone,uniaxial compressive strength,and Brazilian tensile strength.Machine specifications,including revolution per minute and thrust force,were considered to predict the TBM AR.The accuracies of the predictive models were examined using the root mean squares error(RMSE)and the coefficient of determination(R^(2))between the observed and predicted yield by employing a five-fold cross-validation procedure.Results showed that the BO algorithm can capture better hyper-parameters for the XGBoost prediction model than can the default XGBoost model.The robustness and generalization of the BO-XGBoost model yielded prominent results with RMSE and R^(2) values of 0.0967 and 0.9806(for the testing phase),respectively.The results demonstrated the merits of the proposed BO-XGBoost model.In addition,variable importance through mutual information tests was applied to interpret the XGBoost model and demonstrated that machine parameters have the greatest impact as compared to rock mass and material properties. | Jian Zhou Yingui Qiu Shuangli Zhu Danial Jahed Armaghani Manoj Khandelwal Edy Tonnizam Mohamad | 2021 | Underground Space2021，6，5： | 4 |
| 11 | 金樱根中1个新的乌苏烷型三萜皂苷显示文摘目的研究蔷薇科植物金樱子Rosa laevigata根的化学成分。方法采用硅胶、Sephadex LH-20、ODS柱色谱、重结晶及半制备液相分离和纯化,根据理化性质和波谱数据鉴定化合物的结构,并运用MTT法对所分离化合物对人宫颈癌HeLa、人胃腺癌BGC823、人结肠癌HCT-116及人肝癌HepG-2细胞的细胞毒活性进行检测。结果从金樱根乙醇提取物中分离得到了9个化合物,分别鉴定为2α,3β,11α,19α-四羟基乌苏-12-烯-28-羧酸-β-D-吡喃葡萄糖基酯(1)、野鸦椿酸(2)、2α,3β,19α-三羟基乌苏-23-醛-12-烯-28-羧酸-β-D-吡喃葡萄糖基酯(3)、野蔷薇苷(4)、苦莓苷F1(5)、2α,3β,19α-三羟基齐墩果-12-烯-28-羧酸-β-D-吡喃葡萄糖基酯(6)、桦木酸(7)、β-谷甾醇(8)和儿茶素(9);其中化合物7对BGC823及HeLa细胞的半数抑制浓度(IC50)分别为19.0和19.6μmol/L,化合物8对BGC823及HeLa细胞的IC50分别为16.2和15.8μmol/L。结论化合物1为新化合物,命名为金樱子皂苷,化合物3为首次从该植物中分离到,化合物7和8对BGC823及HeLa细胞显示出中等强度的细胞毒活性。 | 李斌 彭彩云 陈钰妍 段淑莉 彭伊玲 Muhammad Danial 王炜 李顺祥 | 2021 | 中草药2021，52，2： | 4 |
| 12 | Predicting the presence of adenomatous polyps during colonoscopy with National Cancer Institute Colorectal Cancer Risk-Assessment Tool显示文摘AIM To evaluate the National Cancer Institute(NCI)Colorectal Cancer(CRC)Risk Assessment Tool as a predictor for the presence of adenomatous polyps(AP) found during screening or surveillance colonoscopy.METHODS This is a retrospective single center observational study.We collected data of adenomatous polyps in each colonoscopy and then evaluated the lifetime CRC risk.We calculated the AP prevalence across risk score quintiles,odds ratios of the prevalence of AP across risk score quintiles,area under curves(AUCs)and Youden’s indexes to assess the optimal risk score cut off value for AP prevalence status.RESULTS The prevalence of AP gradually increased throughout the five risk score quintiles:i.e.,27.63%in the first and 51.35%in the fifth quintile.The odd ratios of AP prevalence in the fifth quintile compared to the first and second quintile were 2.76[confidence interval(CI):1.71-4.47]and 2.09(CI:1.32-3.30).The AUC for all patients was 0.62(CI:0.58-0.66).Youden’s Index indicated the optimal risk score cutoff value discriminating AP prevalence status was 3.60.CONCLUSION Patients with the higher NCI risk score have higher risk of AP and subsequent CRC;therefore,measures to increase the effectiveness of CRC detection in these patients include longer withdrawal time,early surveillance colonoscopy,and choosing flexible colonoscopy over other CRC screening modalities. | Hassan Tariq Muhammad Umar Kamal Harish Patel Ravi Patel Muhammad Ameen Shehi Elona Maram Khalifa Sara Azam Aiyi Zhang Kishore Kumar Ahmed Baiomi Danial Shaikh Jasbir Makker | 2018 | World Journal of Gastroenterology2018，24，34： | 3 |
| 13 | Morphophysiological and molecular evidence supporting the augmentative role of Piriformospora indica in mitigation of salinity in Cucumis melo L.显示文摘Salinity is one of the major limiting factors in plant growth and productivity.Cucumis melo L.is a widely cultivated plant,but its productivity is significantly influenced by the level of salinity in soil.Symbiotic colonization of plants with Piriformospora indica has shown a promotion in plants growth and tolerance against biotic stress.In this study,physiological markers such as ion analysis,antioxidant determination,proline content,electrolyte leakage and chlorophyll measurement were assessed in melon cultivar under two concentrations(100 and 200 mM)of NaCl with and without P.indica inoculation.Results showed that the endophytic inoculation consistently upregulated the level of antioxidants,enhanced plants to antagonize salinity stress.The expression level of an RNA editing factor(SLO2)which is known to participate in mitochondria electron transport chain was analyzed,and its full mRNA sequence was obtained by rapid amplification of cDNA ends(RACE).Under salinity stress,the expression level of SLO2 was increased,enhancing the plant’s capability to adapt to the stress.However,P.indica inoculation further elevated the expression level of SLO2.These findings suggested that the symbiotic association of fungi could help the plants to tolerate the salinity stress. | Danial Hassani Muhammad Khalid Danfeng Huang Yi-Dong Zhang | 2019 | Acta Biochimica et Biophysica Sinica2019，51，3： | 3 |
| 14 | Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays显示文摘Deep excavation during the construction of underground systems can cause movement on the ground,especially in soft clay layers.At high levels,excessive ground movements can lead to severe damage to adjacent structures.In this study,finite element analyses(FEM)and the hardening small strain(HSS)model were performed to investigate the deflection of the diaphragm wall in the soft clay layer induced by braced excavations.Different geometric and mechanical properties of the wall were investigated to study the deflection behavior of the wall in soft clays.Accordingly,1090 hypothetical cases were surveyed and simulated based on the HSS model and FEM to evaluate the wall deflection behavior.The results were then used to develop an intelligent model for predicting wall deflection using the functional linked neural network(FLNN)with different functional expansions and activation functions.Although the FLNN is a novel approach to predict wall deflection;however,in order to improve the accuracy of the FLNN model in predicting wall deflection,three swarm-based optimization algorithms,such as artificial bee colony(ABC),Harris’s hawk’s optimization(HHO),and hunger games search(HGS),were hybridized to the FLNN model to generate three novel intelligent models,namely ABC-FLNN,HHO-FLNN,HGS-FLNN.The results of the hybrid models were then compared with the basic FLNN and MLP models.They revealed that FLNN is a good solution for predicting wall deflection,and the application of different functional expansions and activation functions has a significant effect on the outcome predictions of the wall deflection.It is remarkably interesting that the performance of the FLNN model was better than the MLP model with a mean absolute error(MAE)of 19.971,root-mean-squared error(RMSE)of 24.574,and determination coefficient(R^(2))of 0.878.Meanwhile,the performance of the MLP model only obtained an MAE of 20.321,RMSE of 27.091,and R^(2)of 0.851.Furthermore,the results also indicated that the proposed hybrid models,i.e.,ABC-FLNN,HHO-FLNN,HGS-FLNN,yielded more superior performances than those of the FLNN and MLP models in terms of the prediction of deflection behavior of diaphragm walls with an MAE in the range of 11.877 to 12.239,RMSE in the range of 15.821 to 16.045,and R^(2)in the range of 0.949 to 0.951.They can be used as an alternative tool to simulate diaphragm wall deflections under different conditions with a high degree of accuracy. | Chengyu Xie Hoang Nguyen Yosoon Choi Danial Jahed Armaghani | 2022 | Geoscience Frontiers2022，13，2： | 3 |
| 15 | 逆向型人工全肩关节置换研究进展显示文摘目的总结逆向型人工全肩关节置换的发展及临床应用。方法广泛复习逆向型人工全肩关节置换的相关文献研究,并总结分析。结果逆向型人工全肩关节置换手术适应证广,主要用于治疗无法修复的肩袖撕裂造成的肩关节假性麻痹,肱骨头向前或向上偏移而三角肌功能完整者。临床研究表明,其近期疗效较好,肩胛盂切痕、关节失稳及关节内外旋转受限是其特殊并发症。采用该术式应注意手术入路的选择、确定假体旋转中心以及对伴骨缺损者行肩胛盂及肱骨近端植骨。结论逆向型人工全肩关节置换临床应用时间尚短,远期疗效有待进一步观察明确。另外,随着计算机辅助技术在置换术中的应用,有望进一步提高手术疗效。 | 吴昊 Goutallier Danial | 2015 | 中国修复重建外科杂志2015，29，7： | 3 |
| 16 | Intelligent rockburst prediction model with sample category balance using feedforward neural network and Bayesian optimization显示文摘The rockburst prediction becomes more and more challenging due to the development of deep underground projects and constructions.Increasing numbers of intelligent algorithms are used to predict and prevent rockburst.This paper investigated the drawbacks of neural networks in rockburst prediction,and aimed at these shortcomings,Bayesian optimization and the synthetic minority oversampling technique+Tomek Link(SMOTETomek)were applied to efficiently develop the feedforward neural network(FNN)model for rockburst prediction.In this regard,314 real rockburst cases were collected to establish a database for modeling.The database was divided into a training set(80%)and a test set(20%).The maximum tangential stress,uniaxial compressive strength,tensile strength,stress ratio,brittleness ratio,and elastic strain energy were selected as input parameters.Bayesian optimization was implemented to find the optimal hyperparameters in FNN.To eliminate the effects of imbalanced category,SMOTETomek was adopted to process the training set to obtain a balanced training set.The FNN developed by the balanced training set received 90.48% accuracy in the test set,and the accuracy improved 12.7% compared to the imbalanced training set.For interpreting the FNN model,the permutation importance algorithm was introduced to analyze the relative importance of input variables.The elastic strain energy was the most essential variable,and some measures were proposed to prevent rockburst.To validate the practicability,the FNN developed by the balanced training set was utilized to predict rockburst in Sanshandao Gold Mine,China,and it had outstanding performance(accuracy 100%). | Diyuan Li Zida Liu Peng Xiao Jian Zhou Danial Jahed Armaghani | 2022 | Underground Space2022，7，5： | 2 |
| 17 | Prevalence of advanced liver fibrosis and steatosis in type-2 diabetics with normal transaminases:A prospective cohort study显示文摘BACKGROUND Nonalcoholic fatty liver disease(NAFLD)and type-2 diabetes mellitus(T2DM)have an intricate bidirectional relationship.Individuals with T2DM,not only have a higher prevalence of non-alcoholic steatosis,but also carry a higher risk of progression to nonalcoholic steatohepatitis.Experts still differ in their recommendations of screening for NAFLD among patients with T2DM.AIM To study the prevalence of NAFLD and advanced fibrosis among our patient population with T2DM.METHODS During the study period(November 2018 to January 2020),59 adult patients with T2DM and 26 non-diabetic control group individuals were recruited prospectively.Patients with known significant liver disease and alcohol use were excluded.Demographic data and lab parameters were recorded.Liver elastography was performed in all patients.RESULTS In the study group comprised of patients with T2DM and normal alanine aminotransferase levels(mean 17.8±7 U/L),81%had hepatic steatosis as diagnosed by elastography.Advanced hepatic fibrosis(stage F3 or F4)was present in 12%of patients with T2DM as compared to none in the control group.Patients with T2DM also had higher number of individuals with grade 3 steatosis[45.8%vs 11.5%,(P<0.00001)and metabolic syndrome(84.7%vs 11.5%,P<0.00001)].CONCLUSION A significant number of patients with T2DM,despite having normal transaminase levels,have NAFLD,grade 3 steatosis and advanced hepatic fibrosis as measured by liver elastography. | Jasbir Makker Hassan Tariq Kishore Kumar Madhavi Ravi Danial Haris Shaikh Vivien Leung Umar Hayat Muhammad T Hassan Harish Patel Suresh Nayudu Sridhar Chilimuri | 2021 | World Journal of Gastroenterology2021，27，6： | 2 |
| 18 | Modeling of shear wave velocity in limestone by soft computing methods显示文摘The main purpose of current study is development of an intelligent model for estimation of shear wave velocity in limestone. Shear wave velocity is one of the most important rock dynamic parameters.Because rocks have complicated structure, direct determination of this parameter takes time, spends expenditure and requires accuracy. On the other hand, there are no precise equations for indirect determination of it; most of them are empirical. By using data sets of several dams of Iran and neuro-genetic,adaptive neuro-fuzzy inference system(ANFIS), and gene expression programming(GEP) methods, models are rendered for prediction of shear wave velocity in limestone. Totally, 516 sets of data has been used for modeling. From these data sets, 413 ones have been utilized for building the intelligent model, and 103 have been used for their performance evaluation. Compressional wave velocity(V_ p), density(γ)and porosity(n), were considered as input parameters. Respectively, the amount of R for neuro-genetic and ANFIS networks was 0.959 and 0.963. In addition, by using GEP, three equations are obtained; the best of them has 0.958 R. ANFIS shows the best prediction results, whereas GEP indicates proper equations. Because these equations have accuracy, they could be used for prediction of shear wave velocity for limestone in the future. | Behnia Danial Ahangari Kaveh Moeinossadat Sayed Rahim | 2017 | International Journal of Mining Science and Technology2017，27，3： | 2 |
| 19 | Traumatic myiasis agents in Iran with introducing of new dominant species,Wohlfahrtia magnifica(Diptera:Sarcophagidae)显示文摘Objective:To study agents of animal wound myiasis in various geographical districts of Fars province.Methods:This study has been done in Fars province,located in the southern part of Iran.Sums of 10358 domestic animals have been visited from April 2011 to March 2012.The infected wounds in any parts of animal body were sampled by means of forceps.Results:About 61%of all animal wound myiasis were caused by larvae of Wohlfahrtia magnifica.The most wound myiasis cases due to this species occurred in central part of Fars province.There wasn't any significant difference between sheep and goat in infestation with myiasis(P>0.05).The infestation rate of myiasis in cattle community was 0.86%.Conclusions:The infestation rate of livestock was lower than other works in Iran and some other countries like Saudi Arabia.Chrysomya bezziana has been mentioned as main myiasis agent in Iran.But in this study it cleared that similarly to some European countries,the common animal myiasis agent in Iran is Wohlfahrtia tnagnifica.Introducing new species as principal agent for myiasis can help public health and animal husbandry policy makers to prepare sufficient and effective control and/or preventive measures for this disease. | Javad Rafinejad Kamran Akbarzadeh Yavar Rassi Jamasp Nozari Mohammad Mehdi Sedaghat Mostafa Hosseini Hamzeh Alipour Abdolmajid Ranjbar Danial Zeinali | 2014 | Asian Pacific Journal of Tropical Biomedicine2014，4，6： | 2 |
| 20 | Peroxynitrite is a major contributor to cytokine-induced myocardial contractile failure显示文摘 | Ferdinandy P Danial H Ambrus I | 2000 | Circ Res2000，87，： | 1 |
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