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| 1 | Evaluation of the adsorption potential of titanium dioxide nanoparticles for arsenic removal显示文摘The adsorption potential of titanium dioxide(TiO2) nanoparticles for removing arsenic from drinking water was evaluated.Pure and iron-doped TiO2 particles are synthesized via sol-gel method.The synthesized TiO2 nanoparticles were then immobilized on ordinary sand for adsorption studies.Adsorption isotherms were conducted on the synthesized nanoparticles as well as the sand coated with TiO2 nanoparticles under varying conditions of air and light,namely,the air-sunlight(A-SL),air-light(AL),air-dark(AD) and nitrogen-dark(ND).X-ray diffraction(XRD) analysis showed that the pure and iron-doped TiO2 nanoparticles were in 100% anatase crystalline phase with crystal sizes of 108 and 65 nm,respectively.Adsorption of arsenic on the three adsorbents was non-linear that could be described by the Freundlich and Langmuir adsorption models.Iron doping enhanced the adsorption capacity of TiO2 nanoparticles by arresting the grain growth and making it visible light responsive resulting in a higher affnity for arsenic.Similarly,the arsenic removal by adsorption on the sand coated with TiO2 nanoparticles was the highest among the three types of sand used.In all cases,As(V) was adsorbed more compared with As(Ⅲ).The solution pH appeared to be the most important factor in controlling the amount of arsenic adsorbed. | NABI Deedar ASLAM Irfan QAZI Ishtiaq A | 2009 | Journal of Environmental Sciences2009，21，3： | 14 |
| 2 | Click through Rate Effectiveness Prediction on Mobile Ads Using Extreme Gradient Boosting显示文摘Online advertisements have a significant influence over the success or failure of your business.Therefore,it is important to somehow measure the impact of your advertisement before uploading it online,and this is can be done by calculating the Click Through Rate(CTR).Unfortunately,this method is not eco-friendly,since you have to gather the clicks from users then compute the CTR.This is where CTR prediction come in handy.Advertisement CTR prediction relies on the users’log regarding click information data.Accurate prediction of CTR is a challenging and critical process for e-advertising platforms these days.CTR prediction uses machine learning techniques to determine how much the online advertisement has been clicked by a potential client:The more clicks,the more successful the ad is.In this study we develop a machine learning based click through rate prediction model.The proposed study defines a model that generates accurate results with low computational power consumption.We used four classification techniques,namely K Nearest Neighbor(KNN),Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).The study was performed on the Click-Through Rate Prediction Competition Dataset.It is a click-through data that is ordered chronologically and was collected over 10 days.Experimental results reveal that XGBoost produced ROC-AUC of 0.76 with reduced number of features. | AlAli Moneera AlQahtani Maram AlJuried Azizah Taghareed AlOnizan Dalia Alboqaytah Nida Aslam Irfan Ullah Khan | 2021 | Computers, Materials & Continua2021，，2： | 1 |
| 3 | An Automated and Real-time Approach of Depression Detection from Facial Micro-expressions显示文摘Depression is a mental psychological disorder that may cause a physical disorder or lead to death.It is highly impactful on the socialeconomical life of a person;therefore,its effective and timely detection is needful.Despite speech and gait,facial expressions have valuable clues to depression.This study proposes a depression detection system based on facial expression analysis.Facial features have been used for depression detection using Support Vector Machine(SVM)and Convolutional Neural Network(CNN).We extracted micro-expressions using Facial Action Coding System(FACS)as Action Units(AUs)correlated with the sad,disgust,and contempt features for depression detection.A CNN-based model is also proposed in this study to auto classify depressed subjects from images or videos in real-time.Experiments have been performed on the dataset obtained from Bahawal Victoria Hospital,Bahawalpur,Pakistan,as per the patient health questionnaire depression scale(PHQ-8);for inferring the mental condition of a patient.The experiments revealed 99.9%validation accuracy on the proposed CNN model,while extracted features obtained 100%accuracy on SVM.Moreover,the results proved the superiority of the reported approach over state-of-the-art methods. | Ghulam Gilanie Mahmood ul Hassan Mutyyba Asghar Ali Mustafa Qamar Hafeez Ullah Rehan Ullah Khan Nida Aslam Irfan Ullah Khan | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 4 | Explainable Classification Model for Android Malware Analysis Using API and Permission-Based Features显示文摘One of the most widely used smartphone operating systems,Android,is vulnerable to cutting-edge malware that employs sophisticated logic.Such malware attacks could lead to the execution of unauthorized acts on the victims’devices,stealing personal information and causing hardware damage.In previous studies,machine learning(ML)has shown its efficacy in detecting malware events and classifying their types.However,attackers are continuously developing more sophisticated methods to bypass detection.Therefore,up-to-date datasets must be utilized to implement proactive models for detecting malware events in Android mobile devices.Therefore,this study employed ML algorithms to classify Android applications into malware or goodware using permission and application programming interface(API)-based features from a recent dataset.To overcome the dataset imbalance issue,RandomOverSampler,synthetic minority oversampling with tomek links(SMOTETomek),and RandomUnderSampler were applied to the Dataset in different experiments.The results indicated that the extra tree(ET)classifier achieved the highest accuracy of 99.53%within an elapsed time of 0.0198 s in the experiment that utilized the RandomOverSampler technique.Furthermore,the explainable Artificial Intelligence(EAI)technique has been applied to add transparency to the high-performance ET classifier.The global explanation using the Shapely values indicated that the top three features contributing to the goodware class are:Ljava/net/URL;->openConnection,Landroid/location/LocationManager;->getLastKgoodwarewnLocation,and Vibrate.On the other hand,the top three features contributing to themalware class are Receive_Boot_Completed,Get_Tasks,and Kill_Background_Processes.It is believed that the proposedmodel can contribute to proactively detectingmalware events in Android devices to reduce the number of victims and increase users’trust. | Nida Aslam Irfan Ullah Khan Salma Abdulrahman Bader Aisha Alansari Lama Abdullah Alaqeel Razan Mohammed Khormy Zahra Abdultawab AlKubaish Tariq Hussain | 2023 | Computers, Materials & Continua2023，76，9： | 0 |
| 5 | Umbilical cord-derived mesenchymal stem cells preconditioned with isorhamnetin:potential therapy for burn wounds显示文摘BACKGROUND Impaired wound healing can be associated with different pathological states.Burn wounds are the most common and detrimental injuries and remain a major health issue worldwide.Mesenchymal stem cells(MSCs)possess the ability to regenerate tissues by secreting factors involved in promoting cell migration,proliferation and differentiation,while suppressing immune reactions.Preconditioning of MSCs with small molecules having cytoprotective properties can enhance the potential of these cells for their use in cell-based therapeutics.AIM To enhance the therapeutic potential of MSCs by preconditioning them with isorhamnetin for second degree burn wounds in rats.METHODS Human umbilical cord MSCs(hU-MSCs)were isolated and characterized by surface markers,CD105,vimentin and CD90.For preconditioning,hU-MSCs were treated with isorhamnetin after selection of the optimized concentration(5μmol/L)by cytotoxicity analysis.The migration potential of these MSCs was analyzed by the in vitro scratch assay.The healing potential of normal,and preconditioned hU-MSCs was compared by transplanting these MSCs in a rat model of a second degree burn wound.Normal,and preconditioned MSCs(IH+MSCs)were transplanted after 72 h of burn injury and observed for 2 wk.Histological and gene expression analyses were performed on day 7 and 14 after cell transplantation to determine complete wound healing.RESULTS The scratch assay analysis showed a significant reduction in the scratch area in the case of IH+MSCs compared to the normal untreated MSCs at 24 h,while complete closure of the scratch area was observed at 48 h.Histological analysis showed reduced inflammation,completely remodeled epidermis and dermis without scar formation and regeneration of hair follicles in the group that received IH+MSCs.Gene expression analysis was time dependent and more pronounced in the case of IH+MSCs.Interleukin(IL)-1β,IL-6 and Bcl-2 associated X genes showed significant downregulation,while transforming growth factorβ,vascular endothelial growth factor,Bcl-2 and matrix metallopeptidase 9 showed significant upregulation compared to the burn wound,showing increased angiogenesis and reduced inflammation and apoptosis.CONCLUSION Preconditioning of hU-MSCs with isorhamnetin decreases wound progression by reducing inflammation,and improving tissue architecture and wound healing.The study outcome is expected to lead to an improved cell-based therapeutic approach for burn wounds. | Shazmeen Aslam Irfan Khan Fatima Jameel Midhat Batool Zaidi Asmat Salim | 2020 | World Journal of Stem Cells2020，12，12： | 0 |
| 6 | Tocilizumab in severe COVID-19-A randomized,double-blind,placebo-controlled trial显示文摘Background:The therapeutic effectiveness of interleukin-6 receptor inhibitor in critically ill hospitalized patients with coronavirus disease 2019(COVID-19)is uncertain.Methods:To evaluate the efficacy and safety of the outcome as recovery or death of tocilizumab for severe acute respiratory syndrome-coronavirus-2(SARS-CoV-2)infection,we conducted a randomized,double-blinded,placebo-controlled phase 2 trial in critically ill COVID-19 adult patients.The patients were randomly assigned in a 4:1 ratio to receive standard medical treatment plus the recommended dose of either tocilizumab or the placebo drug.Randomization was stratified.The primary outcome was the recovery or death after administration of tocilizumab or a placebo drug.The secondary outcomes were clinical recovery or worsening of the patients’symptoms and inflammatory markers and discharge from the hospital.Results:Of 190 patients included in this study,152 received tocilizumab,and 38 received a placebo.The duration of hospital stay of the interventional group was 12.9±9.2,while the placebo group had a more extended hospital stay(15.6±8.8).The mortality ratio for the primary outcome,ie,mortality or recovery in the tocilizumab group was 17.8%;p=0.58 by log-rank test).The mortality ratio in the placebo group was 76.3%;p=0.32 by log-rank test).The inflammatory markers in the tocilizumab group significantly declined by day 16 compared to the placebo group.Conclusions:The use of tocilizumab was associated with decreased mortality,earlier improvement of inflamma-tory markers,and reduced hospital stay in patients with severe COVID-19. | Muhammad Irfan Malik Sardar Al Fareed Zafar Fabiha Qayyum Muna Malik Muhammad Sohaib Asghar Muhammad Junaid Tahir Ammarah Arshad Fatima Khalil Hafiza Shafia Naz Mudassar Aslam Jodat Saleem Abdul Aziz Mustafa Usman Azhar Muhammad Naqash Zohaib Yousaf | 2022 | Infectious Medicine2022，1，2： | 0 |
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