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| 1 | Pollutant removal from municipal wastewater employing baffled subsurface flow and integrated surface flow-floating treatment wetlands显示文摘This article reports pollutant removal performances of baffled subsurface flow, and integrated surface flow-floating treatment wetland units, when arranged in series for the treatment of municipal wastewater in Bangladesh. The wetland units(of the hybrid system) included organic, inorganic media, and were planted with nineteen types of macrophytes. The wetland train was operated under hydraulic loading fluctuation and seasonal variation. The performance analyses(across the wetland units) illustrated simultaneous denitrification and organics removal rates in the first stage vertical flow wetland, due to organic carbon leaching from the employed organic media. Higher mean organics removal rates(656.0 g COD/(m2·day)) did not completely inhibit nitrification in the first stage vertical flow system; such pattern could be linked to effective utilization of the trapped oxygen, as the flow was directed throughout the media by the baffle walls. Second stage horizontal flow wetland showed enhanced biodegradable organics removal, which depleted organic carbon availability for denitrification. The final stage integrated wetland system allowed further nitrogen removal from wastewater, via nutrient uptake by plant roots(along with nitrification), and generation of organic carbon(by the dead macrophytes) to support denitrification. The system achieved higher E. coli mortality through protozoa predation, E. coli oxidation, and destruction by UV radiation. In general, enhanced pollutant removal efficiencies as demonstrated by the structurally modified hybrid wetland system signify the necessity of such modification, when operated under adverse conditions such as: substantial input organics loading, hydraulic loading fluctuation, and seasonal variation. | Tanveer Saeed Abdullah Al-Muyeed Rumana Afrin Habibur Rahman Guangzhi Sun | 2014 | Journal of Environmental Sciences2014，26，4： | 7 |
| 2 | Breaking wheat yield barriers requires integrated efforts in developing countries显示文摘Most yield progress obtained through the so called 'Green Revolution', particularly in the irrigated areas of Asia, has reached a limit, and major resistance genes are quickly overcome by the appearance of new strains of disease causing organisms.New plant stresses due to a changing environment are difficult to breed for as quickly as the changes occur.There is consequently a continual need for new research programs and breeding strategies aimed at improving yield potential, abiotic stress tolerance and resistance to new, major pests and diseases.Recent advances in plant breeding encompass novel methods of expanding genetic variability and selecting for recombinants, including the development of synthetic hexaploid, hybrid and transgenic wheats.In addition, the use of molecular approaches such as quantitative trait locus(QTL) and association mapping may increase the possibility of directly selecting positive chromosomal regions linked with natural variation for grain yield and stress resistance.The present article reviews the potential contribution of these new approaches and tools to the improvement of wheat yield in farmer's fields, with a special emphasis on the Asian countries, which are major wheat producers, and contain the highest concentration of resource-poor wheat farmers. | Saeed Rauf Maria Zaharieva Marilyn L Warburton ZHANG Ping-zhi Abdullah M AL-Sadi Farghama Khalil Marcin Kozak Sultan A Tariq | 2015 | Journal of Integrative Agriculture2015，14，8： | 2 |
| 3 | Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method显示文摘In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial pattern.Thereby,electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability.Due to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG domain.In this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as input.To minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant features.To this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the classification.Further,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test accuracy.Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes.Our proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively. | Muhammad Irfan Ali Raza Faisal Althobiani Nasir Ayub Muhammad Idrees Zain Ali Kashif Rizwan Abdullah Saeed Alwadie Saleh Mohammed Ghonaim Hesham Abdushkour Saifur Rahman Omar Alshorman Samar Alqhtani | 2022 | Computers, Materials & Continua2022，，9： | 2 |
| 4 | Evaluation of anti-resistant activity of Auklandia(Saussurea lappa) root against some human pathogens显示文摘Objective:The antimicrobial activity of the ethanol extract of the Auklandia(Saussurea lappa)root plant was investigated to verify its medicinal use in the treatment of microbial infections.Methods:The antimicrobial activity of the ethanol extract was tested against clinical isolates ofsome multidrug-resistant bacteria using the agar well diffusion method.Commercial antibioticswere used as positive reference standards to determine the sensitivity of the clinical isolates.Results:The extracts showed significant inhibitory activity against clinical isolates of methicillinresistantStaphylococcus aureus,Pseudomonas aeruginosa,Escherichia coli,Klebsiella pneumonia,Extended Spectrum Beta-Lactemase,Acinetobacter baumannii.The minimum inhibitory concentration values obtained using the agar dilution test ranged from 2.0μg/μL-12.0μg/μL.In the contrary the water extract showed no activity at all against the tested isolates.Furthermore,theresults obtained by examining anti-resistant activity of the plant ethanolic extract showed thatat higher concentration of the plant extract(12μg)all tested bacteria isolates were inhibited with variable inhibition zones similar to those obtained when we applied lower extract concentrationusing the well diffusion assay.Conclusion:The results demonstrated that the crude ethanolicextract of the Auklandia(Saussurea lappa)root plant has a wide spectrum of activity suggestingthat it may be useful in the treatment of infections caused by the above clinical isolates(humanpathogens). | Sidgi Syed Anwer Hasson Mohammed Saeed Al-Balushi KhazinaAlharthy JumaZaidAl-Busaidi MunaSulimanAldaihani Mohammed Shafeeq Othman Elias Antony Said Omar Habal Talal Abdullah Sallam Ali Abdullah Aljabri Mohamed AhmedIdris | 2013 | Asian Pacific Journal of Tropical Biomedicine2013，3，7： | 2 |
| 5 | Effect of erythromycin before endoscopy in patients presenting with variceal bleeding: a prospective, randomized, double-blind, placebo-controlled trial显示文摘 | Ibrahim Altraif Fayaz A. Handoo Abdulrahman Aljumah Abduljaleel Alalwan Mutasim Dafalla Abdullah Mohamed Saeed Abdulrahman Alkhormi Abdul Karim Albekairy Hani Tamim | 2011 | Gastrointestinal Endoscopy2011，，2： | 1 |
| 6 | Energy Theft Identification Using Adaboost Ensembler in the Smart Grids显示文摘One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques. | Muhammad Irfan Nasir Ayub Faisal Althobiani Zain Ali Muhammad Idrees Saeed Ullah Saifur Rahman Abdullah Saeed Alwadie Saleh Mohammed Ghonaim Hesham Abdushkour Fahad Salem Alkahtani Samar Alqhtani Piotr Gas | 2022 | Computers, Materials & Continua2022，，7： | 1 |
| 7 | Influence of nutrient amendment on the biodegadation of wheat straw during solid state fermentation with Trametes versicolor显示文摘 | SAEED IQBAL ZAFAR NAHEED ABDULLAH MUHAMMED IQBAL | 1996 | International Biodeterioration & Biodegradation1996，38，2： | 1 |
| 8 | Synthesis,Crystal Structure and Spectroscopic Properties of 1,2-Benzothiazine Derivatives:An Experimental and DFT Study显示文摘1,2-Benzothiazine derivatives methyl 3-methoxy-4-oxo-3,4-dihydro-2H-benzo[e] [1,2]thiazine-3-carboxylate 1,1-dioxide(1) and methyl 2-ethyl-3-hydroxy-4-oxo-3,4-dihydro-2Hbenzo[e][1,2]thiazine-3-carboxylate 1,1-dioxide(2) were synthesized, and characterized by spectroscopic techniques; 1H-NMR and infrared(IR) spectroscopy. Crystals of 1 and 2 were grown by slow evaporation of methanol and ethyl acetate, respectively and their crystal structures were investigated by single-crystal X-ray diffraction analysis. Geometric properties were calculated by the B3 LYP method of density functional theory(DFT) at the 6-31G+(d) basis set to compare with the experimental data. Simulated properties were found in strong agreement with the experimental ones. Intermolecular forces have also been modeled in order to investigate the strength of packing and strong hydrogen bonding was observed in both compounds 1 and 2. Electronic properties such as Ionization Potential(IP), Electron Affinities(EA) and coefficients of the highest occupied molecular orbital(HOMO) and the lowest unoccupied molecular orbital(LUMO) of com- pounds 1 and 2 were simulated for the first time. | MUHAMMAD Nadeem Arshad TARIQ Mahmood ATHER Faroque Khan MUHAMMAD Zia-Ur-Rehman ABDULLAH M.Asiri ISLAM Ullah Khan RIFFAT-Un-Nisa KHURSHID Ayub AZAM Mukhtar MUHAMMAD Tariq Saeed | 2015 | Chinese Journal of Structural Chemistry2015，34，1： | 1 |
| 9 | Outcome of posterior chamber phakic intraocular lens (ICL) procedure to correct myopia显示文摘 | Nasser Al Sabaani Abdullah Al Assiri Abdullah Al Torbak Saeed Al Motawa | 2013 | Saudi Journal of Ophthalmology2013，，： | 1 |
| 10 | Machine Learning-Enabled Communication Approach for the Internet of Medical Things显示文摘The Internet ofMedical Things(IoMT)is mainly concernedwith the efficient utilisation of wearable devices in the healthcare domain to manage various processes automatically,whereas machine learning approaches enable these smart systems to make informed decisions.Generally,broadcasting is used for the transmission of frames,whereas congestion,energy efficiency,and excessive load are among the common issues associated with existing approaches.In this paper,a machine learning-enabled shortest path identification scheme is presented to ensure reliable transmission of frames,especially with the minimum possible communication overheads in the IoMT network.For this purpose,the proposed scheme utilises a well-known technique,i.e.,Kruskal’s algorithm,to find an optimal path from source to destination wearable devices.Additionally,other evaluation metrics are used to find a reliable and shortest possible communication path between the two interested parties.Apart from that,every device is bound to hold a supplementary path,preferably a second optimised path,for situations where the current communication path is no longer available,either due to device failure or heavy traffic.Furthermore,the machine learning approach helps enable these devices to update their routing tables simultaneously,and an optimal path could be replaced if a better one is available.The proposed mechanism has been tested using a smart environment developed for the healthcare domain using IoMT networks.Simulation results show that the proposed machine learning-oriented approach performs better than existing approaches where the proposed scheme has achieved the minimum possible ratios,i.e.,17%and 23%,in terms of end to end delay and packet losses,respectively.Moreover,the proposed scheme has achieved an approximately 21%improvement in the average throughput compared to the existing schemes. | Rahim Khan Abdullah Ghani Samia Allaoua Chelloug Mohammed Amin Aamir Saeed Jason Teo | 2023 | Computers, Materials & Continua2023，76，8： | 0 |
| 11 | A gated recurrent unit model to predict Poisson’s ratio using deep learning显示文摘Static Poisson’s ratio(vs)is crucial for determining geomechanical properties in petroleum applications,namely sand production.Some models have been used to predict vs;however,the published models were limited to specific data ranges with an average absolute percentage relative error(AAPRE)of more than 10%.The published gated recurrent unit(GRU)models do not consider trend analysis to show physical behaviors.In this study,we aim to develop a GRU model using trend analysis and three inputs for predicting n s based on a broad range of data,n s(value of 0.1627-0.4492),bulk formation density(RHOB)(0.315-2.994 g/mL),compressional time(DTc)(44.43-186.9 μs/ft),and shear time(DTs)(72.9-341.2μ s/ft).The GRU model was evaluated using different approaches,including statistical error an-alyses.The GRU model showed the proper trends,and the model data ranges were wider than previous ones.The GRU model has the largest correlation coefficient(R)of 0.967 and the lowest AAPRE,average percent relative error(APRE),root mean square error(RMSE),and standard deviation(SD)of 3.228%,1.054%,4.389,and 0.013,respectively,compared to other models.The GRU model has a high accuracy for the different datasets:training,validation,testing,and the whole datasets with R and AAPRE values were 0.981 and 2.601%,0.966 and 3.274%,0.967 and 3.228%,and 0.977 and 2.861%,respectively.The group error analyses of all inputs show that the GRU model has less than 5% AAPRE for all input ranges,which is superior to other models that have different AAPRE values of more than 10% at various ranges of inputs. | Fahd Saeed Alakbari Mysara Eissa Mohyaldinn Mohammed Abdalla Ayoub Ibnelwaleed A.Hussein Ali Samer Muhsan Syahrir Ridha Abdullah Abduljabbar Salih | 2024 | Journal of Rock Mechanics and Geotechnical Engineering2024，16，1： | 0 |
| 12 | Quality of life of COVID-19 recovered patients:a 1-year follow-up study from Bangladesh显示文摘BackgroundThe COVID-19 pandemic posed a danger to global public health because of the unprecedented physical,mental,social,and environmental impact affecting quality of life(QoL).The study aimed to find the changes in QoL among COVID-19 recovered individuals and explore the determinants of change more than 1 year after recovery in low-resource settings.MethodsCOVID-19 patients from all eight divisions of Bangladesh who were confirmed positive by reverse transcription-polymerase chain reaction from June 2020 to November 2020 and who subsequently recovered were followed up twice,once immediately after recovery and again 1 year after the first follow-up.The follow-up study was conducted from November 2021 to January 2022 among 2438 individuals using the World Health Organization Quality of Life Brief Version(WHOQOL-BREF).After excluding 48 deaths,95 were rejected to participate,618 were inaccessible,and there were 45 cases of incomplete data.Descriptive statistics,paired-sample analyses,generalized estimating equation(GEE)analysis,and multivariable logistic regression analyses were performed to test the mean difference in participants’QoL scores between the two interviews.ResultsMost participants(n=1710,70.1%)were male,and one-fourth(24.4%)were older than 46.The average physical domain score decreased significantly from baseline to follow-up,and the average scores in psychological,social,and environmental domains increased significantly at follow-up(P<0.05).By the GEE equation approach,after adjusting for other factors,we found that older age groups(P<0.001),being female(P<0.001),having hospital admission during COVID-19 illness(P<0.001),and having three or more chronic diseases(P<0.001),were significantly associated with lower physical and psychological QoL scores.Higher age and female sex[adjusted odd ratio(aOR)=1.3,95%confidence interval(CI)1.0–1.6]were associated with reduced social domain scores on multivariable logistic regression analysis.Urban or semi-urban people were 49%less likely(aOR=0.5,95%CI 0.4–0.7)and 32%less likely(aOR=0.7,95%CI 0.5–0.9)to have a reduced QoL score in the psychological domain and the social domain respectively,than rural people.Higher-income people were more likely to experience a decrease in QoL scores in physical,psychological,social,and environmental domains.Married people were 1.8 times more likely(aOR=1.8,95%CI 1.3–2.4)to have a decreased social QoL score.In the second interview,people admitted to hospitals during their COVID-19 infection showed a 1.3 times higher chance(aOR=1.3,95%CI 1.1–1.6)of a decreased environmental QoL score.Almost 13%of participants developed one or more chronic diseases between the first and second interviews.Moreover,7.9%suffered from reinfection by COVID-19 during this 1-year time.ConclusionsThe present study found that the QoL of COVID-19 recovered people improved 1 year after recovery,particularly in psychological,social,and environmental domains.However,age,sex,the severity of COVID-19,smoking habits,and comorbidities were significantly negatively associated with QoL.Events of reinfection and the emergence of chronic disease were independent determinants of the decline in QoL scores in psychological,social,and physical domains,respectively.Strong policies to prevent and minimize smoking must be implemented in Bangladesh,and we must monitor and manage chronic diseases in people who have recovered from COVID-19. | Mohammad Delwer Hossain Hawlader Md Utba Rashid Md Abdullah Saeed Khan Mowshomi Mannan Liza Sharmin Akter Mohammad Ali Hossain Tajrin Rahman Sabrina Yesmin Barsha Alberi Afifa Shifat Mosharop Hossian Tahmina Zerin Mishu Soumik Kha Sagar Ridwana Maher Manna Nawshin Ahmed Sree Shib Shankar Devnath Debu Irin Chowdhury Samanta Sabed Mashrur Ahmed Sabrina Afroz Borsha Faraz Al Zafar Sabiha Hyder Abdullah Enam Habiba Babul Naima Nur Miah Md.Akiful Haque Shopnil Roy K.M.Tanvir Hassan Mohammad Lutfor Rahman Mohammad Hayatun Nabi Koustuv Dalal | 2023 | Infectious Diseases of Poverty2023，12，4： | 0 |
| 13 | Modifications of the Optimal Auxiliary Function Method to Fractional Order Fornberg-Whitham Equations显示文摘In this paper,we present a new modification of the newly developed semi-analytical method named the Optimal Auxilary Function Method(OAFM)for fractional-order equations using the Caputo operator,which is named FOAFM.The mathematical theory of FOAFM is presented and the effectiveness of this method is proven by using it with well-known Fornberg-Whitham Equations(FWE).The FOAFM results are compared with other method results along with their exact solutions with the help of tables and plots to prove the validity of FOAFM.A rapidly convergent series solution is obtained from FOAFMand is validated by comparison with other results.The analysis proves that ourmethod is simply applicable,contains less computationalwork,and is rapidly convergent to the exact solution at the first iteration.A series solution to the problem is obtained with the help of FOAFM.The validity of FOAFM results is validated by comparing its results with the results available in the literature.It is observed that FOAFM is simply applicable,contains less computational work,and is fastly convergent.The convergence and stability are obtained with the help of optimal constants.FOAFM is very easy in applicability and provides excellent results at the first iteration for complex nonlinear initial/boundary value problems.FOAFM contains the optimal auxiliary constants through which we can control the convergence as FOAFM contains the auxiliary functions D_(1),D_(2),D_(3)...in which the optimal constants G_(1),G_(2),...and the control convergence parameters exist to play an important role in getting the convergent solution which is obtained rigorously.The computational work in FOAFM is less when compared to other methods and even a low-specification computer can do the computational work easily. | Hakeem Ullah Mehreen Fiza Ilyas Khan Abd Allah A.Mosa Saeed Islam Abdullah Mohammed | 2023 | Computer Modeling in Engineering & Sciences2023，，7： | 0 |
| 14 | High levels of Zinc-α-2-Glycoprotein among Omani AIDS patients on combined antiretroviral therapy显示文摘Objective:To investigate the levels of zinc-α-2-glycoprotein(ZAG) among Omani AIDS patients receiving combined antiretroviral therapy(cART).Methods:A total of 80 Omani AIDS patients(45 males and 33 females),average age of 36 vears.who were receiving cART at the Saltan Qaboos University Hospital(SQUH).Muscat,Oman,were tested for the levels of ZAG.In addition,SO healthy blood donors(46 males and 34 females),average age of 26 years,attending the SOUH Blood Bank,were tested in parallel as a control group.Measurement of the ZAG levels was performed using a competitive enzyme—linked immunosorbent assay and in accordance with the manufacturer's instructions.Results:The ZAG levels were found to he significantly higher among AIDS patients compared to the healthy individuals(P=0.033).A total of 56(70%) of the AIDS patients were found to have higher levels of ZAG and 16(20%) AIDS patients were found to have high ZAG levels,which are significantly(P>0.031) associated with weight loss.Conclusions:ZAG levels are high among Omani AIDS patients on cART and this necessitales the measurement of ZAG on routine basis,as it is associated with weight loss. | Sidgi Syed Anwer Hasson Mohammed Saeed Al-Balushi Muzna Hamed Al Yahmadi Juma Zaid Al-Busaidi Elias Antony Said Mohammed Shafeeq Othman Talal Abdullah Sallam Mohammed Ahmad Idris Ali Abdullah Al-Jabri | 2014 | Asian Pacific Journal of Tropical Biomedicine2014，4，8： | 0 |
| 15 | Augmenting IoT Intrusion Detection System Performance Using Deep Neural Network显示文摘Due to their low power consumption and limited computing power,Internet of Things(IoT)devices are difficult to secure.Moreover,the rapid growth of IoT devices in homes increases the risk of cyber-attacks.Intrusion detection systems(IDS)are commonly employed to prevent cyberattacks.These systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate countermeasures.Attempts have been made in the past to detect new attacks using machine learning and deep learning techniques,however,these efforts have been unsuccessful.In this paper,we propose two deep learning models to automatically detect various types of intrusion attacks in IoT networks.Specifically,we experimentally evaluate the use of two Convolutional Neural Networks(CNN)to detect nine distinct types of attacks listed in the NF-UNSW-NB15-v2 dataset.To accomplish this goal,the network stream data were initially converted to twodimensional images,which were then used to train the neural network models.We also propose two baseline models to demonstrate the performance of the proposed models.Generally,both models achieve high accuracy in detecting the majority of these nine attacks. | Nasir Sayed Muhammad Shoaib Waqas Ahmed Sultan Noman Qasem Abdullah M.Albarrak Faisal Saeed | 2023 | Computers, Materials & Continua2023，，1： | 0 |
| 16 | Text Extraction with Optimal Bi-LSTM显示文摘Text extraction from images using the traditional techniques of image collecting,and pattern recognition using machine learning consume time due to the amount of extracted features from the images.Deep Neural Networks introduce effective solutions to extract text features from images using a few techniques and the ability to train large datasets of images with significant results.This study proposes using Dual Maxpooling and concatenating convolution Neural Networks(CNN)layers with the activation functions Relu and the Optimized Leaky Relu(OLRelu).The proposed method works by dividing the word image into slices that contain characters.Then pass them to deep learning layers to extract feature maps and reform the predicted words.Bidirectional Short Memory(BiLSTM)layers extractmore compelling features and link the time sequence fromforward and backward directions during the training phase.The Connectionist Temporal Classification(CTC)function calcifies the training and validation loss rates.In addition to decoding the extracted feature to reform characters again and linking them according to their time sequence.The proposed model performance is evaluated using training and validation loss errors on the Mjsynth and Integrated Argument Mining Tasks(IAM)datasets.The result of IAM was 2.09%for the average loss errors with the proposed dualMaxpooling and OLRelu.In the Mjsynth dataset,the best validation loss rate shrunk to 2.2%by applying concatenating CNN layers,and Relu. | Bahera H.Nayef Siti Norul Huda Sheikh Abdullah Rossilawati Sulaiman Ashwaq Mukred Saeed | 2023 | Computers, Materials & Continua2023，76，9： | 0 |
| 17 | National guidelines for the diagnosis and treatment of hilar cholangiocarcinoma显示文摘A consensus meeting of national experts from all major national hepatobiliary centres in the country was held on May 26,2023,at the Pakistan Kidney and Liver Institute&Research Centre(PKLI&RC)after initial consultations with the experts.The Pakistan Society for the Study of Liver Diseases(PSSLD)and PKLI&RC jointly organised this meeting.This effort was based on a comprehensive literature review to establish national practice guidelines for hilar cholangiocarcinoma(hCCA).The consensus was that hCCA is a complex disease and requires a multidisciplinary team approach to best manage these patients.This coordinated effort can minimise delays and give patients a chance for curative treatment and effective palliation.The diagnostic and staging workup includes high-quality computed tomography,magnetic resonance imaging,and magnetic resonance cholangiopancreato-graphy.Brush cytology or biopsy utilizing endoscopic retrograde cholangiopancreatography is a mainstay for diagnosis.However,histopathologic confirmation is not always required before resection.Endoscopic ultrasound with fine needle aspiration of regional lymph nodes and positron emission tomography scan are valuable adjuncts for staging.The only curative treatment is the surgical resection of the biliary tree based on the Bismuth-Corlette classification.Selected patients with unresectable hCCA can be considered for liver transplantation.Adjuvant chemotherapy should be offered to patients with a high risk of recurrence.The use of preoperative biliary drainage and the need for portal vein embolisation should be based on local multidisciplinary discussions.Patients with acute cholangitis can be drained with endoscopic or percutaneous biliary drainage.Palliative chemotherapy with cisplatin and gemcitabine has shown improved survival in patients with irresectable and recurrent hCCA. | Faisal Saud Dar Zaigham Abbas Irfan Ahmed Muhammad Atique Usman Iqbal Aujla Muhammad Azeemuddin Zeba Aziz Abu Bakar Hafeez Bhatti Tariq Ali Bangash Amna Subhan Butt Osama Tariq Butt Abdul Wahab Dogar Javed Iqbal Farooqi Faisal Hanif Jahanzaib Haider Siraj Haider Syed Mujahid Hassan Adnan Abdul Jabbar Aman Nawaz Khan Muhammad Shoaib Khan Muhammad Yasir Khan Amer Latif Nasir Hassan Luck Ahmad Karim Malik Kamran Rashid Sohail Rashid Mohammad Salih Abdullah Saeed Amjad Salamat Ghias-un-Nabi Tayyab Aasim Yusuf Haseeb Haider Zia Ammara Naveed | 2024 | World Journal of Gastroenterology2024，30，9： | 0 |
| 18 | Ammoniacal Nitrogen and Organics Removal Modelling in Vertical Flow Wetlands Treating Strong Wastewaters显示文摘This paper reports a comparative evaluation between 2 kinetic models for predicting nitrification and biodegradable organics(BOD5)removal rates in 5 vertical flow(VF)wetland systems,that received strong wastewaters(i.e.tannery,textile and municipal effluents).The models were formulated by combining first order and Monod kinetics,with continuous-stirred tank reactor(CSTR)flow approach.The performance of the 2 models had been evaluated with3 statistical parameters:coefficient of determination(R2),relative root mean square error(RRMSE),and model efficiency(ME).The statistical parameters indicated better performance of the Monod CSTR model(over first order CSTR approach),for correlating ammoniacal nitrogen(NH4+—N)and BOD5removal profiles across VF systems.Higher Monod coefficient values(from Monod CSTR model)coincided with greater input NH4+—N and BOD5loading,and experimentally measured removal rate(g/(m2·d))values.Such trends indicate that NH4+—N and BOD5removals in the VF systems were mainly achieved via biological routes.On the other hand,the rate constants(from the first order CSTR model)did not exhibit such correlations(of Monod coefficients),elucidating their inefficiencies in capturing overall removal mechanisms.The interference of organics removal on nitrification process(in VF wetlands)was identified through Monod coefficients.The deviation between NH4+—N and BOD5Monod coefficients imply incorporation of both coefficients,for calculating the area of a single VF bed.Overall,closer performance of the Monod CSTR model for predicting NH4+—N and BOD5removals indicate its potential application,as a design tool for VF systems. | Tanveer Ferdous Saeed Abdullah A1 Muyeed Guangzhi Sun | 2013 | 湿地科学2013，11，4： | 0 |
| 19 | Hybrid GrabCut Hidden Markov Model for Segmentation显示文摘Diagnosing data or object detection in medical images is one of the important parts of image segmentation especially those data which is less effective to identify inMRI such as low-grade tumors or cerebral spinal fluid(CSF)leaks in the brain.The aim of the study is to address the problems associated with detecting the low-grade tumor and CSF in brain is difficult in magnetic resonance imaging(MRI)images and another problem also relates to efficiency and less execution time for segmentation of medical images.For tumor and CSF segmentation using trained light field database(LFD)datasets of MRI images.This research proposed the new framework of the hybrid k-Nearest Neighbors(k-NN)model that is a combination of hybridization of Graph Cut and Support Vector Machine(GCSVM)and Hidden Markov Model of k-Mean Clustering Algorithm(HMMkC).There are four different methods are used in this research namely(1)SVM,(2)GrabCut segmentation,(3)HMM,and(4)k-mean clustering algorithm.In this framework,on the one hand,phase one is to perform the classification of SVM and Graph Cut algorithm to create the maximum margin distance.This research use GrabCut segmentation method which is the application of the graph cut algorithm and extract the data with the help of scaleinvariant features transform.On the other hand,in phase two,segment the low-grade tumors and CSF using a method adapted for HMkC and extract the information of tumor or CSF fluid by GCHMkC including iterative conditional maximizing mode(ICMM)with identifying the range of distant.Comparative evaluation is also performing by the comparison of existing techniques in this research.In conclusion,our proposed model gives better results than existing.This proposed model helps to common man and doctor that can identify their condition of brain easily.In future,this will model will use for other brain related diseases. | Soobia Saeed Afnizanfaizal Abdullah N.Z.Jhanjhi Mehmood Naqvi Mehedi Masud Mohammed A.AlZain | 2022 | Computers, Materials & Continua2022，，7： | 0 |
| 20 | Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform显示文摘Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)techniques.Especially,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions.From this perspective,an automated AI technique with a digital processing method can be used to improve these signals.This paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG signals.These classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and non-seizure.In addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the brain.Thus,Hadamard coefficients of the EEG signals are obtained via the FWHT.Moreover,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings.Also,a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers.The LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,respectively.The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,respectively.The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals.Eventually,the proposed classifiers provide high classification accuracy compared to previously published classifiers. | Saeed Mohsen Sherif S.M.Ghoneim Mohammed S.Alzaidi Abdullah Alzahrani Ashraf Mohamed Ali Hassan | 2023 | Computers, Materials & Continua2023，，6： | 0 |
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