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Outcomes of surgical correction of Peyronie’s disease with plaque excision and grafting: Comparison of testicular tunica vaginalis graft versus bovine pericardium graft

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Asian Journal of Urology 2024年第3期11卷 497-503页

作者： ali Eslahi Faisal Ahmed Mohammad Reza Askarpour Hossein-ali Nikbakht Iman Shamohammadi Payam Ghasemi Hanieh alimardani Bahareh Ebrahimi Department of Urology School of MedicineShiraz University of Medical SciencesShirazIran Shiraz Geriatric Research Center Shiraz University of Medical SciencesShirazIran Urology Research Center Al-Thora General HospitalDepartment of UrologySchool of MedicineIbb University of Medical SciencesIbbYemen Social Determinants of Health Research Center Health Research InstituteBabol University of Medical SciencesBabolIran

Objective:Peyronie’s disease(PD)is an abnormal wound healing in the penile tunica *** fibrotic plaque excision,different graft materials have been used to repair the defects,but the optimal graft remains *** study aimed to compare the functional outcomes of testicular tunica vaginalis grafts and bovine pericardium grafts in patients with severe ***:A retrospective comparative study was conducted on 33 PD patients undergoing partial plaque excision and grafting from September 2015 to May *** patients were divided into two groups depending on the type of graft *** 15 patients in Group B,testicular tunica vaginalis grafts were used to repair the defect,while for 18 patients in Group A,bovine pericardium grafts were *** of the patient’s age,comorbidities,sexual function,penile curvature,postoperative complications,need for further treatment,change in penile length,and satisfaction were gathered and compared between the *** function was evaluated using the 5-item version of the International Index of Erectile Function(IIEF-5),and a functional less than 20-degree penile curvature after surgery was considered a successful ***:There was no difference in age,comorbidities,degree of curvature,perioperative IIEF-5,operative time,plaque size,or complication *** surgery,a statistically significant improvement in curvature degree(palis or bovine pericard

关键词： Peyronie's disease Plaqueexcision Testiculartunica vaginalis graft Bovinepericardium graft

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Youth voting and institutional change in the post-Arab Spring MENA region

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Economic and Political Studies 2024年第3期12卷 318-344页

作者： ali Fakih Mohammad Hammoud Nagham Sayour Department of Economics Lebanese American UniversityBeirutLebanon Centre for Lebanese Studies BeirutLebanon Department of Economics and Finance Zayed UniversityDubaithe United Arab Emirates

Using a unique and novel dataset on the youth,the SAHWA Youth Survey(2016),we apply probit and ordered probit models to study the determinants of voting behaviour change among the youth in the Middle East and North Africa(MENA)region in the post-Arab Spring *** find that drivers of voting vary depending on whether we consider the voting behaviour in the last elections or the likelihood of voting in the next ***,socioeconomic variables and some Arab Spring factors are significant for both types of ***,institutional variables and personal beliefs only affect the likelihood of voting in the next *** also document heterogeneous effects for the last and next votes by gender.

关键词： Voting behaviour youth Arab Spring institutional change the MENA region

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Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2519-2547页

作者： Hassen Louati ali Louati Elham Kariri Slim Bechikh College of Information Technology Kingdom UniversityRiffa40434Bahrain SMART Lab University of TunisISGTunisTunisia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease *** promising avenue involves the use of chest X-Rays,which are commonly utilized in *** fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic ***,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data *** tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image *** approach accurately classifies radiography images and detects potential chest abnormalities and infections,including ***,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting *** method can help reduce the amount of labeled data required for the task and enhance the overall performance of the *** have validated our method via a series of experiments against state-of-the-art architectures.

关键词： Computer-aided diagnosis deep learning evolutionary algorithms deep compression transfer learning

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Boron Nitride-Integrated Lithium Batteries:Exploring Innovations in Longevity and Performance

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Energy & Environmental Materials 2024年第6期7卷 13-40页

作者： Shayan Angizi Sayed ali Ahmad Alem Mahdi Torabian Maryam Khalaj Dmitri Golberg Amir Pakdel Department of Chemical Engineering McMaster University1280 Main St WHamiltonONL8S 4L7 Canada Institute of Chemistry of Polymeric Materials Montanuniversität LeobenOtto-Glöckel-Strasse 2A-8700 LeobenAustria Department of Chemistry Materials and Chemical EngineeringPolitecnico di MilanoPiazza Leonardo da Vinci 3220133 MilanItaly Department of Materials Science and Engineering Northwestern UniversityEvanstonIL60208 USA Centre for Materials Science and School of Chemistry and Physics Queensland University of Technology(QUT)2 GeorgeBrisbaneQLD4000 Australia Research Centre for Materials Nanoarchitectonics(MANA) National Institute for Materials Science(NIMS)Namiki 1Ibaraki3050044 Japan Department of Mechanical Manufacturing and Biomedical EngineeringTrinity College DublinThe University of DublinDublinD02PN40 Ireland

The current global warming,coupled with the growing demand for energy in our daily lives,necessitates the development of more efficient and reliable energy storage *** batteries(LBs)are at the forefront of emerging power sources addressing these *** studies have shown that integrating hexagonal boron nitride(h-BN)nanomaterials into LBs enhances the safety,longevity,and electrochemical performance of all LB components,including electrodes,electrolytes,and separators,thereby suggesting their potential value in advancing eco-friendly energy *** review provides an overview of the most recent applications of h-BN nanomaterials in *** begins with an informative introduction to h-BN nanomaterials and their relevant properties in the context of LB ***,it addresses the challenges posed by h-BN and discusses existing strategies to overcome these limitations,offering valuable insights into the potential of BN *** review then proceeds to outline the functions of h-BN in LB components,emphasizing the molecular-level mechanisms responsible for performance ***,the review concludes by presenting the current challenges and prospects of integrating h-BN nanomaterials into battery research.

关键词： electrode electrolyte hexagonal boron nitride lithium battery separator

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Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

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Computers, Materials & Continua 2024年第6期79卷 4725-4740页

作者： Djeldjli Halima Benatiallah Djelloul Ghasri Mehdi Tanougast Camel Benatiallah ali Benabdelkrim Bouchra Material Sciences Department Faculty of Material SciencesMathematics and Computer ScienceLaboratory of Sustainable Development and Computer Science(LSDCS)University of AdrarAdrar01000Algeria Department of Civil Engineering University of Sistan and BaluchestanZahedan98167-45845Iran Laboratory of Computer Production and Maintenance Engineering(LGIPM)University of LorraineChieulles57070France Laboratory of Energy Environment and Information System(LEEIS) University of AdrarAdrar01000Algeria

When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first *** study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and *** proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN *** GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were *** model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

关键词： Solar energy systems genetic algorithm neural networks hybrid adaptive neuro fuzzy inference system solar radiation

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An asymmetrical multilevel inverter with minimum voltage stress and fewer components for photovoltaic renewable-energy system

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Clean Energy 2024年第1期8卷 1-22页

作者： Rabail Memon Mukhtiar Ahmed Mahar Abdul Sattar Larik Syed Asif ali shah Department of Electrical Engineering Mehran University of Engineering and TechnologyJamshoroPakistan

The enhanced power quality provided by multilevel inverters(MLIs)has made them more appropriate for medium-and high-power applications,including photovoltaic ***,a prevalent limitation involves the necessity for numerous switches and increased voltage stress across these switches,consequently increasing the overall system *** address these challenges,a new 17-level asymmetrical MLI with fewer components and low voltage stress is proposed for the photovoltaic *** innovative MLI configuration has four direct current(DC)sources and 10 *** on the trinary sequence,the proposed topology uses photovoltaics with boost converters and fuzzy logic controllers as its DC *** equations are used to calculate cru-cial parameters for this proposed design,including total standing voltage per unit(TSVPU),cost function per level(CF/L),component count per level(CC/L)and voltage stress across the *** comparison is conducted by considering switches,DC sources,TSVPU,CF/L,gate driver circuits and CC/L with other existing MLI *** analysis is carried out under various conditions,encompassing different levels of irradiance,variable loads and modulation *** reduce the total harmonic distortion of the suggested topology,the phase opposition disposition approach has been *** suggested framework is simulated in MATLAB®/Simulink®.The results indicate that the proposed topology achieves a well-distributed stress profile across the switches and has CC/L of 1.23,TSVPU of 5 and CF/L of 4.58 and 5.76 with weight coefficients of 0.5 and 1.5,*** values are not-ably superior to those of existing MLI *** results demonstrate that the proposed topology maintains a consistent output at varying irradiance levels with FLCs and exhibits robust performance under variable loads and diverse modulation ***,the total harmonic distortion achieved with phase opposition disposi

关键词： asymmetrical multilevel inverter photovoltaic system renewable energy component count per level cost function per level total standing voltage per unit phase opposition disposition technique

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Data-driven strategy for state of health prediction and anomaly detection in lithium-ion batteries

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Energy and AI 2024年第3期17卷 419-431页

作者： Slimane Arbaoui Ahmed Samet ali Ayadi Tedjani Mesbahi Romuald Boné SDC team ICube laboratoryINSA StrasbourgUniversity of Strasbourg24 Bd de la VictoireStrasbourg67000France SMH team ICube laboratoryINSA StrasbourgUniversity of Strasbourg24 Bd de la VictoireStrasbourg67000France

This study addresses the crucial challenge of monitoring the State of Health(SOH)of Lithium-Ion Batteries(LIBs)in response to the escalating demand for renewable energy systems and the imperative to reduce CO2 *** research introduces deep learning(DL)models,namely Encoder-Long Short-Term Memory(E-LSTM)and Convolutional Neural Network-LSTM(CNN–LSTM),each designed to forecast battery SOH.E-LSTM integrates an encoder for dimensionality reduction and an LSTM model to capture data ***–LSTM,on the other hand,employs CNN layers for encoding followed by LSTM layers for precise SOH ***,we prioritize model explainability by employing a game-theoretic approach known as SHapley Additive exPlanations(SHAP)to elucidate the output of our ***,a method based on pattern mining was developed,synergizing with the model,to identify patterns contributing to abnormal SOH *** insights are presented through informative *** proposed approach relies on the battery dataset from the Massachusetts Institute of Technology(MIT)and showcases promising results in accurately estimating SOH values,in which the E-LSTM model outperformed the CNN–LSTM model with a Mean Absolute Error(MAE)of less than 1%.

关键词： Lithium-ion batteries State of health LSTM CNN Auto-encoders .Pattern mining Explainable artificial intelligence

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Multiperiod optimization of cooling water system with flexible topology network

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Green Chemical Engineering 2024年第4期5卷 461-472页

作者： Bo Liu Ruimei Zhang Ye Liu Yufei Wang ali Almansoori Xiao Feng School of Chemical Engineering and Environment China University of Petroleum Department of Chemical Engineering Khalifa University of Science and Technology CHN Energy Xinjiang Chemical Co.LTD School of Chemical Engineering & Technology Xi'an Jiaotong University

Cooling water systems(CWSs) are extensively utilized in various industries to eliminate the excess heat and converse energy. Studies on CWSs mainly concentrated on finding the optimal cooler network structure. In addition, some works also considered the optimal design under varied operation conditions. However, in these works, once the optimal design of the cooler's network is determined, its arrangement remains fixed and cannot be adapted to accommodate diverse operating conditions. In this work, a flexible topology network concept is proposed to make the adjustment of network structure possible under different operation conditions. The CWS with integrated air cooler and flexible topology network has better overall performance, represented by a mixed integer nonlinear programming(MINLP) model that require advanced tools such as GAMS software. Case studies revealed that the proposed methodology can realize better energy-saving performance, and improve the economic performance under varied operation conditions. The impact of critical flexible nodes on system configuration and economy is achieved by sensitivity analysis.

关键词： Cooling water systems Flexible network Multiperiod optimization Air cooler

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Testing the LCC hypothesis by considering environmental sustainability and economic development:Role of green energy and resource management

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Geoscience Frontiers 2024年第3期15卷 419-430页

作者： Sunil Tiwari Syed ali Raza Shiv Kumar Gupta Irum Shahzadi Mahendra Babu Kuruva Department of Tourism Studies School of Business StudiesCentral University of KeralaIndia Department of Business Administration IQRA UniversityKarachi 75300Pakistan Centre for Mountain Tourism and Hospitality Studies HNB Garhwal Central UniversitySrinagarUttarakhand 246174India Department of Production Engineering São Paulo State University(UNESP)BauruBrazil Department of Business Administration IQRA University Karachi-75300Pakistan Department of Business Management HNB Garhwal Central UniversitySrinagar GarhwalUttarakhand 246174India

This study aims to demystify the role of green energy and green technology in establishing the nexus between behavioural intentions of tourists,technologies,and digital payments by using Perceived value(PV),Compatibility(CO),Perceived Enjoyment(PE),and Social Influence(SI)as a predictor variables,Trust(TR)and Satisfaction(SA)as a mediating variables and Behavioural Intentions(BI)as an outcome *** the empirical estimation,we employ smart PLS-SEM,TAM(Technology Acceptance Model)and SPSS and Tested the LCC *** findings suggest that green energy and perceived value have the highest positive impact on tourists’trust towards digital payments followed by compatibility,social influence and perceived ***,tourists’satisfaction and green technology is one of the important determinants of choosing any digital mode of payment,is mostly influence by perceived value,perceived enjoyment,compatibility and social ***,if we choose between trust and satisfaction,trust plays a significant role in exploring the behavioural intentions of tourists about green energy and green technology followed by tourists’*** addition,Tourists’trust and satisfaction are highly correlated and influence each *** study offers novel policy implications in terms of use of green technology and green energy in enhancing trust and satisfaction of tourists in order to deeper understanding of different dimensions of digital payments and M-wallets,and allowing them to explore the long-term value inherent of digital payments and M-wallets.

关键词： Digital payment Green technology Green energy Tourism LCC hypothesis

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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Computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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