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Re-analysis of temperature dependent neutron capture rates and stellarβ-decay rates of^(95-98)Mo

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Chinese Physics C 2024年第9期48卷 142-149页

作者： Abdul Kabir Jameel-Un Nabi muhammad Tahir Abdul Muneem zain Ul Abideen Space and Astrophysics Research lab National Centre of GIS and Space ApplicationsDepartment of Space ScienceInstitute of Space TechnologyIslamabad 44000Pakistan University of Wah Quaid AvenueWah Cantt 47040PunjabPakistan Faculty of Engineering Sciences GIK Institute of Engineering Sciences and TechnologyTopi 23640Khyber PakhtunkhwaPakistan

The neutron capture rates and temperature dependent stellar beta decay rates of Mo isotopes are investigated within the framework of the statistical code TALYS v1.96 and the proton neutron quasi particle random phase approximation(pn-QRPA)*** Maxwellian average cross-section(MACS)and neutron capture rates for the^(95-98)Mo(n,γ)^(96-99)Mo radiative capture process are analyzed within the framework of the statistical code TALYS v1.96 based on the phenomenological nuclear level density model and gamma strength *** present model-based computations for the MACS are comparable to the existing measured *** sensitivity of stellar weak interaction rates to various densities and temperatures is investigated within the framework of the pn-QRPA *** attention is paid to the impact of thermally filled excited states in the decaying nuclei(^(95-98)Mo)on electron emission and positron capture ***,we compare the neutron capture rates and stellar beta decay *** is found that neutron capture rates are higher than stellar beta decay rates at both lower and higher temperatures.

关键词： cross-section TALYS nuclear level density decay rates capture rates

Sepsis Prediction Using CNNBDLSTM and Temporal Derivatives Feature Extraction in the IoT Medical Environment

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Computers, Materials & Continua 2024年第4期79卷 1157-1185页

作者： Sapiah Sakri Shakila Basheer Zuhaira muhammad zain Nurul Halimatul Asmak Ismail Dua’Abdellatef Nassar Manal Abdullah Alohali Mais Ayman Alharaki Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Kingdom of Saudi Arabia Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityRiyadh11671Kingdom of Saudi Arabia

Background:Sepsis,a potentially fatal inflammatory disease triggered by infection,carries significant healthimplications *** detection is crucial as sepsis can rapidly escalate if left *** in deep learning(DL)offer powerful tools to address this ***:Thus,this study proposeda hybrid CNNBDLSTM,a combination of a convolutional neural network(CNN)with a bi-directional long shorttermmemory(BDLSTM)model to predict sepsis *** the proposed model provides a robustframework that capitalizes on the complementary strengths of both architectures,resulting in more accurate andtimelier ***:The sepsis prediction method proposed here utilizes temporal feature extraction todelineate six distinct time frames before the onset of *** time frames adhere to the sepsis-3 standardrequirement,which incorporates 12-h observation windows preceding sepsis *** models were trained usingthe Medical Information Mart for Intensive Care III(MIMIC-III)dataset,which sourced 61,522 patients with 40clinical variables obtained from the IoT medical *** confusion matrix,the area under the receiveroperating characteristic curve(AUCROC)curve,the accuracy,the precision,the F1-score,and the recall weredeployed to evaluate ***:The CNNBDLSTMmodel demonstrated superior performance comparedto the benchmark and other models,achieving an AUCROC of 99.74%and an accuracy of 99.15%one hour beforesepsis *** results indicate that the CNNBDLSTM model is highly effective in predicting sepsis onset,particularly within a close proximity of one ***:The results could assist practitioners in increasingthe potential survival of the patient one hour before sepsis onset.

关键词： Temporal derivatives hybrid deep learning predicting sepsis onset MIMIC III machine learning(ML) deep learning

Quality Assessment of Online Patient Education Materials for Diabetic Foot

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Journal of Biosciences and Medicines 2024年第9期12卷 28-41页

作者： Dana Hendie Eyman Haj-Ali muhammad Candragupta Jihwaprani Hagar Khaled Marah Alrahal Faisal zain Mohammed Al-Zabidi Nazmus Saquib Juliann Saquib College of Medicine Sulaiman Al Rajhi University Bukaryiah Saudi Arabia

Background: The quality of online Arabic educational materials for diabetic foot syndrome (DFS) is unknown. This study evaluated Arabic websites as patients’ sources of information for DFS. Methods: The study assessed patient-related websites about DFS using a modified Ensuring Quality of Information for Patients (EQIP) tool (score 0 - 35). Specific terms were searched in Google to identify DFS websites;eligibility criteria were applied to 20 pages of search results to select the included websites. Data on country of origin, source types and subtypes, and website traffic were extracted. Additional therapeutic information regarding prevention and conservative, pharmacological, and surgical treatments was also recorded and analyzed. Results: Among 559 websites, 157 were eligible for inclusion. The median EQIP score was 16 out of 35, indicating poor quality in one of three domains (content, identification, or structure). Most sources originated from Arab countries (75.8%) were non-governmental (94.9%), and were medical information websites (46.5%). High-scoring websites were significantly more likely than low-scoring websites to describe information on prevention (30.9% vs. 2.9%, p = 0.001), conservative treatment (34.1% vs. 13%, p = 0.002), or pharmacological treatment (32.5% vs. 16.8%, p = 0.024). There were increased odds of scoring high if a website provided information on prevention (OR = 12.9, 95% CI [1.68 - 98.57], p = 0.014). Conclusion: Most Arabic online patient information on DFS is of poor quality. Quality control measures are needed to ensure accurate health information for the public.

关键词： Diabetic Foot Disease Diabetic Foot Ulcer Online Resources Patient Education Patient Education Materials

Determination of the best materials for development and designing product using a multi-criteria decision-making

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Railway Sciences 2024年第5期3卷 541-557页

作者： Rabia Hassan Zeeshan Ahmad Arfeen Mehreen Kausar Azam zain ul Abiden Akhtar Abubakar Siddique Department of Management Economicsand Industrial Engineering DIGPolitecnico di MilanoMilanItaly Department of Electrical Engineering College of Engineering and SciencesInstitute of Business Management SindhKarachiPakistan Department of Electrical Engineering The Islamia University of Bahawalpur PakistanBahawalpurPakistan Department of Industrial Manufacturing NED University of Engineering and TechnologyKarachiPakistan Department of Information and Communication Engineering The Islamia University of Bahawalpur PakistanBahawalpurPakistan Department of Electrical Engineering Khwaja Fareed University of Engineering and Information TechnologyRahim Yar KhanPakistan muhammad Rashid Department of Electrical Engineering Faculty of ScienceThe Islamia University of Bahawalpur PakistanBahawalpurPakistan

Purpose–Material selection,driven by wide and often conflicting objectives,is an important,sometimes difficult problem in material *** this context,multi-criteria decision-making(MCDM)methodologies are *** approach of MCDM is needed to cater to criteria of material assortment *** firms are now concerned about increasing their productivity using mathematical *** occupy a gap in the previous literature this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of *** addition,by using the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS),the inherent ambiguities of decision-makers in paired evaluations are considered in this *** goes on to construct a mathematical bi-objective model for determining the best item to ***/methodology/approach–The entropy perspective is implemented in this paper to evaluate the weight parameters,while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust *** intermediate pipes are used to join the components of the exhaust *** materials usually used to manufacture intermediate pipe are SUS 436LM,SUS 430,SUS 304,SUS 436L,SUH 409 L,SUS 441 L and SUS *** seven materials are evaluated based on tensile strength(TS),hardness(H),elongation(E),yield strength(YS)and cost(C).A hybrid methodology combining entropy-based criteria weighting,with the TOPSIS for alternative ranking,is pursued to identify the optimal design material for an engineered application in this *** study aims to help while filling the information gap in selecting the most suitable material for use in the exhaust intermediate *** that,the authors searched for and considered eight materials and evaluated them on the following five criteria:(1)TS,(2)YS,(3)H,(4)E and(5)*** first two criteria have been chosen because they can have a lot of influence on the behav

关键词： TOPSIS Multi-criteria decision-making Entropy method Material selection

Deep Learning-Based Robust DC Fault Protection Scheme for Meshed HVDC Grids

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CSEE Journal of Power and Energy Systems 2023年第6期9卷 2423-2434页

作者： muhammad zain Yousaf Hui Liu Ali Raza Ali Mustafa the School of Electrical Engineering Guangxi UniversityNanning 530004China the School of Electrical Engineering University of LahoreLahore 39161Pakistan the School of Electrical Engineering Xi’an Jiaotong UniversityXi’an 710049China

Fast and reliable detection of faults is a significant technical challenge in transient-based protection for a modular multi-level converter(MMC)based high voltage direct current(HVDC)*** is because existing protection schemes rely heavily upon setting a complicated protective threshold,the failure of which causes high DC-fault currents in HVDC grids,and MMC is prone to such strong transient *** this context,this paper proposes a DC-line fault diagnosis technique based on a tuned long-short-term memory(LSTM)algorithm to improve the response and accuracy of transient-based *** discrete wavelet transform(DWT)extracts the transient features of DC-line voltages in the frequency-time *** healthy and faulty samples are incorporated during training even by considering the noise *** training,numerous test samples are run to evaluate the proposed algorithm’s robustness under various fault *** results show the proposed algorithm can detect DC faults and has a high recognition accuracy of 98.6%.Compared to contemporary techniques,it can perform well to identify DC-line faults because of the efficient training of characteristic features.

关键词： High voltage direct current(HVDC) longshort-term-memory(LSTM) primary protection voltage source converters(VSC)

Multi-omics insights into neuronal regeneration and re-innervation

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Neural Regeneration Research 2021年第2期16卷 296-297页

作者： muhammad zain Chauhan Sanjoy K.Bhattacharya University of Arkansas for Medical Sciences(UAMS) Little RockARUSA(Chauhan MZ)Miami Integrative Metabolomics Research CenterBascom Palmer Eye Institute Program In Biomedical Sciences and Neuroscience Graduate Program University of MiamiMiamiFLUSA

The regeneration of peripheral nervous system and central nervous system(CNS)neurons after injury remains *** have come a long way since the identification of a 37 k Da protein specific for regenerating peripheral ner... 详细信息

关键词： al. nerves insight

Neural Machine Translation Models with Attention-Based Dropout Layer

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Computers, Materials & Continua 2023年第5期75卷 2981-3009页

作者： Huma Israr Safdar Abbas Khan muhammad Ali Tahir muhammad Khuram Shahzad Muneer Ahmad Jasni Mohamad zain School of Electrical Engineering and Computer Science(SEECS) National University of Science and TechnologyIslamabadPakistan Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARA40450Shah AlamSelangorMalaysia

In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of *** model has obtained state-of-the-art performance for several language ***,there has been little work exploring useful architectures for Urdu-to-English machine *** conducted extensive Urdu-to-English translation experiments using Long short-term memory(LSTM)/Bidirectional recurrent neural networks(Bi-RNN)/Statistical recurrent unit(SRU)/Gated recurrent unit(GRU)/Convolutional neural network(CNN)and *** results show that Bi-RNN and LSTM with attention mechanism trained iteratively,with a scalable data set,make precise predictions on unseen *** trained models yielded competitive results by achieving 62.6%and 61%accuracy and 49.67 and 47.14 BLEU scores,*** a qualitative perspective,the translation of the test sets was examined manually,and it was observed that trained models tend to produce repetitive output more *** attention score produced by Bi-RNN and LSTM produced clear alignment,while GRU showed incorrect translation for words,poor alignment and lack of a clear ***,we considered refining the attention-based models by defining an additional attention-based dropout *** dropout fixes alignment errors and minimizes translation errors at the word *** empirical demonstration and comparison with their counterparts,we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation *** ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as *** empirically concluded that adding an attention-based dropout layer helps improve GRU,SRU,and Transformer translation and is considerably more efficient in translation quality and speed.

关键词： Natural language processing neural machine translation word embedding attention perplexity selective dropout regularization Urdu Persian Arabic BLEU

LexDeep:Hybrid Lexicon and Deep Learning Sentiment Analysis Using Twitter for Unemployment-Related Discussions During COVID-19

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计算机、材料和连续体（英文） 2023年第4期75卷 1577-1601页

作者： Azlinah Mohamed Zuhaira muhammad zain Hadil Shaiba Nazik Alturki Ghadah Aldehim Sapiah Sakri Saiful Farik Mat Yatin Jasni Mohamad zain Institute for Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA(UiTM)Shah AlamMalaysia College of Computer and Information Sciences Princess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA(UiTM)Shah AlamMalaysia

The COVID-19 pandemic has spread globally,resulting in financialinstability in many countries and reductions in the per capita grossdomestic *** analysis is a cost-effective method for acquiringsentiments based on household income loss,as expressed on social ***,limited research has been conducted in this domain using theLexDeep *** study aimed to explore social trend analytics usingLexDeep,which is a hybrid sentiment analysis technique,on Twitter to capturethe risk of household income loss during the COVID-19 ***,tweet data were collected using Twint with relevant keywords before(9 March2019 to 17 March 2020)and during(18 March 2020 to 21 August 2021)***,the tweets were annotated using VADER(lexiconbased)and fed into deep learning classifiers,and experiments were conductedusing several embeddings,namely simple embedding,Global Vectors,andWord2Vec,to classify the sentiments expressed in the *** performanceof each LexDeep model was evaluated and compared with that of a supportvector machine(SVM).Finally,the unemployment rates before and duringCOVID-19 were analysed to gain insights into the differences in unemploymentpercentages through social media input and *** resultsdemonstrated that all LexDeep models with simple embedding outperformedthe *** confirmed the superiority of the proposed LexDeep modelover a classical machine learning classifier in performing sentiment analysistasks for domain-specific *** terms of the risk of income loss,the unemployment issue is highly politicised on both the regional and globalscales;thus,if a country cannot combat this issue,the global economy will alsobe *** research should develop a utility maximisation algorithmfor household welfare evaluation,given the percentage risk of income lossowing to COVID-19.

关键词： Sentiment analysis sentiment lexicon machine learning imbalanced data deep learning method unemployment rate

An Adaptive Edge Detection Algorithm for Weed Image Analysis

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Computer Systems Science & Engineering 2023年第12期47卷 3011-3031页

作者： Yousef Alhwaiti muhammad Hameed siddiqi Irshad Ahmad College of Computer and Information Sciences Jouf UniversitySakakaAljouf73211Saudi Arabia Department of Computer Science Islamia College(Chartered University)Peshawar25000Pakistan

Weeds are one of the utmost damaging agricultural annoyers that have a major influence on *** have the responsibility to get higher production costs due to the waste of crops and also have a major influence on the worldwide agricultural *** significance of such concern got motivation in the research community to explore the usage of technology for the detection of weeds at early stages that support farmers in agricultural *** weed methods have been proposed for these fields;however,these algorithms still have challenges as they were implemented against controlled ***,in this paper,a weed image analysis approach has been proposed for the system of weed *** this system,for preprocessing,a Homomorphic filter is exploited to diminish the environmental ***,for feature extraction,an adaptive feature extraction method is proposed that exploited edge *** proposed technique estimates the directions of the edges while accounting for non-maximum *** method has several benefits,including its ease of use and ability to extend to other types of ***,low-level details in the formof features are extracted to identify weeds,and additional techniques for detecting cultured weeds are utilized if *** the processing of weed images,certain edges may be verified as a footstep function,and our technique may outperform other operators such as gradient *** relevant details are extracted to generate a feature vector that is further given to a classifier for weed ***,the features have been used in logistic regression for weed *** model was assessed against logistic regression that accurately identified different kinds of weed images in naturalistic *** proposed approach attained weighted average recognition of 98.5%against the weed images ***,it is assumed that the proposed approach might help in the weed classification s

关键词： Weeds images classification enhancement logistic regression agricultural fields remote sensing