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Journal of Ocean Engineering and Science 2024年第2期9卷 112-125页

作者： M.Higazy Norah almalki Shabbir Muhammad a.al-Ghamdi Department of Mathematics and Statistics College of ScienceTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Physics and Engineering Mathematics Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Research Center for Advanced Materials Science(RCAMS) King Khalid UniversityP.O.Box 9004Abha 61413Saudi Arabia Department of Chemistry College of ScienceKing Khalid UniversityP.O.Box 9004Abha 61413Saudi Arabia Department of Physics Faculty of ScienceKing Abdulaziz UniversityP.O.Box 80203Jeddah 21589Saudi Arabia

A new fractional 6D chaotic model is constructed in this paper.The new fractional 6D chaotic model has six positive parameters plus the fractional order with eight nonlinear terms.The complicated chaotic dy-namics of the new fractional 6D model is presented and a.alyzed.The basic properties of this model are studied and its chaotic attractors,dissipative feature,symmetry,equilibrium points,Lyapunov Exponents are investigated.The new dynamics of the 6D fractional model is numerically simulated using Matlab software.In addition,utilizing the graph theory tools certain structural characteristics are calculated.An electrical circuit is built to implement the new 5.4 fractional order 6D model.Finally,an active fractional order controller is proposed to control the new model at different fractional orders.The chaos of the new model is very useful and can be used to produce random keys for data encryption.

关键词： Chaotic systems Fractional calculus Lyapunov exponents Chaotic attractors Adjacency matrix Hermitian matrix Electronic circuit realization

Findings on three endocommensal scuticociliates(Protista,Ciliophora)from freshwater mollusks,including their morphology and molecular phylogeny with descriptions of two new species

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Marine Life Science & Technology 2024年第2期6卷 212-235页

作者： Tao Li Tengyue Zhang Mingjian Liu Zhe Zhang Jiachen Zhang Junhua Niu Xiangrui Chen Saleh a.al‑Farraj Weibo Song Institute of Evolution and Marine Biodiversity Ocean University of China and Key Laboratory of Evolution and Marine Biodiversity(Ministry of Education)Qingdao 266003China The Key Laboratory of Zoological Systematics and Application College of Life SciencesHebei UniversityBaoding 071002China College of Marine Life Sciences Ocean University of ChinaQingdao 266003China School of Marine Sciences Ningbo UniversityNingbo 315800China Zoology Department College of ScienceKing Saud University11451 RiyadhSaudi Arabia Laboratory for Marine Biology and Biotechnology Laoshan LaboratoryQingdao 266237China

Species of the ciliate genera Myxophyllum and Conchophthirus are found as endocommensals of terrestrial and freshwater mollusks,respectively.So far,there have been few studies of these genera and morphological data for most members are often incomplete.In the present work,two new species,Myxophyllum weishanense sp.***.and Conchophthirus paracurtus sp.nov.,and a known species,Conchophthirus lamellidens,were isolated from hosts in Lake Weishan Wetland,China.Taxonomic studies indicate that M.weishanense sp.***.can be recognized mainly by the combination of about 60 somatic kineties on both ventral and dorsal sides and the presence of caudal cilia.Conchophthirus paracurtus sp.***.differs from congeners in its body shape and size,having a glabrous area on the posterior right side,and having fewer somatic kineties.In addition,differences in their ITS2(Internally Transcribed Spacer 2)secondary structures support the discrimination of the two new species from their highly similar congeners.An improved diagnosis for the poorly known species,C.lamel-lidens is also provided.Phylogenetic a.alyses reveal that members of the genus Myxophyllum belong to a fully supported clade that is sister to a large,poorly supported clade consisting of Hemispeiridae,Ancistridae,and several lineages of the nonmonophyletic Cyclidiidae.The Myxophyllum clade also includes Protophyra ovicola JQ956552,a possible misidenti-fication.Sequences of the two new Conchophthirus species cluster with other congeners in a fully supported clade that is unrelated to either the‘typical’thigmotrichs or to pleuronematids,thus conflicting with the traditional classification,and may represent an orphan scuticociliate lineage.

关键词： Ciliates Endocommensal ITS2 secondary structure Mollusks New taxa Wetland

深呼吸练习对冠状动脉旁路移植术后胸腔引流管拔除疼痛水平的影响

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International Journal of Nursing Sciences 2022年第2期9卷 155-161,I0003页

作者： Mohamad I.Jarrah Issa M.Hweidi sirin a.al-dolat Hossam N.alhawatmeh Salwa M.al-Obeisat Lama I.Hweidi Aysam I.Hweidi Osama a.alkouri Faculty of Medicine Jordan University of Science&TechnologyIrbidJordan Faculty of Nursing Jordan University of Science&TechnologyIrbidJordan Faculty of Nursing Al-Ahliyya Amman UniversityAmmanJordan

目的本研究旨在评价缓慢深呼吸放松练习(slow deep breathing relaxation exercise,SDBRE)在冠状动脉旁路移植术(coronary artery bypass grafting,CABG)后患者胸腔引流管拔除(chest tube removal,CTR)中减轻疼痛水平的有效性。方法2019年便利选取约旦心脏转诊机构的重症监护室中50例CABG患者,将其随机分为干预组(25例)和对照组(25例)。干预组患者在CTR前,在常规护理的基础上同时接受SDBRE训练。对照组患者在常规护理措施后进行CTR。在CTR前(Time 1)、CTR后5 min(Time 2)和CTR后15 min(Time 3),采用视觉模拟量表(Visual a.alogue Scale,VAS)来测量患者疼痛水平,以比较两组之间的干预效果。结果数据显示,两组患者的疼痛水平随着时间变化均有下降趋势(H=32.71,P<0.01;H=47.23,P<0.01)。干预组患者疼痛水平在Time 2[3.50(1.15,5.55)比7.90(6.80,9.00),P<0.01]、Time 3[0.00(0.00,1.35)比3.60(2.20,4.25),P<0.01]时均低于对照组。结论在CABG术后胸腔引流管拔除前应用SDBRE,是一种有效减轻疼痛的技术,可以减少对镇痛药的需求及其相关的不良反应。

关键词：呼吸运动胸腔引流管拔除冠状动脉搭桥术疼痛患者约旦

Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

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Computers, Materials & Continua 2024年第3期78卷 4225-4241页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saad Arif Sara Rehman Muhammad Farrukh Qureshi Nagwan Abdel Samee Maali alabdulhafith Yeong Hyeon Gu Mohammed a.al-masni Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial Intelligence College of Software and Convergence TechnologySejong UniversitySeoul05006Korea

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland.Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care.This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques.Sequential forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this study.In ensemble learning,random forest,adaptive boosting,and bagging classifiers are employed.The effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature reduction.Extensive experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,respectively.Beyond enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical applications.It significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed scheme.This research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

关键词： Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis

Quantitative and Qualitative Responses of Hydroponic Tomato Production to Different Levels of Salinity

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Phyton-International Journal of Experimental Botany 2024年第6期93卷 1311-1323页

作者： Khalid a.al-Gaadi Ahmed M.Zeyada ElKamil Tola Abdullah M.alhamdan Khalid A.M.Ahmed Rangaswamy Madugundu Mohamed K.Edrris Department of Agricultural Engineering College of Food and Agriculture SciencesKing Saud UniversityRiyadh11451Saudi Arabia Precision Agriculture Research Chair Deanship of Scientific ResearchKing Saud UniversityRiyadh11451Saudi Arabia Chair of Dates Industry&Technology Deanship of Scientific ResearchKing Saud UniversityRiyadh11451Saudi Arabia Agricultural Research Centre Agricultural Engineering Research Institute(AEnRI)Giza12619Egypt

From economic and nutritional points of view,tomato is,historically,considered one of the most important crops.Without signiﬁcant yield reduction,most commercial cultivars of tomato crops are sensitive to moderate levels of salinity.However,high levels of salt stress can negatively affect the yield and quality of tomato fruits.Therefore,this study was conducted to evaluate the yield and fruit quality of three tomato cultivars(Forester,Ghandowra-F1,and Feisty-Red)cultivated hydroponically,under three different levels of nutrient solution salinity.Evaluation of tomato fruits was performed based on quantity(number and weight of fruits,and total fruit yield),physical quality(color andﬁrmness),and chemical quality(refractometric index“Brix”,pH,EC,K^(+),Na^(+),and NO_(3)-).Experiments were conducted using three salinity levels of the nutrient solution with electrical conductivity values(dSm^(-1))of 2.5(control),6.0,and 9.5.Results showed that the studied tomato cultivars were signiﬁcantly inﬂuenced by high salinity(9.5 dSm^(-1))in comparison to the low(2.5 dSm^(-1))and medium(6.0 dSm^(-1))levels of salinity.On average,the highest fruit weight per plant of 1944.84 g and total fruit yield of 4.42 kgm^(-2) were observed at the low salinity level;however,no signiﬁcant differences were obtained in the two yield factors(single fruit weight and total fruit yield)for the low and medium salinity levels.On the other hand,a signiﬁcant reduction in tomato yield(31%)was associated with the high salinity level compared to the yield at low and medium salinity levels.Results of physical quality parameters showed highly signiﬁcant differences among all salinity levels.On average,the maximum value of color change(1.72)was associated with the medium salinity level,and the maximum value ofﬁrmness(9.61 Ncm^(-1))was recorded at the high salinity level.Salinity levels and tomato cultivars introduced signiﬁcant differences in chemical quality parameters;however,no signiﬁcant differences in these parameters were attributed to the low and mediu

关键词： Quality cultivars yield color ﬁrmness brix

Malware Attacks Detection in IoT Using Recurrent Neural Network(RNN)

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Intelligent Automation & Soft Computing 2024年第2期39卷 135-155页

作者： Abeer Abdullah alsadhan Abdullah a.al-Atawi Hanen karamti Abid Jameel Islam Zada Tan N.Nguyen Computer Science Department Imam Abdulrahman Bin Faisal UniversityDamam32232Saudi Arabia Department of Computer Science Applied CollegeUniversity of TabukTabuk47512Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences and Information Technology Hazara UniversityMansehraPakistan Faculty of Computing Department of Software EngineeringInternational Islamic UniversityIslamabadPakistan Department of Architectural Engineering Sejong UniversitySeoul05006Korea

IoT(Internet of Things)devices are being used more and more in a variety of businesses and for a variety of tasks,such as environmental data collection in both civilian and military situations.They are a desirable attack target for malware intended to infect specific IoT devices due to their growing use in a variety of applications and their increasing computational and processing power.In this study,we investigate the possibility of detecting IoT malware using recurrent neural networks(RNNs).RNNis used in the proposed method to investigate the execution operation codes of ARM-based Internet of Things apps(OpCodes).To train our algorithms,we employ a dataset of IoT applications that includes 281 malicious and 270 benign pieces of software.The trained model is then put to the test using 100 brand-new IoT malware samples across three separate LSTM settings.Model exposure was not previously conducted on these samples.Detecting newly crafted malware samples with 2-layer neurons had the highest accuracy(98.18%)in the 10-fold cross validation experiment.A comparison of the LSTMtechnique to other machine learning classifiers shows that it yields the best results.

关键词： Malware malicious code code obfuscation IoT machine learning deep learning

Spatio-temporal coupled mode theory for nonlocal metasurfaces

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Light(Science & Applications) 2024年第2期13卷 267-282页

作者： Adam Overvig Sander A.Mann Andrea.alù Photonics Initiative Advanced Science Research CenterCity University of New YorkNew YorkNY10031USA Physics Program Graduate Center of the City University of New YorkNew YorkNY10016USA

Diffractive nonlocal metasurfaces have recently opened a broad range of exciting developments in nanophotonics research and applications,leveraging spatially extended—yet locally patterned—resonant modes to control light with new degrees of freedom.While conventional grating responses are elegantly captured by temporal coupled mode theory,current approaches are not well equipped to capture the arbitrary spatial response observed in the nascent field of nonlocal metasurfaces.Here,we introduce spatio-temporal coupled mode theory(STCMT),capable of elegantly capturing the key features of the resonant response of wavefront-shaping nonlocal metasurfaces.This framework can quantitatively guide nonlocal metasurface design while maintaining compatibility with local metasurface frameworks,making it a powerful tool to rationally design and optimize a broad class of ultrathin optical components.We validate this STCMT framework against full-wave simulations of various nonlocal metasurfaces,demonstrating that this tool offers a powerful semi-a.alytical framework to understand and model the physics and functionality of these devices,without the need for computationally intense full-wave simulations.We also discuss how this model may shed physical insights into nonlocal phenomena in photonics and the functionality of the resulting devices.As a relevant example,we showcase STCMT’s flexibility by applying it to study and rapidly prototype nonlocal metasurfaces that spatially shape thermal emission.

关键词： theory resonant spatially

RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

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Computers, Materials & Continua 2024年第5期79卷 3175-3192页

作者： Farah Mohammad Saad al-Ahmadi Ja.al al-Muhtadi Center of Excellence in Information Assurance(CoEIA) King Saud UniversityRiyadh11543Saudi Arabia College of Computer&Information Sciences King Saud UniversityRiyadh11543Saudi Arabia

Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by consumers.The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning techniques.Moreover,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive hardware.Computer-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor performance.In this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as a.alicious and non-malicious activity.The key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft detection.Different advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for classification.The effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification results.The performance evaluation of the proposed model revealed exemplary results across variousmetrics.The accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.

关键词： Electricity theft smart grid RoBERTa GRU transfer learning

Diagnostic accuracy of ePOS score in predicting DNR labeling after ICU admission:A prospective observational study(ePOS-DNR)

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Journal of Intensive Medicine 2024年第2期4卷 216-221页

作者： Omar E.Ramadan Ahmed F.Mady Mohammed a.al-Odat Ahmed N.Balshi Ahmed W.aletreby Taisy J.Stephen Sheena R.Diolaso Jennifer Q.Gano Waleed Th.aletreby Department of Critical Care King Saud Medical CityRiyadhSaudi Arabia Anesthesia Department Faculty of MedicineAin Shams UniversityCairoEgypt Anesthesia Department Faculty of MedicineTanta UniversityTantaEgypt Faculty of Medicine Alexandria UniversityAlexandriaEgypt Nursing Department King Saud Medical CityRiyadhSaudi Arabia

Background Resuscitation can sometimes be futile and making a do-not-resuscitate(DNR)decision is in the best interest of the patient.The electronic poor outcome screening(ePOS)score was developed to predict 6-month poor outcomes of critically ill patients.We explored the diagnostic accuracy of the ePOS score in predicting DNR decisions in the intensive care unit(ICU).Methods This study was conducted at the ICU of a tertiary referral hospital in Saudi Arabia between March and May 2023.Prospectively,we calculated ePOS scores for all eligible consecutive admissions after 48 h in the ICU and recorded the DNR orders.The ability of the score to predict DNR was explored using logistic regression.Youden's ideal cut-off value was calculated using the DeLong method,and different diagnostic accuracy measures were generated with corresponding 95%confidence intervals(CIs).Results We enrolled 857 patients,125 received a DNR order and 732 did not.The average ePOS score of DNR and non-DNR patients was 28.2±10.7 and 15.2±9.7,respectively.ePOS score,as a predictor of DNR order,had an area under receiver operator characteristic(AUROC)curve of 81.8%(95%CI:79.0 to 84.3,Pal cut-off value>17 was associated with a sensitivity of 87.2(95%CI:80.0 to 92.5,Palue of 29.2(95%CI:24.6 to 33.8,Palue of 96.7(95%CI:95.1 to 98.3,P17 may help guide clinical decisions to withhold or commence resuscitative measures.

关键词： Do-not-resuscitate(DNR) Resuscitation Intensive care unit(ICU) Diagnostic accuracy Sensitivity Specificity

Glucokinase regulatory protein rs780094 polymorphism is associated with type 2 diabetes mellitus, dyslipidemia, non-alcoholic fatty liver disease, and nephropathy

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World Journal of Diabetes 2024年第5期15卷 814-817页

作者： Ashraf al Madhoun Department of Genetics and Bioinformatics Dasman Diabetes InstituteDasman 15400Kuwait

In this editorial,we comment on the article by Liu et al published in the recent issue of the World Journal of Diabetes(Relationship between GCKR gene rs780094 polymorphism and type 2 diabetes with albuminuria).Type 2 diabetes mellitus(T2DM)is a chronic disorder characterized by dysregulated glucose homeostasis.The persistent elevated blood glucose level in T2DM significantly increases the risk of developing severe complications,including cardiovascular disease,re-tinopathy,neuropathy,and nephropathy.T2DM arises from a complex interplay between genetic,epigenetic,and environmental factors.Global genomic studies have identified numerous genetic variations associated with an increased risk of T2DM.Specifically,variations within the glucokinase regulatory protein(GCKR)gene have been linked to heightened susceptibility to T2DM and its associated complications.The clinical trial by Liu et al further elucidates the role of the GCKR rs780094 polymorphism in T2DM and nephropathy development.Their findings demonstrate that individuals carrying the CT or TT genotype at the GCKR rs780094 locus are at a higher risk of developing T2DM with albuminuria compared to those with the CC genotype.These findings highlight the importance of genetic testing and risk assessment in T2DM to develop effective preventive strategies and personalized treatment plans.

关键词： Glucokinase regulatory protein rs780094 Type 2 diabetes mellitus Dyslipidemia Non-alcoholic fatty liver disease Nephropathy