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The deterministic condition for the ground reaction force acting point on the combined knee valgus and tibial internal rotation moments in early phase of cutting maneuvers in female athletes

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Journal of Sport and Health Science 2024年第3期13卷 376-386页

作者： Issei Ogasawara Ken Ohta Gajanan S.Revankar Shoji Konda Yohei Shimokochi Hideyuki Koga Ken Nakata department of Health and Sport Sciences Graduate School of MedicineOsaka UniversityToyonaka 560-0043Japan Sports Sensing Co. LTD.Fukuoka 815-0035Japan department of Neurology Graduate School of MedicineOsaka UniversitySuita 565-0871Japan department of Health and Sport management Osaka University of Health and Sport SciencesSennan-gun 590-0496Japan department of Joint Surgery and Sports Medicine Graduate School of Medical and Dental SciencesTokyo Medical and Dental UniversityBunkyo-ku 113-8519Japan

Background:Combined knee valgus and tibial internal rotation(VL+IR)moments have been shown to stress the anterior cruciate ligament(ACL)in several in vitro cadaveric studies.To utilize this knowledge for non-contact ACL injury prevention in sports,it is necessary to elucidate how the ground reaction force(GRF)acting point(center of pressure(CoP))in the stance foot produces combined knee VL+IR moments in risky maneuvers,such as cuttings.However,the effects of the GRF acting point on the development of the combined knee VL+IR moment in cutting are still unknown.Methods:We first established the deterministic mechanical condition that the CoP position relative to the tibial rotational axis differentiates the GRF vector’s directional probability for developing the combined knee VL+IR moment,and theoretically predicted that when the CoP is posterior to the tibial rotational axis,the GRF vector is more likely to produce the combined knee VL+IR moment than when the CoP is anterior to the tibial rotational axis.Then,we tested a stochastic aspect of our theory in a lab-controlled in vivo experiment.Fourteen females performed 60˚cutting under forefoot/rearfoot strike conditions(10 trials each).The positions of lower limb markers and GRF data were measured,and the knee moment due to GRF vector was calculated.The trials were divided into anterior-and posterior-CoP groups depending on the CoP position relative to the tibial rotational axis at each 10 ms interval from 0 to 100 ms after foot strike,and the occurrence rate of the combined knee VL+IR moment was compared between trial groups.Results:The posterior-CoP group showed significantly higher occurrence rates of the combined knee VL+IR moment(maximum of 82.8%)at every time point than those of the anterior-CoP trials,as theoretically predicted by the deterministic mechanical condition.Conclusion:The rearfoot strikes inducing the posterior CoP should be avoided to reduce the risk of non-contact ACL injury associated with the combined knee VL+IR stress.

关键词： Center of pressure Deterministic condition Foot strike pattern Injury mechanism Moment of ground reaction force

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Application of artificial intelligence in diagnosis of pulmonary tuberculosis

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Chinese Medical Journal 2024年第5期137卷 559-561页

作者： Jingli Du Yue Su Juan Qiao Shang Gao Enjun Dong Ruilan Wang Yanhui Nie Jing Ji Zhendong Wang Jianqin Liang Wenping Gong Tuberculosis Control team Senior Department of TuberculosisThe Eighth Medical Center of PLA General HospitalBeijing 100091China department of Computer Science and Technology College of Management Science and EngineeringCapital University of Economics and BusinessBeijing 100071China The Second Ward of Tuberculosis department Senior Department of TuberculosisThe Eighth Medical Center of PLA General HospitalBeijing 100091China Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment Senior Department of TuberculosisThe Eighth Medical Center of PLA General HospitalBeijing 100091China

Tuberculosis(TB)is an infectious disease caused by Mycobacterium tuberculosis.Although the diagnostic technology of pulmonary tuberculosis(PTB)has advanced,accurate and differential diagnoses of PTB are still challenging.In recent years,the rapid development of artificial intelligence(AI)and its wide application in the medical field have provided new opportunities for diagnosing and treating TB and PTB.The machine learning model of AI has not only helped physicians improve diagnostic accuracy,but also enabled them to make early preventive diagnoses for individuals at increased risk of infection.Furthermore,AI can guide physicians to formulate targeted treatment strategies for PTB patients with different conditions.

关键词： tuberculosis diagnosis artificial

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Plasma metabolites and risk of myocardial infarction:a bidirectional Mendelian randomization study

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Journal of Geriatric Cardiology 2024年第2期21卷 219-231页

作者： Dong-Hua LI Qiang WU Jing-Sheng LAN Shuo CHEN You-Yi HUANG Lan-Jin WU Zhi-Qing QIN Ying HUANG Wan-Zhong HUANG Ting ZENG Xin HAO Hua-Bin SU Qiang SU department of Cardiovascular Medicine Minzu Hospital of Guangxi Zhuang Autonomous RegionGuangxiChina Senior department of Cardiology the Sixth Medical CenterChinese PLA General HospitalBeijingChina Library of graduate School Chinese PLA General HospitalBeijingChina department of Cardiology Jiangbin Hospital of Guangxi Zhuang Autonomous RegionGuangxiChina Health management Institute the Second Medical CenterChinese PLA General HospitalBeijingChina

BACKGROUND Myocardial infarction(MI)is a critical cardiovascular event with multifaceted etiology,involving several genetic and environmental factors.It is essential to understand the function of plasma metabolites in the development of MI and unravel its complex pathogenesis.METHODS This study employed a bidirectional Mendelian randomization(MR)approach to investigate the causal relationships between plasma metabolites and MI risk.We used genetic instruments as proxies for plasma metabolites and MI and conducted MR analyses in both directions to assess the impact of metabolites on MI risk and vice versa.In addition,the large-scale genome-wide association studies datasets was used to identify genetic variants associated with plasma metabolite(1400 metabolites)and MI(20,917 individuals with MI and 440,906 individuals without MI)susceptibility.Inverse variance weighted was the primary method for estimating causal effects.MR estimates are expressed as beta coefficients or odds ratio(OR)with 95%CI.RESULTS We identified 14 plasma metabolites associated with the occurrence of MI(P<0.05),among which 8 plasma metabolites[propionylglycine levels(OR=0.922,95%CI:0.881–0.965,P<0.001),gamma-glutamylglycine levels(OR=0.903,95%CI:0.861–0.948,P<0.001),hexadecanedioate(C16-DC)levels(OR=0.941,95%CI:0.911–0.973,P<0.001),pentose acid levels(OR=0.923,95%CI:0.877–0.972,P=0.002),X-24546 levels(OR=0.936,95%CI:0.902–0.971,P<0.001),glycine levels(OR=0.936,95%CI:0.909–0.964,P<0.001),glycine to serine ratio(OR=0.930,95%CI:0.888–0.974,P=0.002),and mannose to trans-4-hydroxyproline ratio(OR=0.912,95%CI:0.869–0.958,P<0.001)]were correlated with a decreased risk of MI,whereas the remaining 6 plasma metabolites[1-palmitoyl-2-arachidonoyl-GPE(16:0/20:4)levels(OR=1.051,95%CI:1.018–1.084,P=0.002),behenoyl dihydrosphingomyelin(d18:0/22:0)levels(OR=1.076,95%CI:1.027–1.128,P=0.002),1-stearoyl-2-docosahexaenoyl-GPE(18:0/22:6)levels(OR=1.067,95%CI:1.027–1.109,P=0.001),alpha-ketobutyrate levels(OR=1.108,95%CI:1.041–1.180,P=0.001),5-acetylamino-6-formylam

关键词： infarction alterations diagnosis

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Emerging molecules,tools,technology,and future of surgical knife in gastroenterology

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World Journal of Gastrointestinal Surgery 2024年第4期16卷 988-998页

作者： Ashok Kumar Anirudh Goyal department of Surgical Gastroenterology Sanjay Gandhi Post Graduate Institute of Medical SciencesLucknow 226014Uttar PradeshIndia

The 21^(st) century has started with several innovations in the medical sciences,with wide applications in health care management.This development has taken in the field of medicines(newer drugs/molecules),various tools and technology which has completely changed the patient management including abdominal surgery.Surgery for abdominal diseases has moved from maximally invasive to minimally invasive(laparoscopic and robotic)surgery.Some of the newer medicines have its impact on need for surgical intervention.This article focuses on the development of these emerging molecules,tools,and technology and their impact on present surgical form and its future effects on the surgical intervention in gastroenterological diseases.

关键词： Newer molecules Tools and technology Gastroenterology Future of surgical knife

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A nested partitioning-based solution method for seru scheduling problem with resource allocation

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Journal of management Science and Engineering 2024年第1期9卷 101-114页

作者： Zhe Zhang Kazuhiro Izui Xiaoling Song Yong Yin Xue Gong School Economics&management Nanjing University of Science and TechnologyNanjing210094China department of Mechanical Engineering and Science Kyoto UniversityKyoto615-8540Japan graduate School of Business Doshisha UniversityKarasuma-ImadegawaKamigyo-kuKyoto602-8580Japan

This paper investigates the production scheduling problems of allocating resources and sequencing jobs in the seru production system(SPS).As a new-type manufacturing mode arising from Japanese production practices,seru production can achieve efficiency,flexibility,and responsiveness simultaneously.The production environment in which a set of jobs must be scheduled over a set of serus according to due date and different execution modes is considered,and a combination optimization model is provided.Motivated by the problem complexity and the characteristics of the proposed seru scheduling model,a nested partitioning method(NPM)is designed as the solution approach.Finally,computational studies are conducted,and the practicability of the proposed seru scheduling model is proven.Moreover,the efficiency of the nested partitioning solution method is demonstrated by the computational results obtained from different scenarios,and the good scalability of the proposed approach is proven via comparative analysis.

关键词： Seru production system Scheduling Combination optimization model Nested partitioning method Resource allocation

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Recent Developments in Authentication Schemes Used in Machine-Type Communication Devices in Machine-to-Machine Communication: Issues and Challenges

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Computers, Materials & Continua 2024年第4期79卷 93-115页

作者： Shafi Ullah Sibghat Ullah Bazai Mohammad Imran Qazi Mudassar Ilyas Abid Mehmood Muhammad Asim Saleem Muhmmad Aasim Rafique Arsalan Haider Ilyas Khan Sajid Iqbal Yonis Gulzar Kauser Hameed department of Computer Engineering Balochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan department of Information Technology Balochistan University of Information TechnologyEngineeringand Management SciencesQuetta87300Pakistan department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia department of management Information Systems College of Business AdministrationKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia department of Software Engineering College of ComputingRiphah International UniversityFaisalabad44000Pakistan department of Electrical Engineering Balochistan University of Information Technology Engineeringand Management SciencesQuetta87300Pakistan department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices(MTCDs) regularly share extensive data without human intervention while making all types of decisions. Thesedecisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeatmonitoring, and several different alert systems. Many of these devices simultaneously share data to form anautomated system. The data shared between machine-type communication devices (MTCDs) is prone to risk dueto limited computational power, internal memory, and energy capacity. Therefore, securing the data and devicesbecomes challenging due to factors such as dynamic operational environments, remoteness, harsh conditions,and areas where human physical access is difficult. One of the crucial parts of securing MTCDs and data isauthentication, where each devicemust be verified before data transmission. SeveralM2Mauthentication schemeshave been proposed in the literature, however, the literature lacks a comprehensive overview of current M2Mauthentication techniques and the challenges associated with them. To utilize a suitable authentication schemefor specific scenarios, it is important to understand the challenges associated with it. Therefore, this article fillsthis gap by reviewing the state-of-the-art research on authentication schemes in MTCDs specifically concerningapplication categories, security provisions, and performance efficiency.

关键词： Authentication cyber security internet of things machine-type communication devices machine-to-machine communication

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Olive Leaf Disease Detection via Wavelet Transform and Feature Fusion of Pre-Trained Deep Learning Models

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Computers, Materials & Continua 2024年第3期78卷 3431-3448页

作者： Mahmood A.Mahmood Khalaf Alsalem department of Information Systems College of Computer and Information SciencesJouf UniversitySakakahKingdom of Saudi Arabia department of Information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityGizaEgypt

Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses.Early detection of these diseases is essential for effective management.We propose a novel transformed wavelet,feature-fused,pre-trained deep learning model for detecting olive leaf diseases.The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images.The model has four main phases:preprocessing using data augmentation,three-level wavelet transformation,learning using pre-trained deep learning models,and a fused deep learning model.In the preprocessing phase,the image dataset is augmented using techniques such as resizing,rescaling,flipping,rotation,zooming,and contrasting.In wavelet transformation,the augmented images are decomposed into three frequency levels.Three pre-trained deep learning models,EfficientNet-B7,DenseNet-201,and ResNet-152-V2,are used in the learning phase.The models were trained using the approximate images of the third-level sub-band of the wavelet transform.In the fused phase,the fused model consists of a merge layer,three dense layers,and two dropout layers.The proposed model was evaluated using a dataset of images of healthy and infected olive leaves.It achieved an accuracy of 99.72%in the diagnosis of olive leaf diseases,which exceeds the accuracy of other methods reported in the literature.This finding suggests that our proposed method is a promising tool for the early detection of olive leaf diseases.

关键词： Olive leaf diseases wavelet transform deep learning feature fusion

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A Study on the Factors Influencing Consumer Purchase Decision Under the Live-Streaming Sales Model

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Journal of Electronic Research and Application 2024年第3期8卷 185-190页

作者： Zhaoxia Zhang Yating Mo Yijun Xia department of Convergence management Woosong UniversityDaejeon 34606Republic of Korea graduate School of Woosong University Daejeon 34606Republic of Korea

In recent years,with the rapid development and popularization of Internet information technology,many new media platforms have risen rapidly,and major e-commerce companies have begun to explore the mode of livestreaming.Especially during the COVID-19 pandemic,due to the lockdown,live-streaming has become an important means of economic development in many places.Owing to its remarkable characteristics of timeliness,entertainment,and interactivity,it has become the latest and trendiest sales mode of e-commerce channels,reflecting huge economic potential and commercial value.This article analyzes two models and their characteristics of live-streaming sales from a practical perspective.Based on this,it outlines consumer purchasing decisions and the factors that affect consumer purchasing decisions under the live-streaming sales model.Finally,it discusses targeted suggestions for using the live-streaming sales model to expand the consumer market,hoping to promote the healthy and steady development of the live-streaming sales industry.

关键词： Live streaming sales model Consumers Purchase decisions Influencing factors

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Left bundle branch pacing set to outshine biventricular pacing for cardiac resynchronization therapy?

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World Journal of Cardiology 2024年第4期16卷 186-190页

作者： Akash Batta Juniali Hatwal department of Cardiology Dayanand Medical College and HospitalLudhiana 141001India department of Internal Medicine Advanced Cardiac CentrePost Graduate Institute of Medical Education&ResearchChandigarh 160012India

The deleterious effects of long-term right ventricular pacing necessitated the search for alternative pacing sites which could prevent or alleviate pacinginduced cardiomyopathy.Until recently,biventricular pacing(BiVP)was the only modality which could mitigate or prevent pacing induced dysfunction.Further,BiVP could resynchronize the baseline electromechanical dssynchrony in heart failure and improve outcomes.However,the high non-response rate of around 20%-30%remains a major limitation.This non-response has been largely attributable to the direct non-physiological stimulation of the left ventricular myocardium bypassing the conduction system.To overcome this limitation,the concept of conduction system pacing(CSP)came up.Despite initial success of the first CSP via His bundle pacing(HBP),certain drawbacks including lead instability and dislodgements,steep learning curve and rapid battery depletion on many occasions prevented its widespread use for cardiac resynchronization therapy(CRT).Subsequently,CSP via left bundle branch-area pacing(LBBP)was developed in 2018,which over the last few years has shown efficacy comparable to BiVP-CRT in small observational studies.Further,its safety has also been well established and is largely free of the pitfalls of the HBP-CRT.In the recent metanalysis by Yasmin et al,comprising of 6 studies with 389 participants,LBBPCRT was superior to BiVP-CRT in terms of QRS duration,left ventricular ejection fraction,cardiac chamber dimensions,lead thresholds,and functional status amongst heart failure patients with left bundle branch block.However,there are important limitations of the study including the small overall numbers,inclusion of only a single small randomized controlled trial(RCT)and a small follow-up duration.Further,the entire study population analyzed was from China which makes generalizability a concern.Despite the concerns,the meta-analysis adds to the growing body of evidence demonstrating the efficacy of LBBP-CRT.At this stage,one must acknowledge that the fact that stil

关键词： Biventricular pacing Cardiac resynchronization therapy Conduction system pacing Left bundle branch-area pacing Left bundle branch block Electromechanical dssynchrony

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Novel Rifle Number Recognition Based on Improved YOLO in Military Environment

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Computers, Materials & Continua 2024年第1期78卷 249-263页

作者： Hyun Kwon Sanghyun Lee department of Artificial Intelligence and Data Science Korea Military AcademySeoulKorea graduate School of Information Security Korea Advanced Institute of Science and TechnologyDaejeonKorea

Deep neural networks perform well in image recognition,object recognition,pattern analysis,and speech recog-nition.In military applications,deep neural networks can detect equipment and recognize objects.In military equipment,it is necessary to detect and recognize rifle management,which is an important piece of equipment,using deep neural networks.There have been no previous studies on the detection of real rifle numbers using real rifle image datasets.In this study,we propose a method for detecting and recognizing rifle numbers when rifle image data are insufficient.The proposed method was designed to improve the recognition rate of a specific dataset using data fusion and transfer learningmethods.In the proposed method,real rifle images and existing digit images are fusedas trainingdata,andthe final layer is transferredto theYolov5 algorithmmodel.The detectionand recognition performance of rifle numbers was improved and analyzed using rifle image and numerical datasets.We used actual rifle image data(K-2 rifle)and numeric image datasets,as an experimental environment.TensorFlow was used as the machine learning library.Experimental results show that the proposed method maintains 84.42% accuracy,73.54% precision,81.81% recall,and 77.46% F1-score in detecting and recognizing rifle numbers.The proposed method is effective in detecting rifle numbers.

关键词： Machine learning deep neural network rifle number recognition detection

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