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application of Fusobacterium nucleatum as a biomarker in gastrointestinal malignancies

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World Journal of Gastrointestinal Oncology 2024年第6期16卷 2271-2283页

作者： Long-Chen Yu Ya-Ping Li Yue-Ming Xin Mai Mao Ya-Xin Pan Yi-Xuan Qu Zheng-Dong Luo Yi Zhang Xin Zhang department of Clinical Laboratory Qilu Hospital of Shandong UniversityShandong Engineering Research Center of Biomarker and Artificial Intelligence ApplicationJinan 250012Shandong ProvinceChina

The morbidity and mortality of gastrointestinal(GI)malignancies are among the highest in the world,posing a serious threat to human *** of the insidious onset of the cancer,it is difficult for patients to be diagnosed at an early stage,and it rapidly progresses to an advanced stage,resulting in poor treatment and *** nucleatum(***)is a gram-negative,sporefree anaerobic bacterium that primarily colonizes the oral cavity and is implicated in the development of colorectal,esophageal,gastric,and pancreatic cancers via various intricate *** development in novel research suggests that *** may function as a biomarker in GI *** the abundance of *** in stool,saliva,and serum samples of patients may aid in the diagnosis,risk assessment,and prognosis monitoring of GI *** editorial systematically describes the biological roles and mechanisms of *** in GI malignancies focusing on the application of *** as a biomarker in the diagnosis and prognosis of GI malignancies to promote the clinical translation of *** and GI tumors-related research.

关键词： Gastrointestinal malignancies Fusobacterium nucleatum Biomarker Diagnosis Prognosis

CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

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computers, Materials & Continua 2024年第3期78卷 3511-3528页

作者： Talal Albalawi P.Ganeshkumar computer Science department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless ***,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT *** this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT ***,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value *** addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the *** effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other *** analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

关键词： IoT security attack detection covariance linear learning embedding selection kernel distributed bayes classifier mongolian gazellas optimization

ReChoreoNet: Repertoire-based Dance Re-choreography with Music-conditioned Temporal and Style Clues

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Machine Intelligence Research 2024年第4期21卷 771-781页

作者： Ho Yin Au Jie Chen Junkun Jiang Yike Guo department of computer Science Hong Kong Baptist UniversityHong Kong 999077China department of computer Science and Engineering The Hong Kong University of Science and EngineeringHong Kong 999077China

To generate dance that temporally and aesthetically matches the music is a challenging problem in three ***,the generated motion should be beats-aligned to the local musical ***,the global aesthetic style should be matched between motion and *** third,the generated motion should be diverse and *** address these challenges,we propose ReChoreoNet,which re-choreographs high-quality dance motion for a given piece of music.A data-driven learning strategy is proposed to efficiently correlate the temporal connections between music and motion in a progressively learned cross-modality embedding *** beats-aligned content motion will be subsequently used as autoregressive context and control signal to control a normalizing-flow model,which transfers the style of a prototype motion to the final generated *** addition,we present an aesthetically labelled music-dance repertoire(MDR)for both efficient learning of the cross-modality embedding,and understanding of the aesthetic connections between music and *** demonstrate that our repertoire-based framework is robustly extensible in both content and *** quantitative and qualitative experiments have been carried out to validate the efficiency of our proposed model.

关键词： Generative model cross-modality learning normalizing flow tempo synchronization style transfer.

Relational Turkish Text Classification Using Distant Supervised Entities and Relations

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computers, Materials & Continua 2024年第5期79卷 2209-2228页

作者： Halil Ibrahim Okur Kadir Tohma Ahmet Sertbas department of computer Engineering Faculty of Engineering and Natural SciencesIskenderun Technical UniversityHatay31200Turkey department of computer Engineering Faculty of EngineeringIstanbul University-CerrahpasaIstanbul34310Turkey

Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing *** study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata(Wikipedia database)database and BERTbased pre-trained Named Entity Recognition(NER)*** on a significant challenge in the field of natural language processing(NLP),the research evaluates the potential of using entity and relational information to extract deeper meaning from *** adopted methodology encompasses a comprehensive approach that includes text preprocessing,entity detection,and the integration of relational *** conducted on text datasets in both Turkish and English assess the performance of various classification algorithms,such as Support Vector Machine,Logistic Regression,Deep Neural Network,and Convolutional Neural *** results indicate that the integration of entity-relation information can significantly enhance algorithmperformance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP *** of this work include the utilization of distant supervised entity-relation information in Turkish text classification,the development of a Turkish relational text classification approach,and the creation of a relational *** demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification,this research aims to support the effectiveness of text-based artificial intelligence(AI)***,it makes significant contributions to the development ofmultilingual text classification systems by adding deeper meaning to text content,thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.

关键词： Text classification relation extraction NER distant supervision deep learning machine learning

Study of baryon number transport dynamics and strangeness conservation effects using-hadron correlations

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Nuclear Science and Techniques 2024年第7期35卷 147-156页

作者： Wei-Jie Dong Xiao-Zhou Yu Si-Yuan Ping Xia-Tong Wu Gang Wang Huan-Zhong Huang Zi-Wei Lin Key Laboratory of Nuclear Physics and Ion-beam application(MOE) Fudan UniversityShanghai 200433China department of Physics and Astronomy University of CaliforniaLos AngelesCA 90095USA department of Physics East Carolina UniversityGreenvilleNC 27858USA

In nuclear collisions at RHIC energies,an excess ofΩhyperons over n is observed,indicating that几has a net ba ryon number despites and 3 quarks being produced in *** ba ryon number inΩmay have been transported from the incident nuclei and/or produced in the baryon-pair production ofΩ with other types of anti-hyperons such *** investigate these two scenarios,we propose to measure the correlations betweenΩand K and betweenΩand *** use two versions,the default and string-melting,of a multiphase transport(AMPT)modelto illustrate the method for measuring the correlation and to demonstrate the general shape ofthe *** present the S 2-hadron correlations from simulated Au+Au collisions at√SNN=7.7 and 14.6 GeV and discuss the dependence on the cllision energy and on the hadronization scheme in these two AMPT *** correlations can be used to explore the mechanism of baryon number transport and the effects of baryon number and strangeness conservation on nuclear collisions.

关键词： Baryon number transport Strangeness conversation Correlation Gluon junction

A Novel Insertion Solution for the Travelling Salesman Problem

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computers, Materials & Continua 2024年第4期79卷 1581-1597页

作者： Emmanuel Oluwatobi Asani Aderemi Elisha Okeyinka Sunday Adeola Ajagbe Ayodele Ariyo Adebiyi Roseline Oluwaseun Ogundokun Temitope Samson Adekunle Pragasen Mudali Matthew Olusegun Adigun department of computer Science Landmark UniversityOmu Aran251103Nigeria SDG 11 Group Landmark UniversityOmu Aran251103Nigeria department of Computing MiVA UniversityAbuja900211Nigeria department of computer Science Ibrahim Badamasi Babangida UniversityLapai911101Nigeria department of computer Science University of ZululandKwadlangezwa3886South Africa department of computer&Industrial Production Engineering First Technical UniversityIbadan200243Nigeria department of Multimedia Engineering Kaunas University of TechnologyKaunasLT-44249Lithuania department of computer Science Colorado State UniversityFort Collins80523USA

The studypresents theHalfMax InsertionHeuristic (HMIH) as a novel approach to solving theTravelling SalesmanProblem (TSP). The goal is to outperform existing techniques such as the Farthest Insertion Heuristic (FIH) andNearest Neighbour Heuristic (NNH). The paper discusses the limitations of current construction tour heuristics,focusing particularly on the significant margin of error in FIH. It then proposes HMIH as an alternative thatminimizes the increase in tour distance and includes more nodes. HMIH improves tour quality by starting withan initial tour consisting of a ‘minimum’ polygon and iteratively adding nodes using our novel Half Max *** paper thoroughly examines and compares HMIH with FIH and NNH via rigorous testing on standard TSPbenchmarks. The results indicate that HMIH consistently delivers superior performance, particularly with respectto tour cost and computational efficiency. HMIH’s tours were sometimes 16% shorter than those generated by FIHand NNH, showcasing its potential and value as a novel benchmark for TSP solutions. The study used statisticalmethods, including Friedman’s Non-parametric Test, to validate the performance of HMIH over FIH and *** guarantees that the identified advantages are statistically significant and consistent in various situations. Thiscomprehensive analysis emphasizes the reliability and efficiency of the heuristic, making a compelling case for itsuse in solving TSP issues. The research shows that, in general, HMIH fared better than FIH in all cases studied,except for a few instances (pr439, eil51, and eil101) where FIH either performed equally or slightly better thanHMIH. HMIH’s efficiency is shown by its improvements in error percentage (δ) and goodness values (g) comparedto FIH and NNH. In the att48 instance, HMIH had an error rate of 6.3%, whereas FIH had 14.6% and NNH had20.9%, indicating that HMIH was closer to the optimal solution. HMIH consistently showed superior performanceacross many benchmarks,

关键词： Nearest neighbour heuristic farthest insertion heuristic half max insertion heuristic tour construction travelling salesman problem

Deep reinforcement learning for online scheduling of photovoltaic systems with battery energy storage systems

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Intelligent and Converged Networks 2024年第1期5卷 28-41页

作者： Yaze Li Jingxian Wu Yanjun Pan the department of Electrical Engineering University of ArkansasFayettevilleAR 72701USA the department of computer Science and computer Engineering University of ArkansasFayettevilleAR 72701USA

A new online scheduling algorithm is proposed for photovoltaic(PV)systems with battery-assisted energy storage systems(BESS).The stochastic nature of renewable energy sources necessitates the employment of BESS to balance energy supplies and demands under uncertain weather *** proposed online scheduling algorithm aims at minimizing the overall energy cost by performing actions such as load shifting and peak shaving through carefully scheduled BESS charging/discharging *** scheduling algorithm is developed by using deep deterministic policy gradient(DDPG),a deep reinforcement learning(DRL)algorithm that can deal with continuous state and action *** of the main contributions of this work is a new DDPG reward function,which is designed based on the unique behaviors of energy *** new reward function can guide the scheduler to learn the appropriate behaviors of load shifting and peak shaving through a balanced process of exploration and *** new scheduling algorithm is tested through case studies using real world data,and the results indicate that it outperforms existing algorithms such as Deep *** online algorithm can efficiently learn the behaviors of optimum non-casual off-line algorithms.

关键词： photovoltaic(PV) battery energy storage system(BESS) Markov decision process(MDP) deep deterministic policy gradient(DDPG)

Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

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computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China department of computer Science University of Science&TechnologyBannu28100Pakistan department of computer Science University of HertfordshireHatfieldAL109ABUK department of computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

An Elastoplastic Fracture Model Based on Bond-Based Peridynamics

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computer Modeling in Engineering & Sciences 2024年第9期140卷 2349-2371页

作者： Liping Zu Yaxun Liu Haoran Zhang Lisheng Liu Xin Lai Hai Mei department of Engineering Structure and Mechanics Wuhan University of TechnologyWuhan430070China Hubei Key Laboratory of Theory and application of Advanced Materials Mechanics Wuhan University of TechnologyWuhan430070China

Fracture in ductile materials often occurs in conjunction with plastic ***,in the bond-based peridynamic(BB-PD)theory,the classic mechanical stress is not defined *** makes it difficult to describe plasticity directly using the classical plastic *** address the above issue,a unified bond-based peridynamics model was proposed as an effective tool to solve elastoplastic fracture *** to the existing models,the proposed model directly describes the elastoplastic theory at the bond level without the need for additional calculation *** results obtained in the context of this model are shown to be consistent with FEM results in regard to force-displacement curves,displacement fields,stress fields,and plastic deformation *** model exhibits good capability of capturing crack propagation in ductile material failure problems.

关键词： Ductile materials plastic deformation bond-based peridynamics isotropic hardening fracture analysis