[{"data":1,"prerenderedAt":46},["ShallowReactive",2],{"article-detail-journal":3,"article-detail:75":17},{"title":4,"description":5,"cover_image":6,"overview":7,"issn":8,"publisher":9,"publishing_mode":10,"impact_factor":11,"impact_factor_5year":11,"submission_to_decision_days":12,"downloads":13,"id":14,"created_at":15,"updated_at":16},"World Journal of Young and Excellent Scholars","World Journal of Young and Excellent Scholars (JYES) is a peer-reviewed, open-access, interdisciplinary journal that supports the academic work of pre-collegiate and adolescent researchers. The journal aims to provide a formal and professional platform for young scholars from around the world to publish their original ideas, research findings, and innovative methods. By connecting secondary education with the broader academic community, JYES helps young researchers take part in scholarly communication at an early stage. The journal is committed to promoting academic excellence through rigorous peer review, high ethical standards, and broad international visibility.\n\nJYES welcomes high-quality submissions that show clear research questions, critical thinking, and strong scientific methods. The journal covers a wide range of STEM fields, including natural sciences, engineering, mathematics, and computer science. Through its open-access model, all published articles are freely available to readers worldwide, helping young scholars gain greater exposure and recognition for their work.","\u002Fimages\u002Fuploads\u002Fjournals\u002F1\u002Febcb34b6ba1e45e09273bbfd391101ab_cover image.png","\u003Cp>\u003Cstrong>Journal Title: \u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>World Journal of Young and Excellent Scholars\u003C\u002Fp>\u003Cp>\u003Cstrong>Journal Type:\u003C\u002Fstrong> \u003C\u002Fp>\u003Cp>Peer-reviewed, open-access, interdisciplinary journal\u003C\u002Fp>\u003Cp>\u003Cstrong>Aim and Mission:\u003C\u002Fstrong> \u003C\u002Fp>\u003Cp style=\"text-align: justify;\">World Journal of Young and Excellent Scholars (JYES) is dedicated to supporting and promoting the scholarship of young researchers, especially pre-collegiate and adolescent scholars. Its mission is to provide a professional publication venue where young investigators can share intellectual discoveries, develop academic confidence, and receive global recognition through a rigorous scholarly process.\u003C\u002Fp>\u003Cp>\u003Cstrong>Scope:\u003C\u002Fstrong> \u003C\u002Fp>\u003Cp>The journal publishes research across the full range of STEM disciplines, including:\u003C\u002Fp>\u003Cp>•Natural Sciences: Physics, Chemistry, Biology, and Environmental Science.\u003C\u002Fp>\u003Cp>•Engineering: Mechanical, Electrical, Civil, and Material Engineering.\u003C\u002Fp>\u003Cp>•Mathematics: Pure and Applied Mathematics, Statistics.\u003C\u002Fp>\u003Cp>•Computer Science: Artificial Intelligence, Data Science, Algorithms, and Software Engineering.\u003C\u002Fp>\u003Cp>\u003Cstrong>Article Types: \u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>Original Research, Reviews, Short Communications.\u003C\u002Fp>\u003Cp>\u003Cstrong>Open Access Policy: \u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>JYES follows a fully open-access publishing model. All articles are freely accessible to readers worldwide without subscription or paywall restrictions.\u003C\u002Fp>\u003Cp>\u003Cstrong>Copyright and License: \u003C\u002Fstrong>\u003C\u002Fp>\u003Cp style=\"text-align: justify;\">Authors retain the copyright to their work. All published papers are distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which allows unrestricted use, sharing, and reproduction, provided the original source is properly cited.\u003C\u002Fp>\u003Cp>\u003Cstrong>Publisher: \u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>Association of Global Intelligent Science and Technology (AGIST)\u003C\u002Fp>\u003Cp>\u003Cstrong>Publication Frequency: \u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>Quarterly (4 issues per year)\u003C\u002Fp>\u003Cp>\u003Cstrong>Peer Review and Publication Timeline:\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>Submission to First Decision: approximately 30 days\u003C\u002Fp>\u003Cp>Submission to Final Acceptance: approximately 70 days\u003C\u002Fp>\u003Cp>Acceptance to Publication: approximately 20 days\u003C\u002Fp>\u003Cp>\u003Cstrong>Publication Frequency: \u003C\u002Fstrong>\u003C\u002Fp>\u003Cp style=\"text-align: justify;\">JYES is supported by AGIST and maintains full editorial independence. All editorial decisions and the double-blind peer-review process are based only on academic quality, scientific validity, and ethical publishing standards, without commercial influence.\u003C\u002Fp>","","AGIST","hybrid",3,90,0,1,"2026-03-18T08:15:34.180691Z","2026-04-08T02:11:52.603854Z",{"id":18,"title":19,"abstract":20,"type":21,"doi":-1,"keywords":22,"authors":28,"author_ids":-1,"issue":35,"page_start":40,"page_end":41,"view_count":42,"download_count":43,"published_date":38,"created_at":44,"funds":45},75,"Unsupervised Customer Segmentation in E-Commerce via PCA-Assisted K-Means Clustering","处理后如下：\n\nBackground In the competitive ecommerce landscape generalized marketing strategies often fail to engage diverse consumer bases Understanding latent customer behaviors without predefined labels requires robust unsupervised learning techniquesObjective This study aims to partition a high dimensional dataset of online shoppers into distinct actionable cohorts using K Means clustering We further seek to visualize these high dimensional clusters using Principal Component Analysis PCAMethods We applied the K Means algorithm Lloyds heuristic to a dataset of 2000 customers based on Annual Income and Spending Score To determine the optimal number of clusters (k) we utilized the Elbow Method and Silhouette Analysis PCA was employed to reduce dimensionality and verify cluster separabilityResults The analysis identified (k=5) distinct customer segments including Prudent Savers Target Group and Big Spenders The model achieved a Silhouette Score of 0.55 indicating dense and well separated clustersConclusion The combination of K Means and PCA provides a powerful framework for discovering market niches enabling businesses to tailor hyper personalized retention strategies\n","regular",[23,24,25,26,27],"Unsupervised Learning","K-Means Clustering","Principal Component Analysis","Customer Segmentatio","Silhouette Score",[29],{"id":30,"display_name":31,"first_name":31,"middle_name":-1,"last_name":8,"orcid":-1,"avatar":-1,"email":32,"affiliation":-1,"bio":-1,"created_at":8,"updated_at":8,"affiliations":33,"articles":34},18,"Dongjin Xie","20232501487@stu.xju.edu.cn",[],[],{"id":36,"volume_number":14,"issue_number":14,"title":-1,"cover_image":37,"publish_date":38,"is_current":39},21,"\u002Fimages\u002Fuploads\u002Fdocuments\u002F79b5a3ffefad4d29addde963b5f8e7e4_iusse1.png","2026-03-28",true,32,40,180,4,"2026-04-01T07:39:31.699697Z",[],1775646838179]