In a scale-free network, only a minority of nodes are connected very often, while the majority of nodes are connected rarely. However, what is the ratio of minority nodes to majority nodes resulting from the Matthew e...In a scale-free network, only a minority of nodes are connected very often, while the majority of nodes are connected rarely. However, what is the ratio of minority nodes to majority nodes resulting from the Matthew effect？ In this paper, based on a simple preferential random model, the poor-rich demarcation points are found to vary in a limited range, and form a poor-rich demarcation interval that approximates to k/m E [3,4]. As a result, the （cumulative） degree distribution of a scale-free network can be divided into three intervals： the poor interval, the demarcation interval and the rich interval. The inequality of the degree distribution in each interval is measured. Finally, the Matthew effect is applied to the ABC analysis of project management.展开更多
A rapid environmental assessment was conducted by the staff of Marine Biology Research Center （CIBIMA）, Faculty of Sciences, Universidad Aut6noma de Santo Domingo （UASD） on the southern coast of the Dominican Repu...A rapid environmental assessment was conducted by the staff of Marine Biology Research Center （CIBIMA）, Faculty of Sciences, Universidad Aut6noma de Santo Domingo （UASD） on the southern coast of the Dominican Republic, to evaluate the effects of the hurricane Matthew on October 3, being the 14th storm of the year 2016 for the Caribbean region. The observations were carried out two weeks after the storm hit. These observations included coastal ecosystems, such as marshes, beaches, lagoons, wetlands, mangrove forests, nearshore sea grasses and coral reefs. The evaluation included observations on the magnitude of the distresses and levels of destruction---changes, produced by the intense weather and upset climate from the storm. The data gathered were recorded following a categorization of impacts. It also included a description of the different coastal communities after being impacted and affected by the storm.展开更多
the Matthew Effect （Matthew Effect） refers to the phenomenon that the stronger is becoming the strong and the weak weaker, which widely used in social psychology, education, finance, and science, and many other fiel...the Matthew Effect （Matthew Effect） refers to the phenomenon that the stronger is becoming the strong and the weak weaker, which widely used in social psychology, education, finance, and science, and many other fields, it has the characteristics of dynamic, universality and continuity. This article embarks from the comprehensive analysis of the concept of ＂Matthew effect＂ , and emphatically discusses the characteristics of the Matthew effect, in the education work performance, and puts forward how to use the ＂Matthew effect＂ to construct good education environment, expect to make a reference to improve education teaching,展开更多
自2020年Dr Matthew Webster接手YG以来,电脑建模便成为YG的标志性特点。YG的音箱都是通过电脑云端夜以继日计算而出,Dr Matthew Webster告诉我一个咂舌的数字,XV3签名版的箱体共计需要1400万个小时才计算而出。而且这项技术应用在音箱...自2020年Dr Matthew Webster接手YG以来,电脑建模便成为YG的标志性特点。YG的音箱都是通过电脑云端夜以继日计算而出,Dr Matthew Webster告诉我一个咂舌的数字,XV3签名版的箱体共计需要1400万个小时才计算而出。而且这项技术应用在音箱制作的全部地方。展开更多
Purpose:The goal of this study is a comparative analysis of the relation between funding(a main driver for scientific research)and citations in papers of Nobel Laureates in physics,chemistry and medicine over 2019-202...Purpose:The goal of this study is a comparative analysis of the relation between funding(a main driver for scientific research)and citations in papers of Nobel Laureates in physics,chemistry and medicine over 2019-2020 and the same relation in these research fields as a whole.Design/methodology/approach:This study utilizes a power law model to explore the relationship between research funding and citations of related papers.The study here analyzes 3,539 recorded documents by Nobel Laureates in physics,chemistry and medicine and a broader dataset of 183,016 documents related to the fields of physics,medicine,and chemistry recorded in the Web of Science database.Findings:Results reveal that in chemistry and medicine,funded researches published in papers of Nobel Laureates have higher citations than unfunded studies published in articles;vice versa high citations of Nobel Laureates in physics are for unfunded studies published in papers.Instead,when overall data of publications and citations in physics,chemistry and medicine are analyzed,all papers based on funded researches show higher citations than unfunded ones.Originality/value:Results clarify the driving role of research funding for science diffusion that are systematized in general properties:a)articles concerning funded researches receive more citations than(un)funded studies published in papers of physics,chemistry and medicine sciences,generating a high Matthew effect(a higher growth of citations with the increase in the number of papers);b)research funding increases the citations of articles in fields oriented to applied research(e.g.,chemistry and medicine)more than fields oriented towards basic research(e.g.,physics).Practical implications:The results here explain some characteristics of scientific development and diffusion,highlighting the critical role of research funding in fostering citations and the expansion of scientific knowledge.This finding can support decision-making of policymakers and R&D managers to improve the effectiveness in allocating financial resources in science policies to generate a higher positive scientific and societal impact.展开更多
Greenblatt and his team have unveiled vertebral skeletal stem cells(vSSCs)as a critical player in the landscape of bone metastasis.This commentary delves into the transformative discoveries surrounding vSSCs,emphasizi...Greenblatt and his team have unveiled vertebral skeletal stem cells(vSSCs)as a critical player in the landscape of bone metastasis.This commentary delves into the transformative discoveries surrounding vSSCs,emphasizing their distinct role in bone metastasis compared to other stem cell lineages.We illuminate the unique properties and functions of vSSCs,which may account for the elevated susceptibility of vertebral bones to metastatic invasion.Furthermore,we explore the exciting therapeutic horizons opened by this newfound understanding.These include potential interventions targeting vSSCs,modulation of associated signaling pathways,and broader implications for the treatment and management of bone metastasis.By shedding light on these game-changing insights,we hope to pave the way for novel strategies that could revolutionize the prognosis and treatment landscape for cancer patients with metastatic bone disease.展开更多
Credit card companies must be able to identify fraudulent credit card transactions so that clients are not charged for items they did not purchase. Previously, many machine learning approaches and classifiers were use...Credit card companies must be able to identify fraudulent credit card transactions so that clients are not charged for items they did not purchase. Previously, many machine learning approaches and classifiers were used to detect fraudulent transactions. However, because fraud patterns are always changing, it is becoming increasingly vital to investigate new frauds and develop the model based on the new patterns. The purpose of this research is to create a machine learning classifier that not only detects fraud but also detects legitimate transactions. As a result, the model should have excellent accuracy, precision, recall, and f1-score. As a result, we began with a large dataset in this study and used four machine learning classifiers: Support Vector Machine (SVM), Decision Tree, Naïve Bayes, and Random Forest. The random forest classifier scored 99.96% overall accuracy with the best precision, recall, f1-score, and Matthews correlation coefficient in the experiments.展开更多
基金National Natural Science Foundation of China(No.61078065)Zhejiang Provincial Natural Science Foundation of China(No.LY13A040006)K.C.Wong Magna Foundation in Ningbo University
基金supported by the "Shu Guang" Project of Shanghai Municipal Education Commission,China (Grant No. 09SG17)EU ELINK-East-West Link for Innovation,Networking and Knowledge Exchange (Grant No. 149674-EM-1-2008-1-UK-ERAMUNDUS)
文摘In a scale-free network, only a minority of nodes are connected very often, while the majority of nodes are connected rarely. However, what is the ratio of minority nodes to majority nodes resulting from the Matthew effect？ In this paper, based on a simple preferential random model, the poor-rich demarcation points are found to vary in a limited range, and form a poor-rich demarcation interval that approximates to k/m E [3,4]. As a result, the （cumulative） degree distribution of a scale-free network can be divided into three intervals： the poor interval, the demarcation interval and the rich interval. The inequality of the degree distribution in each interval is measured. Finally, the Matthew effect is applied to the ABC analysis of project management.
文摘A rapid environmental assessment was conducted by the staff of Marine Biology Research Center （CIBIMA）, Faculty of Sciences, Universidad Aut6noma de Santo Domingo （UASD） on the southern coast of the Dominican Republic, to evaluate the effects of the hurricane Matthew on October 3, being the 14th storm of the year 2016 for the Caribbean region. The observations were carried out two weeks after the storm hit. These observations included coastal ecosystems, such as marshes, beaches, lagoons, wetlands, mangrove forests, nearshore sea grasses and coral reefs. The evaluation included observations on the magnitude of the distresses and levels of destruction---changes, produced by the intense weather and upset climate from the storm. The data gathered were recorded following a categorization of impacts. It also included a description of the different coastal communities after being impacted and affected by the storm.
文摘the Matthew Effect （Matthew Effect） refers to the phenomenon that the stronger is becoming the strong and the weak weaker, which widely used in social psychology, education, finance, and science, and many other fields, it has the characteristics of dynamic, universality and continuity. This article embarks from the comprehensive analysis of the concept of ＂Matthew effect＂ , and emphatically discusses the characteristics of the Matthew effect, in the education work performance, and puts forward how to use the ＂Matthew effect＂ to construct good education environment, expect to make a reference to improve education teaching,
文摘Purpose:The goal of this study is a comparative analysis of the relation between funding(a main driver for scientific research)and citations in papers of Nobel Laureates in physics,chemistry and medicine over 2019-2020 and the same relation in these research fields as a whole.Design/methodology/approach:This study utilizes a power law model to explore the relationship between research funding and citations of related papers.The study here analyzes 3,539 recorded documents by Nobel Laureates in physics,chemistry and medicine and a broader dataset of 183,016 documents related to the fields of physics,medicine,and chemistry recorded in the Web of Science database.Findings:Results reveal that in chemistry and medicine,funded researches published in papers of Nobel Laureates have higher citations than unfunded studies published in articles;vice versa high citations of Nobel Laureates in physics are for unfunded studies published in papers.Instead,when overall data of publications and citations in physics,chemistry and medicine are analyzed,all papers based on funded researches show higher citations than unfunded ones.Originality/value:Results clarify the driving role of research funding for science diffusion that are systematized in general properties:a)articles concerning funded researches receive more citations than(un)funded studies published in papers of physics,chemistry and medicine sciences,generating a high Matthew effect(a higher growth of citations with the increase in the number of papers);b)research funding increases the citations of articles in fields oriented to applied research(e.g.,chemistry and medicine)more than fields oriented towards basic research(e.g.,physics).Practical implications:The results here explain some characteristics of scientific development and diffusion,highlighting the critical role of research funding in fostering citations and the expansion of scientific knowledge.This finding can support decision-making of policymakers and R&D managers to improve the effectiveness in allocating financial resources in science policies to generate a higher positive scientific and societal impact.
文摘Greenblatt and his team have unveiled vertebral skeletal stem cells(vSSCs)as a critical player in the landscape of bone metastasis.This commentary delves into the transformative discoveries surrounding vSSCs,emphasizing their distinct role in bone metastasis compared to other stem cell lineages.We illuminate the unique properties and functions of vSSCs,which may account for the elevated susceptibility of vertebral bones to metastatic invasion.Furthermore,we explore the exciting therapeutic horizons opened by this newfound understanding.These include potential interventions targeting vSSCs,modulation of associated signaling pathways,and broader implications for the treatment and management of bone metastasis.By shedding light on these game-changing insights,we hope to pave the way for novel strategies that could revolutionize the prognosis and treatment landscape for cancer patients with metastatic bone disease.
文摘Credit card companies must be able to identify fraudulent credit card transactions so that clients are not charged for items they did not purchase. Previously, many machine learning approaches and classifiers were used to detect fraudulent transactions. However, because fraud patterns are always changing, it is becoming increasingly vital to investigate new frauds and develop the model based on the new patterns. The purpose of this research is to create a machine learning classifier that not only detects fraud but also detects legitimate transactions. As a result, the model should have excellent accuracy, precision, recall, and f1-score. As a result, we began with a large dataset in this study and used four machine learning classifiers: Support Vector Machine (SVM), Decision Tree, Naïve Bayes, and Random Forest. The random forest classifier scored 99.96% overall accuracy with the best precision, recall, f1-score, and Matthews correlation coefficient in the experiments.
