Applied Mathematics and Sciences: An International Journal (MathSJ)最新文献

{"title":"The Mathematics of the Brain","authors":"S. Zechner","doi":"10.5121/MATHSJ.2019.6102","DOIUrl":"https://doi.org/10.5121/MATHSJ.2019.6102","url":null,"abstract":"It has never been accomplished to describe our behavior mathematically. Due to the fact that human behavior is highly erratic even the understanding of its causes are still sketchy. Assuming that we are all equal in our regulation of thought and behavior there are simply too many differences and partially inconsistencies, the attempts stopped in its onset. For having defined the five major groups of mankind, the sociopath, the artist, the median, the, and the psychopath[1]each group is related to each other but his decision making is hardwired differently and thou probably more easy to grasp than sticking to the consistency of all appearance for the regulation of behavior is quite similar in the five groups but different in its limits. Using the more formalistic tools of mathematics, this can open the review of the equations in verifying or falsifying the predictions on future behavior in an individual, at least after defining the group affiliation. Therefore a self-test has been established[2]to predetermine the group. In a hybrid-species [3], the eight main neuro-receptors in each group to have two optional origins. Measuring each by its own dominating patterns not only the amalgamation in each group can be defined but also the native patterns of the non-hybrid ancestry [4]. Not only the variance of possible combination can distribute to the limits of brain-equations but also the time-axis of our memory, being rather different, illuminating the highly different decision making among offspring of hunters and farmers. A phenomenon probably explaining the variance in processing memory by peripheral distributed groups of ADHD and autism for ADHD memorizes in combining data with importance and such is been given an emotional response to the recall, while autism is mainly been given the exact time reference stored in a continuous frame of timepreference. The latter therefore have problems to distinguish between important and not important and the former lacks a passing timeframe, mirroring the primary form of acquiring resource, farming or hunting. With the boundaries set the graphs of the equation on resource-projection looks highly different by only changing its limits. Having not only focussed on the designation of human groups but also in behavioral shifts over time (from social to non-social) and the exact denomination of similar behavior, some rather simple equation could be defined to predict and consequently proof the predicate. Not only the proper use of words is necessary but also awareness that non-social individuals will often not answer truthfully. A topic that also can be mathematically predicted. The outcome will revolutionize our perception of mankind and ourselves, dawning more than one academic discipline, probably enabling us to go virtual and back again.","PeriodicalId":151714,"journal":{"name":"Applied Mathematics and Sciences: An International Journal (MathSJ)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132141849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive Models for Game Outcomes in Women's Lacrosse","authors":"M. S. Brown","doi":"10.5121/MATHSJ.2019.6101","DOIUrl":"https://doi.org/10.5121/MATHSJ.2019.6101","url":null,"abstract":"This research presents a predictive model for determining the game outcome of a Women’s (Female) Lacrosse game. This is important to coaches regardless of if their team appears to be winning or losing the game. Coaches make decisions throughout the game based upon the belief that they are winning or losing. The model is a Logistic Regression model and can be used with very little data from a game: time remaining and difference between the scores. This could be a valuable tool to coaches that can be used during the game. It is more than 89% accurate. Data used in this research comes from direct matchup games between BigTen Women’s Lacrosse teams. The win probability equations, including coefficients, are presented.","PeriodicalId":151714,"journal":{"name":"Applied Mathematics and Sciences: An International Journal (MathSJ)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117040061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Edge-Neighbor Rupture Degree on Graph Operations","authors":"Saadet Eskiizmirliler, Z. Yorgancioglu","doi":"10.5121/MATHSJ.2018.5301","DOIUrl":"https://doi.org/10.5121/MATHSJ.2018.5301","url":null,"abstract":"Vulnerability and reliability parameters measure the resistance of the network to disruption of operation after the failure of certain stations or communication links in a communication network. An edge subversion strategy of a graph , say , is a set of edge(s) in whose adjacent vertices which is incident with the removal edge(s) are removed from . The survival subgraph is denoted by − . The edgeneighbor-rupture degree of connected graph , , is defined to be = − − | | − − : ⊆ , − ≥ 1 where is any edge-cut-strategy of , − is the number of the components of − , and − is the maximum order of the components of − . In this paper we give some results for the edge-neighbor-rupture degree of the graph operations and Thorny graph types are examined.","PeriodicalId":151714,"journal":{"name":"Applied Mathematics and Sciences: An International Journal (MathSJ)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131002142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}