Tree Diagram Research Articles

The slope of the time-dependent asymmetry, Δ and Δin D 0 → K + K −and D 0 → π + π − decays

The most precise measurement of the the slope of the time-dependent asymmetry defined by the parameter (), of the decay rates of and mesons into the final states in the decays and were recently reported by LHCb collaboration with the values of and In this study, we have obtained and where constrains possible violation. The violation in the decays and have been calculated under the factorization approach. These decay modes include a current-current tree and penguin diagrams that their amplitudes are considered separately, by introducing strong and weak phases with the and parameters, respectively. We have obtained for both of decays and The asymmetries are calculated to be and at Finally, from the sum of the amplitudes, we have calculated the total amplitude and obtained comparable results with experimental values for the branching ratios of decays.

Examining the Root Causes of On-Street Parking Mismanagement in Central Bangkok

Bangkok, a sprawling metropolis and capital of Thailand, has long-struggled with chronic on-street parking mismanagement despite repeated government attempts at regulation. This paper examines the root causes of ineffective on-street parking management in the city, elicited using root cause analysis (RCA), documentary research, field surveys, and in-depth interviews as data sources. In-depth semi-structured interviews were conducted with fifteen government officers, focusing on the mindsets, motivations and incentives of stakeholders, and tree diagrams were developed to visualize cause-and-effect relationships and identify root causes of mismanagement. Our analysis identified eight root causes of ineffective on-street parking management stemming from misalignment of government bodies, including a fragmented institutional framework, lack of interest and commitment from both national and local policymakers, the limited legislative authority of the local government, lack of on-street parking policy, lack of planning and review, ineffective law and regulation enforcement, lack of monitoring mechanisms, and low perceived seriousness of parking infringements. Based on these findings, several priority action steps are proposed to help policymakers in effectively managing on-street parking in Bangkok.

A risk-informed assessment tool to support review of license amendment requests for spent nuclear fuel dry cask storage systems

While risk-informed safety evaluations are common methodologies used by the U.S. Nuclear Regulatory Commission (NRC), their application in the field of commercial spent nuclear fuel (SNF) dry storage has received limited attention. For instance, license amendment requests (LARs) for SNF dry cask storage systems (DCSSs) are evaluated according to U.S. NRC standard review plans (SRPs). However, risk-informed LAR review strategies could improve the understanding of critical system modifications, and lead to more effective, efficient, and consistent LAR review processes.A methodology to develop a risk-informed LAR review tool is presented. Although focused on DCSSs, this methodology can be used to develop similar tools for a broad spectrum of safety-relevant systems. The end product (i.e., the tool) includes a tree diagram to help visualize the review risk significances assigned to a predefined set of system modifications and supporting rationale documentation. Instructions support correct tool application and provide the user with a path to incorporate currently unevaluated modifications in the tool structure.The demonstration of the methodology leads to the conclusion that risk-informed reviews of DCSS LARs are possible, although the tool precision could benefit from additional risk information. Further, the results indicate that many typical LARs involve low-risk or medium-risk modifications.

A Syntactical Analysis on Sentence Structures Spoken by Joe Biden and Donald Trump in the Election Night Speeches

This study attempted to investigate the types of sentence structures spoken by Joe Biden and Donald Trump in the election night speech in the year of 2020. The design of this research was qualitative and quantitative research. The total of 101 sentences of both Joe Biden and Donald Trump were analyzed by using syntactical structure analysis. The instruments of this research were documentation and checklist tables. The data were analyzed by using Miles, Huberman, and Saldana’s theory, and Carnie’s theory was used for the rules in portraying the tree diagram. The results of the study showed that all types of sentence structures appeared in both speeches. In Joe Biden’s speech, there were 40% simple sentence structures, 26.7% complex sentence structures, 16.7% compound sentence structures, and 16.7% compound complex sentence structures. In Donald Trump’s speech, there were 59,2% simple sentence structures, 19.7% complex sentence structures, 15.4% compound sentence structures, and 5.6% compound complex sentence structures. In other words, the simple sentence structures appeared the most dominant in both Donald Trump and Joe Biden speeches. It is recommended that other researchers should study the sentence structures of other American presidents (e.g. Bill Clinton, George W. Bush, and Barrack Obama) in order to expand the descriptions of sentence structures of the US presidents in general.

RISK ANALYSIS OF FALLING FROM HEIGHTS IN THE GROWING CONSTRUCTION INDUSTRY

Based on the data released by the Australian Bureau of Statistics, the number of injuries and fatalities caused by falls from height (FFH) accidents at construction sites is one of the highest making the construction industry relatively unsafe compared to other industries. This study has been made to find out the causes, consequences, and probabilities of construction fall incidents in New South Wales (NSW), Australia. The Bowtie (BT) diagram will be used to illustrate various accident situations in order to evaluate the risk of FFH in NSW, as it has not been used in previous studies. The BT diagram, which is made up of a fault tree (FT) and an event tree, represents the logical interconnections between the causes and consequences of the accidents (ET). FTs are used to calculate the odds of different accident situations. The ET analysis is then used to assess the various outcomes of these accidents, as well as their probabilities. Following that, the schematic modeling will be entered into GeNIe software for results validation, and sensitivity analysis will be used to find the main contributor of FFH incidents. The outcome will aid in the prevention of particular incidents and increase the overall level of safety on construction sites. Based on the data released by the Australian Bureau of Statistics, the number of injuries and fatalities caused by falls from height (FFH) accidents at construction sites is one of the highest, making the construction industry relatively unsafe compared to other industries. This study has been made to find out the most common causes, probabilities, and appropriate mitigation measures in construction fall incidents in New South Wales (NSW), Australia. The findings obtained from NSW Construction Blitz in 2019 was used to analyze the probability of the common potential causes of falling from ladders, voids and edges, scaffolds, formworks and, mobile scaffolds and fall restraints. Five fault tree (FT) diagrams were used to illustrate various accident causes in order to determine the probability of risk of FFH in NSW. Following that, a schematic modeling was entered into Genie software for results validation and to determine the main contributor of FFH incidents. From this study, falling from voids and edges has the highest likelihood of falls in NSW with a probability of 27.15%. This is followed by falling from mobile scaffolds and fall restraints with 7.92%, which is closely followed by falling from scaffolds with 7.68%. While falling from ladders and falling from formworks were not too far behind, with 2.17% and 1.05% respectively. Preventative measures were then discussed for FFH accident scenarios. These findings will aid in the prevention of particular incidents and increase the overall level of safety on construction sites in NSW.

Developing a simple and practical decision model to predict the risk of incident type 2 diabetes among the general population: The Di@bet.es Study

AimsTo develop a simple multivariate predictor model of incident type 2 diabetes in general population. MethodsParticipants were recruited from the Spanish Di@bet.es cohort study with 2570 subjects meeting all criteria to be included in the at-risk sample studied here. Information was collected using an interviewer-administered structured questionnaire, followed by physical and clinical examination. CHAID algorithm, which collects the information of individuals with and without type 2 diabetes, was used to develop a decision tree based type 2 diabetes prediction model. Results156 individuals were identified as having developed type 2 diabetes (6.5% incidence). Fasting plasma glucose (FPG) at the beginning of the study was the main predictive variable for incident type 2 diabetes: FPG ≤ 92 mg/dL (ref.), 92–106 mg/dL (OR = 3.76, 95%CI = 2.36–6.00), > 106 mg/dL (OR = 13.21; 8.26–21.12). More than 25% of subjects starting follow-up with FPG levels > 106 mg/dL developed type 2 diabetes. When FPG <106 mg/dL, other variables (fasting triglycerides (FTGs), BMI or age) were needed. For levels ≤ 92 mg/dL, higher FTGs levels increased risk of incident type 2 diabetes (FTGs > 180 mg/dL, OR = 14.57; 4.89–43.40) compared with the group of FTGs ≤ 97 mg/dL (FTGs = 97–180 mg/dL, OR = 3.12; 1.05–9.24). This model correctly classified 93.5% of individuals. ConclusionsThe type 2 diabetes prediction model is based on FTGs, FPG, age, gender, and BMI values. Utilizing commonly available clinical data and a simple blood test, a simple tree diagram helps identify subjects at risk of developing type 2 diabetes, even in apparently low risk subjects with normal FPG.

Development of SSR markers and genetic diversity analysis based on RAD-seq technology among Chinese populations of Daphnia magna.

Daphnia magna belongs to the Cladocera order and plays an important role in the aquatic ecosystem. With the intensification of water pollution, the wild population of D. magna has declined rapidly in recent years, and insufficient molecular markers have limited effective research and conservation of this species. 26 novel microsatellite (SSR) markers were developed in an artificially domesticated D. magna and 12 wild D. magna populations using restriction site-associated DNA sequencing (RAD-seq). The results showed that the observed heterozygosity (Ho) and expected heterozygosity (He) ranged from 0.083 to 0.999 and 0.085 to 0.862, respectively. The PIC ranged from 0.368 to 0.805. These results indicate that the developed SSR marker is highly polymorphic. Nei's genetic identity (H) ranged from 0.0926 to 0.3462. Shannon's Information index (I) ranged from 0.1333 to 0.4799. Genetic distance and Nei's genetic identity analysis, NJ tree diagram analysis, and PCoA analysis were conducted on populations of D. magna from different regions. The results show that the D. magna genetic relationship between Liaoning and Shanxi, Hunan and Anhui, and Beijing and Hainan are relatively close, while the genetic structure of D. magna in Guangdong, Jiangsu, and Sichuan is quite different from other sampling sites. An analysis of population genetic structure divided the D. magna samples into two major groups. These results indicate that the genetic structure of D. magna differs considerably in different regions. Our research results and the newly developed polymorphic SSR markers for D. magna are of great significance in terms of the genetic breeding of D. magna, identification of wild and artificially domesticated populations and conservation genetics research.

Construction of E-Commerce Economic Management Platform Based on Data Visualization Technology

In the age of information explosion, users are exposed to overwhelmingly visual information every day. The raw unprocessed data are not attractive enough to the human brain. Thankfully, humans are extremely sensitive to graphic image perception and can understand the meaning behind numbers from graphic images. For this reason, data visualization is becoming the best way to communicate with people. In the context of big data, how to display complex data efficiently, what kind of drawing technology to use, and how to improve user interaction with data are urgent problems to be solved by data visualization technology. On the other hand, researchers’ understanding of data visualization is not consistent; there is no clear standard; and it is relatively confusing. Researchers engaged in data visualization and executives engaged in related data visualization industries have different interpretations of it, and the core theoretical basis and research methods are still in the exploration stage. Firstly, I systematically introduce the design principles of data visualization, software development related technologies, data visualization research methods, basic charts, and characteristics of data visualization. Secondly, based on the software development process, I analyzed the key issues of data visualization for the e-commerce system, gave the objectives of the design plan, and designed the whole architecture and development framework of the project. In particular, I designed the class diagram and timing diagram of the control layer, service layer, and data access layer for the visualization of user purchase preferences in the data visualization and analysis module as an example. Then, with the structure and characteristics of the data in the visualization studied, the business logic layer and the front-end visualization layer are implemented, and a good user interaction function is provided for the users. The data visualization is loaded asynchronously and on demand to improve the performance of data visualization and user interaction experience. Finally, the system data is presented with the analysis of the user population’s region, the analysis of user preferences, and the analysis of the product price sales relationship, and the product category tree diagram and the results of the data visualization run are given, providing a comprehensive platform for shopping, data analysis, and data visualization for the next generation e-commerce system.

Reservoir facies classification based on random forest and geostatistics methods in an offshore oilfield

Machine learning methods are increasingly employed in various seismic and petrophysical methods for parameter estimation, interpretation, prediction, and classification. Reservoir facies classification assists the interpretation of seismic data as an important step in petroleum exploration and production monitoring. In this study, we estimate a reservoir facies model by integrating random forest (RF) algorithms and geostatistics modeling. The Surmeh Formation with the Jurassic age is known as one of the most important hydrocarbon reservoirs in the Middle East. The upper part of the Surmeh Formation is equivalent to the Arab Formation, which includes sequences of evaporitic carbonate facies in the study area. Well log data including DT, GR, RHOB, and PHI are used in the RF method for reservoir facies classification. Cross-validation verifies the high accuracy of our classification, with an average accuracy of 95%. The predicted reservoir facies consistently describe the carbonate and evaporitic facies with the geological information of this formation. The decision tree diagrams of the RF algorithm give valuable information on decision limitations and how to select features for efficient computation. We use the classification results for facies modeling. The comparison between facies models and drilling core data shows that the APE value of the sequential indicator simulation model is less than that of the indicator kriging model.

SYNTACTIC AMBIGUITY FOUND IN BUSINESS HEADLINES OF THE NEW YORK TIMES

This study is conducted to analyze the syntactic ambiguity in business headlines on the news of The New York Times. This study aimed at explaining: (1) the ambiguity in the tree diagram. (2) The suitability of the headlines with the content of the news in The New York Times. The data source is the online news in The New York Times. The data are collected by (1)Opening the Google Chrome app, (2) Accessing the official website https://nytimes.com, (3) Choosing the column "business", (4) Collecting headlines via copy and paste and screenshotting news content in a word document, (5) Writing the ten data of headlines into the table, (6) Coding the data, while the procedure of data analysis are: (1) Reading the headlines, (2) Drawing the tree diagrams, (3) Describing the meaning, (3) Interpreting the data, (4) Drawing conclusion. In this research, the writer concluded that ambiguous headlines are represented in tree diagrams based on syntax categories, namely parts of speech and phrases. Also, there are nine news headlines that are in accordance with the news content and one headline that is not in accordance with the news content.

A Machine Learning Approach to Evaluate Variables of Math Anxiety in STEM Students

The relationships between math anxiety and other variables such as students’ motivation and confidence have been extensively studied. The main purpose of the present study was to employ a machine learning approach to provide a deeper understanding of variables associated with math anxiety. Specifically, we applied classification and regression tree models to weekly survey data of science, technology, engineering, and mathematics (STEM) students enrolled in calculus. The tree models accurately identified that the level of confidence is the primary predictor of math anxiety. Students with low levels of confidence expressed high levels of math anxiety. The academic level of students and the number of weekly hours studied were the next two predictors of math anxiety. The junior and senior students had lower math anxiety. Also, those with a higher number of hours studied were generally less anxious. Weekly tree diagrams provided a detailed analysis of the interrelations between math anxiety and variables including academic level, number of hours studied, gender, motivation, and confidence. We noticed that the nature of such interrelations can change during the semester. For instance, in the first week of the semester, confidence was the primary factor, followed by academic level and then motivation. However, in the third week, the order of the interrelation changed to confidence, academic level, and course level, respectively. In summary, decision tree models can be used to predict math anxiety and to provide a more detailed analysis of data associated with math anxiety.

문법 지식의 도식화를 통한 문법 학습 난이도 예측 방안에 대한 연구

This study aims to present a new method for predicting the difficulty levels of grammatical items, using a tree diagram that allows to visually identify which items are difficult to learn. In order to achieve this goal, first I reviewed various studies that focus on intralingual and interlingual interference to find out the conditions of interference. As a large part of the errors of foreign language learners come from the interference, the grammatical items that have a high possibility of causing it can be classified as difficult to learn. Second, I chose a Spanish grammar book used in a Korean university. Its content was divided into propositions, the minimum unit of knowledge, which were classified into a priori and a posteriori knowledge and organized in a tree diagram. It was predicted that when various pieces of grammatical knowledge show a parallel or vertical relationship in the diagram, they have a high possibility of causing intralingual interference, and according to the result of the analysis, this prediction turned out to be partly correct. It was foreseen that when the priori knowledge that the learners have obtained in the acquisition or learning of the mother tongue or second language, coincide partly with the knowledge that has to be learned, the interlingual interference can occur. According to the result of the analysis, this prediction turned out to be correct. This study has limitations as it was conducted without learner error analysis. In other words, the validity of the method presented in this study has not been objectively demonstrated. For this reason, in the follow-up study it is necessary to compare learner errors with the diagram to check its validity.

A Syntactic Analysis of Null Subjects in Academic Discourse

This study uses a minimalist approach to explore the syntactic structures of teachers at the undergraduate level. It aims to find null subject(s) used in the sentence structure of lecturers who teach English in undergraduate classes. For the data collection, two universities of Federal territory in Islamabad were chosen by the researchers. The lectures [of English teachers] delivered in classes were recorded through an audiogenic device which was transcribed word by word manually. The data were analyzed qualitatively and quantitatively. In qualitative analysis, the transcribed material was analyzed with the help of parsing and a tree diagram. In quantitative studies, the occurrences of the syntactic parameters are shown with tabulations. The data analyzed reveals that the undergraduate teachers use language with three types of null subjects in their syntactic structures, i.e., Imperative Null Subject, Non-finite Null Subject, and Truncated Null Subject. The minimal sentence structure pairs impact a different level of language, changing its dimensions.

Analysis of Minimalist Syntactic Complexity in English Writing among Secondary Level Students

This research is an analysis of minimalist syntactic complexity in the syntactic structures of secondary-level students of government schools. It highlights the null constituents in the syntactic structures of students writing in annual examinations. It also finds the occurrences of null constituents in the written syntactic structures of the students. For this purpose, the researchers selected ten government high schools [male students] of district Mardan. In this research, a hundred samples of attempted answer sheets of English paper (class 9 and 10) were collected through a random sampling technique. The theoretical framework of the Noam Chomskys Minimalist program was used for this research. The syntactic structures of the attempted answer sheets of the students were analyzed through parsing (tree diagram) with the perspective of minimalism. The data were analyzed qualitatively and quantitatively; the null constituents are discussed qualitatively, and the occurrences of null constituents shown are quantitatively through tabulation.

Extraction of rules related to marketing mix on customers’ buying behavior using Rough set theory and fuzzy 2-tuple approach

ABSTRACT Marketing mix elements are sets of controllable marketing variables that an organization combines to achieve marketing goals in the target market and meet customer needs. These are the most essential criteria to increase sales and profits. The purpose of this study is to explore how these factors influence customers’ purchasing behavior at chain stores in Tehran. Based on this, Five of Tehran’s best-selling chain stores are chosen, and 125 samples are gathered from each. To collect information, the 2-tuple fuzzy approach is used to avoid losing the linguistic information obtained from customers. The Rough Theory Set and decision tree algorithm are used to analyze the data and extract the rules Five rules guiding customer behavior are identified as essential aspects influencing buying behavior in shopping based on the data analyzed by Rosetta software. A comparison is made between the extracted rules using the proposed Rough Set Theory and the tree diagram of the data acquired by RapidMiner software to assess the result.

Dendrogram Analysis and Statistical Examination for Total Microbiological Mesophilic Aerobic Count of Municipal Water Distribution Network System

The microbiological quality of water for human consumption is a critical safety aspect that should not be overlooked, especially when considering facilities for healthcare and the treatment of ill populations. Thus, the biological stability of water is crucial for the distribution network that delivers potable water to the final users for consumption and other human activities. The present work aimed to study a municipal distribution network system for city water within a healthcare facility. The implementation of the statistical analysis was conducted over long-term data collection, and the comparative study for the microbiological count of the water samples - from different points-of-use was assessed using the non-parametric analysis of the Kruskal-Wallis test. The comparative study involved a preliminary general one-way Analysis of Variance (ANOVA) followed by ad-hoc pairwise comparison. The statistical study involved a correlation matrix and a dendrogram to elucidate the level of association between different sections in the network. The ports C4 and C13 were at the trough in the microbiological count, in contrast to C13, which showed the highest level of the average microbial density. Despite a low to moderate level of correlation between the datasets of the water network, the tree diagram (dendrogram) analysis showed remarkable clustering. Use points could be grouped into three dense groups based on abrupt cuts in the similarity value. The study was useful in the analysis of the pattern and behavior of the microbial quality in a distribution water network in a specific area of the study. This work in turn would help in investigating the areas of improvement and defect spotting, in addition to assessing the biological stability of the water distribution system. The study could be extended to cover other different processed water networks, such as distilled, deionized, and purified water, as well as Water-For-Injection (WFI). Doi: 10.28991/HIJ-2022-03-01-03 Full Text: PDF

The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)

This article provides a checklist of the aquatic animals the Romans appear to have recognized, including an overview of how they classified and named them. Indeed, in addition to providing scientific identifications of the animal referents and philological and/or linguistic data related to their names as they emerge in the ancient texts, the checklist focuses on ethnobiology. In particular, it provides ethnotaxonomic and ethnobiolinguistic data such as ethnotaxonomic rank and ethnobiological name typology (Conklin 1968; Berlin 1992) by which to reconstruct the actual folk taxa, i.e. the kinds or “groups of kinds” of animals each name covered in the ancient speakers' minds (but a few unnamed folk taxa have also emerged). In this respect, tree diagrams are used to represent at a glance the folk taxonomic knowledge of an ideal Roman as to the main higher-order groupings of aquatic animals they were familiar with. The results are analyzed and statistical data are provided as to various relevant ethnobiological variables. The findings accord with Berlin's universals, except for the number of additional levels where folk taxa of the life-form rank can be found, a characteristic already observed for aquatic animals in other folk taxonomies (Pawley 2006). From a more general zooanthropological perspective, these data suggest the Romans' familiarity with the aquatic world and its inhabitants, despite their traditional self-ascribed identity as peasants and soldiers.

Construction of Xinjiang metabolic syndrome risk prediction model based on interpretable models

BackgroundWe aimed to construct simple and practical metabolic syndrome (MetS) risk prediction models based on the data of inhabitants of Urumqi and to provide a methodological reference for the prevention and control of MetS.MethodsThis is a cross-sectional study conducted in the Xinjiang Uygur Autonomous Region of China. We collected data from inhabitants of Urumqi from 2018 to 2019, including demographic characteristics, anthropometric indicators, living habits and family history. Resampling technology was used to preprocess the data imbalance problems, and then MetS risk prediction models were constructed based on logistic regression (LR) and decision tree (DT). In addition, nomograms and tree diagrams of DT were used to explain and visualize the model.ResultsOf the 25,542 participants included in the study, 3,267 (12.8%) were diagnosed with MetS, and 22,275 (87.2%) were diagnosed with non-MetS. Both the LR and DT models based on the random undersampling dataset had good AUROC values (0.846 and 0.913, respectively). The accuracy, sensitivity, specificity, and AUROC values of the DT model were higher than those of the LR model. Based on a random undersampling dataset, the LR model showed that exercises such as walking (OR=0.769) and running (OR= 0.736) were protective factors against MetS. Age 60 ~ 74 years (OR=1.388), previous diabetes (OR=8.902), previous hypertension (OR=2.830), fatty liver (OR=3.306), smoking (OR=1.541), high systolic blood pressure (OR=1.044), and high diastolic blood pressure (OR=1.072) were risk factors for MetS; the DT model had 7 depth layers and 18 leaves, with BMI as the root node of the DT being the most important factor affecting MetS, and the other variables in descending order of importance: SBP, previous diabetes, previous hypertension, DBP, fatty liver, smoking, and exercise.ConclusionsBoth DT and LR MetS risk prediction models have good prediction performance and their respective characteristics. Combining these two methods to construct an interpretable risk prediction model of MetS can provide methodological references for the prevention and control of MetS.

Research on Abnormal Event Classification of Expressway Tunnel based on Security Control

Based on the perspective of tunnel traffic safety operation and control and the human, vehicle, road, and environment system in tunnel operation process, adopting event tree analysis method according to the different influencing factors, generation mechanism and performance characteristics of tunnel abnormal events, this research propose abnormal event classification tree diagram. According to the current abnormal event management and control mechanism in China, the administration departments and disposal facilities associated with different types of abnormal events in expressway tunnel operation are sorted out, which can provide decision-making support for the rapid treatment of abnormal events and realize intelligent control of tunnel.

A structural ambiguity analysis in sentences found in the skeleton key novel

As social beings, humans need other individual to survive. In social life, humans interact and communicate with each other using languages which use to ask for help or exchange information. Often in verbal or non-verbal communication, someone provides information that has two or more different meaning thus confusing the interlocutors, this condition of information call ambiguous. This study entitle “A Structural Ambiguity Analysis In Sentences Found in The Skeleton Key Novel” is aimed to identify one of the types of surface ambiguity in the structural ambiguity in sentences found in The Skeleton Key novel, and to analyze pattern of structural ambiguity in sentences that were found in The Skeleton Key novel by tree diagrams. The data of this study were analyzed by using the qualitative method. From 12 data that were found in the data source, one of the data were analyzed as the sample of this study. From six types of surface ambiguity propose by Kriedler (1998) only one of the types was analyzed for this research. It was a coordinate head with one modifier. In this research, the researcher found that the structure of sentence structure will affect the meaning of the sentence itself. Thus the analysis of sentence structure using tree diagram will produce different meaning according to intented construction in tree diagram.