Logistic Regression Analysis of the Factors Involved in the Failure of Osseointegration and Survival of Dental Implants with an Internal Connection and Machined Collar: A 6-Year Retrospective Cohort Study

Background. Although the long-term success rate of dental implants is currently close to 95%, it is necessary to provide more evidence on the factors related to the failure of osseointegration and survival. Purpose. To establish the risk factors associated with the failure of osseointegration and survival of dental implants with an internal connection and machined collar and to establish a predictive statistical model. Materials and Methods. An analytical, retrospective, and observational clinical study of a sample of 297 implants with a follow-up of up to 76 months. Independent variables related to the implant, patient, and surgical and rehabilitative procedures were identified. The dependent variables were failure of osseointegration and failure of implant survival after prosthetic loading. A survival analysis was carried out by applying the Kaplan-Meier model (significance for p < 0.05 ). The log-rank test and the Cox regression analysis were applied to the factors that presented differences. Finally, the regression logit function was used to determine whether it is possible to predict the risk of implant failure according to the analyzed variables with the data obtained in this study. Results. The percentages of osseointegration and survival were 97.6 and 97.2%, respectively. For osseointegration, there were significant differences according to gender ( p = 0.048 ), and the risk of nonosseointegration was 85% lower in women. Regarding survival, the Cox analysis converged on only two factors, which were smoking and treatment with anticoagulant drugs. The risk of loss was multiplied by 18.3 for patients smoking more than 10 cigarettes per day and by 28.2 for patients treated with anticoagulants. Conclusions. The indicated risk factors should be considered, but the analysis of the results is not sufficient to create a predictive model.

A influência de variáveis contextuais e situacionais na classificação de árbitros de futebol de elite

Objetivo: Este estudo teve como objetivo avaliar a relação entre a classificação de árbitros de futebol de elite e variáveis contextuais e situacionais que caraterizam os jogos arbitrados no decorrer de uma época. Para tal, foi realizada uma regressão ordinal com função Link Logit entre a classificação final e variáveis contextuais e situacionais. As variáveis contextuais revelaram um efeito significativo sobre a classificação final, não se verificando efeitos significativos das variáveis situacionais, sobre a classificação dos árbitros no final da época desportiva. Na globalidade o modelo revelou-se estatisticamente significativo. A probabilidade de obtenção de melhor classificação final dos árbitros aumenta 54.2% com o aumento do número de jogos realizados na I Liga e aumenta 24.8% com aumento do número de jogos equilibrados. Diminui 61.2% com jogos realizados sem equipas Top 3. Os resultados reforçam a influência significativa que os fatores contextuais têm sobre a classificação e avaliação de um árbitro no final da época desportiva. This study aimed to evaluate the relationship between the classification of elite soccer referees in Portugal and the contextual and situational variables of the matches refereed during the 2016-2017 sports season. In order to analyze the relationship between the final classification and the level of the competition, characterization of the game, result of the game and total number of cards displayed per game, an ordinal regression with Link Logit function was used. The results revealed that the contextual variables have a significant effect on the final classification, with no significant effects of the situational variables. In general, the model statistically explains the final classification of the elite soccer referees at the end of the sports season (X2LP (5) = 40.299, p<0.001). The probability of obtaining a better final referees’ classification increases 54.2% with the increase in the number of games played in the I League (OR=1.542), and 24.8% with the increase in the number of balanced games (OR=1.248). Decreases 61.2% with the increase in the number of games without TOP 3(OR=0.388). Finally, in relation to the total number of cards displayed in a game, there were no significant effects on the ranking of referees' performance. In summary, the results reinforce the significant influence that contextual factors have on the classification and assessment of a referee at the end of the sports season. Este trabalho teve como objetivo avaliar a relação entre a classificação dos árbitros de futebol de elite em Portugal e as variáveis contextuais e situacionais dos jogos arbitrados no decorrer da época desportiva 2016-2017. No sentido de analisar qual a relação entre a classificação final e o nível da competição, caraterização do jogo, resultado do jogo e número total de cartões exibidos por jogo foi realizada uma regressão ordinal com função Link Logit. Os resultados, revelaram que as variáveis contextuais, apresentam um efeito significativo sobre a classificação final, não se verificando efeitos significativos das variáveis situacionais, sobre a classificação final dos árbitros de futebol no final da época desportiva. Na globalidade o modelo revelou-se estatisticamente significativo (X2LP (5) = 40.299, p<0.001). A probabilidade de obtenção de melhor classificação final dos árbitros aumenta 54.2% com o aumento do número de jogos realizados na I Liga (OR=1.542) e aumenta 24.8% com aumento do número de jogos equilibrados (OR=1.248). Diminui 61.2% com jogos realizados sem equipas Top 3 (OR=0.388). Por último, em relação ao número total de cartões exibidos num jogo não se verificaram efeitos significativos no ranking de desempenho dos árbitros. Em suma, os resultados reforçam a influência significativa que os fatores contextuais têm uma sobre a classificação e avaliação de um árbitro no final da época desportiva.

The Influence of Sustainable Earnings on Stock Price: Evidence from Publicly Listed Vietnamese Business Enterprises

Expected earnings and stock price are important determinants of investors’ decision. This research is conducted to estimate earnings persistence and examine the relationship between sustainable earnings on price-to-earning (P/E) ratio based on financial statements’ information of 631 publicly listed non-financial companies on Vietnam’s stock market, by using Ordinary Least Squares (OLS) and Logit function. The results show that earnings persistence depends on net operating assets growth, profit margin changes, operating asset turnover changes and past profitability. Besides, both the sustainable and unsustainable components of earnings growth are proved to empirically affect P/E ratio, even though investors underreact to sustainable earnings and overreact to unsustainable earnings. This study helps to improve investors’ perception of their future earnings, investment value and companies’ sustainable growth, particularly in the context of developing stock market of Vietnam which is full of market anomalies.

De wervelkolom van taalverandering

The spine of language changeIn his contribution to the 2005 anniversary issue of the journal Nederlandse Taalkunde, Fred Weerman remarked on the famous S-curve underlying language change, and claimed that a good explanation for this pattern is still lacking. We pick up the thread and assess what 15 years of research have clarified about the nature of the curve. We look at two aspects: the onset of the curve (also known as the ‘actuation problem’), and the sigmoid trajectory (known as ‘propagation’). For the actuation problem, we highlight the role of external variables, notably the role of cities in what kind of changes are more likely to occur. Higher urbanization leads to morphological simplification. For the propagation, we investigate the underlying mathematics of the curve, and its conceptual motivation. We argue that the lesser-known probit function is conceptually more insightful than the commonly used logit function, and marginally outperforms the latter as well, when tested on real data. The difference is so small, however, that in actual practice, the logit function, which is mathematically simpler, may continue to be preferred

Toxicity of two Engelmann spruce (Pinaceae) monoterpene chemotypes from the southern Rocky Mountains to North American spruce beetle (Coleoptera: Scolytidae)

Engelmann spruce, Picea engelmannii Parry ex Engelm. (Pinaceae), in the southern Rocky Mountains is composed of two distinct phloem monoterpene chemotypes that differ in relative abundances of multiple monoterpenes, particularly α-pinene and Δ3-carene (hereafter, the “α-pinene chemotype” and the “Δ3-carene chemotype”). Here, relative toxicity of these chemotypes is tested on spruce beetle (Dendroctonus rufipennis Kirby) (Coleoptera: Scolytinae), a phloeophagous herbivore that colonises trees of both types. Synthetic monoterpene blends representing each chemotype were tested across a range of concentrations (0, 10, 50, 100, 200, and 500 µg/L) in the lab, and probability of survival of adult beetles exposed to each blend was modelled using a logit function. Logit curves were solved to determine LC25, LC50, and LC75 of each monoterpene blend. On average, probability of beetle survival was lower when exposed to the Δ3-carene chemotype than when exposed to the α-pinene chemotype. However, both chemotypes were completely lethal to beetles at concentrations exceeding 100 µg/L. Adult body mass did not affect survival probability. It is concluded that spruce phloem chemotypes may differ in their toxicity to spruce beetles, with potential consequences for patterns of host-tree colonisation by spruce beetle.

A Practical Traffic Assignment Model for Multimodal Transport System Considering Low-Mobility Groups

In this study, we created a practical traffic assignment model for a multimodal transport system considering low-mobility groups with the aim of providing the foundation of transportation network design for low-mobility individuals. First, the route choice equilibrium for walking, non-vehicle, and private car modes is described using the logit function, which is formulated as a variational inequality problem considering different low-mobility groups. Then, the practicalities related to travel times at intersections, traffic barricades between different lanes, and fuel fees of private cars are integrated to design a generalized travel cost function. Last, the method of successive weight averages is used to solve the proposed model. The model and its solution are verified based on a real case study of the city of Wenling in China. The sensitivity of adjustment parameters related to travel costs are analyzed, the practicality of the proposed model is explored, and the results of traffic assignment for different low-mobility groups are discussed.

Probit or Logit? Which is the better model to predict the longevity of seeds?

The prediction of seed longevity (P50) is traditionally performed by the use of the Probit model. However, due to the fact that the survival data are of binary origin (0,1), the fit of the model can be compromised by the non-normality of the residues. Consequently, this leads to prediction losses, despite the data being partially smoothed by Probit and Logit models. A possibility to reduce the effect of non-normality of the data would be to apply the principles of the central limit theorem, which states that non-normal residues tend to be normal as the n sample is increased. The Logit and Probit models differ in their normal and logistic distribution. Therefore, we developed a new estimation procedure by using a small increase of the n sample and tested it in the Probit and Logit functions to improve the prediction of P50. The results showed that the calculation of P50 by increasing the n samples from 4 to 6 replicates improved the index of correctness of the prediction. The Logit model presented better performance when compared with the Probit model, indicating that the estimation of P50 is more adequate when the adjustment of the data is performed by the Logit function.

Degrees of Rationality in Agent-Based Retail Markets

The imperfect decision-making of human buyers participating in retail markets varies from fundamental models that assume rational economic choices: even in markets with identical items human buyers are not rational, i.e., buyers do not always choose the cheapest option. Recent developments in artificial intelligence and e-commerce enable market participation by software agents that are (almost) perfectly rational due to their computational capacity. However, the increasing degree of buyers’ rationality might have unfavorable effects on retail markets with regards to the competition between sellers and the resulting prices. In this paper, we study the effects of varying degrees of buyers’ rationality on the competition and the prices buyers face in retail markets with identical items. We use the multinomial logit function to model different degrees of buyers’ rationality. We further model the competition between sellers using k-level reasoning: each seller computes the price to offer (best response strategy) with regards to its belief for the competition. First, we derive an analytical best response strategy (price) of a seller given the competing prices and the degree of buyers’ rationality, and show that there exists an optimal degree of buyers’ rationality that minimizes the price. Last, we use evolutionary game theory to show that perfect rationality leads to unstable competition dynamics increasing the overall cost for buyers. In contrast, bounded rationality leads to smoother dynamics and lower cost for buyers. Our insights raise the need to revisit design objectives for software agents in retail markets in light of their wider systematic impact.

Patent Keyword Analysis using Time Series and Copula Models

The technological keywords extracted from patent documents have much information about a developed technology. We can understand the technological structure of a product by examining the results of patent analysis. So far, much research has been done on patent data analysis. The technological keywords of patent documents contain representative information on the developed technology. As such, the patent keyword is one of the most important factors in patent data analysis. In this paper, we propose a patent data analysis model combining a integer valued time series model and copula direction dependence for integer valued patent keyword analysis over time. Most patent keywords are frequency values and keywords often change over time. However, the existing patent keywords analysis works do not account for two major factors: integer value and time. For modeling integer valued keyword data with time factor, we use a copula directional dependence model based on marginal regression with a beta logit function and integer valued generalized autoregressive conditional heteroskedasticity model. Using the proposed model, we find technological trends and relations in the target technological domain. To illustrate the performance and implication of our paper, we carry out experiments using the patent documents applied and registered by Apple company. This study contributes to the effective planning for the research and development of technologies by utilizing the evolution of technology over time.

Choice of choice models: Theory of signal detectability outperforms Bradley-Terry-Luce choice model

Identifying the best framework for two-choice decision-making has been a goal of psychology theory for many decades (Bohil, Szalma, & Hancock, 2015; Macmillan & Creelman, 1991). There are two main candidates: the theory of signal detectability (TSD) (Swets, Tanner Jr, & Birdsall, 1961; Thurstone, 1927) based on a normal distribution/probit function, and the choice-model theory (Link, 1975; Luce, 1959) that uses the logistic distribution/logit function. A probit link function, and hence TSD, was shown to have a better Bayesian Goodness of Fit than the logit function for every one of eighteen diverse psychology data sets (Open-Science-Collaboration, 2015a), conclusions having been obtained using Generalized Linear Mixed Models (Lindstrom & Bates, 1990; Nelder & Wedderburn, 1972) . These findings are important, not only for the psychology of perceptual, cognitive and social decision-making, but for any science that use binary proportions to measure effectiveness, as well as the meta-analysis of such studies.