scholarly journals Pet Reptiles—Are We Meeting Their Needs?

Animals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2964
Author(s):  
Alexandre Azevedo ◽  
Leonor Guimarães ◽  
Joel Ferraz ◽  
Martin Whiting ◽  
Manuel Magalhães-Sant’Ana
Keyword(s):  
At Risk ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Ethical Debate ◽  
In Captivity ◽  
Poor Welfare

The ability to meet the needs of each species in captivity is at the heart of the ethical debate on the acceptability of keeping reptiles and other animals as pets. Little is known about the ability of reptile owners to understand their pets’ behavior and to meet their welfare requirements. In this study, we surveyed pet reptile owners in Portugal (N = 220) to assess their behavioral knowledge and the provision of essential husbandry needs. Although two-thirds of respondents (68%) scored very good to excellent in terms of knowledge of their pet reptile’s behaviors, only 15% of respondents met four essential reptile husbandry needs (temperature, lighting, diet and refuge) and 43% met two or less. None of the respondents reported their reptile’s welfare as very poor, and only a single respondent reported it as poor. Logistic regression model showed that while snake owners had fourteen times higher odds of reporting adequate husbandry provision, lizard owners had the highest odds of reporting good or very good welfare despite providing less of their animals’ basic husbandry needs. These results suggest that many pet reptiles in Portugal live in, at best, ‘controlled deprivation’ and are at risk of suffering poor welfare throughout their captive lives. Moreover, behaviors indicative of poor welfare and captivity stress were considered ‘normal’ by up to one quarter of respondents. We suggest that the frequency of these behaviors in pet reptiles has led to their acceptance as normal, precluding the search for ways to prevent them. These results suggest that campaigns aimed at challenging the current norm for adequate reptile welfare are warranted.

scholarly journals A Logistic Regression Model For The Enhancement Of Student Retention: The Identification Of At-Risk Freshmen

2011 ◽  
Vol 1 (8) ◽  
Author(s):  
Joseph G. Glynn ◽  
Paul L. Sauer ◽  
Thomas E. Miller
Keyword(s):  
At Risk ◽  
Logistic Regression ◽  
Regression Model ◽  
Early Identification ◽  
Student Persistence ◽  
Logistic Regression Model ◽  
Intervention Program ◽  
School Experiences ◽  
Freshman Orientation ◽  
High School Experiences

A logistic regression model will be developed to provide early identification of freshmen at risk of attrition. The early identification is accomplished literally within a couple of weeks after freshman orientation. The dependent variable of interest is persistence, and it is a binary, nominal variable. Students who proceed from freshman matriculation to graduation without ever having dropped out are labeled persistors. Freshman matriculates who leave college either temporarily or permanently are classified as dropouts. The independent variables employed to predict attrition include demographics, high school experiences, and attitudes, opinions, and values as reported on a survey administered during freshman orientation. The model and its results will be presented along with a brief description of the institutional intervention program designed to enhance student persistence.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p < 0,0001), education (p < 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

scholarly journals Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

2021 ◽  
Vol 11 (14) ◽  
pp. 6594
Author(s):  
Yu-Chia Hsu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Logistic Regression Model ◽  
National Football League ◽  
Performance Metrics ◽  
Betting Market

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.

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