Access to waste management services is crucial for urban sustainability, impacting public health, environmental well-being, and overall quality of life. This study employs logistic regression analysis on survey data collected from 1,032 household heads residing in Nouakchott, the capital of Mauritania. The survey investigated key household factors that determine access to waste management services. The findings reveal a significant interplay among waste service provision, the presence of cisterns, housing type and size, and access to electricity. Socioeconomic disparity in service access, with poorer housing formats like shacks receiving substandard services. In contrast, areas with robust electrification report better service access, although inconsistencies remain amid power outages. The research highlights the challenges faced by Riyadh municipality, particularly rapid growth and inadequate infrastructure, which hinder waste management efficiency. Overall, the results not only illuminate Nouakchott’s unique challenges in service provision but also propose actionable recommendations for a sustainable urban future. These recommendations aim to inform and guide targeted policies for improving living conditions and environmental sustainability in urban Mauritania.

The germination percentage (GP) is commonly employed to estimate the viability of a seed population. Statistical methods such as analysis of variance (ANOVA) and logistic regression are frequently used to analyse GP data. While ANOVA has a long history of usage, logistic regression is considered more suitable for GP data due to its binomial nature. However, both methods have inherent issues that require attention. In this study, we address previously unexplored challenges associated with these methods and propose the utilization of a likelihood ratio test as a solution. We demonstrate the advantages of employing the likelihood ratio test for GP data analysis through simulations and real data analysis.

Alternating Dat-Nom/Nom-Dat verbs in Icelandic are notorious for instantiating two diametrically opposed argument structures: the Dat-Nom and the Nom-Dat construction. We conduct a systematic study of the relevant verbs to uncover the factors steering the alternation. This involves a comparison of 15 verbs, five alternating ones, and as a control, five Nom-Dat verbs and five non-alternating Dat-Nom verbs. Our findings show that alternating verbs instantiate the Nom-Dat construction 54% of the time and the Dat-Nom construction 46% of the time on average for four of five verbs when both arguments are full NPs. However, in configurations with a nominative pronoun, the Nom-Dat construction takes precedence over the Dat-Nom construction. Also, for the double-NP configuration, a logistic regression analysis identifies indefiniteness and length as two key predictors, apart from nominative case marking. We demonstrate that the latter systematically correlates with discourse-prominence, which we show, upon closer inspection, correlates with topicality.

Climate models are primary tools for investigating processes in the climate system, projecting future changes, and informing decision makers. The latest generation of models provides increasingly complex and realistic representations of the real climate system, while there is also growing awareness that not all models produce equally plausible or independent simulations. Therefore, many recent studies have investigated how models differ from observed climate and how model dependence affects model output similarity, typically drawing on climatological averages over several decades. Here, we show that temperature maps of individual days drawn from datasets never used in training can be robustly identified as “model” or “observation” using the CMIP6 model archive and four observational products. An important exception is a prototype storm-resolving simulation from ICON-Sapphire which cannot be unambiguously assigned to either category. These results highlight that persistent differences between simulated and observed climate emerge at short timescales already, but very high-resolution modeling efforts may be able to overcome some of these shortcomings. Moreover, temporally out-of-sample test days can be assigned their dataset name with up to 83% accuracy. Misclassifications occur mostly between models developed at the same institution, suggesting that effects of shared code, previously documented only for climatological timescales, already emerge at the level of individual days. Our results thus demonstrate that the use of machine learning classifiers, once trained, can overcome the need for several decades of data to evaluate a given model. This opens up new avenues to test model performance and independence on much shorter timescales.

Elephant ranges in Asia overlap with human-use areas, leading to frequent and often negative two-way interactions, a fraction of which result in human fatalities. Minimizing such negative interactions rests on gaining a mechanistic understanding of their patterns and underlying processes. In Chhattisgarh (India), a rewilding population of 250–300 elephants that have recently expanded their range from neighbouring states through dispersal has been causing annual losses of >60 human lives. Using logistic regression models, we examined the influences of eight plausible predictors of the occurrence of elephant-related human fatality incidents. We found that 70% of incidents occurred in areas with high-intensity habitat use by elephants; the other 30% were in areas of intermediate and sporadic elephant habitat use. The probability of human fatalities was high along the roads connecting settlements and in areas with frequent house break-ins by elephants, and this probability was also affected by the spatial geometry of forest patches. Immediate practical options to minimize fatal interactions include community-based early-warning systems and the use of portable barriers around settlements. Judicious landscape-level land-use planning aimed at maintaining the resilience of remnant intact elephant habitats will be critical to preventing the dispersal of elephants into suboptimal habitats, which can create complex conflict situations.

Consider the problem of determining the Bayesian credibility mean $E(X_{n+1}|X_1,\cdots, X_n),$ whenever the random claims $X_1,\cdots, X_n,$ given parameter vector $\boldsymbol{\Psi},$ are sampled from the K-component mixture family of distributions, whose members are the union of different families of distributions. This article begins by deriving a recursive formula for such a Bayesian credibility mean. Moreover, under the assumption that using additional information $Z_{i,1},\cdots,Z_{i,m},$ one may probabilistically determine a random claim $X_i$ belongs to a given population (or a distribution), the above recursive formula simplifies to an exact Bayesian credibility mean whenever all components of the mixture distribution belong to the exponential families of distributions. For a situation where a 2-component mixture family of distributions is an appropriate choice for data modelling, using the logistic regression model, it shows that: how one may employ such additional information to derive the Bayesian credibility model, say Logistic Regression Credibility model, for a finite mixture of distributions. A comparison between the Logistic Regression Credibility (LRC) model and its competitor, the Regression Tree Credibility (RTC) model, has been given. More precisely, it shows that under the squared error loss function, it shows the LRC’s risk function dominates the RTC’s risk function at least in an interval which about $0.5.$ Several examples have been given to illustrate the practical application of our findings.

Acquired immune deficiency syndrome (UNAIDS) has risen as the serious public health problem across the world. Knowledge about HIV/AIDS is the cornerstone for prevention and treatment. Research is needed to explore the attitude and the effect of different demographic, geographic, and socioeconomic and media exposure factors on males knowledge about HIV in Pakistan. In this study, latest secondary data are used from Pakistan Demographic and Health Survey 2017-18. Sample results show that the majority of the respondents (70%) have knowledge about AIDS. Regression Modeling reveals that man’s knowledge about HIV/AIDS is associated with age, place of residence, educational level, wealth index, ethnicity and media exposure factors. Males of age group 35-39, with higher education, belonging to Pukthon ethnicity, having exposure to mass media on a daily basis and belonging to richest wealth quintile has high Knowledge of HIV/AIDS. For example, the regression model predicts that men between the ages of 35 and 39 from Islamabad who live in urban areas, have higher education, are of Pukhtoon ethnicity, are the head of the household, belong to the richest quintile, work in professional occupations, and use media exposure factors on a daily basis would have probability of 97% of having knowledge of HIV/AIDS. But there is still need to focus to increase the men’s knowledge of HIV/AIDS.

Previous research has established that higher levels of trait Honesty-Humility (HH) are associated with less dishonest behavior in cheating paradigms. However, only imprecise effect size estimates of this HH-cheating link are available. Moreover, evidence is inconclusive on whether other basic personality traits from the HEXACO or Big Five models are associated with unethical decision making and whether such effects have incremental validity beyond HH. We address these issues in a highly powered reanalysis of 16 studies assessing dishonest behavior in an incentivized, one-shot cheating paradigm (N = 5,002). For this purpose, we rely on a newly developed logistic regression approach for the analysis of nested data in cheating paradigms. We also test theoretically derived interactions of HH with other basic personality traits (i.e., Emotionality and Conscientiousness) and situational factors (i.e., the baseline probability of observing a favorable outcome) as well as the incremental validity of HH over demographic characteristics. The results show a medium to large effect of HH (odds ratio = 0.53), which was independent of other personality, situational, or demographic variables. Only one other trait (Big Five Agreeableness) was associated with unethical decision making, although it failed to show any incremental validity beyond HH.

Although there has been significant research on the relationship between alcohol consumption and demographic and psychological influences, this does not consider the effect of social influence among older drinkers and if these effects differ between men and women. One aspect of social influence is social capital. The aim of this paper is to examine whether relational and cognitive social capital are associated with higher or lower risk of alcohol use among adults aged 50 years or older and to assess the extent to which this relationship differs between men and women. To investigate this, data were collected from a cross-sectional questionnaire survey of adults over the age of 50 in the United Kingdom who were recruited from general practitioners. The sample consisted of 9,984 individuals whose mean age was 63.87 years. From these data, we developed proxy measures of social capital and associate these with the respondent's level of alcohol consumption as measured on the Alcohol Use Disorders Identification Test (AUDIT-10) scale. In the sample, just over 20 per cent reported an increasing risk or dependency on alcohol. Using two expressions of social capital – relational (social relationships) and cognitive (knowledge acquisition and understanding) – we found that greater levels of both are associated with a reduced risk of higher drinking risk. Being female had no significant effect when combined with relational capital but did have a significant effect when combined with cognitive capital. It is argued that interventions to enhance social relations among older people and education to help understand alcohol risks would be helpful to protect older people from the damaging effects of excessive alcohol consumption.

There is an ongoing debate on whether wine reviews provide meaningful information on wine properties and quality. However, few studies have been conducted aiming directly at comparing the utility of wine reviews and numeric measurements in wine data analysis. Based on data from close to 300,000 wines reviewed by Wine Spectator, we use logistic regression models to investigate whether wine reviews are useful in predicting a wine's quality classification. We group our sample into one of two binary quality brackets, wines with a critical rating of 90 or above and the other group with ratings of 89 or below. This binary outcome constitutes our dependent variable. The explanatory variables include different combinations of numerical covariates such as the price and age of wines and numerical representations of text reviews. By comparing the explanatory accuracy of the models, our results suggest that wine review descriptors are more accurate in predicting binary wine quality classifications than are various numerical covariates—including the wine's price. In the study, we include three different feature extraction methods in text analysis: latent Dirichlet allocation, term frequency-inverse document frequency, and Doc2Vec text embedding. We find that Doc2Vec is the best performing feature extraction method that produces the highest classification accuracy due to its capability of using contextual information from text documents. (JEL Classifications: C45, C88, D83)