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The reduction in energy consumption in shipping is a crucial issue to achieve a more sustainable sector. Nevertheless, investments in energy efficiency are inhibited by barriers. Consequently, under a Principal-Agent approach, this study aims to analyze the factors affecting the investment preference for either technical or operational measures. To date, the research problem has barely been addressed from a similar approach. This work further integrates agency theory with the identification of barriers and drivers, as well as the cost–benefit ratio from both an environmental and a financial perspective. This makes it possible to consider shipping management from a more comprehensive perspective. The study sample is current and representative (658 individual bulk carriers). The research was carried out utilizing two binominal logistic models that provide similar results when testing the proposed hypotheses. The outcomes show that regulatory factors, such as the distance of a vessel's technical emissions from EEDI requirements (standardized coefficients: −2.8352 and −2.5069), and Principal-Agent problems, such as split incentives (standardized coefficients: −1.0059 and −0.9828), have the greatest influence on investment preferences. As a consequence of Principal-Agent problems, vessels operating under Time Charter contracts are less likely to invest in technical measures than in operational ones. Verified information and activity promote technical measures. Maritime regulation promotes technical measures in younger vessels, especially those meeting only the minimum requirements. Better knowledge can help achieve a more environmentally responsible shipping sector. The role of shipowners and charterers should be highlighted, and transparency should be promoted to enable well-informed decisions to be made. [ABSTRACT FROM AUTHOR]

With the growing development and deployment of automated vehicles (AVs), it is crucial to understand the associated risks and factors contributing to collisions involving AVs. California possesses an immense amount of publicly available data from AV testing due to the requirement laid out by the California Department of Motor Vehicles (DMV), which requires all automated vehicle operators to report collisions for any level of severity. However, this information is reported in specific forms and requires a laborious task to aggregate data from these reports. This study creates an automated data extraction system for these reports and analyses collision characteristics using logistic regression models as well as XGBoost models with SHapley Additive exPlanation (SHAP) interpretation. Additionally, these characteristics are matched with the characteristics of non-automated vehicles (non-AVs) for the same region. The results of the study indicate that rear-end collisions are the most common type of collision observed in currently deployed AVs. The analysis further revealed an increased likelihood of injury-prone rear-end collisions in AVs at intersections compared to non-AVs. Transportation policymakers and researchers should take these safety concerns into account when addressing AV deployment and developing appropriate measures to mitigate collision risks in mixed fleet conditions. [ABSTRACT FROM AUTHOR]

This study investigates the factors that influence households' willingness to comply with municipal solid waste (MSW) management regulations in the Kumasi Metropolis of Ghana. Data from a survey conducted in 400 households in Kumasi were analyzed using descriptive statistics and a binomial logistic regression model. The study found that households' willingness to comply with MSW management regulations is influenced by education, employment, income, monthly expenditure on MSW management, familiarity with MSW management regulations and regulation benefit expectations (P<0.05). The city authorities should educate households on the benefits they stand to gain from their strict compliance with MSW management regulations. [ABSTRACT FROM AUTHOR]

Aims: To measure the current trends of cannabis use in Germany, measure trends in the proportion of heavy cannabis users and estimate future cannabis use rates. Design: Repeated waves of the Epidemiological Survey on Substance Abuse, a cross‐sectional survey conducted between 1995 and 2021 with a two‐stage participant selection strategy where respondents completed a survey on substance use delivered through the post, over the telephone or on‐line. Setting: Germany. Participants/cases: German‐speaking participants aged between 18 and 59 years living in Germany who self‐reported on their cannabis use in the past 12 months (n = 78 678). With the application of a weighting scheme, the data are nationally representative. Measurements Questions on the frequency of cannabis use in the past 12 months and self‐reported changes in frequency of use due to the COVID‐19 pandemic. Findings The prevalence of past 12‐month cannabis users increased from 4.4% [95% confidence interval (CI) = 3.7, 5.1] in 1995 to 10.0% (95% CI = 8.9, 11.3) in 2021. Modeling these trends revealed a significant increase that accelerated over the past decade. The proportion of heavy cannabis users [cannabis use (almost) daily or at least 200 times per year] among past‐year users has remained steady from 1995 (11.4%, 95% CI = 7.7, 16.5) to 2018 (9.5%, 95% CI = 7.6, 11.9), but significantly increased to 15.7% (95% CI = 13.1, 18.8) in 2021 during the COVID‐19 pandemic. Extrapolating from these models, the prevalence of 12‐month cannabis users in 2024 is expected to range between 10.4 and 15.0%, while the proportion of heavy cannabis users is unclear. Conclusions: Trends from 1995 to 2021 suggest that the prevalence of past 12‐month cannabis users in Germany will continue to increase, with expected rates between 10.4 and 15.0% for the German‐speaking adult population, and that at least one in 10 cannabis users will continue to use cannabis heavily (almost daily or 200 + times in the past year). [ABSTRACT FROM AUTHOR]

Epidemics are dangerous and difficult to prevent and control, especially in urban areas. Clarifying the correlation between the COVID-19 Outbreak Frequency and the urban spatial environment may help improve cities’ ability to respond to such public health emergencies. In this study, we firstly analyzed the spatial distribution characteristics of COVID-19 Outbreak Frequency by correlating the geographic locations of COVID-19 epidemic-affected neighborhoods in the city of Beijing with the time point of onset. Secondly, we created a geographically weighted regression model combining the COVID-19 Outbreak Frequency with the external spatial environmental elements of the city. Thirdly, different grades of epidemic-affected neighborhoods in the study area were classified according to the clustering analysis results. Finally, the correlation between the COVID-19 Outbreak Frequency and the internal spatial environmental elements of different grades of neighborhoods was investigated using a binomial logistic regression model. The study yielded the following results. (i) Epidemic outbreak frequency was evidently correlated with the urban external spatial environment, among building density, volume ratio, density of commercial facilities, density of service facilities, and density of transportation facilities were positively correlated with COVID-19 Outbreak Frequency, while water and greenery coverage was negatively correlated with it. (ii) The correlation between COVID-19 Outbreak Frequency and the internal spatial environmental elements of neighborhoods of different grades differed. House price and the number of households were positively correlated with the COVID-19 Outbreak Frequency in low-end neighborhoods, while the number of households was positively correlated with the COVID-19 Outbreak Frequency in mid-end neighborhoods. In order to achieve spatial justice, society should strive to address the inequality phenomena of income gaps and residential differentiation, and promote fair distribution of spatial environments.

Using a large database of continuous glucose monitoring (CGM) data, this study aimed to gain insights into the association between pre-exercise food ingestion timing and reactive hypoglycemia. A group of 6,761 users self-reported 48,799 pre-exercise food ingestion events and logged minute-by-minute CGM, which was used to detect reactive hypoglycemia (<70 mg/dL) in the first 30 min of exercise. A linear and a non-linear binomial logistic regression model was used to investigate the association between food ingestion timing and the probability of experiencing reactive hypoglycemia. An analysis of variance was conducted to compare the predictive ability of the models. On average, reactive hypoglycemia was detected in 8.34 ± 3.04% of the total events, with <15% of individuals experiencing hypoglycemia in >20% of their events. The majority of the reactive hypoglycemia events were found with pre-exercise food timing between ~30 and ~90 min, with a peak at ~60 min. The superior accuracy (62.05 vs 45.1%) and F-score (0.75 vs 0.59) of the non-linear vs the linear model were statistically superior (P < 0.0001). These results support the notion of an unfavourable 30-to-90 min pre-exercise food ingestion time window which can significantly impact the likelihood of reactive hypoglycemia in some individuals. [ABSTRACT FROM AUTHOR]

Predicting preservice teachers' performance on their certification examination may meaningfully help Educators Preparation Programs (EPPs) to adapt and integrate learning frameworks that can improve their passing rates. This study used multiple linear regression (MLR) and binomial logistic regression (BLR) to explore potential variables that may impact the preparedness of 170 pre-service teachers to pass the science core of the official EC-6 Texas Examinations for Educator Standards (TExES) certification examination. The study was conducted by issuing a practice EC-6 TExES certification examination in a pretest and post-test manner during the semester that the participating cohort were enrolled in BIOL 1082, a mandatory science course EC-6 preservice teachers need to take prior to the official state EC-6 TExES certification exam. Additionally, the cohort took an online QualtricsTM survey that collected ex post facto and other demographics data. The independent variables explored in this study included: final grade in BIOL 1082, classification, transfer status, prior college science coursework, enrollment status, family's college history, and current GPA. The dependent variable used was the post-test score on the practice EC-6 exam. The independent variable, grade in BIOL 1082 was revealed to be the single best predictor of preservice teachers' performance on the science practice examination across both the MLR and BLR models. The BLR models had a higher prediction accuracy of preservice teachers who would most likely fail the practice test than those who may pass at a prediction rate at approximately 79% accuracy. Based on the 67 out of 170 preservice teachers who passed the post-test, the accuracy of predicting failures may be a useful tool that EPPs can use in identifying students who may be at risk of failing and thus implement necessary interventions and other educational strategies. [ABSTRACT FROM AUTHOR]

There are few existing studies from a spatial perspective that explore the mechanism of the living environment, relocated direction and relocated distance on the elderly's physical and mental health. Based on the 2019 household survey questionnaire data, this study utilizes a binomial logistic regression model and residents' in-depth interviews to study relocated behavior in Nanjing. We analyzed the spatial–temporal characteristics of relocated behaviors, the accumulation effect at different stages and the influence of relocated intention on physical and mental health among the elderly. The findings are as follows: (1) The mode of relocation among the elderly includes long-term cumulative effects and short-term effects. Frequent relocation has a cumulative negative impact on the physical and mental health of the elderly. (2) The elderly relocated in the short-term had a great negative impact on their mental health but had no significant impact on their physical health. (3) In the last relocation, active relocation had a significantly positive impact on physical health. The "centrifugal relocation" from the main urban area to the surrounding new cities has a significant positive impact on mental health. Moreover, long-distance relocations adversely affected mental health. [ABSTRACT FROM AUTHOR]

Working poverty in Canada is shrouded in mystery and misconception due to the little attention given to this area in research and policy-making. We performed an analysis of working poverty in Canada by using data from the Canadian Income Survey and conducted a binomial logistic regression to discuss the general profile of the working poor and the determinants of working poverty. We found that the working poor have high rates of post-secondary education and worked full-time hours. Moreover, being an immigrant, a single-earner (e.g., unattached individual, lone-parent family, single-earning household), and being self-employed were determinants of working poverty. [ABSTRACT FROM AUTHOR]

The rise of real-time information communication through smartphones and wireless networks enabled the growth of ridesharing services. While personal rideshare services (individuals riding alone or with acquaintances) initially dominated the market, the popularity of pooled ridesharing (individuals sharing rides with people they do not know) has grown globally. However, pooled ridesharing remains less common in the U.S., where personal vehicle usage is still the norm. Vehicle design and rideshare services may need to be tailored to user preferences to increase pooled rideshare adoption. Based on a large, national U.S. survey (N = 5385), the results of exploratory and confirmatory factor analyses suggested that four key factors influence riders' willingness to consider pooled ridesharing: comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. A binomial logistic regression was conducted to determine how the four factors influence one's willingness to consider pooled ridesharing. The two factors that positively influence riders' willingness to consider pooled ridesharing are vehicle technology/accessibility (B = 1.10) and convenience (B = 0.94), while lack of passenger safety (B = −0.63) and comfort/ease of use (B = −0.17) are pooled ridesharing deterrents. Understanding user-centered design and service factors are critical to increase the use of pooled ridesharing services in the future. [ABSTRACT FROM AUTHOR]

Disasters triggered by natural hazards are becoming more frequent and more intense, causing damage to infrastructure and causing loss of life. One way to reduce disaster risk is by evacuating the hazardous area. However, despite the amount of literature that exists on evacuation behavior, there is still a lack of agreement on which variables can be used as predictors for individuals (or households) to actually evacuate. This lack of agreement can be related to the many variables that can affect the evacuation decision, from demographics, geographic, the hazard itself, and also local or cultural differences that may influence evacuation. Hence, it is essential to analyze and understand these variables based on the specifics of a case study. This study aims to find the most significant variables to be used as predictors of evacuation on the island of Sint Maarten, using data collected after the disaster caused by Hurricane Irma in September 2017. The results suggest that the variables gender, homeownership, percentage of property damage, quality of information, number of storeys of the house, and the vulnerability index are the most significant variables influencing evacuation decisions on the island. We believe the results of this paper offer a clear view to risk managers on the island as to which variables are most important in order to increase evacuation rates on Sint Maarten and to plan more efficiently for future evacuations. In addition, the variables found in this study have the potential to be the base information to set up, validate, and calibrate evacuation models. [ABSTRACT FROM AUTHOR]

The classification of a country's economic status as developed or developing often involves factors such as life expectancy and its underlying variables. This research aims to compare the performance of three machine learning algorithms, namely KNN (K-Nearest Neighbors), naive Bayes, and binomial logistic regression, in classifying the economic status of countries as developed or developing. The data used in this study is "Life Expectancy (WHO) Fixed," obtained from the Kaggle website. The first statistical analysis conducted was Principal Component Analysis (PCA) using 16 predictor variables. PCA resulted in three principal components capable of explaining 71.41% of the variance, which were subsequently used in the KNN, naive Bayes, and binomial logistic regression methods. The analysis results from the KNN, naive Bayes, and binomial logistic regression methods produced F1-scores of 100\%, 98.19%, and 97.36%, respectively.

The aim of this study is to contribute to the evidence regarding variables related to emotional symptom severity and to use them to exemplify the potential usefulness of logistic regression for clinical assessment at primary care, where most of these disorders are treated. Cross-sectional data related to depression and anxiety symptoms, sociodemographic characteristics, quality of life (QoL), and emotion-regulation processes were collected from 1,704 primary care patients. Correlation and analysis of variance (ANOVA) tests were conducted to identify those variables associated with both depression and anxiety. Participants were then divided into severe and nonsevere emotional symptoms, and binomial logistic regression was used to identify the variables that contributed the most to classify the severity. The final adjusted model included psychological QoL (p <.001, odds ratio [ OR ] =.426, 95% CI [.318,.569]), negative metacognitions (p <.001, OR = 1.083, 95% CI [1.045, 1.122]), physical QoL (p <.001, OR =.870, 95% CI [.841,.900]), brooding rumination (p <.001, OR = 1.087, 95% CI [1.042, 1.133]), worry (p <.001, OR = 1.047, 95% CI [1.025, 1.070]), and employment status (p =.022, OR [.397, 2.039]) as independent variables, ρ2 =.326, area under the curve (AUC) =.857. Moreover, rumination and psychological QoL emerged as the best predictors to form a simplified equation to determine the emotional symptom severity (ρ2 =.259, AUC =.822). The use of statistical models like this could accelerate the assessment and treatment-decision process, depending less on the subjective point of view of clinicians and optimizing health care resources. [ABSTRACT FROM AUTHOR]

Identifying the causes of road traffic crashes (RTCs) and contributing factors is of utmost importance for developing sustainable road network plans and urban transport management. Driver-related factors are the leading causes of RTCs, and speed is claimed to be a major contributor to crash occurrences. The results reported in the literature are mixed regarding speed-crash occurrence causality on rural and urban roads. Even though recent studies shed some light on factors and the direction of effects, knowledge is still insufficient to allow for specific quantifications. Thus, this paper aimed to contribute to the analysis of speed-crash occurrence causality by identifying the road features and traffic flow parameters leading to RTCs associated with driver errors along an access-controlled major highway (761.6 km of Highway 15 between Taif and Medina) in Saudi Arabia. Binomial logistic regression (BNLOGREG) was employed to predict the probability of RTCs associated with driver errors (p < 0.001), and its results were compared with other supervised machine learning (ML) models, such as random forest (RF) and k-nearest neighbor (kNN) to search for more accurate predictions. The highest classification accuracy (CA) yielded by RF and BNLOGREG was 0.787, compared to kNN's 0.750. Moreover, RF resulted in the largest area under the ROC (a receiver operating characteristic) curve (AUC for RF = 0.712, BLOGREG = 0.608, and kNN = 0.643). As a result, increases in the number of lanes (NL) and daily average speed of traffic flow (ASF) decreased the probability of driver error-related crashes. Conversely, an increase in annual average daily traffic (AADT) and the availability of straight and horizontal curve sections increased the probability of driver-related RTCs. The findings support previous studies in similar study contexts that looked at speed dispersion in crash occurrence and severity but disagreed with others that looked at absolute speed at individual vehicle or road segment levels. Thus, the paper contributes to insufficient knowledge of the factors in crash occurrences associated with driver errors on major roads within the context of this case study. Finally, crash prevention and mitigation strategies were recommended regarding the factors involved in RTCs and should be implemented when and where they are needed. [ABSTRACT FROM AUTHOR]

After the exclusion of iron deficiency and β-thalassemia, molecular research for α-thalassemia is recommended to investigate microcytic anemia. Aiming to suggest more efficiently the molecular analysis for individuals with a greater chance of having a symptomatic form of the disease, we have developed and validated a new decision tool to predict the presence of two or more deletions of α-thalassemia, increasing considerably the pre-test probability. The model was created using the variables: the percentage of HbA2, serum ferritin and mean corpuscular volume standardized by age. The model was trained in 134 patients and validated in 160 randomly selected patients from the total sample. We used Youden's index applied to the ROC curve methodology to establish the optimal odds ratio (OR) cut-off for the presence of two or more α-globin gene deletions. Using the OR cut-off of 0.4, the model's negative predictive value (NPV) was 96.8%; the cut-off point accuracy was 85.4%; and the molecular analysis pre-test probability increased from 25.9% to 65.4% after the use of the proposed model. This tool aims to assist the physician in deciding when to perform molecular studies for the diagnosis of α-thalassemia. The model is useful in places with few financial health resources. [ABSTRACT FROM AUTHOR]

The rise of real-time information communication through smartphones and wireless networks enabled the growth of ridesharing services. While personal rideshare services (individuals riding alone or with acquaintances) initially dominated the market, the popularity of pooled ridesharing (individuals sharing rides with people they do not know) has grown globally. However, pooled ridesharing remains less common in the U.S., where personal vehicle usage is still the norm. Vehicle design and rideshare services may need to be tailored to user preferences to increase pooled rideshare adoption. Based on a large, national U.S. survey (N = 5385), the results of exploratory and confirmatory factor analyses suggested that four key factors influence riders’ willingness to consider pooled ridesharing: comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. A binomial logistic regression was conducted to determine how the four factors influence one’s willingness to consider pooled ridesharing. The two factors that positively influence riders’ willingness to consider pooled ridesharing are vehicle technology/accessibility (B = 1.10) and convenience (B = 0.94), while lack of passenger safety (B = −0.63) and comfort/ease of use (B = −0.17) are pooled ridesharing deterrents. Understanding user-centered design and service factors are critical to increase the use of pooled ridesharing services in the future.

After the exclusion of iron deficiency and β-thalassemia, molecular research for α-thalassemia is recommended to investigate microcytic anemia. Aiming to suggest more efficiently the molecular analysis for individuals with a greater chance of having a symptomatic form of the disease, we have developed and validated a new decision tool to predict the presence of two or more deletions of α-thalassemia, increasing considerably the pre-test probability. The model was created using the variables: the percentage of HbA2, serum ferritin and mean corpuscular volume standardized by age. The model was trained in 134 patients and validated in 160 randomly selected patients from the total sample. We used Youden’s index applied to the ROC curve methodology to establish the optimal odds ratio (OR) cut-off for the presence of two or more α-globin gene deletions. Using the OR cut-off of 0.4, the model’s negative predictive value (NPV) was 96.8%; the cut-off point accuracy was 85.4%; and the molecular analysis pre-test probability increased from 25.9% to 65.4% after the use of the proposed model. This tool aims to assist the physician in deciding when to perform molecular studies for the diagnosis of α-thalassemia. The model is useful in places with few financial health resources.

Copyright of Zbornik Radova: Univerzitet 'Džemal Bijedić' u Mostaru, Ekonomski Fakultet is the property of Dzemal Bijedic University of Mostar, Faculty of Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of this study is to conduct an analysis of the investment made by the fifteen largest sovereign wealth funds on listed European companies. The analysis is divided into two sections: a descriptive one and a statistical one. The methodology used for this purpose consisted of mining data from Orbis database and running a binomial logistic regression. The main results show that, in the first place, the Norwegian fund is the one that invests in a larger amount of companies and European countries. Another significant result indicates that the United Kingdom is the country that receives the most investment. Finally, the results lead also to the conclusion that, concerning investing, sovereign wealth funds are influenced by a set of factors such as company size, profitability, and leverage, whereas the company’s home country and the economic sector it belongs are not determining factors.

This study aimed to estimate rice farms efficiency using qualitative response models. The survied field included three districts in Najaf (Al Mishkhab, Al Hirah, Al Manadhira) during agricultural season 2017, including a random sample of 80 farms, representing 7.5% of the total farms. To achieve the goals, analysis was divided into two stages, the first stage was measuring the economic efficiency according to entry guidance of rice farms, while the second stage involved the analysis of factors affecting the economic efficiency of rice farms using the binomial logistic regression model to obtain a mathematical relationship between the dependent variable (response variable), which represents the economic efficiency of rice farms and the independent variables represented by age, experience and education of the owners, geographical area and type of possession for the farms. DEAP software was used to estimate economic efficiency and SPSS software to analyze the logistic regression of the factors affecting economic efficiency. The results showed that the average economic efficiency according to the Economic efficiency was 66.7%, which means that rice farmers can obtain the same production using 66.7% of the total costs i.e. there is a 33% waste in economic resources in the research sample.The logistic regression results indicated that each of experience (more than 35 years), education (university degree) and farm area (greater than 15 dunums) had a significant effect on the efficiency of the rice crop farms. [ABSTRACT FROM AUTHOR]

The present study attempts to identify factors that influence the readiness of Hungarian agriculturalproducers’ to effectively and efficiently cooperate with each other. Analyses based on the binomiallogistic regression method have revealed a clear correlation between activities conducive to cooperation,the demographics of producers, farming conditions and the level of trust. Findings highlight that producerswho live in smaller rural settlements as well as those who are younger and have a higher level ofeducational attainment show a higher level of activity associated with cooperation that can be statisticallyjustified whereas farms managed by women are less inclined to cooperate. Calculations indicate that asthe size of holdings increases, their readiness for cooperation increases too, while in terms of the type offarming, crop farmers are more open to cooperate effectively than livestock farmers. Finally, this model hasrevealed a significant positive correlation between producers’ readiness to cooperate and their level of trust.

• Data-driven EBF and Binomial LR model applied for flood susceptibility modeling. • The ensemble EBF-LR had better performance (87.9% prediction rate) than others. • The flood susceptibility maps validated by real world flood location and ROC curve. Flooding is the overflow of water from stream, river, lake and sea that occurs all over the world and has disastrous effects on human society and environment. Frequent severe flood event in eastern India cause of death and damages every year so, the development of flood susceptibility method is needed for identifying the flood vulnerability areas to reduce the damages. Techniques of Remote Sensing (RS) and Geographical Information System (GIS) can help to flood susceptibility modeling by the accrued and analyzing huge amount of data in short time. The main objectives of this study are to determine the effectiveness of Evidence Belief Function (EBF), binomial Logistic Regression (LR) and ensemble of EBF and LR (EBF-LR) model with RS and GIS techniques for flood susceptibility mapping and spatial prediction of flood-susceptible areas in the Koiya river basin of West Bengal, India. Eight flood conditioning factors; Land use and land cover (LULC), soil, rainfall, normalized differences vegetation index (NDVI), distance to river, elevation, topographic wetness index (TWI) and stream power index (SPI) have been used, and total 264 historical flooding points were mapped, and randomly divided in to training (70%) and validating (30%) dataset. Flood susceptibility map has been generated by applying EBF, LR and ensemble EBF-LR method with the help of training and eight causative factors dataset. The maps have been divided in to six classes; extremely low, very low, low, moderate, high and very high. The receiver operating characteristic (ROC) curve has been used to accuracy assessment of the susceptibility map, and the area under curve (AUC) disclosures 87.9%, 85.2% and 84.1% prediction rate for the EBF-LR, EBF and LR model, respectively. This study is helpful to flood management program, dissection makers and planning in local administrative level. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on the relationship between the subjective evaluation of air quality and the quality of life (QOL) of middle-aged and elderly residents in China. The 2018 China Health and Retirement Longitudinal Study (CHARLS) project database is the key sources of data, from which 16,736 valid samples were used in our research. Multivariate linear regression analysis and binomial logistic regression model were applied to detect the impact of the subjective evaluation of air quality on QOL, which was evaluated in two dimensions, which are health utility and experienced utility, using the health utility EQ-5D score and the experienced utility of life satisfaction score. Our results show that there is a significant positive correlation between the subjective evaluation of air quality and the two dimensions of QOL. Age, education, marital status and sleep status also have a relatively great impact on the QOL of residents. This worked studied the overall QOL of middle-aged and elderly residents in China, while policy suggestions regarding high-quality air public goods are also given in the paper.

Epidemics are dangerous and difficult to prevent and control, especially in urban areas. Clarifying the correlation between the COVID-19 Outbreak Frequency and the urban spatial environment may help improve cities' ability to respond to such public health emergencies. In this study, we firstly analyzed the spatial distribution characteristics of COVID-19 Outbreak Frequency by correlating the geographic locations of COVID-19 epidemic-affected neighborhoods in the city of Beijing with the time point of onset. Secondly, we created a geographically weighted regression model combining the COVID-19 Outbreak Frequency with the external spatial environmental elements of the city. Thirdly, different grades of epidemic-affected neighborhoods in the study area were classified according to the clustering analysis results. Finally, the correlation between the COVID-19 Outbreak Frequency and the internal spatial environmental elements of different grades of neighborhoods was investigated using a binomial logistic regression model. The study yielded the following results. (i) Epidemic outbreak frequency was evidently correlated with the urban external spatial environment, among building density, volume ratio, density of commercial facilities, density of service facilities, and density of transportation facilities were positively correlated with COVID-19 Outbreak Frequency, while water and greenery coverage was negatively correlated with it. (ii) The correlation between COVID-19 Outbreak Frequency and the internal spatial environmental elements of neighborhoods of different grades differed. House price and the number of households were positively correlated with the COVID-19 Outbreak Frequency in low-end neighborhoods, while the number of households was positively correlated with the COVID-19 Outbreak Frequency in mid-end neighborhoods. In order to achieve spatial justice, society should strive to address the inequality phenomena of income gaps and residential differentiation, and promote fair distribution of spatial environments., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yang, Li, Li, Huang, Zhang, Lu, Zhao and Yang.)

Copyright of German Journal of Exercise & Sport Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Ultrasonic vocalizations (USVs) have been established as an animal model of emotional status and are often utilized in drug abuse studies as motivational and emotional indices. Further USV functionality has been demonstrated in our recent work showing accurate identification of selectively-bred high versus low alcohol-consuming male rats ascertained exclusively from 22 to 28 kHz and 50–55 kHz FM USV acoustic parameters. With the hypothesis that alcohol-sensitive sex differences could be revealed through USV acoustic parameters, the present study examined USVs and alcohol consumption in male and female selectively bred high-alcohol drinking (HAD-1) rats. For the current study, we examined USV data collected during a 12-week experiment in male and female HAD-1 rats. Experimental phases included Baseline (2 weeks), 4-h EtOH Access (4 weeks), 24-h EtOH Access (4 weeks) and Abstinence (2 weeks). Findings showed that both male and female HAD-1 rats spontaneously emitted a large number of 22–28 kHz and 50–55 kHz FM USVs and that females drank significantly more alcohol compared to males over the entire course of the experiment. Analyses of USV acoustic characteristics (i.e. mean frequency, duration, bandwidth and power) revealed distinct sex-specific phenotypes in both 50–55 kHz FM and 22–28 kHz USV transmission that were modulated by ethanol exposure. Moreover, by using a linear combination of these acoustic characteristics, we were able to develop binomial logistic regression models able to discriminate between male and female HAD-1 rats with high accuracy. Together these results highlight unique emotional phenotypes in male and female HAD-1 rats that are differentially modulated by alcohol experience. [ABSTRACT FROM AUTHOR]

Copyright of Revista Facultad de Ciencias Economicas: Investigacion y Reflexion is the property of Revista Facultad de Ciencias Economicas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of this paper is to contribute to the understanding of the importance of different determinants and their impact on the severity of injuries of individuals in road traffic accidents, so that measures that are supposed to prevent or reduce severe injury consequences can be developed. In this paper three research models have been built. Model I was built to study the impact of demographic factors (gender and age) on the individual’s likelihood to wear a seat-belt while Model II to study the impact of demographic factors (gender and age) and the impact of wearing a seat belt on the likelihood that fatal injuries of individuals in road traffic accidents occur. Model III was formed to study the impact of several environmental factors on the likelihood that the accident involves severe or fatal injuries of road traffic accident participants. Altogether our study revealed that middle-aged individuals (over 25 years and up to 65 years old) are less likely to wear a seat belt and at the same time more likely to suffer fatal injuries in road traffic accidents. This is the result that implies that the targeted policy measures to the population between 25 and 65 years of age are needed to reduce the fatal injuries occurrence in Slovenia.

Ultrasonic vocalizations (USVs) are mediated through specific dopaminergic and cholinergic neural pathways and serve as real-time measures of positive and negative emotional status in rodents. Although most USV studies focus primarily on USV counts, each USV possesses a number of characteristics shown to reflect activity in the associated neurotransmitter system. In the present study, we recorded spontaneously emitted USVs from alcohol-naïve high alcohol drinking (HAD-1) and low alcohol drinking (LAD-1) rats. Using our recently developed WAAVES algorithm, we quantified four acoustic characteristics (mean frequency, duration, power, and bandwidth) from each 22-28 kHz and 50-55 kHz frequency-modulated (FM) USV. This rich USV representation allowed us to apply advanced statistical techniques to identify the USV acoustic characteristics that distinguished HAD-1 from LAD-1 rats. Linear mixed models (LMM) examined the predictability of each USV characteristic in isolation and linear discriminant analysis (LDA), and binomial logistic regression examined the predictability of linear combinations of the USV characteristics as a group. Results revealed significant differences in acoustic characteristics between HAD-1 and LAD-1 rats in both 22-28 kHz and 50-55 kHz FM USVs. In other words, these rats selectively bred for high- and low-alcohol consumption can be identified as HAD-1 or LAD-1 rats with high classification accuracy (approximately 92-100%) exclusively based on their emitted 22-28 kHz and 50-55 kHz FM USV acoustic characteristics. In addition, acoustic characteristics of 22-28 kHz and 50-55 kHz FM USVs emitted by alcohol-naïve HAD-1 and LAD-1 rats significantly correlate with their future alcohol consumption. Our current findings provide novel evidence that USV acoustic characteristics can be used to discriminate between alcohol-naïve HAD-1 and LAD-1 rats, and may serve as biomarkers in rodents with a predisposition for, or against, excessive alcohol intake. [ABSTRACT FROM AUTHOR]

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One of the first soil forming processes in marine and fluviatile clay soils is ripening, the irreversible change of physical and chemical soil properties, especially consistency, under influence of air. We used Bayesian binomial logistic regression (BBLR) to update the map showing unripened subsoils for a reclamation area in the west of The Netherlands. Similar to conventional binomial logistic regression (BLR), in BBLR the binary target variable (the subsoil is ripened or unripened) is modelled by a Bernoulli distribution. The logit transform of the `probability of success' parameter of the Bernoulli distribution was modelled as a linear combination of the covariates soil type, freeboard (the desired water level in the ditches, compared to surface level) and mean lowest groundwater table. To capture all available information, Bayesian statistics combines legacy data summarized in a ‘prior’ probability distribution for the regression coefficients with actual observations. Our research focused on quantifying the influence of priors with different information levels, in combination with different sample sizes, on the resulting parameters and maps. We combined subsamples of different size (ranging from 5% to 50% of the original dataset of 676 observations) with priors representing different levels of trust in legacy data and investigated the effect of sample size and prior distribution on map accuracy. The resulting posterior parameter distributions, calculated by Markov chain Monte Carlo simulation, vary in centrality as well as in dispersion, especially for the smaller datasets. More informative priors decreased dispersion and pushed posterior central values towards prior central values. Interestingly, the resulting probability maps were almost similar. However, the associated uncertainty maps were different: a more informative prior decreased prediction uncertainty. When using the ‘overall accuracy’ validation metric, we found an optimal value for the prior information level, indicating that the standard deviation of the legacy data regression parameters should be multiplied by 10. This effect is only detectable for smaller datasets. The Area Under Curve validation statistic did not provide a meaningful optimal multiplier for the standard deviation. Bayesian binomial logistic regression proved to be a flexible mapping tool but the accuracy gain compared to conventional logistic regression was marginal and may not outweigh the extra modelling and computing effort. [ABSTRACT FROM AUTHOR]

Background: Ending preventable maternal and newborn morbidity and mortality remains a core component of the Sustainable Development Goals (SDG) which is a continuation of the Millennium Development Goals (MDG). It has been documented that most women in developing countries give birth at home without the intervention of skilled attendants despite the prevailing multitude risks associated with childbirth for both mother and the unborn child. The study was aimed at identifying the most significant factors influencing selection of health facility based delivery among reproductive age mothers. Data and methods: Data was obtained from Benin Demographic and Health Survey (BDHS) of 2011-2012 using percent distribution, binomial logistic regression and multinomial regression. Out of 16,599 interviewed only 9,111 were eligible for the present study. Results: Most (76.0%) of the respondents gave birth at public health centers, 11% in private health centers and the rest outside health institutions. Out of 17 variables examined, 13 were found (using Akaike Information Criterion in a stepwise algorithm) to be the most important factors for institutional delivery. Among them, Antenatal Care (ANC) frequency, mother's education, and household wealth index had most impact on institutional deliveries with positive and linear relationships. Most of the educated women (secondary or above) were more likely to give birth in a private health facility (Private Health OR=5.2, 95% CI 2.47-10.88; Public Health OR=3.7, 95% CI 1.82-7.6). Similar results were found among the richest women (Private Health OR=8.6, 95% CI 3.5-21.3; Public Health OR=4.4, 95% CI 1.90-10.4). Female education and economic status were the most important factors for institutional delivery preference. Conclusion: Encouraging regular ANC visits (at least the four recommended visits), and girls' education up to secondary school will help getting women to deliver in a well-equipped health facility. There is also a need to investigate thoroughly through qualitative researches reasons behind their preference of health facility for reproductive health services, including institutional delivery. [ABSTRACT FROM AUTHOR]

Background Picea chihuahuana, which is endemic to Mexico, is currently listed as “Endangered” on the Red List. Chihuahua spruce is only found in the Sierra Madre Occidental (SMO), Mexico. About 42,600 individuals are distributed in forty populations. These populations are fragmented and can be classified into three geographically distinct clusters in the SMO. The total area covered by P. chihuahuana populations is less than 300 ha. A recent study suggested assisted migration as an alternative to the ex situ conservation of P. chihuahuana, taking into consideration the genetic structure and diversity of the populations and the predictions regarding the future climate of the habitat. However, detailed background information is required to enable development of plans for protecting and conserving species and for successful assisted migration. Thus, it is important to identify differences between populations in relation to environmental conditions. The genetic diversity of populations, which affect vigor, evolution and adaptability of the species, must also be considered. In this study, we examined 14 populations of P. chihuahuana, with the overall aim of discriminating the populations and form clusters of this species. Methods Each population was represented by one 50 × 50 m plot established in the center of its respective location. Climate, soil, dasometric, density variables and genetic and species diversities were assessed in these plots for further analyses. The putatively neutral and adaptive AFLP markers were used to calculate genetic diversity. Affinity Propagation (AP) clustering technique and k-means clustering algorithm were used to classify the populations in the optimal number of clusters. Later stepwise binomial logistic regression was applied to test for significant differences in variables of the southern and northern P. chihuahuana populations. Spearman’s correlation test was used to analyze the relationships among all variables studied. Results The binomial logistic regression analysis revealed that seven climate variables, the geographical longitude and sand proportion in the soil separated the southern from northern populations. The northern populations grow in more arid and continental conditions and on soils with lower sand proportion. The mean genetic diversity using all AFLP studied of P. chihuahuana was significantly correlated with the mean temperature in the warmest month, where warmer temperatures are associated to larger genetic diversity. Genetic diversity of P. chihuahuana calculated with putatively adaptive AFLP was not statistically significantly correlated with any environmental factor. Discussion Future reforestation programs should take into account that at least two different groups (the northern and southern cluster) of P. chihuahuana exist, as local adaptation takes place because of different environmental conditions.

The problem concerning the prediction of the aggressive status of prostate cancer (PCa) is examined on the basis of preoperative data. This problem is solved using data on 360 patients with the established (aggressive or indolent) postoperative status of the disease. The collection of factors containing five informative indicators of prediction (from primarily accessible sixteen) is revealed and employed to create the diagnostic index used to predict the aggressive PCa status. In compliance with cross-validation data, the prognostic algorithm enables us to find the group involving 55% of patients with an aggressive status in the absence of patients with an indolent PCa status. The three-class prediction algorithm making it possible to ascertain whether any patient belongs to the group with the low, high, or uncertain risk of the aggressive disease stage is proposed. [ABSTRACT FROM AUTHOR]