Air Pollution Exposure and Risk of Spontaneous Pneumothorax in Children: A Longitudinal, Nationwide Study

Spontaneous pneumothorax (SP) involves the spontaneous appearance of air in the pleural space. Atmospheric pressure, temperature change, and seasonal factors may precipitate SP, but its association with air pollution remains unclear. Therefore, we conducted this nationwide, retrospective population-based study to evaluate the risk of SP in Taiwanese children exposed to air pollution. We collected data on SP incidence from the Longitudinal Health Insurance Database; the Taiwan Air Quality-Monitoring Database provided daily concentrations of nitric oxide (NO), nitrogen dioxide (NO2), and hydrocarbons in 2000–2012. SP risk was evaluated for four quartiles (Q1, Q2, Q3, Q4). The NO adjusted hazard ratios (aHRs) for Q2, Q3, and Q4 compared to Q1 were 1.11 (95% confidence interval (CI): 0.77–1.61), 1.24 (95% CI: 0.88–1.76), and 1.66 (95% CI: 1.17–2.34), respectively. The NO2 aHRs for Q2, Q3, and Q4 were 1.12 (95% CI: 0.77–1.64), 1.31 (95% CI: 0.0.90–1.90), and 1.51 (95% CI: 1.04–2.19), respectively. Hydrocarbons aHRs for Q2, Q3, and Q4 were 0.87 (95% CI: 0.64–1.18), 1.16 (95% CI: 0.90–1.49), and 1.40 (95% CI: 1.06–1.85), respectively. Increased exposure to NO, NO2, and hydrocarbons is associated with increased SP risk in Taiwanese children.

Application of GARCH Forecasting Method in Predicting The Number of Rail Passengers (Thousands of People) in Jabodetabek Region

PT. Kereta Api Indonesia and PT. KAI Commuter Jabodetabek records time series data in the form of the number of train passengers (thousand people) in Jabodetabek Region in 2011-2020. One of the time series methods that can be used to predict the number of train passengers (thousand people) in Jabodetabek area is ARIMA method. ARIMA or also known as Box-Jenkins time series analysis method is used for short-term forecasting and does not accommodate seasonal factors. If the assumption of residual homoscedasticity is violated, the ARCH / GARCH method can be used, which explicitly models changes in residual variety over time. This study aims to model and forecast the number of train passengers (thousand people) in Jabodetabek area in 2021. Based on data analysis and processing using ARIMA method, the best model is ARIMA (1,1,1) with an AIC value of 2,159.87 and with ARCH / GARCH method, the best model is GARCH (1,1) with an AIC value of 18.314. Forecasting results obtained based on the best model can be used as a reference for related parties in managing and providing public transportation facilities, especially trains.

Prediction model of seasonality in the construction industry based on the accidentality phenomenon

The construction industry is an economic sector that is characterized by seasonality. Seasonal factors affect the volume of production, which in turn affects the accident rate. The aim of the research presented in the article was to develop a model for predicting the number of people injured in occupational accidents in the construction industry. Based on the analysis of statistical data and previous studies, the occurrence of certain regularities of the accidentality phenomenon was found, namely the long-term trend over many years, as well as seasonality and cyclicality over the course of a year. The found regularities were the basis for the assumptions that were made for the construction of the model. A mathematical model was built in the non-linear regression dimension. The model was validated by comparing the results of prediction errors generated by the developed model with the results of prediction errors generated by other known models, such as ARIMA, SARIMA, linear and polynomial models, which take into account the seasonality of the phenomenon. The constructed model enables the number of people injured in accidents in the construction industry in selected months of future years to be predicted with high accuracy. The obtained results can be the basis for making appropriate decisions regarding preventive and prophylactic measures in the construction industry. Commonly known mathematical tools available in the STATISTICA package were used to solve the given task.

A Thoughtful Insight on Women Entrepreneur’s Investment Attitude

This study endeavors to be cognizant of the investment paradigm of women entrepreneurs and reveal their ambitions, professionalism, and desire to form a robust framework in the context of economic development. These persistent attributes of women entrepreneurs for economic development persuaded us to investigate factors that influence women’s attitude to make a long-term investment decision in their business regardless of uncertainty. This study adopted a deductive approach and assessed data using the PLS-SEM technique through Smart PLS 3.3.3. Around 330 adequate responses from Karachi and Lahore using a self-designed structured questionnaire revealed that women’s investment attitude has a positive significant mediating effect on social, behavioral factors, and investment decisions. Whereas, women’s investment attitude did not depict a positive significant mediating effect on personal factors and investment decisions. It was quite interesting to know that uncertainty did not reveal a significant moderating effect between investment attitude and investment decision. The study highlights measures suggested empowering women entrepreneurs who strive to enhance their performance and achieve sustainable development goals without being discouraged by society. Moreover, focusing risk-taking attributes to set an example for those who do not come forth. The novelty of the study in the context of women entrepreneur’s investment attitude well contributes to the existing literature and recommends future scholars to expand the horizon of the existing area of the study in the context of cultural, demographic, and seasonal factors, which are also affecting women entrepreneur’s investment decisions.

Factors Associated with Timely Surgery for Semi-elective Distal Radius Fracture Fixation

Background In the United Kingdom, national guidance recommends intra-articular distal radius fractures should undergo surgery within 72 hours and extra-articular fractures within 7 days. Purpose We investigated if hospitals can provide timely surgery and meet national guidelines in patients who are sent home following distal radius fracture (DRF) to return for planned surgery. The influence of patient, hospital, and seasonal factors on wait to surgery are investigated. Patients and Methods We reviewed Hospital Episode Statistics (HES) data between April 2009 and March 2013. Proportion of procedures being performed within 3 and 7 days was calculated. A linear regression model was created to investigate the relationship between wait for surgery and patient and hospital factors. Results A total of 9,318 patients were sent home to return for planned acute DRF surgery during the 4-year study period. Mean time to surgery was 3.04 days (range 1–days, standard deviation [SD] 3.14). A total of 6,538 patients underwent surgery within 3 days (70.2%) and 8,747 within 7 days (93.9%). Patients listed for surgery and sent home to return waited longer if listed toward the end of the week. Less surgery was performed at weekends, and patients were less likely to be listed for semielective trauma surgery. Conclusions Acute semielective DRF fixation is generally performed within targets for extra-articular fractures but there is scope for improvement for intra-articular fractures. Day of presentation and increasing number of comorbidities increase wait for surgery. Hospital trusts should focus on improving pathways for patients with multiple comorbidities and strategies to improve accessibility of these services at weekends.

Social and Seasonal Factors Contribute to Shifts in Male African Elephant (Loxodonta africana) Foraging and Activity Patterns in Kruger National Park, South Africa

African savannah elephants (Loxodonta africana) are well-known as ecosystem engineers with the ability to modify vegetation structure. The present study aimed to examine how male elephant foraging behaviour is affected across (a) season (wet versus dry); (b) time of day (before or after noon); (c) presence or absence of other elephants; and (d) reproductive state (musth versus no musth). Six radio-collared adult elephant bulls were observed twice per week from June 2007–June 2008 in Kruger National Park (KNP), South Africa. Using generalized linear mixed effect modeling, results indicate that elephant bulls graze more during the wet season and browse more during the dry season. To potentially offset the costs associated with thermoregulation during the heat of the day, KNP elephants spent more time foraging during the morning, and more time resting during the afternoon. Male elephants also foraged significantly less when they were associated with females compared to when they were alone or with other males. This is likely due to male–female associations formed mainly for reproductive purposes, thus impeding on male foraging behaviours. In contrast, the condition of musth, defined by the presence of related physical signs, had no significant effect on foraging behaviour.

Analyzing a phenological anomaly in Yucca of the southwestern United States

Yucca in the American desert Southwest typically flowers in early spring, but a well-documented anomalous bloom event occurred during an unusually cold and wet late fall and early winter 2018–2019. We used community science photographs to generate flowering presence and absence data. We fit phenoclimatic models to determine which climate variables are explanatory for normal flowering, and then we tested if the same conditions that drive normal blooming also drove the anomalous blooming event. Flowering for Yucca brevifolia (Joshua tree) and Yucca schidigera (Mojave yucca) is driven by complex, nonlinear interactions between daylength, temperature, and precipitation. To our surprise, early-season flowering odds are highest in colder and drier conditions, especially for Joshua trees, but increase with precipitation late-season. However, the models used to fit normal blooming overpredicted the number of anomalous blooms compared to what was actually observed. Thus, predicting anomalous flowering events remains a challenge for quantitative phenological models. Because our model overpredicted the number of anomalous blooms, there are likely other factors, such as biotic interactions or other seasonal factors, which may be especially important in controlling what is presumed to be rare, out-of-season flowering in desert-adapted Yucca.