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In recent years, online lending has created many risks while providing lending convenience to Chinese individuals and small and medium-sized enterprises. The timely assessment and prediction of the status of industry indicators is an important prerequisite for effectively preventing the spread of risks in China’s new financial formats. The role of investor sentiment should not be underestimated. We first use the BERT model to divide investor sentiment in the review information of China’s online lending third-party information website into three categories and analyze the relationship between investor sentiment and quantitative indicators of online lending product transactions. The results show that the percentage of positive comments has a positive relationship to the borrowing interest rate of P2P platforms that investors are willing to participate in for bidding projects. The percentage of negative comments has an inverse relationship to the borrowing period. Second, after introducing investor sentiment into the long short-term memory (LSTM) model, the average RMSE of the three forecast periods for borrowing interest rates is 0.373, and that of the borrowing period is 0.262, which are better than the values of other control models. Corresponding suggestions for the risk prevention of China’s new financial formats are made.

Introduction Mapping and population size estimates of people who inject drugs (PWID) provide information needed for monitoring coverage of programs and planning interventions. The objectives of this study were to provide the locations and numbers of PWID in eight cities in Afghanistan and extrapolate estimates for the country as a whole. Methods Multiple population size estimation methods were used, including key informant interviews for mapping and enumeration with reverse tracking, unique object and service multipliers, capture-recapture, and wisdom of the crowds. The results of the several methods were synthesized using the Anchored Multiplier–a Bayesian approach to produce point estimates and 95% credible intervals (CI). Using the prevalence of PWID in the eight cities and their correlation with proxy indicators, we extrapolated the PWID population size for all of Afghanistan. Results Key informants and field mapping identified 374 hotspots across the eight cities from December 29, 2018 to March 20, 2019. Synthesizing results of the multiple methods, the number of male PWID in the eight study cities was estimated to be 11,506 (95% CI 8,449–15,093), corresponding to 0.69% (95% CI 0.50–0.90) of the adult male population age 15–64 years. The total number of women who injected drugs was estimated at 484 (95% CI 356–633), corresponding to 0.03% (95% CI 0.02–0.04) of the adult female population. Extrapolating by proxy indicators, the total number of PWID in Afghanistan was estimated to be 54,782 (95% CI 40,250–71,837), men and 2,457 (95% CI 1,823–3,210) women. The total number of PWID in Afghanistan was estimated to be 57,207 (95% CI 42,049–75,005), which corresponds to 0.37% (95% CI 0.27–0.48) of the adult population age 15 to 64 years. Discussion This study provided estimates for the number of PWID in Afghanistan. These estimates can be used for advocating and planning services for this vulnerable at-risk population.

Background Distrust, and more broadly, public perception of government’s handling of a crisis, has been a widely studied topic within health crisis research and suggests that these perceptions are significantly associated with the behavior of its citizens. Purpose To understand which aspects of the public’s perception of government handling of the COVID-19 pandemic predicted engagement of protective behaviors among older adults, who are the most vulnerable to COVID-19. Methods Participants were recruited from an ongoing biopsychosocial study on aging amongst community-dwelling older adults. There were two rounds of data collection, during the national lockdown and post-lockdown. The average length of follow-up was 5.88 months. N = 421 completed the first round of data collection and N = 318 subsequently completed the second round of questionnaires. Results During the lockdown, perceptions that pandemic-related measures in place were sufficient, effective, timely, provided a sense of safety, important information was easily accessible, and government handling of the pandemic could be trusted, were found to significantly predict engagement in protective behaviors. During post-lockdown, only perceptions that measures in place were sufficient, provided a sense of safety, and important information was easily accessible, remained significant predictors. The perception that COVID-19 measures were clear and easy to understand now became a significant predictor. Conclusions Public perceptions of government handling of the pandemic predicted engagement in protective behaviors but were less important during post-lockdown. To effectively engage older adults in protective behavior, our findings suggest for pandemic-related information to be accessible, introducing timely safety measures, and having easy-to-understand instructions for nuanced measures.

To better prevent the potential risks in Internet-based Supply Chain Financing (SCF) products, this paper optimizes and evaluates the Internet-based SCF-oriented Credit Risk Evaluation (CRE) method. Firstly, this paper summarizes 12 risk factors of SCF business, establishes a Risk Assessment Index System (RAIS) with good consistency and stability; then, the principles of Backpropagation (BP) Neural Network (NN) is expounded together with Support Vector Machines (SVM) and Genetic Algorithm (GA) model. Consequently, a CRE model is implemented by the NN tools in MATLAB based on the collection of multiple groups of SCF-oriented risk assessment samples. Subsequently, the assessment samples are trained and tested. Finally, the SCF-oriented CRE model is proposed and verified. The results show that the BP-GA model has presented high prediction consistency with the actual classification. According to the comparison of classification results of SVM, BP model, and BP-GA model, the classification accuracy of test samples of the proposed Internet-based SCF-oriented CRE system using BP-GA model reaches 97.19%; the Type I and Type II error rate of the CRE system based on BP-GA model is 7.2% and 14.21%, respectively. Therefore, a suitable SCF-oriented CRE method is put forward for China’s commercial banks along with scientific and feasible suggestions to manage SCF-oriented credit risks more reasonably and effectively.

With the steady progress of China’s opening-up policy, how to avoid the financial risks brought by opening-up is a valuable research topic at present while promoting economic development. As an innovative business model connecting the real economy and the virtual economy, the Internet of Things (IoT) finance provides standardized technical support for the expansion of trade and finance. In financial data analysis, deep learning (DL) has become an important means to predict financial market movements, process text information and improve trading strategies. Analysis is conducted on the influence of trade and financial opening on the volatility of real exchange rate. Through the empirical test of panel data of 45 major countries in the world, the pooled ordinary least square (OLS) method and instrumental variable method are used to evaluate the influence of trade and financial opening of sample countries on the volatility of real exchange rate. The main conclusions are that trade openness is negatively correlated with the volatility of real exchange rate, and financial openness is positively correlated with the volatility of real exchange rate. A certain reference is provided for reducing the fluctuation of real exchange rate in the process of opening to the world.

We estimate and forecast childhood obesity by age, sex, region, and urban-rural residence in Thailand, using a Bayesian approach to combining multiple source of information. Our main sources of information are survey data and administrative data, but we also make use of informative prior distributions based on international estimates of obesity trends and on expectations about smoothness. Although the final model is complex, the difficulty of building and understanding the model is reduced by the fact that it is composed of many smaller submodels. For instance, the submodel describing trends in prevalences is specified separately from the submodels describing errors in the data sources. None of our Thai data sources has more than 7 time points. However, by combining multiple data sources, we are able to fit relatively complicated time series models. Our results suggest that obesity prevalence has recently starting rising quickly among Thai teenagers throughout the country, but has been stable among children under 5 years old.

This paper utilizes spatial econometric reenactments to examine the geographic effects of different types of environmentally friendly power on corban discharges. The example covers 31 nations in the Asia-Pacific district during the time frame 2000 to 2018. The spatial connection in the model was affirmed by symptomatic testing, and the spatial Durbin model was picked as the last model. Results show that Gross domestic product per capita, receptiveness to business sectors, unfamiliar direct venture, energy force, and urbanization critically affect CO2 emanations. In correlation, just wind and sunlight-based energy have added to a generous abatement in ozone harming substance emanations in nations over the long run. In contrast, hydropower, bioenergy, and geothermal energy discoveries have been irrelevant. A cross-sectional examination worldview delineated that nations with more elevated sunlight-based energy yield have higher CO2 outflows, while nations with lower levels have lower CO2 emanations. The presence of spatial impacts in the model gave off an impression of the negative consequences for homegrown CO2 outflows of Gross domestic product per capita and exchange transparency of adjoining nations. Furthermore, energy power and higher creation of sustainable power in adjoining nations will prompt lower homegrown CO2 outflows.

Previous research indicates that mate retention strategies are associated with mate value and affect relationship satisfaction. The current research aimed to replicate previous findings in a non-WEIRD society (Iran) and to extend this research by investigating the moderating roles of individual and coalitional mate retention. Participants (n = 754; 416 women) in a committed, heterosexual relationship from two independent samples reported (1) their relationship satisfaction, (2) their partner’s mate value, (3) the frequency of performing individual mate retention, and (4) the frequency of requesting coalitional mate retention. Results indicated that there were positive associations between mate value, individual and coalitional Benefit-Provisioning mate retention behaviors, and relationship satisfaction. We found negative associations between individual and coalitional Cost-Inflicting mate retention behaviors and relationship satisfaction. We found that mate retention moderated the relationship between mate value and relationship satisfaction. Limitations of the current study are noted, and future directions are discussed.

Objectives This study intends to build and compare two kinds of forecasting models at different time scales for hemorrhagic fever incidence in China. Methods Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) were adopted to fit monthly, weekly and daily incidence of hemorrhagic fever in China from 2013 to 2018. The two models, combined and uncombined with rolling forecasts, were used to predict the incidence in 2019 to examine their stability and applicability. Results ARIMA (2, 1, 1) (0, 1, 1)12, ARIMA (1, 1, 3) (1, 1, 1)52 and ARIMA (5, 0, 1) were selected as the best fitting ARIMA model for monthly, weekly and daily incidence series, respectively. The LSTM model with 64 neurons and Stochastic Gradient Descent (SGDM) for monthly incidence, 8 neurons and Adaptive Moment Estimation (Adam) for weekly incidence, and 64 neurons and Root Mean Square Prop (RMSprop) for daily incidence were selected as the best fitting LSTM models. The values of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the models combined with rolling forecasts in 2019 were lower than those of the direct forecasting models for both ARIMA and LSTM. It was shown from the forecasting performance in 2019 that ARIMA was better than LSTM for monthly and weekly forecasting while the LSTM was better than ARIMA for daily forecasting in rolling forecasting models. Conclusions Both ARIMA and LSTM could be used to build a prediction model for the incidence of hemorrhagic fever. Different models might be more suitable for the incidence prediction at different time scales. The findings can provide a good reference for future selection of prediction models and establishments of early warning systems for hemorrhagic fever.

Using microdata from the Chinese General Social Survey (CGSS 2010), this paper investigates whether there are green returns to education in China, where educational attainment promotes pro-environmental attitudes and behaviors. We establish causality by exploiting the exogenous variation induced by the implementation of the Compulsory Schooling Law (CSL) in China. We find evidence that educational attainment is associated with higher levels of pro-environmental attitudes and behaviors, and these estimates are robust to various robustness checks. Further analysis reveals that the acquisition of environmental knowledge is the channel that drives the effect of education on pro-environmental attitudes and behaviors. Finally, the effects of education are heterogeneous across individuals.

The Coronavirus Disease 2019 has resulted in a transition from physical education to online learning, leading to a collapse of the established educational order and a wisdom test for the education governance system. As a country seriously affected by the pandemic, the health of the Indian higher education system urgently requires assessment to achieve sustainable development and maximize educational externalities. This research systematically proposes a health assessment model from four perspectives, including educational volume, efficiency, equality, and sustainability, by employing the Technique for Order Preference by Similarity to an Ideal Solution Model, Principal Component Analysis, DEA-Tobit Model, and Augmented Solow Model. Empirical results demonstrate that India has high efficiency and an absolute health score in the higher education system through multiple comparisons between India and the other selected countries while having certain deficiencies in equality and sustainability. Additionally, single-target and multiple-target path are simultaneously proposed to enhance the Indian current education system. The multiple-target approach of the India-China-Japan-Europe-USA process is more feasible to achieve sustainable development, which would improve the overall health score from .351 to .716. This finding also reveals that the changes are relatively complex and would take 91.5 years considering the relationship between economic growth rates and crucial indicators. Four targeted policies are suggested for each catching-up period, including expanding and increasing the social funding sources, striving for government expenditure support to improve infrastructures, imposing gender equality in education, and accelerating the construction of high-quality teachers.

Understanding how textual information impacts financial market volatility has been one of the growing topics in financial econometric research. In this paper, we aim to examine the relationship between the volatility measure that is extracted from GARCH modelling and textual news information both publicly available and from subscription, and the performances of the two datasets are compared. We utilize a latent Dirichlet allocation method to capture the dynamic features of the textual data overtime by summarizing their statistical outputs, such as topic distributions in documents and word distributions in topics. In addition, we transform various measures representing the popularity and diversity of topics to form predictors for a rolling regression model to assess the usefulness of textual information. The proposed method captures the statistical properties of textual information over different time periods and its performance is evaluated in an out-of-sample analysis. Our results show that the topic measures are more useful for predicting our volatility proxy, the unexplained variance from the GARCH model than the simple moving average. The finding indicates that our method is helpful in extracting significant textual information to improve the prediction of stock market volatility.

Recent technological advances have facilitated the collection of large-scale administrative data and the online surveying of the Indian population. Building on these we propose a strategy for more robust, frequent and transparent projections of the Indian vote during the campaign. We execute a modified MrP model of Indian vote preferences that proposes innovations to each of its three core components: stratification frame, training data, and a learner. For the post-stratification frame we propose a novel Data Integration approach that allows the simultaneous estimation of counts from multiple complementary sources, such as census tables and auxiliary surveys. For the training data we assemble panels of respondents from two unorthodox online populations: Amazon Mechanical Turks workers and Facebook users. And as a modeling tool, we replace the Bayesian multilevel regression learner with Random Forests. Our 2019 pre-election forecasts for the two largest Lok Sahba coalitions were very close to actual outcomes: we predicted 41.8% for the NDA, against an observed value of 45.0% and 30.8% for the UPA against an observed vote share of just under 31.3%. Our uniform-swing seat projection outperforms other pollsters—we had the lowest absolute error of 89 seats (along with a poll from ‘Jan Ki Baat’); the lowest error on the NDA-UPA lead (a mere 8 seats), and we are the only pollster that can capture real-time preference shifts due to salient campaign events.

This study investigates international students’ acculturation attitude preference and the influence factors (i.e., gender, duration in China, host and English proficiency, adult attachment style and social ties) on each acculturation attitude (i.e, integration, assimilation, separation and marginalization attitude) in China. A group of 229 international students within China responded online. Results from cluster analysis based on process model of acculturation revealed that, integration was the most prevalent acculturation attitude of international students in China. Additionally, logistic regressions analysis indicated that duration in China was negatively associated with adoption of separation and marginalization attitude also higher level of English proficiency predicted the preference of marginalization attitude. However, Chinese proficiency was positively associated with integration attitude adoption, host ties were positively and significantly associated with adoption of assimilation and integration attitude, but negatively associated with the usage of marginalization attitude. Lastly, avoidance attachment was found negatively associating with the integration attitude. This study provides valuable insights for international student education policymakers to assist international students in order to adapt to a new environment quickly and effectively.

Background There is a lack of nationally representative estimates for the consequences of early childhood undernutrition on preadolescent outcomes in India. Understanding this relationship is helpful to develop interventions that not only prevent child undernutrition but also mitigate its consequences. Methods and findings In this cohort study, we analyzed prospectively gathered data from 2 waves of the India Human Development Survey (IHDS) to investigate the association of undernutrition during early childhood (0 to 5 years) in 2004 to 2005 with physical and cognitive outcomes during preadolescent (8 to 11 years) years in 2011 to 2012. These surveys interviewed 41,554 households across all 33 states and union territories in India in 2004 to 2005 and reinterviewed 83% of the households in 2011 to 2012. Primary exposure was assessed using the Composite Index of Anthropometric Failure (CIAF) based on 2004 to 2005 survey. Primary outcomes were short stature (height-for-age z-score [HAZ], Apurv Soni and co-workers study child nutrition and developmental outcomes in India., Author summary Why was this study done? India has the largest number of undernourished children under the age of 5. Previous studies report an absence of gender disparities among child undernutrition in India despite well-documented disparities in adulthood. Current national programs for addressing child undernutrition focus on the first 5 years of life and do not include gender-focused interventions. What did the researchers do and find? We carried out a population-based cohort study based on data gathered prospectively from nationally representative survey of 41,554 households in India in 2004 to 2005 and a follow-up in 2011 to 2012 among 83% of the same households. This study leverages India’s only nationally representative panel dataset to report that undernutrition in the first 5 years of life is associated with increased odds of adverse physical outcomes, i.e., having short stature and being very thin and cognitive outcomes, i.e., unable to read and do arithmetic during preadolescent years. Female children who were undernourished were the most vulnerable for experiencing these adverse outcomes, and the gender disparities widened with age during the preadolescent period. As an example, the adjusted probability of short stature for undernourished female children increased from 31% among 8 year olds to 45% among 11 year olds. There was no such increase observed among male children. Presence of a woman in the household with more than secondary level of education mitigated the disadvantage associated with both being undernourished and being a female child. What do these findings mean? Sex differences in physical and cognitive outcomes emerge during the preadolescent period and are associated with early childhood nutrition. Higher level of female education within the household is an important protective factor for these disparities. Elevation of women’s status through improved female education should be central to the national level programs in India focused on maternal and child health.

BackgroundE-learning is a relatively trending system of education that has been placed over conventional campus-based learning worldwide, especially since the emergence of the COVID-19 pandemic. This study aimed to assess e-learning readiness among university students of a developing country like Bangladesh and identify the independent predictors of e-learning readiness.MethodsFrom 26 December 2020 to 11 January 2021, a total of 1162 university students who had enrolled for e-learning completed a semi-structured questionnaire. Data were collected online via “Google Form” following the principles of snowball sampling through available social media platforms in Bangladesh. A multivariable linear regression model was fitted to investigate the association of e-learning readiness with perceived e-learning stress and other independent predictor variables.ResultsA total of 1162 university students participated in this study. The results indicated that with the increase of students’ perceived e-learning stress score, the average e-learning readiness score was significantly decreased (β = -0.43, 95% CI: -0.66, -0.20). The students did not seem ready, and none of the e-learning readiness scale items reached the highest mean score (5.0). The age, gender, divisional residence, preference of students and their parents, devices used, and having any eye problems were significantly associated with the students’ e-learning readiness.ConclusionDuring the prolonged period of the COVID-19 pandemic, e-learning implication strategies are needed to be assessed systematically with the level of readiness and its’ impacts among students for the continuation of sound e-learning systems. The study findings recommend evaluating the e-learning readiness of university students and the mental health outcomes during the COVID-19 catastrophe in Bangladesh.

With the development of recent years, the field of deep learning has made great progress. Compared with the traditional machine learning algorithm, deep learning can better find the rules in the data and achieve better fitting effect. In this paper, we propose a hybrid stock forecasting model based on Feature Selection, Convolutional Neural Network and Bidirectional Gated Recurrent Unit (FS-CNN-BGRU). Feature Selection (FS) can select the data with better performance for the results as the input data after data normalization. Convolutional Neural Network (CNN) is responsible for feature extraction. It can extract the local features of the data, pay attention to more local information, and reduce the amount of calculation. The Bidirectional Gated Recurrent Unit (BGRU) can process the data with time series, so that it can have better performance for the data with time series attributes. In the experiment, we used single CNN, LSTM and GRU models and mixed models CNN-LSTM, CNN-GRU and FS-CNN-BGRU (the model used in this manuscript). The results show that the performance of the hybrid model (FS-CNN-BGRU) is better than other single models, which has a certain reference value.

Given scientific and technological advancements, expectations of online medical education are increasing. However, there is no way to predict the effectiveness of online clinical clerkship curricula. To develop a prediction model, we conducted cross-sectional national surveys in Japan. Social media surveys were conducted among medical students in Japan during the periods May–June 2020 and February–March 2021. We used the former for the derivation dataset and the latter for the validation dataset. We asked students questions in three areas: 1) opportunities to learn from each educational approach (lectures, medical quizzes, assignments, oral presentations, observation of physicians’ practice, clinical skills practice, participation in interprofessional meetings, and interactive discussions with physicians) in online clinical clerkships compared to face-to-face, 2) frequency of technical problems on online platforms, and 3) satisfaction and motivation as outcome measurements. We developed a scoring system based on a multivariate prediction model for satisfaction and motivation in a cross-sectional study of 1,671 medical students during the period May–June 2020. We externally validated this scoring with a cross-sectional study of 106 medical students during February–March 2021 and assessed its predictive performance. The final prediction models in the derivation dataset included eight variables (frequency of lectures, medical quizzes, oral presentations, observation of physicians’ practice, clinical skills practice, participation in interprofessional meetings, interactive discussions with physicians, and technical problems). We applied the prediction models created using the derivation dataset to a validation dataset. The prediction performance values, based on the area under the receiver operating characteristic curve, were 0.69 for satisfaction (sensitivity, 0.50; specificity, 0.89) and 0.75 for motivation (sensitivity, 0.71; specificity, 0.85). We developed a prediction model for the effectiveness of the online clinical clerkship curriculum, based on students’ satisfaction and motivation. Our model will accurately predict and improve the online clinical clerkship curriculum effectiveness.

International openness can affect regional innovation through more export opportunities, enhanced import competition and the spillover effects of foreign direct investment. Many studies have been conducted based on different countries for capturing the determinants of regional innovation, but very little literature is available with contradictory findings for the case of China. Based on 19 years’ panel data of 31 Chinese provinces, this paper analyzes the impact of international openness on regional innovation measured by the number of patent grants. The positive effects of overall trade and a higher proportion of exports and imports to GDP are significant and robust across different model specifications, indicating that an increase in international openness can promote regional innovating activities in China. The causal relationship of all the variables depicted by path analysis matches the results of the system GMM model. Higher intellectual property protection provides each region with the opportunity to obtain economic benefits from innovation and then make a higher investment in R&D activities. Besides, the lag effect of regional innovation capability can also explain a large part of local innovating activities. In our subsample regressions, the positive effect of trade openness on innovation is majorly manifested in developed areas like eastern provinces.

Drawing upon a dataset of cross-border mergers and acquisitions (M&A) events of Chinese enterprises from 2010 to 2017, this study investigates the impact of corporate social responsibility (CSR) on the completion of cross-border M&A with a focus on the moderating role of institutional distance. The results highlight the significance of CSR on the completion of cross-border M&A. The robustness tests including changing estimation model, new measurements, propensity score matching, and instrumental variable tests show that the main results are consistent. Second, both formal and informal institutional distance have positive moderating effects of CSR on the completion of cross-border M&A.

To examine which factors affect the performance of technology business incubators in China, the present study proposes an entrepreneurial ecosystem framework with four key areas, i.e., people, technology, capital, and infrastructure. We then assess this framework using a three-year panel data set of 857 national-level technology business incubators in 33 major cities from 28 provinces in China, from 2015 to 2017. We utilize factor analysis to downsize dozens of characteristics of these technology business incubators into seven factors related to the four proposed areas. Panel regression model results show that four of the seven factors related to three areas of the entrepreneurial ecosystem, namely people, technology, and capital areas, have statistically significant associations with an incubator’s performance when applied to the overall national data set. Further, seven factors related to all four areas have various statistically significant associations with an incubator’s performance in five major regional data set. In particular, a technology related factor has a consistently statistically significant association with the performance of the incubator in both national model and the five regional models, as we expected.

Background It is well known that equity assessment of the spatial distribution of primary health care facilities (PHCFs) is significant to optimize the allocation of health care resources and enhance the ability to react to public health emergencies, yet there is much discussion about the poor effectiveness of assessment for many cities due to the limitations of the insufficient ability of traditional data to reflect residents’ demands, etc. In many cities where PHCFs are inequity distributed, this is urgently needed for the government. Methods Using Fuzhou City, China as a case study, we propose a comprehensive method for assessing the equity that consisting of two frameworks based on the Geographic Information System. The first framework is assessing resident demand based on daily demand and potential demand, and the second is assessing PHCFs supply based on road impedance. This method combines an index system based on multi-source data and a spatial matching analysis between resident demand and PHCFs supply based on the supply-demand disparity index. Results The demand degree for the PHCFs and the supply degree of accessing the PHCFs of different residential areas differ to great extents. The equity of the spatial distribution of PHCFs shows well overall, but there is still a lack of equity in local areas. The number of the residential areas with the lowest equity, insufficient supply and oversupply accounted for 10.31% and 39.00% respectively; the former is mainly gathered in a concentrated form at the edge and in a scattered form inside, and the latter is distributed in the interior mostly in a concentrated manner. Conclusion The key findings highlight several aspects of improving the layout of PHCFs such as strengthen an in-depth analysis on residents’ demand. This study contributes to a more accurate equity assessment, and further improves the layout of health care facilities.

Regional collaboration and the division of economic zones have been widely discussed for sustainable development. This paper aims at building a framework for analyzing the provincial division of economic zone (DEZ) via considering spatial interaction among regions based on the improved gravity model and clustering approaches. The proposed framework of province DEZ is applied in the case study of Hunan Province, China. The results reveal that Chenzhou city in the south of Hunan Province is always excluded from the DEZ due to its larger external gravity from other cities in neighboring provinces. It also shows that the city components of economic zones (EZs) have experienced a fluctuation at a higher degree from 2002 to 2006 to 2009 while it reached to a stable phase in 2013. Furthermore, cross-provincial regional integration and the highway construction have a significant impact on the change of city components of EZ. The findings are of great potential in regional planning that should be incorporated to the toolkit of regional policy and sustainable development for local governments.

Higher education is considered as the engine of the economic development of a country due to its role in cultivating human capital. The provision of higher education is regarded as a productive investment in human capital for improving nation’s productive capacity. However, there is a large gap in enrollment between students of different socioeconomic statuses. The ever-widening socio-economic inequalities between individuals from disadvantaged and advantaged backgrounds make the government’s efforts to enroll a higher number of students to pursue higher education challenging in developing countries, as the students’ socioeconomic status plays a decisive role in their priority to attain higher education. This study anticipated quantifying the impact of socio-economic indicators and underlying situations on students’ enrollment in higher education in Pakistan. A descriptive study, involving correlation, principal component analysis (PCA), clustering, and stepwise regression using 15-years data of enrollment and socio-economic indicators was conducted. The correlation between different socio-economic indicators and students’ enrollment was positive and highly significant (0.73 to 0.99), except for the unemployment rate (- 0.39 to -0.57). PCA showed that the first two components were the most influential with 93.85% of the total variation. Enrollment (total and male) showed significant relationships with general government expenditure and unemployment rate, while female enrollment showed a significant relationship with general government expenditure. Findings revealed that socio-economic factors can serve as a significant predictor of students’ enrollment in higher education. The minimum values of correlation coefficient (R) and adjusted R2 for enrollment were ranged from 0.875 to 0.748 (female enrollment), while maximum values (0.987 to 0.993 and 0.973 to 0.983), respectively were observed for total enrollment. The findings will assist educationists, social scientists, and policymakers to better understand the association between socio-economic indicators and student enrollment in higher education for formulating future education policies for enhancing enrollment in higher education.

Background Dengue is the most rapidly spreading mosquito-borne viral disease of humans worldwide, including southeast Asia region. This review provides a comprehensive overview of questionnaire-related dengue studies conducted in the Philippines and evaluates their reliability and validity in these surveys. Methods A review protocol constructed by a panel of experienced academic reviewers was used to formulate the methodology, research design, search strategy and selection criteria. An extensive literature search was conducted between March–June 2020 in various major electronic biomedical databases including PubMed, EMBASE, MEDLINE and ScienceDirect. A systematic review and meta-analysis (PRISMA) were selected as the preferred item reporting method. Results Out of a total of 34 peer-reviewed dengue-related KAP studies that were identified, 15 published from 2000 to April 2020 met the inclusion criteria. Based on the meta-analysis, a poor mean score was obtained for each of knowledge (68.89), attitude (49.86) and preventive practice (64.69). Most respondents were equipped with a good knowledge of the major clinical signs of dengue. Worryingly, 95% of respondents showed several negative attitudes towards dengue prevention, claiming that this was not possible and that enacting preventive practices was not their responsibility. Interestingly, television or radio was claimed as the main source of gaining dengue information (range 50–95%). Lastly, only five articles (33.3%) piloted or pretested their questionnaire before surveying, of which three reported Cronbach’s alpha coefficient (range 0.70 to 0.90). Conclusion This review indicates that to combat the growing public health threat of dengue to the Philippines, we need the active participation of resident communities, full engagement of healthcare personnel, promotion of awareness campaigns, and access to safe complementary and alternative medicines. Importantly, the psychometric properties of each questionnaire should be assessed rigorously.

Background Cyberbullying is a modern form of bullying that could be practiced electronically or on the internet. It is related to different mental health issues such as depression, which can affect both the cyberbully and the victim. Although a few studies have been conducted regarding the prevalence of cyberbullying and cyber-victimization among the younger generation in Qatar, no studies have been conducted among young adults despite studies showing that they are also prone to cyberbullying. Methods This is a cross-sectional study to investigate the prevalence and the relationship between cyberbullying, cyber-victimization, and depression symptoms among Qatar University students. A self-administered close-ended electronic questionnaire was used to assess student’s cyberbullying/cyber-victimization behaviors and depression symptoms. The Revised Cyberbullying Inventory scale (RCBI-II) and Patient Health questionnaire-9 (PHQ-9) were utilized to measure involvement in cyberbullying and depression symptoms, respectively. A total of 836 students participated in the study. Pearson Chi-Square test and binary logistic regression were conducted to analyze the data. Results Results indicated the majority of students have been involved in cyberbullying as follows: 6.8% cyberbullies, 29.2% cybervictims, 35.8% cyberbully-victims, and 28.2% not involved in either. Approximately 50% of the students scored a ten or higher on the PHQ9 test indicating symptoms of depression. Moreover, significant associations were found between cyberbullying experiences and gender (p = 0.03), depression and gender (p = 0.046), and between cyberbullying experiences and depression (p Conclusion Our findings indicate that among Qatar University students, cyberbullying and cyber-victimization are prevalent behaviors that could be associated with the high reported rates of depression symptoms.

In the game of softball, the batter should possess the necessary skills to hit the ball toward various directions with high initial speed. However, because various factors influence each other, there are limitations to the range that can be controlled by the batter’s skill. This study was aimed at extracting the impact characteristics associated with the launch speed/direction and batted ball spin and clarifying the important skills required to improve the batter’s hitting performance. In our experiments, 20 female softball players, who are members of the Japan women’s national softball team, hit balls launched from a pitching machine. The movements of the ball and bat before, during, or after the impact were recorded using a motion capture system. Stepwise multiple regression analysis was performed to extract factors relating the side spin rate. The undercut angle (elevation angle between the bat’s trajectory and the common normal between the ball and bat: ΔR2 = 0.560) and the horizontal bat angle (azimuth of bat’s long axis at ball impact: ΔR2 = 0.299) were strongly associated with the side spin rate (total R2 = 0.893, p < 0.001). The undercut angle in opposite-field hitting was significantly larger than that in pull-side hitting (p < 0.001). The side spin rate was associated with the undercut angle because the bat’s distal (barrel) side inclined downward (–29.6 ± 8.7°) at impact. The ball exit velocity was higher when it was hit at a smaller undercut angle (R2 = 0.523, p < 0.001). Therefore, it is deemed desirable to focus on maximizing the ball exit velocity rather than ball spin because the ball–bat impact characteristics vary inevitably depending on the launch direction. Meanwhile, the use of the ball delivery machine and the slower pitched ball are the limiting factors in the generalization of the findings.

Despite widespread scholarly interest in values and attitudes among Muslim populations, relatively little work has focused on specific attitudes popularly thought to indicate anti-modern or anti-liberal tendencies within Islam. In this article, we use data from the Pew Research Center from 2008-2012 to examine support for violent practices among Muslims in thirty-five countries. Support for violent practices is defined by three questions on the acceptability of killing apostates, the stoning of adulterers, and severe corporal punishment for thieves. Using multilevel models that capture country-level variability, we analyze the relationship between support for violent practices and education, religiosity, and development. In general, we find that support for violent practices is less common among individuals with more education and less religiosity and who come from more developed countries. However, when we examine variation across countries, we see evidence of substantial heterogeneity in the association of education and religiosity with support for violent practices. We find that education is more liberalizing in more liberal countries and in less developed countries. The effects of religiosity are also related to country-level context but vary depending on how religiosity is measured. Overall, the variation we observe across countries calls into question a civilizational approach to studying values among Muslim populations and points to a more detailed multiple modernities approach.

Larval surveillance is the central approach for monitoring dengue vector populations in Indonesia. However, traditional larval indices are ineffective for measuring mosquito population dynamics and predicting the dengue transmission risk. We conducted a 14-month ovitrap surveillance. Eggs and immature mosquitoes were collected on a weekly basis from an urban village of Bandung, namely Sekejati. Ovitrap-related indices, namely positive house index (PHI), ovitrap index (OI), and ovitrap density index (ODI), were generated and correlated with environmental variables, housing type (terraced or high-density housing), ovitrap placement location (indoor or outdoor; household or public place), and local dengue cases. Our results demonstrated that Aedes aegypti was significantly predominant compared with Aedes albopictus at each housing type and ovitrap placement location. Ovitrap placement locations and rainfall were the major factors contributing to variations in PHI, OI, and ODI, whereas the influences of housing type and temperature were subtle. Indoor site values were significantly positively correlated to outdoor sites’ values for both OI and ODI. OI and ODI values from households were best predicted with those from public places at 1- and 0-week lags, respectively. Weekly rainfall values at 4- and 3-week lags were the best predictors of OI and ODI for households and public places, respectively. Monthly mean PHI, OI, and ODI were significantly associated with local dengue cases. In conclusion, ovitrap may be an effective tool for monitoring the population dynamics of Aedes mosquitoes, predicting dengue outbreaks, and serving as an early indicator to initiate environmental clean-up. Ovitrap surveillance is easy for surveyors if they are tasked with a certain number of ovitraps at a designated area, unlike the existing larval surveillance methodology, which entails identifying potential breeding sites largely at the surveyors’ discretion. Ovitrap surveillance may reduce the influence of individual effort in larval surveillance that likely causes inconsistency in results., Author summary The dengue virus, transmitted by Aedes vectors, has been continuously spreading in tropical and subtropical countries, causing illness and fatality. Given the lack of a cost-effective dengue vaccine, the vector control approach for reducing the Aedes population remains the key method for mitigating dengue transmission. For a successful vector control program, an effective vector surveillance system is crucial for precisely predicting the spatial and temporal risk of a dengue outbreak. The ovitrap system improves data collection efficiency, aiding long-term dengue vector monitoring activities. This study is one of the few long-term dengue vector surveillance programs in Indonesia and provides compelling evidence of the need to improve the existing conventional larval surveillance system. The results demonstrated that two dengue vector mosquitoes, A. aegypti and A. albopictus, were present in the study area, and A. aegypti was more prevalent than A. albopictus. We observed an interactive relationship between ovitrap placement and rainfall in the dynamics of ovitrap-related indices; understanding this relationship allows for timely initiation of vector control and intervention strategies. We conclude that the ovitrap surveillance system is a sensitive tool for monitoring the population dynamics of Aedes vectors, predicting dengue outbreaks, and potentially improving community-based conventional larval surveillance.

Background The values of individuals and organizations are the core factors driving and guiding nurses’ decision-making and actions. Previous studies mainly focused on the impact of organizational commitment and other influencing factors on turnover intention. Aim To explore the mediating effect of personal and organizational values matching the relationship between organizational commitment and turnover intention of nursing staff. Methods A cross-sectional survey of 490 subjects in four tertiary hospitals in Tianjin was conducted by convenient sampling. Multivariate regression analysis and structural equation models were used to test each hypothesis. Results The results showed that there is a negative correlation between personal and organizational values, organizational commitment and turnover intention, and personal and organizational values played an indirect intermediary role between organizational commitment and turnover intention. Conclusions Organizational commitment reduces nurses’ turnover intention indirect through personal and organizational values paths. Leaders can improve nurses’ values as members of the organization, so as to participate in their own work more actively. Implications for nursing management Managers should effectively reduce the turnover rate and stabilize the nursing team by improving the organizational commitment and personal and organizational values of clinical nurses.

With social and economic environment changes occurring in the world and deepening of the urbanization process, China’s urban development exhibits a new phenomenon of growth and shrinkage fluctuations. The resource-based city shrinkage phenomenon is particularly prominent. Research on the commonalities and patterns of similar groups should be enhanced. We constructed an urban shrinkage evaluation index system from the three dimensions of population, economy and space. Accordingly, we explored the spatiotemporal evolution characteristics of 175 resource-based cities in China from a multidimensional perspective with the entropy method, shrinkage model and transfer matrix method. The results indicated that most resource-based cities in China occurred in the non-shrinking state, but their development speed gradually decreased or even presented stagflation. The shrinkage measure-related results in the different dimensions revealed that the number of shrinking cities is increasing. The population, economic and comprehensive shrinkage levels were mainly slight and remained stable. The number of cities experiencing moderate and severe shrinkage was relatively small and mostly encompassed short-term shrinkage. Spatial shrinkage demonstrated a clear administrative hierarchy difference. Moreover, the spatial distribution range of shrinking cities in each dimension expanded and exhibited obviously similar characteristics, i.e., shrinking cities were relatively concentrated in Northeast China, while they were more scattered in other regions. Furthermore, the geodetector technique was applied to reveal the influencing factors of resource-based city growth and shrinkage. Based on the results, the change in the secondary industry output value share at the start of the study was the primary factor. The impact of each employment structure indicator from 2014 to 2018 was particularly significant. Comprehensive exploration of the shrinkage characteristics of this particular group of cities and their development behavior from a multidimensional perspective can provide an important reference for the transformation and high-quality development of resource-based cities.