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The problem of loneliness among college students has attracted extensive attention from researchers. As a fundamental psychological framework, personality traits are associated with loneliness. Thus, we conducted a survey of 688 college students in Mainland China to examine the relationship between the HEXACO personality traits and loneliness. Regression analysis revealed a negative association between loneliness and the personality traits of agreeableness, conscientiousness, and openness to experience, and a positive association between loneliness and the personality traits of honesty–humility, emotionality, and extraversion. This model explained 33.78% of the variance in loneliness. Implications of the findings are discussed. [ABSTRACT FROM AUTHOR]

To verify the association between personality traits and academic performance in a university setting, we collected the grade point average (GPA) and six personality characteristics (honesty–humility, emotionality, extraversion, agreeableness, conscientiousness, and openness to experience) of 349 students at a university in China. According to the results, there were positive associations between GPA and honesty–humility, emotionality, extraversion, agreeableness, conscientiousness, and openness to experience. Additionally, honesty–humility, extraversion, agreeableness, and openness to experience explained 30.72% of the variance in GPA. On the basis of the above findings, two conclusions can be made: First, undergraduates' GPA is associated with these six personality traits; second, undergraduates' honesty–humility, extraversion, agreeableness, and openness to experience may help to explain the variance in their GPA. [ABSTRACT FROM AUTHOR]

Locust plagues inflict severe agricultural damage. Climate change-induced extreme events like rainfall and droughts have expanded locust habitats. These new areas, often beyond routine monitoring, could become potential high-risk locust areas (PHRLA). Quantitatively understanding the outbreak mechanism driving drought and flood dynamics is crucial for identifying PHRLA, but such studies are scarce. To address this gap, we conducted a case study on locust outbreaks in Xiashan Reservoir, the largest reservoir in Shandong Province, China, in 2017 and 2018. Using time series satellite imagery and meteorological products, we quantitatively analyzed how drought–flood dynamics and temperature affect locust habitats, reproduction, and aggregation. Employing an object-oriented random forest classifier, we generated locust habitat classification maps with 93.77% average overall accuracy and Kappa coefficient of 0.90. Combined with meteorological analysis, we found that three consecutive drought years from 2014 to 2016 reduced the water surface area by 75%, expanding suitable habitats (primarily reeds and weeds) to cover 60% of the reservoir. Warm winters and high temperatures during locust key growth periods, coupled with expanding suitable habitats, promoted multi-generational locust reproduction. However, substantial flooding events in 2017 and 2018, driven by plentiful rainfall during key growth periods, reduced suitable habitats by approximately 54% and 29%, respectively. This compression led to high locust density, causing the locust plague and high-density spots of locusts (HDSL). Our study elucidates locust plague outbreak mechanisms under dynamic drought and flood environments. Based on this, we propose an approach to identify PHRLA by monitoring changes in drought and flood patterns around water bodies and variations in suitable habitat size and distribution, as well as surrounding topography. These findings hold significant implications for enhancing locust monitoring and early warning capabilities, reducing pesticide usage, and ensuring food and ecological security and sustainable agriculture. [ABSTRACT FROM AUTHOR]

Frequent forest fires cause severe damage to the ecological service functions of forest ecosystems. Machine learning (ML) techniques have gained widespread use for forest fire susceptibility mapping due to their potent nonlinear learning capabilities. However, prior research has devoted insufficient attention to negative sample sampling, leading to overestimated fire susceptibility. Furthermore, the dynamic factors used for fire prediction are often extracted based on fixed time windows (FTW), making it challenging to accurately capture the spatial heterogeneity in forest cumulative dryness status (CDS). In this paper, we introduced a space-humidity-constrained (SHC-based) sampling method to generate negative samples with specific humidity. It was achieved by imposing distance and aridity constraints on randomly generated points, thus enhancing their representativeness. We also proposed a dynamic factor extraction method based on dynamic time windows (DTW), constrained by daily or cumulative precipitation thresholds (Th cp), to obtain fine-grained forest CDS information by limiting the statistical time windows of dynamic factors. Our results demonstrated that the forest fire susceptibility modeling approach, incorporating the above two methods for sample sets and feature sets construction, significantly enhanced the prediction performance of three ML models, namely random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP), underscoring its stability and robustness in improving model performance. The RF model outperformed the SVM and MLP models in terms of both performance improvement and prediction accuracy. The application of these two proposed methods improved the prediction accuracy of the RF model by more than 3% (FTW of at least 35 days) and 5% (Th cp of at least 40 mm), respectively. As Th cp increased, the performance enhancement became even more pronounced (accuracy can increase by over 8% when Th cp is 60 mm). The novel modeling approach, rooted in an understanding of the mechanisms underlying forest fires and adeptly capturing the forest spatially heterogeneous CDS information, can significantly improve the mapping accuracy. Moreover, coupled with the adaptive acquisition advantage of dynamic factors, this study is able to generate near real-time fire susceptibility maps, which hold greater practical value for aiding decision-makers in formulating effective prevention and management strategies. [Display omitted] • Cumulative dryness status information was considered in modeling forest fire susceptibility. • A space-humidity-constrained sampling method for negative samples was introduced. • A dynamic factor extraction method based on dynamic time windows was developed. • The proposed ML modeling approach effectively improved the prediction performance. • The RF model outperforms the SVM and MLP models and fire risk maps of the GBA were generated. [ABSTRACT FROM AUTHOR]

The psyllid Cacopsylla chinensis (Yang & Li) (Hemiptera: Psyllidae) is a serious pest of pears in China. To determine and contrast the fitness of the psyllid on two endemic cultivars of Pyrus bretschneideri (i.e. BHXS and BSL) and two introduced cultivars of Pyrus communis (i.e. CB and CRB), we analyzed data on the development, survival, and fecundity from C. chinensis individuals reared on the four cultivars. The age-stage, two-sex life table theory was used in order to enable the inclusion of males in the analysis as well as a means of identifying the variation in developmental durations among individuals. Results indicated that C. chinensis can successfully develop and reproduce on all four pear cultivars. However, based on the lower preadult survival rate, longer preadult duration, longer total preoviposition period (TPOP), and lower fecundity that occurred on both cultivars of P. communis , these two cultivars are less favorable hosts for C. chinensis than the P. bretschneideri cultivars. The lower intrinsic rate of increase (r), finite rate of increase (λ), and net reproduction rate (R 0) on CB and CRB pears showed these two introduced cultivars are more resistant to C. chinensis than the endemic BHXS and BSL pears. These resistant cultivars would be appropriate candidates for managing C. chinensis. We used the bootstrap technique to estimate the uncertainty of the population parameters (r , λ, R 0, etc.), while also demonstrating that it can be used for estimating the 0.025 and 0.975 percentile confidence intervals of the age of survival rate. [ABSTRACT FROM AUTHOR]

There is an association between personality traits and generalized anxiety disorder (GAD). In order to differentiate the association between various personality traits and GAD, a survey was conducted among PhD students in northern China (mainland). Three conclusions could be drawn with the help of regression analysis. At first, there is a positive association between Honesty-Humility (H H)/Emotionality (E)/Conscientiousness (C)/Openness to experience (O) and GAD, whereas there is a negative association between Agreeableness (A)/eXtraversion (X) and GAD. Secondly, age, gender, major, monthly income, H H, E, X, A, C and O could explain a 21.80 % variance in GAD. Thirdly, E and C are two robust factors that associated with GAD among PhD students. [ABSTRACT FROM AUTHOR]

The physiological adaptations of biological control agents may determine where and when they will be successful. Agasicles hygrophila Selman & Vogt, has been introduced into China for biological control of an invasive weed, Alternanthera philoxeroides (Mart.) Griseb. The extent to which A. hygrophila can withstand low temperatures was tested to determine whether this physiological trait can be used to predict its potential distribution and effectiveness as a biological control agent. Our results showed that A. hygrophila eggs, larvae, pupae and adults had similar super cooling point temperatures (SCPs) which were not affected by acclimatization. Mortality increased with decreasing temperatures and with length of exposure to low temperatures. SCP was the limiting survival temperature, and chilling injury was one of the main causes of death. The upper limit of chill injury zone (ULCIZ) was 6.7 °C and the sum of injurious temperature (SIT) was about 15.0 degree days. Based on adult cold tolerance, the distribution of A. hygrophila in China is unlikely to extend beyond areas that experience temperatures at or below −8 °C, and the flea beetles will probably overwinter successfully only in areas where the annual minimum air temperature remains above 6.7 °C. These predictions were compared with known distribution data and seem to be a reliable and economical method to predict effectiveness of potential of biological control agents in regions with extreme climates. [ABSTRACT FROM AUTHOR]

The Oriental migratory locust is a destructive agricultural pest in China. Large-scale locust area (the area possessing suitable breeding habitat for locusts and has locust infestation) extraction and its evolution analysis are essential for locust ecological control. Existing methods seldom consider the spatial differences in the locust development and habitat landscape structures in large areas. To analyze these effects, our study proposed a landscape-based habitat suitability model (LHS model) for large-scale locust area extraction based on remote sensing data, taking the middle and lower reaches of the Yellow River (MLYR) as an example. Firstly, the DD model was used to simulate locust development and obtain habitat factors of the corresponding dates; secondly, the patch distribution of different land cover classes and their adjacent landscape characteristics were analyzed to determine the landscape-based factors memberships; finally, the habitat suitability index was calculated by combining the factors memberships and weights to extract the locust area. Compared with the patch-based model using moving windows (patch based-analytic hierarchy process model, R2 = 0.77), the LHS model accuracy improved significantly (R2 = 0.83). Our results showed that the LHS model has a better application prospect in large-scale locust area extraction. By analyzing the locust areas evolution along the MLYR extracted using the LHS model, we found human activities were the main factors affecting the locust areas evolution from 2016 to 2020, including: (1) planting the plants that locusts do not like and urbanization caused the decrease of the locust area; (2) the wetland protection policies may cause the increase of the locust area. The model and research results help locust control and prevention to realize the sustainable development of agriculture. [ABSTRACT FROM AUTHOR]

Non-target plant selection tests using 20 representative species showed that Agasicles hygrophila could feed on Alternanthera sessilis, but the mature larvae did not pupate successfully. A. hygrophila could not complete their life cycle on the other plant species tested. Thus, A. hygrophila is safe for controlling Alternanthera philoxeroides in China. [ABSTRACT FROM AUTHOR]

• An integrated patch-based habitat suitability assessment method for nationwide locust area extraction is proposed. • Dynamic changes of locust area and its evolution in China from 1995 to 2017 are analyzed. • The influences of land use/cover change on locust area evolution are quantified. • Suggestions for locust area management and monitoring are provided. The Oriental migratory locust (Locusta migratoria manilensis) is a threat to agriculture in China. The locust area is defined as an area possessing a suitable breeding habitat for this locust and has a locust outbreak at least once per decade. Ecological control of the locust area is an effective approach to manage locust with minimal environmental degradation. The formation and dynamic changes of locust areas are greatly affected by land use/cover changes (LUCC). Clarifying the LUCC in the locust area is significant for implementing rational ecological control strategies and detecting timely potential locust areas. This paper proposed a patch-based habitat suitability assessment (PHSA) method for nationwide locust area extraction. Then, a multi-temporal binary coding method was used to detect the locust area dynamic changes from 1995 to 2017. Subsequently, the relationship between LUCC and the extinction and formation of locust areas in different ecotypes was quantitatively investigated. Results show that the area of the locust area has greatly reduced over the past 20 years, and the distribution range has a large degree of temporal and spatial variation. The extinction of the locust area is closely related to the LUCC from cropland and wetland to woodland and artificial surface. It is also related to changes from grassland to cropland and woodland. The newly formed locust area shows opposite LUCC trends. These results provide practical strategies and guidance for ecological control and early monitoring of locust areas. They are instrumental in reducing pesticide use and achieving more sustainable agriculture. [ABSTRACT FROM AUTHOR]

Background: Shift work may cause insomnia and sleepiness in individuals. The present study aimed to exam shift work disorder (SWD), and to investigate their associations with individual characteristics.Methods: A total of 1833 shift workers were assessed using the Pittsburg Sleep Quality Index, Epworth Sleepiness Scale (ESS), Composite Scale of Morningness (CSM), Circadian Type Inventory (CTI), Center for Epidemiologic Studies-Depression Scale (CES-D), Beck Anxiety Inventory (BAI) and other self-compiled socio-demographic questionnaires.Results: In the current sample, 17.1% shift workers have experienced insomnia symptoms, 20.9% were tested for daytime sleepiness, and 19.9% were categorized as having SWD. Logistics regressions revealed that history of mental disorders (OR = 2.04, 95% CI = 1.30-3.21), chronic physical illness (OR = 1.53, 95% CI = 1.17-1.99), CES-D scores (OR = 1.03, 95% CI = 1.02-1.05), BAI scores (OR = 1.04, 95% CI = 1.03-1.06), languid/vigorous tendencies (OR = 1.06, 95% CI = 1.03-1.10) were positively associated with the onset of SWD, while morningness (OR = 0.97, 95% CI = 0.94-0.99) decreased the odds of SWD onset.Conclusions: These findings suggested that attention should be drawn to individuals with mental and chronic diseases in when scheduling work shifts. While SWD and its associates should be considered when providing psychological services to shift workers. [ABSTRACT FROM AUTHOR]

Introduction: The high co-occurrence of tobacco smoking and depression is a major public health concern during the novel coronavirus disease-2019 pandemic. However, no studies have dealt with missing values when assessing depression. Therefore, the present study aimed to examine the effect of tobacco smoking on depressive symptoms using a multiple imputation technique., Methods: This research was a longitudinal study using data from four waves of the China Health and Retirement Longitudinal Study conducted between 2011 and 2018, and the final sample consisted of 74,381 observations across all four waves of data collection. The present study employed a multiple imputation technique to deal with missing values, and a fixed effects logistic regression model was used for the analysis., Results: The results of fixed effects logistic regression showed that heavy smokers had 20% higher odds of suffering from depressive symptoms than those who never smoked. Compared to those who never smoked, for short-term and moderate-term quitters, the odds of suffering from depressive symptoms increased by 30% and 22%, respectively. The magnitudes of the odds ratios for of the variables short-term quitters, moderate-term quitters, and long-term quitters decreased in absolute terms with increasing time-gaps since quitting. The sub-group analysis for men and women found that heavy male smokers, short-term and moderate-term male quitters had higher odds of suffering from depressive symptoms than those who never smoked. However, associations between smoking status and depressive symptoms were not significant for women., Conclusions: The empirical findings suggested that among Chinese middle-aged and older adults, heavy smokers and short-term and moderate-term quitters have increased odds of suffering from depressive symptoms than those who never smoked. Moreover, former smokers reported that the probability of having depressive symptoms decreased with a longer duration since quitting. Nevertheless, the association between depressive symptoms and smoking among Chinese middle-aged and older adults is not straightforward and may vary according to gender. These results may have important implications that support the government in allocating more resources to smoking cessation programs to help middle-aged and older smokers, particularly in men., 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 © 2022 Du, Wu, Kang, Zhao and Li.)