Investigating the Effects of Personality on the Safety Behavior of Gold Mine Workers: A Moderated Mediation Approach.

Based on prior research on the relationship between personality and safety behavior, we construct a moderated mediation model that tests the effects of each of the Big Five personality traits (openness, conscientiousness, agreeableness, extraversion, and neuroticism) on the safety behavior of gold miners in Ghana. The model included safety competency as a mediator between the five personality traits and safety behavior. Management commitment to safety was used as a moderator to test the strength of the mediation of safety competency. Data was collected from 344 individuals employed across six large-scale gold mining companies in Ghana using a questionnaire survey. Amos 26 was used to conduct confirmatory factor analysis. The hypotheses were tested using Hayes PROCESS macros models 4 and 7 on SPSS 26. Findings show that openness and extraversion have an insignificant direct influence on safety behavior. Neuroticism negatively affects safety behavior. In contrast, conscientiousness and agreeableness positively affect safety behavior. Mediation analysis revealed that safety competency partially mediates the relationships between (1) conscientiousness and safety behavior and (2) agreeableness and safety behavior. The relationship between extraversion and safety behavior is fully mediated by safety competency. Additionally, we discovered that management safety commitment has a significant conditional indirect effect (Index of moderated mediation = 0.168 95% CI = [0.122;0.222]) on the relationship between conscientiousness and safety behavior through safety competency. Management safety commitment also significantly moderated (Index of moderated mediation = 0.075 95% CI = [0.021;0.120]) the relationship between agreeableness and safety behavior through safety competency.

Lactate increases tumor malignancy by promoting tumor small extracellular vesicles production via the GPR81-cAMP-PKA-HIF-1α axis.

Lactate and tumor cell-derived extracellular vesicles (TEVs) both contribute to tumor progression. However, it is still unclear whether lactate can accelerate tumor development by directly promoting TEV production. Here, we show that lactate decreases intracellular cAMP levels and subsequent PKA activation via GPR81, which inhibits the PKA-induced ubiquitination of HIF-1α that causes degradation. Then, the HIF-1α-mediated transcription of Rab27a is enhanced, leading to increased TEV release. In this way, lactate promotes lung metastasis by murine melanoma. In addition, we show that serum lactate levels are positively correlated with serum EV levels and Rab27a and HIF-1α protein levels in the tumor tissues of lung cancer patients. Thus, our results reveal a novel mechanism underlying lactate-mediated tumor progression induced by TEVs and provide new strategies for tumor therapy.

Evacuation in Buildings Based on BIM: Taking a Fire in a University Library as an Example.

As a typical public place, a university library has a large collection of books with heavy fire load, dense population, and large flow of people. The situation of safe evacuation in case of fire is very serious. This study utilizes Revit, Pyrosim, and Pathfinder software to research evacuation of a university library. First, a Building Information Modeling (BIM) is constructed based on Revit software in 1:1 scale. Second, the evacuation passage with the highest utilization rate was determined through Pathfinder software. According to the "most unfavorable principle," the location near it was assumed to be where the fire occurred. Pyrosim software was used to determine the smoke spread, visibility, CO concentration, temperature, and other conditions at each stairway exit in case of fire. Finally, the evacuation situation is compared with that after man-made route planning. The results indicate that evacuation exits 1#, 7#, 13#, 19#, and 23# have the highest utilization rate. The safe evacuation time was 739.275 s, which was shortened to 638.025 s after man-made route planning, a 13.67% increase in evacuation efficiency. Evacuation efficiency can be significantly improved by increasing broadcast guidelines, adding signs, widening staircases, and other optimization suggestions, which can provide reference for the study of evacuation effects in public places and the improvement of the layout of public facilities.

Comprehensive evaluation of water ecological environment in watersheds: a case study of the Yangtze River Economic Belt, China.

The Yangtze River Economic Belt, an inland economic zone with global influence, has shown a trend of prosperous economic development in recent years. Economic development, water pollution, resource depletion, and other environmental problems continue to emerge. The steady state of the water ecological environment is an important aspect of ecological security. To investigate the regional water ecological security state, this study constructs a comprehensive evaluation indicator system within the framework of "driving force-carrying source-state-management" (DCSM). The entropy weight method was used to determine the weight of each indicator, and the weighted rank sum ratio model was introduced to classify the water ecological environment of the Yangtze River Economic Belt from 2010 to 2019. Finally, an adversarial interpretative structure model is used to refine the ranking of each region. The results show that the bearing state and driving force subsystems are closely related to the water ecological environment. The top three indicators are wastewater discharge of industrial added value of 10,000 yuan, water consumption per 10,000 yuan of industrial gross product, and water consumption per 10,000 yuan of tertiary gross domestic product. In addition, there are clear differences in the water ecological environment of the Yangtze River Economic Belt. The classification results show that Zhejiang and Jiangsu are rated as "excellent''; Yunnan, Guizhou, Anhui, and Jiangxi are in the "good" level; and Sichuan, Hunan, Chongqing, and Hubei are in the "medium" level. Shanghai is "poor." As a whole, the downstream is superior, the upstream is second, and the midstream is poor in an asymmetric "U"-shaped distribution. During the study period, the overall state of water ecology in the Yangtze River Economic Belt was at a medium level and has not yet reached a safe and steady state. The performance of areas with traditional industrialization as the main development path was poor. Therefore, it is necessary to pay attention to the overall water ecological security in the basin in the future, strengthen the regulatory role of the government's water ecological management, promote reform of traditional industries and resource-based regions, and achieve the sustainable development of the water ecological environment.

Survival benefit of combinatorial osimertinib rechallenge and entrectinib in an EGFR-mutant NSCLC patient with acquired LMNA-NTRK1 fusion following osimertinib resistance.

Acquired resistance to osimertinib is inevitable and heterogeneous despite its documented efficacy against EGFR-mutated non-small cell lung cancer (NSCLC). Subsequent therapeutic options assume the dominant form of the resistance mechanism; however, the more rare oncogenic driver, NTRK1 fusion, has also reportedly conferred osimertinib resistance. Nevertheless, clear-cut options when NSCLCs are driven by EGFR mutation and the subsequent NTRK fusion are lacking. This is a case of NSCLC wherein exon 19 deletion in EGFR (19del) and acquired LMNA-NTRK1 fusion were accompanied by the persistence of EGFR T790M. The patient underwent peritoneal metastasis after multiple targeted therapies: gefitinib, osimertinib, chemotherapy, and anlotinib plus docetaxel (in clinical trials). Osimertinib was subsequently re-administered with the NTRK fusion inhibitor entrectinib, resulting in remission of peritoneal metastases even after slow progression of pancreatic metastasis over the following 5 months. An extensive literature review to identify the efficacies of therapies for NTRK fusion as the means to acquired resistance to EGFR TKIs revealed that blocking both the EGFR mutation and the subsequent NTRK fusion can provide clinical benefits following EGFR TKIs resistance; however, the efficacy and safety of combination therapies must be further investigated. To precisely manage EGFR-mutated NSCLCs, it is also essential to identify the resistance mechanisms by repeating biopsies.

Morphological and physiological variation of soybean seedlings in response to shade.

Soybean (Glycine max) is a legume species that is widely used in intercropping. Quantitative analyses of plasticity and genetic differences in soybean would improve the selection and breeding of soybean in intercropping. Here, we used data of 20 varieties from one year artificial shading experiment and one year intercropping experiment to characterize the morphological and physiological traits of soybean seedlings grown under shade and full sun light conditions. Our results showed that shade significantly decreased biomass, leaf area, stem diameter, fraction of dry mass in petiole, leaf mass per unit area, chlorophyll a/b ratio, net photosynthetic rate per unit area at PAR of 500 µmol m-2 s-1 and 1,200 µmol m-2 s-1 of soybean seedling, but significantly increased plant height, fraction of dry mass in stem and chlorophyll content. Light × variety interaction was significant for all measured traits, light effect contributed more than variety effect. The biomass of soybean seedlings was positively correlated with leaf area and stem diameter under both shade and full sunlight conditions, but not correlated with plant height and net photosynthetic rate. The top five (62.75% variation explained) most important explanatory variables of plasticity of biomass were that the plasticity of leaf area, leaf area ratio, leaflet area, plant height and chlorophyll content, whose total weight were 1, 0.9, 0.3, 0.2, 0.19, respectively. The plasticity of biomass was positively correlated with plasticity of leaf area and leaflet area but significant negative correlated with plasticity of plant height. The principal component one account for 42.45% variation explain. A cluster analysis further indicated that soybean cultivars were classified into three groups and cultivars; Jiandebaimaodou, Gongdou 2, and Guixia 3 with the maximum plasticity of biomass. These results suggest that for soybean seedlings grown under shade increasing the capacity for light interception by larger leaf area is more vital than light searching (plant height) and light conversion (photosynthetic rate).

Risk Prediction of Coal and Gas Outburst in Deep Coal Mines Based on the SAPSO-ELM Algorithm.

Effective risk prevention and management in deep coal mines can reduce the occurrences of outburst accidents and casualties. To address the low accuracy and inefficiency of coal-gas outburst prediction in deep coal mines, this study proposes a deep coal-gas outburst risk prediction method based on kernal principal component analysis (KPCA) and an improved extreme learning machine (SAPSO-ELM) algorithm. Firstly, high-dimensional nonlinear raw data were processed by KPCA. Secondly, the extracted sequence of outburst-causing indicator principal components were used as the input variables for the simulated annealing particle swarm algorithm (SAPSO), which was proposed to optimize the input layer weights and implied layer thresholds of the ELM. Finally, a coal and gas outburst risk prediction model for a deep coal mine based on the SAPSO-ELM algorithm was developed. The research results show that, compared with the ELM and PSO-ELM algorithms, the SAPSO-ELM optimization algorithm significantly improved the accuracy of risk prediction for coal-gas outbursts in deep coal mines, and the accuracy rate was as high as 100%. This study enriches the theory and methods of safety management in deep coal mines, and effectively helps coal mine enterprises in improving their ability to manage coal-gas outburst risks.

Risk Assessment of Deep Coal and Gas Outbursts Based on IQPSO-SVM.

Coal and gas outbursts seriously threaten the mining safety of deep coal mines. The evaluation of the risk grade of these events can effectively prevent the occurrence of safety accidents in deep coal mines. Characterized as a high-dimensional, nonlinear, and small-sample problem, a risk evaluation method for deep coal and gas outbursts based on an improved quantum particle swarm optimization support vector machine (IQPSO-SVM) was constructed by leveraging the unique advantages of a support vector machine (SVM) in solving small-sample, high-dimension, and nonlinear problems. Improved quantum particle swarm optimization (IQPSO) is used to optimize the penalty and kernel function parameters of SVM, which can solve the optimal local risk and premature convergence problems of particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO) in the training process. The proposed algorithm can also balance the relationship between the global search and local search in the algorithm design to improve the parallelism, stability, robustness, global optimum, and model generalization ability of data fitting. The experimental results prove that, compared with the test results of the standard SVM, particle swarm optimization support vector machine (PSO-SVM), and quantum particle swarm optimization support vector machine (QPSO-SVM) models, IQPSO-SVM significantly improves the risk assessment accuracy of coal and gas outbursts in deep coal mines. Therefore, this study provides a new idea for the prevention of deep coal and gas outburst accidents based on risk prediction and also provides an essential reference for the scientific evaluation of other high-dimensional and nonlinear problems in other fields. This study can also provide a theoretical basis for preventing coal and gas outburst accidents in deep coal mines and help coal mining enterprises improve their safety management ability.

Comprehensive Evaluation of Deep Coal Miners' Unsafe Behavior Based on HFACS-CM-SEM-SD.

The unsafe behavior of miners seriously affects the safety of deep mining. A comprehensive evaluation of miners' unsafe behavior in deep coal mines can prevent coal mine accidents. This study combines HFACS-CM, SEM, and SD models to evaluate miners' unsafe behaviors in deep coal mining. First, the HFACS-CM model identifies the risk factors affecting miners' unsafe behavior in deep coal mines. Second, SEM was used to analyze the interaction between risk factors and miners' unsafe behavior. Finally, the SD model was used to simulate the sensitivity of each risk factor to miners' unsafe behavior to explore the best prevention and control strategies for unsafe behavior. The results showed that (1) environmental factors, organizational influence, unsafe supervision, and unsafe state of miners are the four main risk factors affecting the unsafe behavior of miners in deep coal mines. Among them, the unsafe state of miners is the most critical risk factor. (2) Environmental factors, organizational influence, unsafe supervision, and the unsafe state of miners have both direct and indirect impacts on unsafe behaviors, and their immediate effects are far more significant than their indirect influence. (3) Environmental factors, organizational influence, and unsafe supervision positively impact miners' unsafe behavior through the mediating effect of miners' unsafe states. (4) Mental state, physiological state, business abilities, resource management, and organizational climate were the top five risk factors affecting miners' unsafe behaviors. Taking measures to improve the adverse environmental factors, strengthening the organization's supervision and management, and improving the unsafe state of miners can effectively reduce the risk of miners' unsafe behavior in deep coal mines. This study provides a new idea and method for preventing and controlling the unsafe behavior of miners in deep coal mines.

Hot topics and frontier evolution in college flipped classrooms based on mapping knowledge domains.

With the outbreak of COVID-19 and the development of online teaching, the online flipping teaching mode has attracted increasing attention. Systematic analysis of the research status and development trend of the flipped classrooms is significant for guiding the improvement of the quality of online flipped teaching. This study used the metrology software CiteSpace to draw a scientific knowledge map of relevant research in the web of science database from 2013 to 2021. It performed visual analysis of research authors, research institutions and countries, keyword clustering, keywords co-occurrence, and keyword time zone distribution. The results showed that: (1) The flipped classrooms research has attracted increasing attention from the social and educational circles, however, the relationship between relevant research authors, institutions, and countries is not close enough, and there is little cooperation. We need to strengthen cooperation further and realize the sharing of high-quality resources; (2) Based on keyword co-occurrence cluster analysis, this study identified three hot topics, namely, preparation before class, classroom activities and consolidation after class; (3) According to the keyword time zone map, this study divided three frontier evolution trends: exploration period, adaptation period, and growth period; (4) Finally, with the spread of novel coronavirus, it is suggested to promote the online flipped classroom teaching mode, and put forward reasonable suggestions from the perspective of teachers, students and researchers, and look forward to the future digital development direction of the flipped classroom.

Effects of Online Learning on College Students in Eastern China: A Structural Equation Model.

With the spread of COVID-19 worldwide, online education is rapidly catching on, even in some underdeveloped countries and regions. Based on Bandura's ternary learning theory and literature review, this paper takes online learning of college students as the research object and conducts an empirical survey on 6,000 college students in East China. Based on literature review and factor analysis and structural equation model, this paper discusses the relationship among learning cognition, learning behavior, and learning environment in online learning of college students. The learning behavior includes interactive communication, self-discipline mechanism, classroom learning, and study after class. The learning environment includes teaching ability, knowledge system, platform support, process control, and result evaluation; learning cognition includes learning motivation, information perception, and adaptability. It is found that the learning environment has a significant positive impact on learning behavior, and learning cognition has a significant positive impact on learning behavior. It is uncertain whether the learning environment significantly impacts learning cognition. At the learning environment level, the teaching ability (0.59) has the most significant impact on the learning environment, followed by result evaluation (0.42), platform support (0.40), process control (0.33), and knowledge system (0.13). In terms of the influence on learning behavior, classroom learning has the most significant impact (0.79), followed by self-discipline mechanism (0.65), study after class (0.54), and interactive communication (0.44). In terms of learning cognition, information perception (0.62) has the most significant influence, followed by learning motivation (0.50) and adaptability (0.41). This paper suggests strengthening the construction of platforms and digital resources to create a more competitive learning environment. Improve process management and personalized online services, constantly stimulate students' enthusiasm for independent online learning, and cultivate students' online independent learning ability to promote the sustainable and healthy development of online education.

Risk Factors Identification of Unsafe Acts in Deep Coal Mine Workers Based on Grounded Theory and HFACS.

The risk factors affecting workers' unsafe acts were comprehensively identified by Human Factors Analysis and Classification System (HFACS) and grounded theory based on interview data and accident reports from deep coal mines. Firstly, we collected accident case and field interview data from deep coal mines issued by authoritative institutions. Then, the data were coded according to grounded theory to obtain relevant concepts and types. The HFACS model was used to classify the concepts and categories. Finally, the relationship between core and secondary categories was sorted out by applying a story plot. The results show that risk factors of unsafe acts of deep coal mine workers include environmental factors, organizational influence, unsafe supervision and unsafe state of miners, and the main manifestations of unsafe acts are errors and violations. Among them, the unsafe state of miners is the intermediate variable, and other factors indirectly affect risky actions of coal miners through unsafe sates. Resource management, organizational processes and failure to correct problems are the top three risk factors that occur more frequently in unsafe acts. The three most common types of unsafe act are unreasonable labor organization, failure to enforce rules, and inadequate technical specifications. By combining grounded theory and the HFACS framework to analyze data, risk factors for deep coal miners can be quickly identified, and more precise and comprehensive conceptual models of risk factors in unsafe acts of deep coal miners can be obtained.

Targeted gene next-generation sequencing reveals genomic profile in a cohort of 46 Chinese patients with breast cancer.

BACKGROUND: The present study aimed to explore the genomic profile in a cohort of Chinese patients with breast cancer (BC), as well as provide potential strategies for clinic treatment in specific subset of BC patients. METHODS: Paired samples from 46 BC patients were subjected to DNA extraction and 537 gene targeted next-generation sequencing. RESULTS: In total, 742 somatic mutations were detected in these patients, which involved 303 genes. TP53 and PIK3CA were the most frequently mutated genes, with a mutation rate of 45.65% and 26.09%. C>T, T>C and C>A comprised the main single nucleotide base variation for this Chinese cohort. Triple negative breast cancer (TNBC) group had more TP53-mutated patients than the Non-TNBC group (p = 0.0229). In addition, the cohort was also divided into 'Young' and 'Old' groups based on the age of onset. Compared with the 'Young' group, the 'Old' group had more frameshift mutations (p = 0.0190), less missense mutations (p = 0.0269) and more HOXA11-mutated patients (p = 0.0197). Additionally, the HOXA11mt (HOXA11 gene mutated) group had more frameshift mutations than the HOXA11wt (HOXA11 gene without mutation) group (p < 0.0001). In KEGG (i.e. Kyoto Encyclopedia of Genes and Genomes) analysis, the HOXA11wt group had more gene mutations involved in the T cell receptor signaling pathway (p = 0.0197), Jak-STAT signaling pathway (p = 0.0380) and the HIF-1 signaling pathway (p = 0.0489) than the HOXA11mt group. In the present study, the heterogeneity of somatic mutations was revealed between different tumor subgroups, including TNBC/Non-TNBC, age of onset (Young/Old) and HOXA11 mutation (HOXA11mt /HOXA11wt ). CONCLUSIONS: The present study revealed the heterogeneity of gene mutation and clinical variables among BC subtypes and might provide guidance for developing a potential target for clinical treatment.

Research on the Construction of a Comprehensive Evaluation Model of Psychological Factors in Coal Mine Workers' Safety: Investigation and Analysis of 1,500 Miners in East China.

With China's economic and social development entering a new era, the improvement of miners' living standards and safety production conditions in coal mine are bound to have a new impact on the safety needs of miners. In order to explore the structural changes of miners' safety demands in the new era, this research adopts the second-order confirmatory factor analysis method to investigate miners from six coal mining enterprises based on Koffka's cognitive psychology theory. Firstly, according to the interaction between the behavioral environment and the self-regulation of coal miners, six potential variables affecting miners' safety psychology, such as material satisfaction, non-skill internal causes, professionalism, emotional attribution, safety atmosphere, and organizational management, are selected. Then, each potential variable is subdivided into 3 observation variables, for a total of 18 observation variables, and a 3-tier comprehensive structural model of miners' safety psychology is constructed that takes into account both evaluation and path integration. The results showed that, affected by the interaction of various potential variables, the degree and intensity of the influence of each factor on miners' safety psychology were different. Among them, emotional attribution was the most significant factor affecting miners' safety psychology, while the influence of organizational management was slightly less important than emotional attribution. Organizational management had a positive impact on material satisfaction and non-skill internal factors. Occupational literacy, material satisfaction, and safety atmosphere had strong impacts on miners' safety psychology. But the impact of non-skill factors on miners' safety psychology was lower than other factors, which is different to previous studies on this aspect.

Knowledge Mapping Analysis of Public Health Emergency Management Research Based on Web of Science.

At present, major public health emergencies frequently occur worldwide, and it is of great significance to analyze the research status and latest developments in this field to improve the ability of public health emergency management in various countries. This paper took 5,143 related studies from 2007 to 2020 from the Web of Science as research object and used CiteSpace, VOSviewer, and other software to perform co-word analysis, social network analysis, and cluster analysis. The results and conclusions were as follows: (1) the related research identified three periods: the exploration, growth, and outbreak period; (2) chronologically: the relevant research evolved from medical and health care for major diseases to emergency management and risk assessment of public health emergencies and then researched the novel coronavirus (COVID-19) pneumonia epidemic; (3) clustering analysis of high-frequency keywords, identifying three research hotspots: "disaster prevention and emergency medical services," "outbreak and management of infectious diseases in Africa," and "emergency management under the COVID-19 pneumonia epidemic." Finally, this study combined the data and literature analysis to point out possible future research directions: from the research of the COVID-19 pneumonia epidemic to the research of general major public health emergencies, thinking and remodeling of the national public health emergency management system, and exploring the establishment of an efficient international emergency management cooperation mechanism.

Influencing Factors, Formation Mechanism, and Pre-control Methods of Coal Miners' Unsafe Behavior: A Systematic Literature Review.

Coal mine accidents are mainly caused by the unsafe behavior of workers. Studying workers' unsafe behaviors can help in regulating such behaviors and reducing the incidence of accidents. However, there is a dearth of systematic literature review in this area, which has hindered mine managers from fully understanding the unsafe behavior of workers. This study aims to address this research gap based on the literature retrieved from the Web of Science. First, a descriptive statistical analysis is conducted on the year, quantity, publications, and keywords of the literature. Second, the influencing factors, formation mechanism, and pre-control methods of coal miners' unsafe behavior are determined and discussed, and the research framework and future research directions of this study are proposed. The study results will help mine safety managers fully understand the influencing factors, formation mechanism, and pre-control methods of workers' unsafe behavior, and lay a theoretical foundation for the future research direction in this field.

Causes of construction injuries: a review.

This article aims to identify the causes of construction injuries (CIs), summarize them into categories, provide input in a concise form for reference and recommend possible solutions. A total of 967 peer-reviewed papers were retrieved, and 42 papers were finally identified and analyzed in detail. Causes of CIs were categorized into four major factors- management commitment factors, behavioral factors, psychological factors and demographic factors - and each reviewed separately. The main finding of the study is that management commitment and behavioral factors are the main sources of CIs. The finding regarding experience and age is the contribution of this article. Thus, further joint research needs to be conducted using mixed methods to provide better and in-depth understanding of the association between the empirical findings and accident causation theories to design effective safety policies. Further, the public body responsible for safety implementation should design mass-media awareness advertisements to increase general public awareness.

Analysis of network public opinion on COVID-19 epidemic based on the WSR theory.

Objective: To obtain the influencing factors of public opinion reactions and to construct a basic framework of the factors causing the occurrence of online public opinion in the epidemic area. Methods: The hot news comments on microblogs during the epidemic in Shanghai were collected and analyzed with qualitative analysis, grounded theory, and the "Wuli-Shili-Renli" (WSR) methodology as an auxiliary method. Results: (1) Three core categories of the Wuli system, the Shili system, and the Renli system, 15 main categories, and 86 categories that influence the development of network public opinion are obtained. (2) WSR Elements Framework Of Network Public Opinion (WSR-EFONPO) is established. (3) The WSR-EFONPO is explained. Conclusion: The framework of factors for the occurrence of network public opinion is proposed, and the development process of network public opinion under COVID-19 is sorted out, which is of great theoretical value in guiding the public in the epidemic area to form reasonable behavior.

Estimating the contribution of plant traits to light partitioning in simultaneous maize/soybean intercropping.

Spatial configuration and plant phenotypic plasticity contribute to increased light capture in relay intercropping, but there is little information on whether these factors also increase light capture in simultaneous intercropping. We developed and validated a three-dimensional functional-structural plant model to simulate light capture in maize and soybean sole crops and intercrop scenarios, using species traits observed in sole crops and intercrops. The intercrop maize phenotype had 2% greater light capture than the sole crop phenotype in a pure stand. The soybean intercrop phenotype had 5-10% lower light capture than the sole crop phenotype in a pure stand. The intercrop configuration increased the light capture of maize by 29% and reduced the light capture of soybean by 42%, compared with the light capture expected from sole crops. However, intercrop configuration only marginally affected total light capture by the intercrop system (+1%). Testing of individual soybean plant traits revealed that plasticity in leaf dimensions was the main reason for differences in light capture by soybean in simulated sole crops and intercrops. The results of this study illustrate a major shift of light capture from shorter species (soybean) to the taller component (maize) in a simultaneous strip intercrop. Plastic plant traits modulate this overall effect, but only marginally.

Citizen Environmental Behavior From the Perspective of Psychological Distance Based on a Visual Analysis of Bibliometrics and Scientific Knowledge Mapping.

Global warming and other climate issues seriously threaten global sustainable development. As citizen environmental behavior can have a positive impact on the environment, it is of great theoretical significance and practical reference value to study the impact of psychological distance theory on citizen environmental behavior. This study obtained 2,633 related studies from 1980 to 2020 from the Web of Science as research objects, and used CiteSpace, VOSviewer, Netdraw, and other software programs to perform a bibliometric analysis, which can show the complex relationship implied in the citation, intuitively grasp the development context and hot frontier in this field, and help scholars better study citizen environmental behavior. The results and conclusions are as follows. (1) The related research covered three periods: infancy, growth, and outbreak. (2) Chronologically, the relevant research evolved from building models to analyze the impact of environmental enrichment on human environmental behavior, to studying the motivation behind citizen environmental behavior, and finally to paying attention to sustainable development. (3) A cluster analysis of high-frequency keywords identified three research hotspots: "drivers of psychological distance of environmental change perception on citizen environmental behavior," "impact of social distance on adolescents' behavior," and "construal level theory and citizen green behavior." Based on these findings, possible future research directions were identified, including changing from a single theory to a combination of multiple theories to comprehensively study citizen environmental protection behavior; analyzing the motivation of citizen environmental behavior and summarizing the general motivation of environmental behavior according to its internal relationship; and determining how to narrow the global psychological distance, strengthen the awareness of the community of human destiny, and explore the establishment of an efficient global climate cooperation mechanism.