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Bangladesh has been ranked as one of the world's top countries affected by climate change, particularly in terms of agricultural crop sector. The purpose of this study is to identify spatial and temporal changes and trends in long-term climate at local and national scales, as well as their implications for rice yield. In this study, Modified Mann-Kendall and Sen's slope tests were used to detect significant trends and the magnitude of changes in temperature and rainfall. The temperature and rainfall data observed and recorded at 35 meteorological stations in Bangladesh over 65-years in the time span between the years 1949 and 2013 have been used to detect these changes and trends of variation. The results show that mean annual Tmean, Tmin, and Tmax have increased significantly by 0.13°C, 0.13°C, and 0.13°C/decade, respectively. The most significant increasing trend in seasonal temperatures for the respective Tmean, Tmin, and Tmax was 0.18°C per decade (post-monsoon), 0.18°C/decade (winter), and 0.23°C/decade (post-monsoon), respectively. Furthermore, the mean annual and pre-monsoon rainfall showed a significant increasing trend at a rate of 4.20 mm and 1.35 mm/year, respectively. This paper also evaluates climate variability impacts on three major rice crops, Aus, Aman, and Boro during 1970–2013. The results suggest that crop yield variability can be explained by climate variability during Aus, Aman, and Boro seasons by 33, 25, and 16%, respectively. Maximum temperature significantly affected the Aus and Aman crop yield, whereas rainfall significantly affected all rice crops' yield. This study sheds light on sustainable agriculture in the context of climate change, which all relevant authorities should investigate in order to examine climate-resilient, high-yield crop cultivation. [ABSTRACT FROM AUTHOR]

Background: During the Omicron pandemic, clinical first-line nurses played a crucial role in healthcare. Their innovative behavior enhanced the quality of nursing and served as a vital factor in driving the sustainable development of the nursing discipline and healthcare industry. Many previous studies have confirmed the significance of nurses' innovative behavior worldwide. However, the correlations among innovative behaviors, organizational innovation climate, self-transcendence, and their mediating roles in Chinese clinical first-line nurses need further research. Methods: A cross-sectional study was conducted, and the quality reporting conformed to the STROBE Checklist. From March 2022 to February 2023, a convenience sample of 1,058 Chinese clinical first-line nurses was recruited from seven tertiary grade-A hospitals of Tianjin city in Northern China. The Demographic Characteristics Questionnaire, Nurse Innovative Behavior Scale (NIBS), Nurse Organizational Innovation Climate Scale, and the Self-Transcendence Scale were used. The data was analyzed using descriptive statistics, correlation, and process plug-in mediation effect analyses. Results: The total scores of innovative behavior, organizational innovation climate, and self-transcendence were 33.19 ± 6.71, 68.88 ± 12.76, and 41.25 ± 7.83, respectively. Innovative behavior was positively correlated with the organizational innovation climate (r = 0.583, p < 0.01) and self-transcendence (r = 0.635, p < 0.01). Self-transcendence partially mediated mediating role between innovative behavior and organizational innovation climate, accounting for 41.7%. Conclusion: The innovative behavior, organizational innovation climate, and self-transcendence among the first-line nurses during the Omicron pandemic were relatively moderate, which needs improving. Organizational innovation climate can directly affect the innovative behavior among Chinese clinical first-line nurses and indirectly through the mediating role of self-transcendence. It is recommended that nursing managers adjust their management strategies and techniques based on the unique characteristics of nurses during the pandemic. This includes fostering a positive and inclusive environment for organizational innovation, nurturing nurses' motivation and awareness for innovation, enhancing their ability to gather information effectively, overcoming negative emotions resulting from the pandemic, and promoting personal growth. These efforts will ultimately enhance nursing quality and satisfaction during the Omicron pandemic. [ABSTRACT FROM AUTHOR]

The engagement in research, as the primary form of learning engagement for PhD students, is crucial for enhancing their competitive edge. Academic passion, a key determinant of learning engagement, plays a significant role in driving the research enthusiasm of PhD students. However, the "black box" concerning whether and how academic passion influences PhD students' research engagement remains to be explored. Addressing this gap, the present study draws upon self-determination theory, adopts the "motivation-behavior-effect" analytical framework, and incorporates ambidextrous learning as a mediator to elucidate the specific pathway through which academic passion impacts PhD students' engagement in research activities. Furthermore, it examines the facilitating role of the academic climate in this process. From December 2022 to March 2023, a questionnaire survey was conducted, collecting 522 responses from PhD students across 25 universities in China. The survey primarily assessed the PhD students' academic passion, ambidextrous learning behaviors (including tendencies towards exploratory and exploitative learning), and their perceived academic climate, investigating how these factors collectively influence their engagement in research activities. The questionnaire data were analyzed using a combination of SEM and bootstrapping with SPSS 26.0 and Mplus 8.3 software. The findings reveal that academic passion significantly positively affects PhD students' research engagement; ambidextrous learning (exploratory and exploitative learning) mediates the relationship between academic passion and PhD students' research engagement; and the academic climate effectively facilitates the transformation of PhD students' academic passion into ambidextrous learning (exploratory and exploitative learning). The study's conclusions not only foster PhD students' enthusiasm for research but also enhance learning effectiveness and innovation vitality, providing a theoretical basis for reforming the doctoral training system. [ABSTRACT FROM AUTHOR]

The main objective of this research is to examine whether the implementation of new human resource management practices (NHRM) can enhance innovative performance (IP) by fostering innovation capability (NC). Additionally, it examines the moderating influence of an innovative climate (IC) and its associated attributes on the association with new human resource management and innovation capability, a factor that has been relatively overlooked in prior research. The study's data was obtained from 398 employees in the pharmaceutical sector of Pakistan, and hierarchical regression analysis was utilized to analyze the data. The results from the mediating and moderating analyses underscore the significance of NHRM practices, innovation capability, and an innovative climate, along with its specific attributes, in promoting innovative performance through factors such as operational efficiency, suitable practices, and employees' willingness to participate in organizational endeavors. Furthermore, the moderated mediation analysis findings reveal that the influence of innovation capability as a mediator is strengthened when the workplace climate is more conducive to innovation. These findings have implications for both theoretical understanding and practical application, particularly in similar developing countries. The study offers insights that can be generalized to developing nations with comparable economic and social structures. [ABSTRACT FROM AUTHOR]

To analyse large numbers of texts, social science researchers are increasingly confronting the challenge of text classification. When manual labeling is not possible and researchers have to find automatized ways to classify texts, computer science provides a useful toolbox of machine-learning methods whose performance remains understudied in the social sciences. In this article, we compare the performance of the most widely used text classifiers by applying them to a typical research scenario in social science research: a relatively small labeled dataset with infrequent occurrence of categories of interest, which is a part of a large unlabeled dataset. As an example case, we look at Twitter communication regarding climate change, a topic of increasing scholarly interest in interdisciplinary social science research. Using a novel dataset including 5,750 tweets from various international organizations regarding the highly ambiguous concept of climate change, we evaluate the performance of methods in automatically classifying tweets based on whether they are about climate change or not. In this context, we highlight two main findings. First, supervised machine-learning methods perform better than state-of-the-art lexicons, in particular as class balance increases. Second, traditional machine-learning methods, such as logistic regression and random forest, perform similarly to sophisticated deep-learning methods, whilst requiring much less training time and computational resources. The results have important implications for the analysis of short texts in social science research. [ABSTRACT FROM AUTHOR]