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Carbon dioxide sequestration simply by reproduction with the fast-growing Azolla spp.
For example, women economists face gendered standards in publishing, and women political scientists are less likely to have their work cited than men. RGD peptide molecular weight Furthermore, data show that salaries become stagnant as the representation of women in these fields increases. These disparities reflect cultural biases in perceptions of women's competence stemming from social role theory. We discuss best practices to address these problems, focusing on the ADVANCE organizational change programs funded by the National Science Foundation that target (a) improving academic climate, (b) providing professional development, and (c) fostering social networking. Federally supported interventions can reveal systemic gender biases in academia and reduce gender disparities for women academics in the social sciences.Mindfulness-based training programs are highly established in competitive and recreational sports. One of the best-known approaches is the Mindfulness-Acceptance-Commitment Approach (MAC) by Gardner and Moore), which integrates mindfulness aspects of awareness, non-judgmental attitude, and focus. Based on these aspects, Thienot and colleagues developed and validated an English language sport-specific questionnaire, the so-called Mindfulness Inventory for Sport (MIS), for the assessment of mindfulness skills in athletes. The aim of this study is to psychometrically test a German language version of the MIS (MIS-D). To assess the psychometric properties, the MIS-D was examined in an online survey with an integrated test-retest design (n = 228) for reliability (internal consistency; test-retest reliability), validity (factorial; convergent), and measurement invariance (gender; competition type). The present results support the psychometric quality of the German language version of the MIS. Necessary replications should among others focus on checking the measurement invariance for further relevant subgroups.Algorithms have become increasingly relevant in supporting human resource (HR) management, but their application may entail psychological biases and unintended side effects on employee behavior. This study examines the effect of the type of HR decision (i.e., promoting or dismissing staff) on the likelihood of delegating these HR decisions to an algorithm-based decision support system. Based on prior research on algorithm aversion and blame avoidance, we conducted a quantitative online experiment using a 2×2 randomly controlled design with a sample of N = 288 highly educated young professionals and graduate students in Germany. This study partly replicates and substantially extends the methods and theoretical insights from a 2015 study by Dietvorst and colleagues. While we find that respondents exhibit a tendency of delegating presumably unpleasant HR tasks (i.e., dismissals) to the algorithm-rather than delegating promotions-this effect is highly conditional upon the opportunity to pretest the algorithm, as well as individuals' level of trust in machine-based and human forecast. Respondents' aversion to algorithms dominates blame avoidance by delegation. This study is the first to provide empirical evidence that the type of HR decision affects algorithm aversion only to a limited extent. Instead, it reveals the counterintuitive effect of algorithm pretesting and the relevance of confidence in forecast models in the context of algorithm-aided HRM, providing theoretical and practical insights.This study aims to determine the specific impact of employees' perceptions of transformational change on in-role performance and how stress assessment can mediate the relationship between transformational change and in-role performance. According to the cognitive appraisal theory, the same individual has different appraisals of the same stressors, including challenge, and hindrance appraisal. As an important stressor, transformational change also affects individuals differently depending on their assessments. This study integrates employees' challenge or hindrance appraisal of transformational change into a conceptual model to distinguish between the roles of the two appraisals. It examines it as a mediating mechanism between transformational change and in-role performance. Additionally, 313 employees who recently experienced transformational change were used as samples to test the hypothesis. The results show that transformational change negatively affects employees' in-role performance; hindrance appraisal negatively mediates the relationship between transformational change and in-role performance, and challenge appraisal positively mediates the relationship between transformational change and in-role performance. The originality and value of this research extend the application of stress appraisals in organizational change management. Research shows that, in the context of major change, employees' in-role performance is reduced by the impact of transformational change. However, when employees positively appraise organizational change, the negative effects of change are weakened.In those theories or empirical-evident model of sexual offending, they all recognized which major life event would cause the sex offense in some conditions, therefore the onset crime of sexual offenders were not only a mark of personal history, but also could reflect the heterogeneity of sexual offenders. Our purpose is to study the onset crime typology of sexual offender and their difference in specialization, problem of psychology marks, and negative developmental experiences. We analyzed the pre-conviction data from 3,750 sexual offenders and their risk assessment data. The research results found that onset typology of sex crime would persist their criminal career into sexual offending, and through the group comparisons, the study pointed out differences in risk factors domain and adverse development experiences. We also discussed those research results and their meaning of risk management.This study investigates the influence of self-determination motivations on accountant employees' psychological wellbeing with the mediating role of positive affectivity and the moderating role of psychological safety. Multivariate analysis and structural equation modeling are used to analyze a three-way time-lagged sample data of 391 accountant employees. Results indicate that positive affectivity positively mediates the relationship between extrinsic motivation and psychological wellbeing and between intrinsic motivation and psychological wellbeing. Furthermore, psychological safety positively moderates the relationship between extrinsic motivation and positive affectivity and between intrinsic motivation and positive affectivity. In addition, psychological safety also positively moderates the relationship between positive affectivity and psychological wellbeing. The findings of this study provide implications for researchers and business managers in managing and enhancing accountant employees' psychological wellbeing.In order to study the application of the deep learning (DL) method in music genre recognition, this study introduces the music feature extraction method and the deep belief network (DBN) in DL and proposes the parameter extraction feature and the recognition classification method of an ethnic music genre based on the DBN with five kinds of ethnic musical instruments as the experimental objects. A national musical instrument recognition and classification network structure based on the DBN is proposed. On this basis, a music library classification retrieval learning platform has been established and tested. The results show that, when the DBN only contains one hidden layer and the number of neural nodes in the hidden layer is 117, the basic convergence accuracy is approximately 98%. The first hidden layer has the greatest impact on the prediction results. When the input sample feature size is one-third of the number of nodes in the first hidden layer, the network performance is basically convergent. The DBN is the best way for softmax to identify and classify national musical instruments, and the accuracy rate is 99.2%. Therefore, the proposed DL algorithm performs better in identifying music genres.Rhythm is key to language acquisition. Across languages, rhythmic features highlight fundamental linguistic elements of the sound stream and structural relations among them. A sensitivity to rhythmic features, which begins in utero, is evident at birth. What is less clear is whether rhythm supports infants' earliest links between language and cognition. Prior evidence has documented that for infants as young as 3 and 4 months, listening to their native language (English) supports the core cognitive capacity of object categorization. This precocious link is initially part of a broader template listening to a non-native language from the same rhythmic class as (e.g., German, but not Cantonese) and to vocalizations of non-human primates (e.g., lemur, Eulemur macaco flavifrons, but not birds e.g., zebra-finches, Taeniopygia guttata) provide English-acquiring infants the same cognitive advantage as does listening to their native language. Here, we implement a machine-learning (ML) approach to ask whether there areions and cognition may be subserved by their perceptual sensitivity to rhythmic and spectral elements available on the surface of these vocalizations, and that these may guide infants' identification of candidate links to cognition.In this manuscript, we introduce a theoretical model of climate radicalization that integrates social psychological theories of perceived unfairness with historical insights on radicalization to contribute to the knowledge of individuals' processes of radicalization and non-radicalization in relation to climate change. We define climate radicalization as a process of growing willingness to pursue and/or support radical changes in society that are in conflict with or could pose a threat to the status quo or democratic legal order to reach climate goals. We describe how perceptions of unfairness can play a pivotal role in processes of climate change related radicalization. Without taking any position or judgment regarding climate concerns and associated actions, we suggest that although these behaviors drive many people to participate in peaceful climate protest, they may also lead others to radicalize into breaking the law to achieve their climate goals, possibly in violent ways. This process of climate radica of our model are discussed.Soundscape has been valued and practiced in classical Chinese garden designs. Some authentic patterns were even mentioned and used in gardening books hundreds of years ago. Though these patterns are well-known, how they work in a classic Chinese garden is still unclear. In this study, we chose one of the most famous soundscapes called Tingyuxuan (Listening to the Sound of Rain Hall) in Zhuozhengyuan (Humble Administrator's Garden), Suzhou as the object. A video of the Tingyuxuan was captured on a rainy day, along with its sound. Twenty-four participants were asked to view this video twice (once with audio, once muted, in a random order). Eye-movement data and the subjective evaluation of participants were collected. The results showed that the participants' visual attention is influenced by the sound of rain and helps them identify and observe the main element of the soundscape. Furthermore, participants experienced more positive feelings when viewing the video with the audio on.
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