Green transformational leadership and employee organizational citizenship behavior for the environment in the manufacturing industry: A social information processing perspective

The employee organizational citizenship behavior for the environment (OCBE) contributes to the improvement of the organization’s environment, its study is increasing in number. However, the psychological mechanism of promoting employee OCBE is still a missing link. Drawing on the theory of social information processing, this study seeks to establish the impact of green transformational leadership on employee OCBE and the mediating role of green organizational climate in this nexus. In addition, we have integrated environmental concerns to better explain the impact of this differentiation. The results show that: green transformational leadership has a significant positive impact on employee OCBE, and green organizational climate has a mediating effect on the impact of green transformational leadership on employee OCBE. Furthermore, environmental concern not only has a positive moderating effect on the influence of green transformational leadership on green organizational climate, but also positively moderates the impact of the influence of green transformational leadership on employee OCBE. This paper reveals the internal psychological mechanism of improving employee OCBE and provides ideas for promoting the sustainable development of enterprises

Pattern formation in oscillatory complex networks consisting of excitable nodes

Oscillatory dynamics of complex networks has recently attracted great attention. In this paper we study pattern formation in oscillatory complex networks consisting of excitable nodes. We find that there exist a few center nodes and small skeletons for most oscillations. Complicated and seemingly random oscillatory patterns can be viewed as well-organized target waves propagating from center nodes along the shortest paths, and the shortest loops passing through both the center nodes and their driver nodes play the role of oscillation sources. Analyzing simple skeletons we are able to understand and predict various essential properties of the oscillations and effectively modulate the oscillations. These methods and results will give insights into pattern formation in complex networks, and provide suggestive ideas for studying and controlling oscillations in neural networks.Comment: 15 pages, 7 figures, to appear in Phys. Rev.

Structure and control of self-sustained target waves in excitable small-world networks

Small-world networks describe many important practical systems among which neural networks consisting of excitable nodes are the most typical ones. In this paper we study self-sustained oscillations of target waves in excitable small-world networks. A novel dominant phase-advanced driving (DPAD) method, which is generally applicable for analyzing all oscillatory complex networks consisting of nonoscillatory nodes, is proposed to reveal the self-organized structures supporting this type of oscillations. The DPAD method explicitly explores the oscillation sources and wave propagation paths of the systems, which are otherwise deeply hidden in the complicated patterns of randomly distributed target groups. Based on the understanding of the self-organized structure, the oscillatory patterns can be controlled with extremely high efficiency.Comment: 16 pages 5 figure

COMPARISON OF SOME BIOMECHANICS PARAMETERS OF BREASTSTROKE SWIMMERS IN FLUME AND SWIMMING POOL

The purpose of this study was to compare some parameters of breaststroke swimmers in a swimming pool with those for breaststroke swimming in the flume, to search whether there is some difference between two test circumstances of swimming pool and flume in technical parameters. Four male breaststroke swimmers aged between16 and 18 years were studied. Subjects were required to swim in a 25m pool for best or familiar stroke length and tried to decrease stroke rate, and performed at three minute intervals at speeds ranging from 70% to 100% of the best performance of individuals. Subjects were familiarized to flume swimming on the day prior to be tested, then swam at the same speed based upon conversion from pool in swimming flume. According to testing we found that stroke rate, stroke length and efficiency index for pool and swimming flume at corresponding speeds were similar. Of course, there was as expected significant difference in the stroke rate and stroke length used between subjects to swim at the various speeds

Ten-year changes in the prevalence of overweight, obesity and central obesity among the Chinese adults in urban Shanghai, 1998–2007 — comparison of two cross-sectional surveys

BACKGROUND: In China, obesity is expected to increase rapidly in both urban and rural areas. However, there have been no comprehensive reports on secular trends in obesity prevalence among Chinese adults in urban Shanghai, which is the largest city in southern China. METHODS: In 1998–2001 and again in 2007–2008, two independent population-based cross-sectional surveys were conducted in Shanghai to investigate the prevalence of metabolic disorders. These surveys obtained height, waist circumference (WC), and weight measurements for Chinese adults aged between 20 and 74 years who lived in urban communities. From the 1998–2001 survey, 4,894 participants (2,081 men and 2,813 women, mean age: 48.9 years) were recruited, and 4,395 participants (1,599 men and 2,796 women, mean age: 49.8 years) were recruited from the 2007–2008 survey. Using the World Health Organization criteria, overweight was defined as 25 kg/m(2) ≤ BMI < 30 kg/m(2) and obesity as BMI ≥ 30 kg/m(2). Central obesity was defined as WC ≥ 90 cm in men or ≥85 cm in women. The differences in prevalence of obesity, central obesity and overweight between the two surveys were tested using multivariable logistic regression analyses. RESULTS: Compared to the 1998–2001 survey, in the 2007–2008 survey the BMI distribution for men and the WC distribution for both genders is shifted significantly to the right along the x-axis (all p < 0.001). Over the ten years, the prevalence of combined overweight and obesity increased 24% (from 31.5% to 39.1%, p < 0.001) in men, but decreased 8% (from 27.3% to 25.0%; p < 0.01) in women. The prevalence of central obesity increased 40% in men (from 19.5% to 27.3%; p < 0.01), but the increase was not significant in women (15.0% to 17.1%; p = 0.051). In the total population, only central obesity showed a significant change between the populations in the two surveys, increasing 29% (from 17.3% to 22.4%; p < 0.001). CONCLUSIONS: Over this 10 year period, central obesity increased significantly in the Shanghai adult population. However, the prevalence of combined overweight and obesity was significantly increased in men but not in women

Serum electrolyte levels in relation to macrovascular complications in Chinese patients with diabetes mellitus

BACKGROUND: The prevalence of diabetes in China is increasing rapidly. However, scarce data are available on serum electrolyte levels in Chinese adults with diabetes, especially in those with cardiovascular complications. This study measured serum electrolyte levels and examined their relationship with macrovascular complications in Chinese adults with diabetes. METHODS: The three gender- and age-matched groups were enrolled into this analysis, which were 1,170 subjects with normal glucose regulation (NGR), 389 with impaired glucose regulation (IGR) and 343 with diabetes. Fasting plasma glucose (FPG), 2-hour post-load plasma glucose (2hPG), glycosylated hemoglobin A1c (HbA1c) and serum electrolyte levels were measured. Data collection included ankle brachial index results. RESULTS: Serum sodium and magnesium levels in the diabetes group were significantly decreased compared to the NGR group (sodium: 141.0 ± 2.4 vs. 142.1 ± 2.0 mmol/l; magnesium: 0.88 ± 0.08 vs. 0.91 ± 0.07 mmol/l, all P < 0.01), while the serum calcium level was significantly increased (2.36 ± 0.11 vs. 2.33 ± 0.09 mmol/l, P < 0.01). Multiple linear regression showed that serum sodium and magnesium levels in the diabetes group were negatively correlated with FPG, 2hPG and HbA1c (sodium: Std β = −0.35, -0.19, -0.25; magnesium: Std β = −0.29, -0.17, -0.34, all P < 0.01), while the serum calcium level was positively correlated with HbA1c (Std β = 0.17, P < 0.05). In diabetic subjects, serum sodium, magnesium and potassium levels were decreased in the subjects with the elevation of estimated glomerular filtration rates (P < 0.05). ANCOVA analysis suggested that serum magnesium level in subjects with diabetic macrovascular complications was significantly decreased compared with diabetic subjects without macrovascular complications after the effect of some possible confounding being removed (P < 0.05). CONCLUSIONS: Serum sodium and magnesium levels were decreased in Chinese subjects with diabetes, while the observed increase in calcium level correlated with increasing glucose level. Diabetic patients with macrovascular complications had lower serum magnesium level than those with no macrovascular complications

Pleiotropy of FRIGIDA enhances the potential for multivariate adaptation.

An evolutionary response to selection requires genetic variation; however, even if it exists, then the genetic details of the variation can constrain adaptation. In the simplest case, unlinked loci and uncorrelated phenotypes respond directly to multivariate selection and permit unrestricted paths to adaptive peaks. By contrast, 'antagonistic' pleiotropic loci may constrain adaptation by affecting variation of many traits and limiting the direction of trait correlations to vectors that are not favoured by selection. However, certain pleiotropic configurations may improve the conditions for adaptive evolution. Here, we present evidence that the Arabidopsis thaliana gene FRI (FRIGIDA) exhibits 'adaptive' pleiotropy, producing trait correlations along an axis that results in two adaptive strategies. Derived, low expression FRI alleles confer a 'drought escape' strategy owing to fast growth, low water use efficiency and early flowering. By contrast, a dehydration avoidance strategy is conferred by the ancestral phenotype of late flowering, slow growth and efficient water use during photosynthesis. The dehydration avoidant phenotype was recovered when genotypes with null FRI alleles were transformed with functional alleles. Our findings indicate that the well-documented effects of FRI on phenology result from differences in physiology, not only a simple developmental switch

Diff-ID: An Explainable Identity Difference Quantification Framework for DeepFake Detection

Despite the fact that DeepFake forgery detection algorithms have achieved impressive performance on known manipulations, they often face disastrous performance degradation when generalized to an unseen manipulation. Some recent works show improvement in generalization but rely on features fragile to image distortions such as compression. To this end, we propose Diff-ID, a concise and effective approach that explains and measures the identity loss induced by facial manipulations. When testing on an image of a specific person, Diff-ID utilizes an authentic image of that person as a reference and aligns them to the same identity-insensitive attribute feature space by applying a face-swapping generator. We then visualize the identity loss between the test and the reference image from the image differences of the aligned pairs, and design a custom metric to quantify the identity loss. The metric is then proved to be effective in distinguishing the forgery images from the real ones. Extensive experiments show that our approach achieves high detection performance on DeepFake images and state-of-the-art generalization ability to unknown forgery methods, while also being robust to image distortions

Interpretable Machine Learning for Weather and Climate Prediction: A Survey

Advanced machine learning models have recently achieved high predictive accuracy for weather and climate prediction. However, these complex models often lack inherent transparency and interpretability, acting as "black boxes" that impede user trust and hinder further model improvements. As such, interpretable machine learning techniques have become crucial in enhancing the credibility and utility of weather and climate modeling. In this survey, we review current interpretable machine learning approaches applied to meteorological predictions. We categorize methods into two major paradigms: 1) Post-hoc interpretability techniques that explain pre-trained models, such as perturbation-based, game theory based, and gradient-based attribution methods. 2) Designing inherently interpretable models from scratch using architectures like tree ensembles and explainable neural networks. We summarize how each technique provides insights into the predictions, uncovering novel meteorological relationships captured by machine learning. Lastly, we discuss research challenges around achieving deeper mechanistic interpretations aligned with physical principles, developing standardized evaluation benchmarks, integrating interpretability into iterative model development workflows, and providing explainability for large foundation models.Comment: 26 pages, 5 figure