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This study also provides empirical evidence to clarify the mediating mechanism of GSCM in the link between environmental orientation and firm performance of SMEs. In terms of practical implications, this study provides knowledge for managers of SMEs to better understand the important role of environmental orientation and green supply chain management. Findings of this study provide knowledge for managers of SMEs to make their business policies better.Antibiotics are often considered as weapons conferring a competitive advantage to their producers in their ecological niche. JAK inhibition However, since these molecules are produced in specific environmental conditions, notably phosphate limitation that triggers a specific metabolic state, they are likely to play important roles in the physiology of the producing bacteria that have been overlooked. Our recent experimental data as well as careful analysis of the scientific literature led us to propose that, in conditions of moderate to severe phosphate limitation-conditions known to generate energetic stress-antibiotics play crucial roles in the regulation of the energetic metabolism of the producing bacteria. A novel classification of antibiotics into types I, II, and III, based on the nature of the targets of these molecules and on their impact on the cellular physiology, is proposed. Type I antibiotics are known to target cellular membranes, inducing energy spilling and cell lysis of a fraction of the population to provide nutrients, and especially phosphate, to the surviving population. Type II antibiotics inhibit respiration through different strategies, to reduce ATP generation in conditions of low phosphate availability. Lastly, Type III antibiotics that are known to inhibit ATP consuming anabolic processes contribute to ATP saving in conditions of phosphate starvation.This study aimed to assess the environmental footprint of dairy farms rearing a dual-purpose breed, and to evaluate, through alternative scenario analyses, the fattening of calves and the cultivation of hemp as strategies for reducing the environmental impact of these farms. Eleven farms were evaluated for global warming (GWP), acidification (AC) and eutrophication (EUP) potential. The Life Cycle Assessment method with three scenarios, REAL, based on real data, BEEF, where calves were fattened in farm, and HEMP, where hemp was cultivated in farms, were considered. If referred to 1 m2 of utilizable agricultural land, the GWP, AC and EUP were 1.6 kgCO2eq, 21.7 gSO2eq and 7.1 gPO43-eq, respectively. If referring to 1 kg of fat and protein corrected milk, the emissions were 1.1-1.4 kgCO2eq, 14.8-19.0 gSO2eq, and 5.0-6.4 gPO43-eq, depending on the allocation method adopted. The emissions were associated positively with culling rate and negatively with production intensity. In BEEF and HEMP scenarios, the emissions were reduced by 8-11% and by 1-5%, respectively. Fattening the calves, evaluating the cultivation of alternative plants and improving the productive and reproductive efficiency of animals could be effective strategies for reducing the environmental footprint of the farm.The main purpose of this study was to examine the relationships between school-based sedentary behavior, physical activity, and health-related outcomes, including cardiorespiratory fitness, weight status, and health-related quality of life (HRQOL) among Hispanic children. The participants were 374 children (192 boys, 182 girls; Mage = 9.64) recruited from four elementary schools from 3rd grade through to 5th grade. Sedentary behavior and physical activity behaviors (light physical activity [LPA] and moderate-to-vigorous physical activity (MVPA)) during school were measured by accelerometers. Cardiorespiratory fitness and weight status were measured using the FITNESSGRAM®, while HRQOL was measured using the PedsQL 4.0TM Spanish version, a validated questionnaire. Sedentary behavior was negatively correlated with cardiorespiratory fitness and HRQOL but positively associated with weight status. MVPA was positively correlated with cardiorespiratory fitness and HRQOL, but negatively associated with weight status and sedentary behavior. Multiple regressions demonstrated that sedentary behavior significantly predicted cardiorespiratory fitness and weight status, whereas MVPA significantly predicted HRQOL. With the current public health priority aiming to reduce health disparities in minority populations, the findings of this study provide important insights. Educators, health care providers, or other professionals working with Hispanic children are encouraged to focus on reducing sedentary behavior and promoting physical activity to improve their health-related outcomes.With the rapid development of flexible vision sensors and visual sensor networks, computer vision tasks, such as object detection and tracking, are entering a new phase. Accordingly, the more challenging comprehensive task, including instance segmentation, can develop rapidly. Most state-of-the-art network frameworks, for instance, segmentation, are based on Mask R-CNN (mask region-convolutional neural network). However, the experimental results confirm that Mask R-CNN does not always successfully predict instance details. The scale-invariant fully convolutional network structure of Mask R-CNN ignores the difference in spatial information between receptive fields of different sizes. A large-scale receptive field focuses more on detailed information, whereas a small-scale receptive field focuses more on semantic information. So the network cannot consider the relationship between the pixels at the object edge, and these pixels will be misclassified. To overcome this problem, Mask-Refined R-CNN (MR R-CNN) is proposed, in which the stride of ROIAlign (region of interest align) is adjusted. In addition, the original fully convolutional layer is replaced with a new semantic segmentation layer that realizes feature fusion by constructing a feature pyramid network and summing the forward and backward transmissions of feature maps of the same resolution. The segmentation accuracy is substantially improved by combining the feature layers that focus on the global and detailed information. The experimental results on the COCO (Common Objects in Context) and Cityscapes datasets demonstrate that the segmentation accuracy of MR R-CNN is about 2% higher than that of Mask R-CNN using the same backbone. The average precision of large instances reaches 56.6%, which is higher than those of all state-of-the-art methods. In addition, the proposed method requires low time cost and is easily implemented. The experiments on the Cityscapes dataset also prove that the proposed method has great generalization ability.