Characterization of subglacial landscapes by a two-parameter roughness index

Peer reviewedPublisher PD

Green supply chain integration, supply chain agility and green innovation performance: Evidence from Chinese manufacturing enterprises

Despite widespread attention on the significance of green supply chain integration (GSCI), there is still limited research on how GSCI can improve firms’ green innovation performance. From the perspective of the natural resource-based view and dynamic capability theory, based on the theoretical logic of “resource-capability-performance”, this study aims to explore the relationship between GSCI and firms’ green innovation performance and its intrinsic mechanism. In order to test the research model, this study collected survey data from 405 Chinese manufacturing firms and tested them by using hierarchical regression and bootstrap analysis. The results show that all three dimensions of GSCI, namely, green internal integration, green supplier integration, and green customer integration, have positive effects on supply chain agility. In addition, supply chain agility has a significant positive impact on green product and process innovation. This study also finds that supply chain agility plays a partially mediating role between all three dimensions of GSCI and green product and process innovation; that is, GSCI can further promote firms’ green innovation performance by improving supply chain agility. The results of this study not only enrich the theoretical research on the driving factors of firms’ green innovation but also provide policy implications for manufacturing firms and government policy-makers regarding the implementation and promotion of green innovation practices

Ginkgo biloba extract EGb 761® improves cognition and overall condition after ischemic stroke: Results from a pilot randomized trial

Background: Patients who experienced an ischemic stroke are at risk for cognitive impairment. Quantified Ginkgo biloba extract EGb 761® has been used to treat cognitive dysfunction, functional impairment and neuropsychiatric symptoms in mild cognitive impairment and dementia.Objectives: To assess the cognitive-related effects of EGb 761® treatment in patients after acute ischemic stroke, as well as the feasibility of patient selection and outcome measures.Methods: We conducted a randomized, multicentric, open-label trial at 7 centers in China. Patients scoring 20 or lower on the National Institutes of Health Stroke Scale were enrolled between 7 and 14 days after stroke onset and randomly assigned to receive 240 mg per day of EGb 761® or no additional therapy for 24 weeks in a 1:1 ratio. Both groups received standard treatments for the prevention of recurrent stroke during the trial. General cognitive function and a battery of cognitive tests for sub-domains were evaluated at 24 weeks. All patients were monitored for adverse events.Results: 201 patients ≥50 years old were included, with 100 assigned to the EGb 761® group and 101 to the reference group. The mean change from baseline on the global cognitive function as assessed by the Montreal Cognitive Assessment score was 2.92 in the EGb 761® group and 1.33 in the reference group (between-group difference: 1.59 points; 95% confidence interval [CI], 0.51 to 2.67; p < 0.005). For cognitive domains, EGb 761® showed greater effects on the Hopkins Verbal Learning Test Total Recall (EGb 761® change 1.40 vs. reference −0.49) and Form 1 of the Shape Trail Test (EGb 761® change −38.2 vs. reference −15.6). Potentially EGb 761®-related adverse events occurred in no more than 3% of patients.Conclusion: Over the 24-week period, EGb 761® treatment improved overall cognitive performance among patients with mild to moderate ischemic stroke. Our findings provide valuable recommendations for the design of future trials, including the criteria for patient selection.Clinical Trial Registration:www.isrctn.com, identifier ISRCTN11815543

Preventive Effects of Collagen Peptide from Deer Sinew on Bone Loss in Ovariectomized Rats

Deer sinew (DS) has been used traditionally for various illnesses, and the major active constituent is collagen. In this study, we assessed the effects of collagen peptide from DS on bone loss in the ovariectomized rats. Wister female rats were randomly divided into six groups as follows: sham-operated (SHAM), ovariectomized control (OVX), OVX given 1.0 mg/kg/week nylestriol (OVX + N), OVX given 0.4 g/kg/day collagen peptide (OVX + H), OVX given 0.2 g/kg/day collagen peptide (OXV + M), and OVX given 0.1 g/kg/day collagen peptide (OXV + L), respectively. After 13 weeks of treatment, the rats were euthanized, and the effects of collagen peptide on body weight, uterine weight, bone mineral density (BMD), serum biochemical indicators, bone histomorphometry, and bone mechanics were observed. The data showed that BMD and concentration of serum hydroxyproline were significantly increased and the levels of serum calcium, phosphorus, and alkaline phosphatase were decreased. Besides, histomorphometric parameters and mechanical indicators were improved. However, collagen peptide of DS has no effect on estradiol level, body weight, and uterine weight. Therefore, these results suggest that the collagen peptide supplementation may also prevent and treat bone loss

Multi-stage Depressurization Analysis of Subsea Production System Based on LedaFlow Software

This paper briefly summarizes that during the depressurization operation in subsea pipeline for maintenance or prevention of hydrate formation, because the flare knock-out drum pump is designed for ambient temperature, and cannot deal with the low temperature fluid in the depressurization process. Therefore, the low temperature fluid needs to be heated in the knock-out drum to meet the temperature requirement of pump. In order to avoid the volume of low temperature fluid exceeding the surge volume of the flare knock-out drum, the concept of multi stage depressurization is innovatively proposed in this paper, which meets both the requirements of pump temperature and the surge volume of flare drum. Taking a gas field project in the South China Sea as an example, the simulation analysis of depressurization temperature and pressure is carried out by using different size of the blowdown valve. It provides a good reference for the similar project in future

Quantifying Basal Roughness and Internal Layer Continuity Index of Ice Sheets by an Integrated Means with Radar Data and Deep Learning

Understanding englacial and subglacial structures is a fundamental method of inferring ice sheets’ historical evolution and surface mass balance. The internal layer continuity index and the basal roughness are key parameters and indicators for the speculation of the relationship between the ice sheet’s internal structure or bottom and ice flow. Several methods have been proposed in the past two decades to quantitatively calculate the continuity index of ice layer geometry and the roughness of the ice–bedrock interface based on radar echo signals. These methods are mainly based on the average of the absolute value of the vertical gradient of the echo signal amplitude and the standard deviation of the horizontal fluctuation of the bedrock interface. However, these methods are limited by the amount and quality of unprocessed radar datasets and have not been widely used, which also hinders further research, such as the analysis of the englacial reflectivity, the subglacial conditions, and the history of the ice sheets. In this paper, based on geophysical processing methods for radar image denoising and deep learning for ice layer and bedrock interface extraction, we propose a new method for calculating the layer continuity index and basal roughness. Using this method, we demonstrate the ice-penetrating radar data processing and compare the imaging and calculation of the radar profiles from Dome A to Zhongshan Station, East Antarctica. We removed the noise from the processed radar data, extracted ice layer continuity features, and used other techniques to verify the calculation. The potential application of this method in the future is illustrated by several examples. We believe that this method can become an effective approach for future Antarctic geophysical and glaciological research and for obtaining more information about the history and dynamics of ice sheets from their radar-extracted internal structure

Quantifying Basal Roughness and Internal Layer Continuity Index of Ice Sheets by an Integrated Means with Radar Data and Deep Learning

Understanding englacial and subglacial structures is a fundamental method of inferring ice sheets’ historical evolution and surface mass balance. The internal layer continuity index and the basal roughness are key parameters and indicators for the speculation of the relationship between the ice sheet’s internal structure or bottom and ice flow. Several methods have been proposed in the past two decades to quantitatively calculate the continuity index of ice layer geometry and the roughness of the ice–bedrock interface based on radar echo signals. These methods are mainly based on the average of the absolute value of the vertical gradient of the echo signal amplitude and the standard deviation of the horizontal fluctuation of the bedrock interface. However, these methods are limited by the amount and quality of unprocessed radar datasets and have not been widely used, which also hinders further research, such as the analysis of the englacial reflectivity, the subglacial conditions, and the history of the ice sheets. In this paper, based on geophysical processing methods for radar image denoising and deep learning for ice layer and bedrock interface extraction, we propose a new method for calculating the layer continuity index and basal roughness. Using this method, we demonstrate the ice-penetrating radar data processing and compare the imaging and calculation of the radar profiles from Dome A to Zhongshan Station, East Antarctica. We removed the noise from the processed radar data, extracted ice layer continuity features, and used other techniques to verify the calculation. The potential application of this method in the future is illustrated by several examples. We believe that this method can become an effective approach for future Antarctic geophysical and glaciological research and for obtaining more information about the history and dynamics of ice sheets from their radar-extracted internal structure

The impact of corporate digital strategic orientation on innovation output

Given the development of the digital economy, the shift to digitalization is an inevitable direction for corporate strategic planning. This empirical study investigates the impact of corporate digital strategic orientation on innovation output. It also examines the moderating effects of executive equity and compensation incentives on the relationship between corporate digital strategic orientation and innovation output. We selected a sample of Chinese listed companies and adopted the Heckman two-stage and two-stage least square (2 S LS) methods to control for potential endogenous problems. Our findings indicate that corporate digital strategic orientation significantly enhances innovation output. Additionally, we found that executive compensation and equity incentives positively moderate the impact of corporate digital strategic orientation on innovation output, with equity incentives having a greater moderating effect than compensation incentives. Further analysis shows that the impact of corporate digital strategic orientation on innovation output is greater in non-manufacturing industries and non-state-owned enterprises. Our study provides policy insights on how companies can enhance their innovation capability in the digital economy