Thermal Performance Analysis of an Underground Closed Chamber with Human Body Heat Sources under Natural Convection

In this article, a combined experimental and numerical study has been performed to investigate the thermal performance of a mine refuge chamber (MRC) under natural convection. In the current study, a 20-hour heating experiment is carried out in a fifty-person MRC laboratory and the heat lamps are utilized to simulate the human heat loss. A new analytical model is proposed to predict the air temperature and validated against the experimental data. Sensitivity analysis is performed to further investigate the effects of the thermal parameters of the rock. Results indicated that: (1) two different air temperature increase stages, rapid and slow increase stages, are observed in the MRC; (2) A new analytical method for predicting the air temperature in MRC under natural convection is proposed, it shows that the air temperature increasing trend becomes slow with the increase of the thermal conductivity, density and specific heat capacity of the rock; (3) the surface heat transfer coefficient on the vertical walls reaches the largest and it increases linearly with air temperature.Peer reviewe

Air Quality Control in Mine Refuge Chamber with Ventilation through Pressure Air Pipeline

A combined experimental and numerical study was performed to improve the performance of the ventilation system in a mine refuge chamber (MRC). In the experiment, CO2 cylinders and dispersion pipes were used to simulate the CO2 release of 50 people, and 0.1 L/min per person of fresh air was provided by an air compressor. A new analytical model for a 50-person MRC was proposed and validated against the experimental data. Sensitivity analysis was carried out to investigate the effects of several control factors. The results indicated the following: (1) The ventilation system layout has a significant influence on the CO2 concentration distribution in an MRC, while the uniformity of the CO2 concentration distribution in the MRC may not be effective with increased number of air inlets. (2) Under a well-arranged ventilation system in the 50-person MRC, the average CO2 concentration can be controlled at less than 0.5% with a ventilation rate of 0.1 m3/min per person, and less than 0.2% with a ventilation rate of 0.3 m3/min per person. (3) A quantitative correlation exists between the CO2 concentration and ventilation volume rate, as well as the CO2 release rate, for an MRC under a well-arranged ventilation system.Peer reviewe

Antioxidant rich grape pomace extract suppresses postprandial hyperglycemia in diabetic mice by specifically inhibiting alpha-glucosidase

Abstract Background Postprandial hyperglycemia is an early defect of type 2 diabetes and one of primary anti-diabetic targets. Treatment of postprandial hyperglycemia can be achieved by inhibiting intestinal α-glucosidase, the key enzyme for oligosaccharide digestion and further glucose absorption. Grape pomace is winemaking byproduct rich in bioactive food compounds such as phenolic antioxidants. This study evaluated the anti-diabetic potential of two specific grape pomace extracts by determining their antioxidant and anti-postprandial hyperglycemic activities in vitro and in vivo. Methods The extracts of red wine grape pomace (Cabernet Franc) and white wine grape pomace (Chardonnay) were prepared in 80% ethanol. An extract of red apple pomace was included as a comparison. The radical scavenging activities and phenolic profiles of the pomace extracts were determined through the measurement of oxygen radical absorbance capacity, DPPH radical scavenging activity, total phenolic content and flavonoids. The inhibitory effects of the pomace extracts on yeast and rat intestinal α-glucosidases were determined. Male 6-week old C57BLKS/6NCr mice were treated with streptozocin to induce diabetes. The diabetic mice were then treated with vehicle or the grape pomace extract to determine whether the oral intake of the extract can suppress postprandial hyperglycemia through the inhibition of intestinal α-glucosidases. Results The red grape pomace extract contained significantly higher amounts of flavonoids and phenolic compounds and exerted stronger oxygen radical absorbance capacity than the red apple pomace extract. Both the grape pomace extracts but not the apple pomace extract exerted significant inhibition on intestinal α-glucosidases and the inhibition appears to be specific. In the animal study, the oral intake of the grape pomace extract (400 mg/kg body weight) significantly suppressed the postprandial hyperglycemia by 35% in streptozocin-induced diabetic mice following starch challenge. Conclusion This is the first report that the grape pomace extracts selectively and significantly inhibits intestinal α-glucosidase and suppresses postprandial hyperglycemia in diabetic mice. The antioxidant and anti-postprandial hyperglycemic activities demonstrated on the tested grape pomace extract therefore suggest a potential for utilizing grape pomace-derived bioactive compounds in management of diabetes

Influence of the Arctic Oscillation on the Vertical Distribution of Wintertime Ozone in the Stratosphere and Upper Troposphere over Northern Hemisphere

The influence of the Arctic Oscillation (AO) on the vertical distribution of stratospheric ozone in the Northern Hemisphere in winter is analyzed using observations and an offline chemical transport model. Positive ozone anomalies are found at low latitudes (0–30°N) and there are three negative anomaly centers in the northern mid- and high latitudes during positive AO phases. The negative anomalies are located in the Arctic middle stratosphere (~30 hPa, 70–90°N), Arctic upper troposphere/lower stratosphere (UTLS, 150–300 hPa, 70–90°N), and mid-latitude UTLS (70–300 hPa, 30–60°N). Further analysis shows that anomalous dynamical transport related to AO variability primarily controls these ozone changes. During positive AO events, positive ozone anomalies between 0–30°N at 50–150 hPa are related to the weakened meridional transport of the Brewer–Dobson circulation (BDC) and enhanced eddy transport. The negative ozone anomalies in the Arctic middle stratosphere are also caused by the weakened BDC, while the negative ozone anomalies in the Arctic UTLS are caused by the increased tropopause height, weakened BDC vertical transport, weaker exchange between the mid-latitudes and the Arctic, and enhanced ozone depletion via heterogeneous chemistry. The negative ozone anomalies in the mid-latitude UTLS are due mainly to enhanced eddy transport from the mid-latitudes to the equatorward of 30°N, while the transport of ozone-poor air from the Arctic to the mid-latitudes makes a minor contribution. Interpreting AO-related variability of stratospheric ozone, especially in the UTLS, would be helpful for the prediction of tropospheric ozone variability caused by AO

One-pot biosynthesis of N-acetylneuraminic acid from chitin via combination of chitin-degrading enzymes, N-acetylglucosamine-2-epimerase, and N-neuraminic acid aldolase

N-acetylneuraminic acid (Neu5Ac) possesses the ability to promote mental health and enhance immunity and is widely used in both medicine and food fields as a supplement. Enzymatic production of Neu5Ac using N-acetyl-D-glucosamine (GlcNAc) as substrate was significant. However, the high-cost GlcNAc limited its development. In this study, an in vitro multi-enzyme catalysis was built to produce Neu5Ac using affordable chitin as substrate. Firstly, exochitinase SmChiA from Serratia proteamaculans and N-acetylglucosaminosidase CmNAGase from Chitinolyticbacter meiyuanensis SYBC-H1 were screened and combined to produce GlcNAc, effectively. Then, the chitinase was cascaded with N-acetylglucosamine-2-epimerase (AGE) and N-neuraminic acid aldolase (NanA) to produce Neu5Ac; the optimal conditions of the multi-enzyme catalysis system were 37°C and pH 8.5, the ratio of AGE to NanA (1:4) and addition of pyruvate (70 mM), respectively. Finally, 9.2 g/L Neu5Ac could be obtained from 20 g/L chitin within 24 h along with two supplementations with pyruvate. This work will lay a good foundation for the production of Neu5Ac from cheap chitin resources

Screening ANLN and ASPM as bladder urothelial carcinoma-related biomarkers based on weighted gene co-expression network analysis

Introduction: Bladder cancer (BLCA) is one of the most common malignancies in the urinary system with a poor prognosis and high treatment costs. Identifying potential prognostic biomarkers is significant for exploring new therapeutic and predictive targets of BLCA.Methods: In this study, we screened differentially expressed genes using the GSE37815 dataset. We then performed a weighted gene co‐expression network analysis (WGCNA) to identify the genes correlated with the histologic grade and T stage of BLCA using the GSE32548 dataset. Subsequently, Kaplan Meier survival analysis and Cox regression were used to further identify prognosis‐related hub genes using the datasets GSE13507 and TCGA‐BLCA. Moreover, we detected the expression of the hub genes in 35 paired samples, including BLCA and paracancerous tissue, from the Shantou Central Hospital by qRT‐polymerase chain reaction.Results: This study showed that Anillin (ANLN) and Abnormal spindle-like microcephaly-associated gene (ASPM) were prognostic biomarkers for BLCA. High expression of ANLN and ASPM was associated with poor overall survival.The qRT‐PCR results revealed that ANLN and ASPM genes were upregulated in BLCA, and there was a correlation between the expression of ANLN and ASPM in cancer tissues and paracancerous tissue. Additionally, the increasing multiples in the ANLN gene was obvious in high-grade BLCA.Discussion: In summary, this preliminary exploration indicated a correlation between ANLN and ASPM expression. These two genes, serving as the risk factors for BLCA progression, might be promising targets to improve the occurrence and progression of BLCA

Designing forest biodiversity experiments : general considerations illustrated by a new large experiment in subtropical China

Funded by German Research Foundation. Grant Number: DFG FOR 891/1 and 2 National Natural Science Foundation of China. Grant Numbers: NSFC 30710103907, 30930005, 31170457 , 31210103910 Swiss National Science Foundation (SNSF) Sino-German Centre for Research Promotion in BeijingPeer reviewedPublisher PD

Designing forest biodiversity experiments: general considerations illustrated by a new large experiment in subtropical China


 Biodiversity-ecosystem functioning (BEF) experiments address ecosystem-level consequences of species loss by comparing communities of high species richness with communities from which species have been gradually eliminated. BEF experiments originally started with microcosms in the laboratory and with grassland ecosystems. A new frontier in experimental BEF research is manipulating tree diversity in forest ecosystems, compelling researchers to think big and comprehensively.
 We present and discuss some of the major issues to be considered in the design of BEF experiments with trees and illustrate these with a new forest biodiversity experiment established in subtropical China (Xingangshan, Jiangxi Province) in 2009/2010. Using a pool of 40 tree species, extinction scenarios were simulated with tree richness levels of 1, 2, 4, 8 and 16 species on a total of 566 plots of 25.8 × 25.8 m each.
 The goal of this experiment is to estimate effects of tree and shrub species richness on carbon storage and soil erosion; therefore, the experiment was established on sloped terrain. The following important design choices were made: (i) establishing many small rather than fewer larger plots, (ii) using high planting density and random mixing of species rather than lower planting density and patchwise mixing of species, (iii) establishing a map of the initial 'ecoscape' to characterize site heterogeneity before the onset of biodiversity effects and (iv) manipulating tree species richness not only in random but also in trait-oriented extinction scenarios.
 Data management and analysis are particularly challenging in BEF experiments with their hierarchical designs nesting individuals within-species populations within plots within-species compositions. Statistical analysis best proceeds by partitioning these random terms into fixed-term contrasts, for example, species composition into contrasts for species richness and the presence of particular functional groups, which can then be tested against the remaining random variation among compositions.
 We conclude that forest BEF experiments provide exciting and timely research options. They especially require careful thinking to allow multiple disciplines to measure and analyse data jointly and effectively. Achieving specific research goals and synergy with previous experiments involves trade-offs between different designs and requires manifold design decisions.&#13

A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China

Wind speed forecasting plays a key role in wind-related engineering studies and is important in the management of wind farms. Current forecasting models based on different optimization algorithms can be adapted to various wind speed time series data. However, these methodologies cannot aggregate different hybrid forecasting methods and take advantage of the component models. To avoid these limitations, we propose a novel combined forecasting model called SSA-PSO-DWCM, i.e., particle swarm optimization (PSO) determined weight coefficients model. This model consisted of three main steps: one is the decomposition of the original wind speed signals to discard the noise, the second is the parameter optimization of the forecasting method, and the last is the combination of different models in a nonlinear way. The proposed combined model is examined by forecasting the wind speed (10-min intervals) of wind turbine 5 located in the Penglai region of China. The simulations reveal that the proposed combined model demonstrates a more reliable forecast than the component forecasting engines and the traditional combined method, which is based on a linear method

A Hybrid Multi-Step Rolling Forecasting Model Based on SSA and Simulated Annealing—Adaptive Particle Swarm Optimization for Wind Speed

With the limitations of conventional energy becoming increasing distinct, wind energy is emerging as a promising renewable energy source that plays a critical role in the modern electric and economic fields. However, how to select optimization algorithms to forecast wind speed series and improve prediction performance is still a highly challenging problem. Traditional single algorithms are widely utilized to select and optimize parameters of neural network algorithms, but these algorithms usually ignore the significance of parameter optimization, precise searching, and the application of accurate data, which results in poor forecasting performance. With the aim of overcoming the weaknesses of individual algorithms, a novel hybrid algorithm was created, which can not only easily obtain the real and effective wind speed series by using singular spectrum analysis, but also possesses stronger adaptive search and optimization capabilities than the other algorithms: it is faster, has fewer parameters, and is less expensive. For the purpose of estimating the forecasting ability of the proposed combined model, 10-min wind speed series from three wind farms in Shandong Province, eastern China, are employed as a case study. The experimental results were considerably more accurately predicted by the presented algorithm than the comparison algorithms