Search

As wort is the sweet starter liquid for beer, its quality needs to be strictly controlled. It is generally accepted that wort turbidity is of importance in terms of the brewing process and the resulting beer quality. This study investigated the major ingredients of wort turbidity using the malting barley cultivar Dan'er, grown in Jiangsu province, China. It was found that dextrins of low molecular weight constituted the vast majority of the polysaccharides causing wort turbidity. To solve the turbidity problem with the Dan'er malt, the present study supplemented 80 U g−1 of β-amylase, and as an activator, an extra 50 mg L−1 of Ca2+ to the malting process of Dan'er barley. The turbidity of the resulting congress wort of the Dan'er malt declined to <5.0 EBC units, which then met the quality guidelines of the brewery. The results of the present study may also help in developing new turbidity detection methods and yield breeding clues for quality improvement of the barley cultivars in the Jiangsu province of China. Copyright © 2016 The Institute of Brewing & Distilling [ABSTRACT FROM AUTHOR]

To better understand the relationship of the urban, economic, and energy systems in Jiangsu, the most well-developed province in China, the nexus of urbanization, energy consumption, and economic growth is explored. Autoregressive distributed lag (ARDL) bounds testing is applied to examine the cointegration relationship of the variables. After confirming the long-run equilibrium among the variables, the short-run and long-run coefficients are estimated by ARDL error-correction model (ARDL-ECM). We find that Jiangsu is an energy-dependent economy at the present time while urbanization has become a growth engine for her economic development. Suggestions for reducing energy usage while keeping the economy growing at a proper speed during urbanization are also presented. [ABSTRACT FROM PUBLISHER]

Jiangsu is one of the most developed regions in China. Its' urbanization rate increased to 61.9 % in 2011, faster than the whole country level (51.27 %). Exploring the influence of urbanization on energy consumption in Jiangsu may help find the relationship between energy consumption and urbanization. The main results received in this paper are as follows. (1) Urbanization accelerated the produce energy consumption over the study period. The produce energy consumption jumped from 50.7 Mtce in 1995 to 166.6 Mtce in 2011. (2) Due to the rapid urbanization, the gap between residential energy consumption for urban and rural regions gradually has become wide since 2002. However, the gap in residential energy consumption per capita between urban and rural has changed narrow since 1995. (3) The grey relational degree between urbanization and office energy consumption is the biggest. However, the relational grade for construction energy consumption is relatively low. (4) The energy intensity effect plays an important role in decreasing produce energy consumption at the aggregate level, followed by the economic structure effect. The economic activity density effect, urbanization rate effect and population effect are found to be primarily responsible for driving produce energy consumption growth over the study period. [ABSTRACT FROM AUTHOR]

Highlights: [•] The CO2 emissions of Jiangsu over the period 1995–2009 are calculated based on the presented method. [•] This paper examines the occurrence of a decoupling between economic activity and CO2 emission. [•] Jiangsu economy experienced weak decoupling and strong decoupling except 2003–2005. [ABSTRACT FROM AUTHOR]

Abstract: Jiangsu Province has become one of the most developed regions in China. Economic growth in Jiangsu has occurred along with rising CO2 emission levels. A deeper understanding of how energy-related CO2 emissions have evolved in Jiangsu Province is very important in formulating future policies. Thus it is very necessary to investigate the driving forces governing CO2 emissions and their evolution. The decoupling index combined with the LMDI (Log Mean Divisia Index) method is used to analyze the contribution of the factors which influence energy-related CO2 emissions in Jiangsu Province over the period 1995–2009. The results show that economic activity is the critical factor in the growth of energy-related CO2 emissions in Jiangsu Province, and the energy intensity effect plays the dominant role in decreasing CO2 emissions. The period from 2003 to 2005 represents a re-coupling effect; the periods 1996–1997 and 2000–2001 indicate strong decoupling effect, while the remaining time intervals show weak decoupling effect. [Copyright &y& Elsevier]

Abstract: Since the start of economic reform in 1979, Jiangsu Province has become one of the most developed regions in China. To ensure adequate energy supply and sustainable development of economy and environment, it is necessary to analyze Jiangsu Province’s energy balance. The energy flow chart is taken as a useful tool for sorting out and displaying energy statistics data. The purpose of this paper is to draft the Jiangsu Province’s energy flow chart for 2009, which is used to study the characteristics of energy production and consumption in Jiangsu Province. We find that: (1) In 2009, energy imported from other regions accounted for 96.1% of the total primary energy supply. However, its energy supply mainly came from coal. (2) Jiangsu thermal power and heating efficiencies in 2009 were higher than those of Guangdong and China in 2007. However, its coal processing and petroleum refineries efficiencies in 2009 were lower than those of Guangdong and China in 2007. (3) Electricity supply in Jiangsu Province mainly depended on thermal power, which accounted for 88.97% of the total electricity supply. However, 96.36% fuel for thermal power was coal. (4) The share of final energy consumption of the secondary industrial sector was 81.28% in 2009. [Copyright &y& Elsevier]

The Oka varicella vaccine strain remains neurovirulent and can establish lifelong latent infection, raising safety concerns about vaccine-related herpes zoster. In this study, we aimed to evaluate the immunogenicity and safety of a skin-attenuated and neuro-attenuated varicella vaccine candidate (v7D vaccine). We did this randomised, double-blind, controlled, phase 2a clinical trial in Jiangsu, China. Healthy children aged 3–12 years with no history of varicella infection or vaccination were enrolled and randomly assigned (1:1:1:1) to receive a single subcutaneous injection of the v7D vaccine at 3·3 log 10 plaque forming units (PFU; low-dose v7D group), 3·9 log 10 PFU (medium-dose v7D group), and 4·2 log 10 PFU (high-dose v7D group), or the positive control varicella vaccine (vOka vaccine group). All the participants, laboratory personnel, and investigators other than the vaccine preparation and management staff were masked to the vaccine allocation. The primary outcome was assessment of the geometric mean titres (GMTs) and seroconversion rates of anti-varicella zoster virus immunoglobulin G (IgG) induced by different dose groups of v7D vaccine at 0, 42, 60, and 90 days after vaccination in the per-protocol set for humoral immune response analysis. Safety was a secondary outcome, focusing on adverse events within 42 days post-vaccination, and serious adverse events within 6 months after vaccination. This study was registered on Chinese Clinical Trial Registry, ChiCTR2000034434. On Aug 18–21, 2020, 842 eligible volunteers were enrolled and randomly assigned treatment. After three participants withdrew, 839 received a low dose (n=211), middle dose (n=210), or high dose (n=210) of v7D vaccine, or the vOka vaccine (n=208). In the per-protocol set for humoral immune response analysis, the anti-varicella zoster virus IgG antibody response was highest at day 90. At day 90, the seroconversion rates of the low-dose, medium-dose, and high-dose groups of v7D vaccine and the positive control vOka vaccine group were 100·0% (95% CI 95·8–100·0; 87 of 87 participants), 98·9% (93·8–100·0; 87 of 88 participants), 97·8% (92·4–99·7; 91 of 93 participants), and 96·4% (89·8–99·2; 80 of 83 participants), respectively; the GMTs corresponded to values of 30·8 (95% CI 26·2–36·0), 31·3 (26·7–36·6), 28·2 (23·9–33·2), and 38·5 (31·7–46·7). The v7D vaccine, at low dose and medium dose, elicited a humoral immune response similar to that of the vOka vaccine. However, the high-dose v7D vaccine induced a marginally lower GMT compared with the vOka vaccine at day 90 (p=0·027). In the per-protocol set, the three dose groups of the v7D vaccine induced a similar humoral immune response at each timepoint, with no statistically significant differences. The incidence of adverse reactions in the low-dose, medium-dose, and high-dose groups of v7D vaccine was significantly lower than that in the vOka vaccine group (17% [35 of 211 participants], 20% [41 of 210 participants], and 13% [27 of 210 participants] vs 24% [50 of 208 participants], respectively; p=0·025), especially local adverse reactions (10% [22 of 211 participants], 14% [30 of 210 participants] and 9% [18 of 210 participants] vs 18% [38 of 208 participants], respectively; p=0·016). None of the serious adverse events were vaccine related. The three dose groups of the candidate v7D vaccine exhibit similar humoral immunogenicity to the vOka vaccine and are well tolerated. These findings encourage further investigations on two-dose vaccination schedules, efficacy, and the potential safety benefit of v7D vaccine in the future. The National Natural Science Foundation of China, CAMS Innovation Fund for Medical Sciences, the Fundamental Research Funds for the Central Universities, and Beijing Wantai. For the Chinese translation of the abstract see Supplementary Materials section. [ABSTRACT FROM AUTHOR]

• An artificial neural networks (ANN) was improved by simulated annealing (SA). • The improved ANN was utilized to calculate the objective function of SA for sampling optimization. • The improved ANN outperformed regression kriging in sampling optimization using SA. Optimization of the sampling design is significant for generating accurate soil maps based on samples. Simulated annealing (SA) with the mean squared prediction error (MSE) as objective function has been proven as an effective method for sampling optimization with prior samples for the MSE calculation. In this study, an improved artificial neural network (the improved ANN) was developed firstly and then utilized to calculate the objective function of SA for sampling optimization (named as the improved ANN_SA). The proposed approach was evaluated to generate optimized samples with a series of samples sizes (from 10 to 500 points) for mapping soil organic matter (SOM) content from the existing 5054 samples in a study area with 1067 km2 in Jiangsu Province, China. A commonly-used sampling optimization using SA with th e objective function calculated by regression kriging (named as RK_SA) was taken as a reference method. The two sampling optimization approaches were compared in mapping SOM using three prediction methods, the improved ANN, regression kriging (RK) and random forest. The results showed that the proposed sampling optimization approach generally achieved more accurate prediction over different sampling sizes. The maximum improvement of prediction accuracy by using the p roposed sampling strategy vs. the reference strategy was 12.5%, 53.6%, and 15.5% when using the improved ANN, RK, and random forest as soil mapping methods, respectively. Moreover, the improved ANN and random forest generated more accurate soil predictions than RK with the proposed sampling optimization approach. The superiority of the proposed sampling optimization was more obvious in low sampling densities (smaller than 0.3 points/km2). We conclude that the improved ANN_SA sampling is a potential effective sampling optimization approach, and machine learning is a promising method for soil mapping when using this sampling approach. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new approach by introducing spatial variability into Neyman allocation is proposed. • The proposed approach is suitable for design-based sampling and model-based sampling. • Accounting for spatial variability in allocation improves prediction accuracy. • The approach performed better than Neyman allocation at all sample densities. Stratified sampling is one of the most commonly used sampling strategies for soil survey and mapping. An important issue in stratified sampling is the sample size allocation for each stratum. Neyman allocation is a typical sample size allocation approach in which the main idea is to assign more samples to a stratum with a larger internal standard deviation. By applying the spatial variability for sample size allocation, we proposed a new allocation approach (SVNA) for stratified spatial sampling. In this new approach, more samples were allocated to strata with both a bigger statistical standard deviation and larger spatial variability of the target variable. SVNA was evaluated using a simulated experiment based on real-world soil data of Jiangsu province, China. The target variable was soil organic matter (SOM), and land-use type was utilized for stratification. Simple random sampling (SRS) and spatial simulated annealing algorithm (SSA) were applied to determine sample locations. Results showed that the performance of SVNA generally was superior to Neyman allocation in terms of SOM content prediction at all sampling densities (4.28–0.19 points per km2) regardless of the application of SRS or SSA for sample location determination. The maximum reduction of mean squared error (MSE) for the SOM content prediction using SVNA was 11% using SRS and was 10% using SSA. Compared with Neyman allocation, SVNA obtained greater prediction accuracies in strata with large spatial variability and similar or slightly smaller prediction accuracies for strata with small spatial variability. Furthermore, the prediction accuracy of the SVNA approach tended to decrease when the sampling density was 0.37 points per km2, and this sampling density threshold was not the same for each stratum. We concluded that SVNA is an efficient sample size allocation approach for stratified sampling in soil survey and mapping. [ABSTRACT FROM AUTHOR]