Progress on the Improvement of Quality and Functional Properties of Fermented Milk by Complex Strains of Bacteria

Fermented milk has a long history of being fermented by lactic acid bacteria. Fermented milk contains many elements, including protein, minerals, and vitamins. Fermented milk is gaining more and more attention from customers as people’s desire for a high quality of life improves. Most of the fermented milk on the market today are prepared with single strains or traditional lactic acid bacteria (Streptococcus thermophilus and Lactobacillus bulgaricus) as fermenting agents. However, this production method results in issues like an excessively long fermentation time, a mildly inferior taste, and poor stability. Compound strains have recently gained attention in the field of fermented milk preparation. By utilizing interactions and synergies between various strains, it is possible to increase the quantity and diversity of metabolites, enhancing the quality and functional properties of fermented milk and compensating for some of the shortcomings of conventional fermented milk in terms of product morphology and sensory experience. This study examines how complexing strains have accelerated pH reduction, improved the product’s sensory qualities, rheological characteristics, and water-holding capacity, as well as increased their capacity for lipid-lowering, anti-inflammatory, antioxidant, and bacteriostatic effects. Finally, the future research paths for fermented milk innovation are intended to offer suggestions for the diverse, functionalized, and precise production of fermented milk

Spatio-Temporal Evolution and Influencing Factors of Green Development in the Yellow River Basin of China

Globally, nations and regions have pushed for “green development (GD)”, a sustainable development strategy that considers the integrated growth of “economy–environment–society”. As it is an area of China that provides an ecological function and is an important energy base, it is necessary to explore the current situation and factors influencing GD in the Yellow River Basin (YRB). Therefore, first, this paper constructs a GD indicator system from a multi-dimensional perspective, measures the GD of 79 prefecture-level cities in the YRB from 2006 to 2019 by using the entropy method, and analyzes the evolution of time series according to the results. We found that the YRB’s GD showed an overall increase during the study period, rising from 0.1261 to 0.2195, but the level was low. Second, we analyzed the spatial characteristics of the YRB’s GD using a spatial analysis method and concluded that GD varied significantly across cities in the YRB. The YRB presented spatial distribution characteristics with obvious “quad-core pieces”, and there was a high intensity of spatial correlation and agglomeration. The spatial center of gravity of GD moved toward the southeast year by year. Third, we examined the influencing factors of the GD of the YRB through the spatial Durbin model. The study found that the spatial spillover effect on GD in the YRB was obvious, and the reasons affecting the GD of the YRB were heterogeneous. Finally, according to the conclusions of this research, we propose differentiated policies that are suitable for GD in the YRB

Do Ecological Restoration Projects Improve Water-Related Ecosystem Services? Evidence from a Study in the Hengduan Mountain Region

Land use/land cover (LULC) and climate change are major driving forces that impact ecosystem services and affect human well-being directly and indirectly. Under the future interaction between LULC and climate change, the impact of different land management and climate change scenarios on water-related services is uncertain. Based on this, the CLUMondo model, which focuses on land use intensity, was used to simulate the land system under different land management scenarios in the future. By coupling the downscaled climate scenario data, this study used the InVEST and RUSLE models to estimate the annual water yield and soil erosion in 2050 in the Hengduan Mountain region and analyzed the variation differences in different sub-watersheds. The results indicated that, under the influence of LULC and climate change, when compared with the amount for 2020, the soil erosion in the Hengduan Mountain region in 2050 was reduced by 1.83, 3.40, and 2.91% under the TREND scenario, FOREST scenario, and CONSERVATION scenario, respectively, while the water yield decreased by 5.05, 5.37, and 5.21%, respectively. Moreover, the change in soil erosion in the study area was affected by precipitation and closely related to the precipitation intensity, and the impact of climate change on the water yield was significantly greater than that of LULC change. The spatial heterogeneity of soil erosion and water yield was obvious at the sub-watershed scale. In the future, soil erosion control should be strengthened in the northern regions, while water resource monitoring and early warning should be emphasized in the central-eastern regions. Our results provide scientific guidance for policy makers to formulate better LULC policies to achieve regional water and soil balance and sustainable management

Spatio-Temporal Evolution and Influencing Factors of Green Development in the Yellow River Basin of China

Globally, nations and regions have pushed for “green development (GD)”, a sustainable development strategy that considers the integrated growth of “economy–environment–society”. As it is an area of China that provides an ecological function and is an important energy base, it is necessary to explore the current situation and factors influencing GD in the Yellow River Basin (YRB). Therefore, first, this paper constructs a GD indicator system from a multi-dimensional perspective, measures the GD of 79 prefecture-level cities in the YRB from 2006 to 2019 by using the entropy method, and analyzes the evolution of time series according to the results. We found that the YRB’s GD showed an overall increase during the study period, rising from 0.1261 to 0.2195, but the level was low. Second, we analyzed the spatial characteristics of the YRB’s GD using a spatial analysis method and concluded that GD varied significantly across cities in the YRB. The YRB presented spatial distribution characteristics with obvious “quad-core pieces”, and there was a high intensity of spatial correlation and agglomeration. The spatial center of gravity of GD moved toward the southeast year by year. Third, we examined the influencing factors of the GD of the YRB through the spatial Durbin model. The study found that the spatial spillover effect on GD in the YRB was obvious, and the reasons affecting the GD of the YRB were heterogeneous. Finally, according to the conclusions of this research, we propose differentiated policies that are suitable for GD in the YRB

“Distance‐Driven” Versus “Density‐Driven”: Understanding the Role of “Source‐Case” Distance and Gathering Places in the Localized Spatial Clustering of COVID‐19—A Case Study of the Xinfadi Market, Beijing (China)

The frequent occurrence of local COVID‐19 today gives a strong necessity to better understand the effects of "source‐case" distance and gathering places, which are often considered to be the key factors of the localized spatial clustering of an epidemic. In this study, the localized spatial clustering of COVID‐19 cases, which originated in the Xinfadi market in Beijing from June–July 2020, was investigated by exploring the spatiotemporal characteristics of the clustering using descriptive statistics, point pattern analysis, and spatial autocorrelation calculation approaches. Spatial lag zero‐inflated negative binomial regression model and geographically weighted Poisson regression with spatial effects were also introduced to explore the factors which influenced the clustering of COVID‐19 cases at the micro spatial scale. It was found that the local epidemic can be significantly divided into two stages which are asymmetric in time. A significant spatial spillover effect of COVID‐19 was identified in both global and local modeling estimation. The dominant role of the “source‐case” distance effect, which was reflected in both global and local scales, was revealed. Relatively, the role of gathering places is not significant at the initial stage of the epidemic, but the upward trend of the significance of some places is obvious. The trend from "distance‐driven" to "density‐driven" of the localized spatial clustering of COVID‐19 was predicted. The effectiveness of blocking the transformation trend will be a key issue for the global response to the local COVID‐19

Improving PM2.5 Air Quality Model Forecasts in China Using a Bias-Correction Framework

Chinese cities are experiencing severe air pollution in particular, with extremely high PM2.5 levels observed in cold seasons. Accurate forecasting of occurrence of such air pollution events in advance can help the community to take action to abate emissions and would ultimately benefit the citizens. To improve the PM2.5 air quality model forecasts in China, we proposed a bias-correction framework that utilized the historic relationship between the model biases and forecasted and observational variables to post-process the current forecasts. The framework consists of four components: (1) a feature selector that chooses the variables that are informative to model forecast bias based on historic data; (2) a classifier trained to efficiently determine the forecast analogs (clusters) based on clustering analysis, such as the distance-based method and the classification tree, etc.; (3) an error estimator, such as the Kalman filter, to predict model forecast errors at monitoring sites based on forecast analogs; and (4) a spatial interpolator to estimate the bias correction over the entire modeling domain. One or more methods were tested for each step. We applied five combinations of these methods to PM2.5 forecasts in 2014–2016 over China from the operational AiMa air quality forecasting system using the Community Multiscale Air Quality (CMAQ) model. All five methods were able to improve forecast performance in terms of normalized mean error (NME) and root mean square error (RMSE), though to a relatively limited degree due to the rapid changing of emission rates in China. Among the five methods, the CART-LM-KF-AN (a Classification And Regression Trees-Linear Model-Kalman Filter-Analog combination) method appears to have the best overall performance for varied lead times. While the details of our study are specific to the forecast system, the bias-correction framework is likely applicable to the other air quality model forecast as well

A Novel Predictor for Micro-Scale COVID-19 Risk Modeling: An Empirical Study from a Spatiotemporal Perspective

Risk assessments for COVID-19 are the basis for formulating prevention and control strategies, especially at the micro scale. In a previous risk assessment model, various “densities” were regarded as the decisive driving factors of COVID-19 in the spatial dimension (population density, facility density, trajectory density, etc.). However, this conclusion ignored the fact that the “densities” were actually an abstract reflection of the “contact” frequency, which is a more essential determinant of epidemic transmission and lacked any means of corresponding quantitative correction. In this study, based on the facility density (FD), which has often been used in traditional research, a novel micro-scale COVID-19 risk predictor, facility attractiveness (FA, which has a better ability to reflect “contact” frequency), was proposed for improving the gravity model in combination with the differences in regional population density and mobility levels of an age-hierarchical population. An empirical analysis based on spatiotemporal modeling was carried out using geographically and temporally weighted regression (GTWR) in the Qingdao metropolitan area during the first wave of the pandemic. The spatiotemporally nonstationary relationships between facility density (attractiveness) and micro-risk of COVID-19 were revealed in the modeling results. The new predictors showed that residential areas and health-care facilities had more reasonable impacts than traditional “densities”. Compared with the model constructed using FDs (0.5159), the global prediction ability (adjusted R2) of the FA model (0.5694) was increased by 10.4%. The improvement in the local-scale prediction ability was more significant, especially in high-risk areas (rate: 107.2%) and densely populated areas (rate in Shinan District: 64.4%; rate in Shibei District: 57.8%) during the outset period. It was proven that the optimized predictors were more suitable for use in spatiotemporal infection risk modeling in the initial stage of regional epidemics than traditional predictors. These findings can provide methodological references and model-optimized ideas for future micro-scale spatiotemporal infection modeling

Exploring Spatio-Temporal Variations of Ecological Risk in the Yellow River Ecological Economic Belt Based on an Improved Landscape Index Method

Intense human activities have led to profound changes in landscape patterns and ecological processes, generating certain ecological risks that seriously threaten human wellbeing. Ecological risk assessment from a landscape perspective has become an important tool for macroecosystem landscape management. This research improves the framework and indices of the ecological risk assessment from a landscape perspective, evaluates the land use pattern and landscape ecological risk dynamics in the Yellow River Ecological Economic Belt (YREEB), analyzes the spatiotemporal variation, and identifies key areas for ecological risk management. The results indicate the following: The main land use types in the region are grassland and cropland, but the area of cropland and grassland decreased during the study period, and with the accelerated urbanization, urban land is the only land use type that continued to increase over the 20-year period. The ecological risk in the YREEB tended to decrease, the area of low ecological risk zones increased, while the area of high ecological risk zones gradually decreased. Most areas are at medium risk level, but the risk in central Qinghai and Gansu is obviously higher, and there is a dispersed distribution of local high- and low-risk zones. A total of 37.7% of the study area is identified as critical area for future risk management, and the potential for increased risk in these areas is high. These results can provide a basis for sustainable development and planning of the landscape and the construction of ecological civilization in ecologically fragile areas