With the disruption of digital technologies, customers have emerged as co-producers in order to reduce costs and enhance productivity. Customer-driven business logic recognizes customer capabilities and sustainable innovation as key factors in the performance of customized manufacturing enterprises. The potential relationship between customers and producers has become a new area of inquiry. Existing research rarely delves into the impact of customer predictive ability on the process of customized production activities, particularly in relation to sustainable innovation, which remains inadequately characterized. Ships serve as a typical representation of customized enterprises. This study explores the underlying drivers of sustainable innovation through customer demand orientation by examining 20-year historical patterns in the global shipping and shipbuilding markets, considering the environmental dependency and temporal correlation characteristics of the shipbuilding market (producers) and the shipping market (consumers). By employing time series and panel data in a machine learning algorithm, specifically the random forest model, the study reveals a strong and statistically significant correlation between new ship deliveries and the Baltic Dry Index (BDI), with larger value ships having a more pronounced impact on the consumer market. The correlation analysis confirms that these two variables, in combination, can comprehensively reflect customer predictive ability and serve as crucial decision criteria for customer investment in new ship production. Furthermore, based on principal component analysis of customer predictive ability and ship innovation levels, as well as Granger causality tests, the study demonstrates that customer predictive ability is a Granger cause of sustainable innovation in customized production. Customer predictive ability influences sustainable innovation in customized enterprises to varying degrees. This research provides valuable insights for shipbuilding companies in terms of engaging in sustainable innovation in international markets and understanding the value of international market customers.

Previous investigations have illuminated the significant association between the gut microbiome and a broad spectrum of health conditions, including obesity, diabetes, cardiovascular diseases, and psychiatric disorders. Evidence from certain studies suggests that dysbiosis of the gut microbiota may play a role in the etiology of obesity and diabetes. Moreover, it is acknowledged that dietary habits, pharmacological interventions, psychological stress, and other exogenous factors can substantially influence the gut microbial composition. For instance, a diet rich in fiber has been demonstrated to increase the population of beneficial bacteria, whereas the consumption of antibiotics can reduce these advantageous microbial communities. In light of the established correlation between the gut microbiome and various pathologies, strategically altering the gut microbial profile represents an emerging therapeutic approach. This can be accomplished through the administration of probiotics or prebiotics, which aim to refine the gut microbiota and, consequently, mitigate the manifestations of associated diseases. The present manuscript evaluates recent literature on the relationship between gut microbiota and metabolic syndrome published over the past three years and anticipates future directions in this evolving field.

Establishing an effective evaluation model to analyze the actual environmental level of the multi-indicator systems is an urgent challenge. However, due to the complexity of the environmental system, the factors that determine the practical system are usually interrelated. To solve this problem, many methods have been proposed and verified. However, the disadvantage of unreasonable weight determination and only single-indicator assessment limits the practical application of these analysis methods. Here, we established an optimized entropy weight model and integrate it with the fuzzy comprehensive evaluation (FCE) method to quantify the complex environmental system. By introducing the linear programming method into the entropy weight solution, we can obtain high accuracy weight values and strong resistance to extreme data. Theoretical and simulation results demonstrate that our method can enhance the precision of weight calculation and evaluation results for multi-factor systems. Our work develops an effective method to quantify the complex environment system comprehensively and is significant to real applications of evaluation method.

Physical inactivity is a well-known risk factor for various non-communicable diseases (NCDs). Sufficient physical activity (PA) is essential for the prevention of NCDs and thus it is imperative to study the current status of PA and its influencing factors among rural residents in China. A population-based survey was conducted in rural areas of Shandong, Shanxi and Yunnan Provinces using a stratified random sampling method. The International Physical Activity Questionnaire Short Form (IPAQ-S) was used to collect the data on PA. A total of 3780 rural residents participated in the survey. The result showed that 22.2% of rural residents were physical inactivity. The proportion of rural residents reporting practice of physical exercise was 54.4%. The most frequently performed physical exercise was walking/brisk walking(78.3%).Binary logistic regression analyses showed that being female, people at age between 15 to 34 years or 60 years old and above, employees of governmental departments/retirees, school students, the unemployed, people with NCDs were risk factors of PA and ethnic minority groups,smoking and alcohol consumption were risk factors of physical exercise. Health promotion programme aiming at increasing people’s PA in rural China is needed and it should focus on the populations groups of the female, people at age 60 years and above ,school students, the unemployed, and people with NCDs.

Red cabbage (RC), a cruciferous vegetable rich in various bioactive substances, can significantly reduce the risk factors of several chronic metabolic diseases, but its mechanism remains clear. Given that the relationship between diet, gut microbiota and health, a diet -induced mice obesity model was used to elucidate the effects of RC on gut microbial composition and interactions in mice. After 24-hour dietary intervention, high-fat (HF) diet and intake of RC led to increased Firmicutes/Bacteroidetes (F/B) ratios in the feces of mice. RC also reduced the relative abundance of Bifidobacteria, Lactobacillus and Akkermansia muciniphila in mice fed low fat (LF) diet. After 8-weeks dietary intervention, RC significantly changed the structure and the ecological network of gut microbial community. Particularly, RC inhibited an HF-diet induced increase of AF12 in mice, and this genus was positively correlated with body weight, low-density lipoprotein level and fecal bile acid of mice. Unclassified Clostridiales, specifically increased by RC consumption, was also found to negatively correlate with hepatic free cholesterol levels in mice. Overall, our results demonstrated that RC modulation of gut microbial composition and interactions is associated with the attenuation of HF diet-induced body weight gain and altered cholesterol metabolism of mice.

The dynamics of hydrological lateral nutrient fluxes contribute to our understanding of ecological functions related to energy, materials, and organism flows across various spatiotemporal scales. To explore the connectivity between multiple spatial flow processes, we conducted a one-year field measurement to assess lateral hydrologic carbon (C) and nitrogen (N) fluxes over the continental shelf in the Yangtze estuary. We observed a significant correlation between the differences in remote sensing-based estimates of gross primary production (GPP) (∆GPPMODIS) and the differences in eddy covariance (EC) tower-based GPP (∆GPPEC) at both high-elevation and low-elevation sites. Over the course of a year, our predicted daily maximum tidal elevation (TE) closely matched the observed values in the creek, which facilitated the development of theoretical models to simulate biogeochemical cycling processes and aquatic ecosystem functions. Our findings indicate that the studied saltmarsh acts as a net exporter of dissolved total C (DTC) while serving as a net sink for dissolved total N (DTN). Furthermore, there is a significant correlation in the total dissolved stoichiometry of the C/N ratio between imports and exports. These findings highlight the importance of integrating ecological stoichiometric principles to gain a deeper understanding of the complex relationships between physical, chemical, and biological processes, particularly within the context of the meta-ecosystem framework. Additionally, when considering reciprocal hydrological lateral C and N flows, single ecosystem can function both as sources and sinks within the meta-ecosystem framework.

The effective integration of aerial remote sensing data and ground multi-source data has always been one of the difficulties of quantitative remote sensing. A new monitoring mode is designed which installs the hyperspectral imager on the UAV and places a buoy spectrometer on the river. Water samples are collected simultaneously to obtain in situ assay data of total phosphorus, total nitrogen, COD, turbidity and chlorophyll during data collection. The cross correlogram spectral matching (CCSM) algorithm is used to match the data of the buoy spectrometer with the UAV spectral data to reduce the UAV data noise significantly. An absorption characteristics recognition algorithm (ACR) is designed to realize a new method for comparing UAV data with laboratory data. This method takes into account the spectral characteristics and the correlation characteristics of test data synchronously. It is concluded that the most accurate water quality parameters can be calculated by using the regression method under five scales after the regression tests of multiple linear regression method (MLR), support vector machine method (SVM) and neural network (NN) method. This new working mode of integrating spectral imager data with point spectrometer data will become a trend in water quality monitoring.

Although researchers have proposed various indentation size effect (ISE) models, these models often involve different form and number of parameters that can make our wonder which is the best in existed ISE models. Three types of ISE test data, namely, normal ISE, reverse ISE and transition of normal to reverse ISE, are used to evaluate the sixteen ISE models. The comparatively study indicates that H-J, N-G-Fs, N-G-H, N-G-A and N-G-Qs models can accurately predict the normal ISE. The reason for this is that the friction stress that is not related to dislocation activities or the indentation size effect of plastic zone has been introduced into these models. Therefore, these two factors should be considered in future ISE models. The sixteen ISE models are originally proposed to describe the normal ISE of different materials. However, to our surprise, some of these models are able to capture the reverse ISE and the transition of normal to reverse ISE of different materials. The determination coefficients (DC) of the sixteen ISE models are also determined for different materials. For reverse ISE, the highest DC value for NiCSi, TC4 and PED Ni are given by the EXP, N-G-Fs and N-G-A models, respectively. For the transition of normal to reverse ISE, the N-G-YC, N-G-Fs, and N-G-H models produce the maximum DC for ZrO2, Cu and Y2O3-ZrO2, respectively. Moreover, the mean DC of the N-G-Fs model is the maximum among the sixteen ISE models, followed by the N-G-H model, but they cannot accurately predict the reverse ISE. Therefore, the N-G-Fs and N-G-H models should be further modified to accurately predict the reverse ISE.