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Existing research has examined the results of women’s political leadership participation (WPLP) and the reasons for the lack of advancement of women to management positions. However, little research has been adopting a more comprehensive framework and configuration perspective to investigate the determinants of WPLP. By integrating institutional theory and institutional complementarities theory, this study aims to construct an institution–culture–structure framework to investigate the multiple driving mechanisms of WPLP.

Drawing on the fuzzy set qualitative comparative analysis method and a sample of 66 countries, the authors identify multiple equifinal combinations of conditions related to high and not-high levels of WPLP.

According to the results, the authors summarize five pathways influencing WPLP. These pathways include education and culture-driven pattern, political institutions-driven pattern, political institutions and structure-driven pattern, integrated-driven pattern and political institutions and culture restrictive pattern.

The authors shed new light on the driving mechanism of WPLP and contribute to research on making full out of women’s leadership.

This study aims to the complex and unpredictable terrain environment of the Qinghai-Tibet Plateau scientific research station, such as cement road, wetland, gravel desert, snowfield, ice surface, grassland, slimy ground, steep slope, step, etc., a reconfigurable walking mechanism based on two movement modes of wheel and triangular crawler was proposed.

By analyzing the deformation mechanism of the walking mechanism, a reconfigurable wheel-crawler-integrated walking mechanism and the configuration scheme are designed. The analysis of the kinematics and mechanical properties of the swing arm system and the deformation mechanism of the walking mechanism.

The reconfigurable wheel-crawler-integrated walking mechanism can be switched between the wheel and triangular crawler modes by driving the deformation mechanism. Through the numerical simulation of its movement process, and the trial production and experiment of the prototype, indicates the validity of the reconfigurable wheel-crawler-integrated walking mechanism design.

The work of this paper provides a reconfigurable wheel-crawler-integrated-walking mechanism, which can be used by robots in the Qinghai-Tibet Plateau scientific research station. It has excellent reconfigurability and can effectively improve the robot’s adaptability to complex terrain.

Considering the small sample size and non-linear characteristics of historical energy consumption data from certain provinces in Southwest China, the authors propose a hybrid accumulation operator and a hybrid accumulation grey univariate model as a more accurate and reliable methodology for forecasting energy consumption. This method can provide valuable decision-making support for policy makers involved in energy management and planning.

The hybrid accumulation operator is proposed by linearly combining the fractional-order accumulation operator and the new information priority accumulation. The new operator is then used to build a new grey system model, named the hybrid accumulation grey model (HAGM). An optimization algorithm based on the JAYA optimizer is then designed to solve the non-linear parameters θ, r, and γ of the proposed model. Four different types of curves are used to verify the prediction performance of the model for data series with completely different trends. Finally, the prediction performance of the model is applied to forecast the total energy consumption of Southwest Provinces in China using the real world data sets from 2010 to 2020.

The proposed HAGM is a general formulation of existing grey system models, including the fractional-order accumulation and new information priority accumulation. Results from the validation cases and real-world cases on forecasting the total energy consumption of Southwest Provinces in China illustrate that the proposed model outperforms the other seven models based on different modelling methods.

The HAGM is used to forecast the total energy consumption of the Southwest Provinces of China from 2010 to 2020. The results indicate that the HAGM with HA has higher prediction accuracy and broader applicability than the seven comparative models, demonstrating its potential for use in the energy field.

The HAGM(1,1) is used to predict energy consumption of Southwest Provinces in China with the raw data from 2010 to 2020. The HAGM(1,1) with HA has higher prediction accuracy and wider applicability compared with some existing models, implying its high potential to be used in energy field.

Theoretically, this paper presents, for the first time, a hybrid accumulation grey univariate model based on a new hybrid accumulation operator. In terms of application, this work provides a new method for accurate forecasting of the total energy consumption for southwest provinces in China.

Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.

As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.

The empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.

With these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.

To address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.

This study was designed to investigate how the material properties of the tea-drinking receptacle interact with a participant's motivation and preference for extracting and using information obtained via haptic perception, namely the need for touch (NFT), to influence his or her tea-drinking experience.

72 blindfolded participants were instructed to sample room temperature tea beverages served in a cup that was made of ceramic, glass, paper or plastic. They were then asked to rate how familiar they were with the taste of the beverage, to rate how pleasant the taste was and to specify how much they would like to pay for it (i.e. willingness-to-pay ratings).

The material of the receptacles used to serve the tea exerted a significant influence over the pleasantness ratings of the tea and interacted with the participants' NFT, exerting a significant influence over their willingness to pay for the tea. Specifically, high-NFT participants were willing to pay significantly more for the same cup of tea when it was served in a ceramic cup rather than in a paper cup, whereas the low-NFT participants' willingness to pay for the tea was unaffected by the material of the receptacles.

Our findings suggest that consumers may not be equally susceptible to the influence of the receptacle in which tea, or any other beverage, is served. Our findings also demonstrate how the physical properties of a receptacle interact with a consumer's motivation and preference to influence his or her behavior in the marketplace.

This article examines the impact of different policy instruments on livestock farmers' willingness to recycle manure. The results shed light on the optimal policy combination.

A game theoretical framework is constructed to illustrate farmers' optimal strategies under different policies. Theoretical results are empirically tested by survey data from beef cattle farmers in Central China.

Empirical results show that penalties work better than subsidies if each type of policy is implemented separately. The authors also find a positive interaction between subsidy and penalty policies, suggesting that a combination of subsidy and penalty policies produces the best outcome in incentivizing livestock farmers to recycle manure. Furthermore, planting and breeding simultaneously have the strongest effect on increasing livestock farmers' willingness to recycle manure, suggesting that the combination of planting and breeding can be an optimal strategy for manure management.

This study is based on firsthand survey data and provides new evidence on the effectiveness of alternative environmental policies on manure recycling.

The authors explicitly evaluate the dynamic impact of five most concerned supply chain disruption scenarios, including: (1) a short-term shortage and price jump of corn supply in hog farms; (2) a shortage of market hogs to packing facilities; (3) disruption in breeding stock adjustments; (4) disruption in pork import; and (5) a combination of scenario (1)–(4).

The agricultural supply chain experienced tremendous disruptions from the COVID-19 pandemic. To evaluate the impact of disruptions, the authors employ a system dynamics model of hog market to simulate and project the impact of COVID-19 on China hog production and pork consumption. In the model the authors explicitly characterize the cyclical pattern of hog market. The hog cycle model is calibrated using market data from 2018–2019 to represent the market situation during an ongoing African swine fever.

The authors find that the impacts of supply chain disruption are generally short-lived. Market hog transportation disruption has immediate impact on price and consumption. But the impact is smoothed out in six months. Delay in import shipment temporarily reduces consumption and raises hog price. A temporary increase of corn price or delay in breeding stock acquisition does not produce significant impact on national hog market as a whole, despite mass media coverage on certain severely affected regions.

This is the first evaluation of short-term supply chain disruption on China hog market from COVID-19. The authors employ a system dynamics model of hog markets with an international trade component. The model allows for monthly time step analysis and projection of the COVID-19 impact over a five-year period. The results and discussion have far-reaching implications for agricultural markets around the world.

The purpose of this paper is to understand the effect of base media on the tribological performance and tribochemistry of bismuth thiophosphate additive.

The oil‐water double soluble additive bismuth dithiophosphate was prepared and identified. The contributions of the two base media on the additive tribological behavior and the tribofilm components were comparatively studied.

The extreme pressure (EP) and friction‐reducing properties are remarkably improved with water substituted for paraffin as the base medium. The EP performance of the lubricating media containing this additive mainly results from the tribochemical reaction film on the rubbing surface, not from the viscosity of the base media. In water or paraffin medium, the adsorption process of this additive from the lubricant bulk onto the rubbing surface and the components and the properties of the tribochemical reaction films formed are different, which have important effect on the tribological performance.

The paper mainly focuses on how the water medium with polarity and the liquid paraffin base medium with non‐polarity affect on the tribological performance of the bismuth thiophosphate additive.

The research has found a water‐oil double soluble lubrication additive with outstanding EP and friction‐reducing performance.

The designed experiment provides a new approach to further learn the action mechanism of thiophosphate additive.

The purpose of this paper is to measure Farmer’s adoption tendency towards drought shock, risk-taking networks and modern irrigation technology.

Based on this assumption, this paper evaluated the data gathered from 498 household surveys of Zhangye, Gansu province, PRC, by using the binary probit model. First, the empirical data was analyzed for evaluating the impact of drought shock and risk-taking tendencies on the adoption of modern irrigation technology by farmers. Second, the authors introduced informal risk-bearing networks with formal risks. Final, based on the empirical results, the sustainability test, along with the marginal effect analysis and the degree of impact was carried out.

The results show that the drought shock has a significantly deferent effect on the modern irrigation technology of the farmers. The probability of using technology for each level of drought loss is reduced by 15.02%. The risk-taking network has a significant role in promoting the modern irrigation technology of farmers. The probability of adoption for each additional unit of rural household labor security supply, the likelihood of adoption by farmers increased 23.11%, the probability of approval for each level of relative support, and neighborhood assistance by farmers increased by 13.11% and 17.88% respectively. This study further revealed that insurance purchases enabled farmers to adopt new irrigation technology with the probability increased by 24.99%; easily available bank loans increased the probability of farmers using irrigation technology by 31.89%. From the perspective of interactions between farmers, the risk-taking network can alleviate the inhibitory effect of drought impact towards the adoption of irrigation technology. Among the control variables, the number of years of education, the age of farming, the degree of arable land, the distance from home to the market, and the price of water all has significant effects on the adoption of modern irrigation technology by farmers.

The novelty of the study is that it illustrated the interactive influence of drought shock and risk-taking networks on the farmer’s adoption tendencies of modern irrigation technologies, the inner relationship among drought impact, the risk-taking network and the farmer’s adoption behavior and provide an interactive relationship between the formal risk-taking network and the non-risk-taking network in farmer’s technology adoption.