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The purpose of this paper is to study the electrochemical properties of electrode material on activated carbon double layer capacitors. It also tries to develop a prediction model to evaluate pore size value.

Back-propagation neural network (BPNN) prediction model is used to evaluate pore size value. Also, an improved heuristic approach genetic algorithm (HAGA) is used to search for the optimal relationship between process parameters and electrochemical properties.

A three-layer ANN is found to be optimum with the architecture of three and six neurons in the first and second hidden layer and one neuron in output layer. The simulation results show that the optimized design model based on HAGA can get the suitable process parameters.

HAGA BPNN is proved to be a practical and efficient way for acquiring information and providing optimal parameters about the activated carbon double layer capacitor electrode material.

Nitrogen oxides (NO_x) have been considered as primarily responsible for many serious environmental problems. Removing NO is the key task to remove NO_x hazards. To clarify, NO removal process for pitch-based spherical-activated carbons (PSACs), an online prediction and optimization technique in real-time based on support vector machine algorithm in regression (support vector regression [SVR]) is discussed. The purpose of this paper is to develop a predictor and optimizer system on selective catalytic reduction of NO (SCRN) using experimental data and data-driven SVR intelligence methods.

Predictor and optimizer using developed SVR have been proposed. To modify the training efficiency of SVR, the authors especially customize batch normalization and k-fold cross-validation techniques according to the unique characteristics of PSACs model.

The results present that SVR provides a property regression model since it can linkage linear and non-linear process and property relationships in few experimental data sets. Also, the integrated normalization and k-fold cross-validation show a satisfying improvement and results for SVR optimization. The predicted results of predictor and optimizer in single and double factor systems are in excellent agreement with the experimental data.

SCRN-PO for predicting and optimization SCRN problems is developed by data-driven methods. The outperformed SCRN-PO system is used to predict multiple-factors property parameters and obtain optimum technological parameters in real-time. Also, experiment duration is greatly shortened.

This paper aims to propose a soft actuator that combines a sponge-based actuating structure and a layer-jamming-based stiffness-improving structure in a cavity.

The proposed soft actuator consists of film-constrained sponge units (FCSUs) and jamming layers. The FCSUs in the proposed soft actuator bend under vacuum pressure, causing bending deformation of the entire actuator. The jamming layers are strongly coupled through friction under vacuum pressure, increasing the stiffness of the entire actuator. The performance of the proposed soft actuator was examined by measuring its stiffness, bending deformation and response performance. A four-finger soft robotic gripper was proposed based on the proposed soft actuator.

Through experiments, it was shown that the proposed soft actuator exhibited acceptable bending deformation, stiffness and response. Moreover, the proposed four-finger soft gripper could effectively grasp objects in daily life.

In this study, the authors proposed a novel bending actuator (with a volume of approximately 43.2 cm³) based on FCSUs and jamming layers. To the best of the authors’ knowledge, this is the first study to combine a sponge-based actuating structure and a layer-jamming structure in a cavity to achieve simultaneous change in actuation and stiffness. The soft actuator exhibited good bending deformation and high stiffness simultaneously under vacuum pressure. Consequently, it could be used effectively to fabricate soft grippers.

Traditional laboratory measurements of soil water diffusivity (D) and soil water retention curve (SWRC) are always time-consuming and labor-intensive. Therefore, this paper aims to present a simple and robust test method for determining D and SWRC without reducing accuracy.

In this study, a D model of unsaturated soil was established based on Gardner–Russo model and then a combination of Gardner–Russo model with one-dimensional horizontal absorption method to obtain n and a parameters of Gardner–Russo model. One-dimensional horizontal absorption experiments on loam, silt loam and sandy clay loam were conducted to obtain the relationships between measured infiltration rate and cumulative infiltration with wetting front distance. Based on the obtained relationships, the measured infiltration data from the one-dimensional horizontal absorption tests were used to calculate n and a parameters and further constructing D and SWRC.

Both the calculated D and SWRC inversed from the infiltration data were in good agreement with the measured ones that obtained from the traditional horizontal absorption method and the centrifuge method, respectively. Error analysis indicated that only the infiltration data are enough to reliably synchronously determine D and SWRC.

A simple and robust method is proposed for synchronous determination of soil water diffusivity and water retention curve.

Hand, foot and mouth disease (HFMD) is a common infectious disease in infants and children. HFMD has caused millions of cases and a large epidemic worldwide. A number of studies have shown that the incidence of HFMD is closely related to various factors such as meteorological factors, environmental air pollution factors and socio-economic factors. However, there are few studies that systematically consider the impact of various factors on the incidence of HFMD. The paper aims to discuss these issues.

This study used grey correlation analysis and principal component analysis (PCA) method to systematically analyse the impact of meteorological factors, health resource factors, socio-economic factors and environmental air pollution factors on the incidence of HFMD in Shenzhen.

The incidence of HFMD in Shenzhen was affected by multiple factors. Grey correlation analysis found eight influencing factors which are as follows: volume of industrial waste gas emission; the days of air quality equal to or above grade; the volume of industrial nitrogen oxide emission; precipitation; the mean air temperature; the gross domestic product; the expenditure for medical and health care; and the gross domestic product per capita. PCA found that the gross domestic product, the volume of industrial soot emission, the relative humidity, and the days of air quality equal to or above grade have a higher load value.

This study is the one of the first studies that apply the grey correlation analysis to analyse the influencing factors of HFMD in the English literature, which to some extent fills up the blank in this field.

The purpose of this paper is to propose a subcontractor selection model to fully consider the impact of construction enterprise demands on subcontractor selection. The objectives are to understand the translating process of specific enterprise demands to the evaluating criteria and the weight calculation process.

A three-stage model of subcontractor selection was designed based on quality function deployment (QFD), analytic hierarchy process (AHP) and improved grey correlation analysis (IGCA). In the proposed model, specific enterprise demands are translated by the QFD method, and the weights of the criteria are determined by the IGCA. The AHP is used to quantify the exporters' experience and construct the judgment matrix, which is used as inputting of the grey correlation analysis.

The proposed model provides a feasible process for subcontractor selection by fully considering the actual requirements of the project. By combining the company requirements and project requirement to put forward the requirements of the target subcontractor, the selection process ensures that the selected subcontractor and the project have a higher “fit”

Few researches on construction subcontractor selection have taken into account the “voice” of the company's stakeholders. Therefore, this paper designs a three-stage construction subcontractor selection model by introducing QFD to achieve the transmission of “voice” in the subcontractor selection process, so as to take full consideration of the project objectives and the needs of the company's stakeholders. Meanwhile, in order to decrease the subjective of weight calculation, this paper designs an AHP-IGCA allocation method to determine the weight of relevant indicators. By integrating the proposed weight calculation method and QFD method, the subcontractor selection results become more reasonable and objective.

Psychosocial factors have received increasing attention regarding significantly influencing safety in the construction industry. This research attempts to comprehensively summarize psychosocial factors related to safety performance of construction workers. In the context of coronavirus disease 2019, some typical psychosocial factors are selected to further analyze their influence mechanism of safety performance.

First, a literature review process was conducted to identify and summarize relevant psychosocial factors. Then, considering the impact of the epidemic, hypotheses on the relationship between six selected psychosocial factors (i.e. work stress, role ambiguity, work–family conflict, autonomy, social support and interpersonal conflict) and safety performance were proposed, and a hypothetical model was developed based on job demands-resources theory. Finally, a meta-analysis was used to examine these hypotheses and the model.

The results showed these psychosocial factors indirectly influenced workers’ safety performance by impacting on their occupational psychology condition (i.e. burnout and engagement). Work stress, role ambiguity, work–family conflict and interpersonal conflict were negatively related to safety performance by promoting burnout and affecting engagement. Autonomy and social support were positively related to safety performance by improving work engagement and reducing burnout.

This research is the pioneer systematically describing the overall picture of psychosocial factors related to the safety performance of construction workers. Through deeply discussed the mechanism of psychosocial factors and safety performance, it could provide a reference for the theory and application of psychosocial factors in the field of construction safety management.

The relationship between farm size and greenhouse gas (GHG) emissions has not been clearly defined. This paper aims to assess and compare the impact of farm size on greenhouse gas (GHG) emissions derived from wheat and maize production in the North China Plain (NCP), one of the most important agricultural regions in China.

A field survey through face-to-face interviews was conducted to collect the primary data, and life cycle assessment method, a worldwide comparable framework, was then adopted to characterize the farm-size effect on greenhouse gas (GHG) wheat and maize production in NCP.

It was confirmed that GHG emissions from N fertilizer production and use were the primary contributor to total carbon footprint (CF). As farm size increased, maize yield increased but wheat yield barely changed, while area-scaled and yield-scaled CF declined for both crops. These results were supposed to relate to utilize the inputs more efficiently resulting from increased application of modern agriculture methods on larger operations. It was also found maize not only had higher grain yields, but possessed much smaller CFs. More notably, the reduction of CF with farm size seemed to be more sensitive for maize as compared to wheat. To further mitigate GHG emissions, farm size should better be larger for wheat than for maize.

This study provides useful information guide for Chinese agriculture in increasing crop production, raising farm income and relieving environmental burdens caused by the misuse of agricultural resources.

Advances in material agency driven by artificial intelligence (AI) have facilitated breakthroughs in material adaptivity enabling smart objects to autonomously provide individualized smart services, which makes smart objects act as social actors embedded in the real world. However, little is known about how material adaptivity fosters the infusion use of smart objects to maximize the value of smart services in customers' lives. This study examines the underlying mechanism of material adaptivity (task and social adaptivity) on AI infusion use, drawing on the theoretical lens of social embeddedness.

This study adopted partial least squares structural equation modeling (PLS-SEM), mediating tests, path comparison tests and polynomial modeling to analyze the proposed research model and hypotheses.

The results supported the proposed research model and hypotheses, except for the hypothesis of the comparative effects on infusion use. Besides, the results of mediating tests suggested the different roles of social embeddedness in the impacts of task and social adaptivity on infusion use. The post hoc analysis based on polynomial modeling provided a possible explanation for the unsupported hypothesis, suggesting the nonlinear differences in the underlying influencing mechanisms of instrumental and relational embeddedness on infusion use.

The formation mechanisms of AI infusion use based on material adaptivity and social embeddedness help to develop the business strategies that enable smart objects as social actors to exert a key role in users' daily lives, in turn realizing the social and economic value of AI.

This study advances the theoretical research on material adaptivity, updates the information system (IS) research on infusion use and identifies the bridging role of social embeddedness of smart objects as agentic social actors in the AI context.

Accurate prediction of seasonal power consumption trends with impact disturbances provides a scientific basis for the flexible balance of the long timescale power system. Consequently, it fosters reasonable scheduling plans, ensuring the safety of the system and improving the economic dispatching efficiency of the power system.

First, a new seasonal grey buffer operator in the longitudinal and transverse dimensional perspectives is designed. Then, a new seasonal grey modeling approach that integrates the new operator, full real domain fractional order accumulation generation technique, grey prediction modeling tool and fruit fly optimization algorithm is proposed. Moreover, the rationality, scientificity and superiority of the new approach are verified by designing 24 seasonal electricity consumption forecasting approaches, incorporating case study and amalgamating qualitative and quantitative research.

Compared with other comparative models, the new approach has superior mean absolute percentage error and mean absolute error. Furthermore, the research results show that the new method provides a scientific and effective mathematical method for solving the seasonal trend power consumption forecasting modeling with impact disturbance.

Considering the development trend of longitudinal and transverse dimensions of seasonal data with impact disturbance and the differences in each stage, a new grey buffer operator is constructed, and a new seasonal grey modeling approach with multi-method fusion is proposed to solve the seasonal power consumption forecasting problem.

The highlights of the paper are as follows:

1. A new seasonal grey buffer operator is constructed.

2. The impact of shock perturbations on seasonal data trends is effectively mitigated.

3. A novel seasonal grey forecasting approach with multi-method fusion is proposed.

4. Seasonal electricity consumption is successfully predicted by the novel approach.

5. The way to adjust China's power system flexibility in the future is analyzed.

A new seasonal grey buffer operator is constructed.

The impact of shock perturbations on seasonal data trends is effectively mitigated.

A novel seasonal grey forecasting approach with multi-method fusion is proposed.

Seasonal electricity consumption is successfully predicted by the novel approach.

The way to adjust China's power system flexibility in the future is analyzed.