Search Results (7)

The solenoid valve component is the core part affecting the total mass of space propulsion system, and the accuracy of the solenoid valve mass model directly impacts the accuracy of the system mass estimation and optimization design. This study focuses on the solenoid valves used in gas path control for cold gas propulsion systems. The relationship between the gas flow rate and volume flow rate of the solenoid valve is derived. By analyzing the parameters affecting the mass of the solenoid valves, a general calculation mass model of the gas solenoid valve used in cold gas propulsion is proposed based on strength theory. Combining with the existing general calculation mass model for liquid solenoid valves and collecting mass data of 16 gas solenoid valves and 33 liquid solenoid valves used in space propulsion system, the mass calculation formulas of the gas and liquid solenoid valves are obtained by employing several mathematical fitting methods, including quadratic polynomial surface, Manski formula, bivariate power function, and pressure-corrected polynomial. The accuracy of different mass model formulas is compared to assess their performance in calculating the solenoid valve mass. The results show that the quadratic surface formula can better reflect the relationship between the mass of the gas solenoid valves and the valve parameters within the medium volume flow range of 1 × 10⁻⁹ to 3.9 × 10⁻³ m³/s and the proof pressure range of 0.4 to 49.74 MPa. For the calculation of liquid solenoid valve mass, the accuracy of quadratic polynomial surface fitting, bivariate power function equation, and univariate polynomial equation with pressure correction is comparable within the liquid volume flow range of 1.8 × 10⁻⁷ to 1.28 × 10⁻⁴ m³/s and the inlet pressure range of 0.99 to 4.24 MPa; the appropriate calculation formula can be selected based on the pressure conditions in the liquid solenoid valve chamber in practical applications. Sensitivity analysis shows a consistent trend for gas and liquid solenoid valves: proof pressure (gas valves) or inlet working pressure (liquid valves) are the dominant factors affecting valve mass, while volume flow rate has a moderate impact. The proposed solenoid valve mass model in this study can be used to calculate the mass of gas solenoid valves for space cold gas propulsion systems and liquid solenoid valves for liquid rocket thrusters with thrust below 1000 N, providing an important reference for the mass modeling and optimization design of the space propulsion systems. Full article

Addressing the global challenge of sustainable waste management, this research investigates the influence of social dynamics and digital connectivity on rural residents’ willingness to adopt waste classification practices, essential for sustainable environmental management. Through a comprehensive analysis of 5413 rural participants surveyed in the China Labor-force Dynamic Survey (CLDS), this study employs a novel mixed-methods approach. It integrates quantitative analysis with the Manski social interaction framework and a Recursive Bivariate Probit model to explore the intricate interplay between community interactions, internet access, and environmental behaviors. Our methodology stands out for its unique combination of social theory and econometric modeling to address a pressing environmental issue. Results highlight a significant effect of mobile internet use and social interactions within communities on enhancing willingness towards waste classification. Notably, digital connectivity emerges as a key facilitator of environmental engagement, mediating social influences, and fostering a collective approach to waste management. Considering these insights, we propose targeted policy interventions that blend digital strategies with traditional community engagement efforts. Recommendations include crafting digital literacy programs and leveraging social media to bolster community-centric environmental governance. By harnessing the synergistic potential of digital tools and social dynamics, these strategies aim to elevate the effectiveness of waste classification initiatives in rural China, offering a scalable model for environmental sustainability. Full article

As an essential subject of rural environmental governance, farmers’ environmental governance behavior directly affects the level and efficiency of rural environmental governance. In traditional rural society, the characteristics of “acquaintance society”, “circle doctrine”, and “clan society” have led to farmers’ behaviors being influenced and constrained by their surrounding social support and social relations. Therefore, the interaction between farmers will affect the effectiveness of rural environmental governance, and the interaction effect will also affect the implementation of policies in rural environmental governance. In the strategic context of the policy of “building a beautiful and harmonious countryside that is desirable to live and work in” and “promoting green development and harmonious coexistence between human beings and nature” put forward by the 20th National Congress, we follow the principles of Reduce, Reuse, and Recycle from the perspective of circular economy, taking farmers as our research subject. We take the behavior of domestic garbage disposal as an example and, relying on the National Social Science Foundation project, use field research data and refer to neighbor groups and neighboring village groups. We use the Manski model to test the interaction effect of the two groups, analyze the interaction between individual farmers and the interaction between neighboring villages, and, finally, prove that there is an endogenous interaction effect and a situational interaction effect between the neighbor group and neighboring villages. Endogenous interaction effects, contextual interaction effects, and association effects exist between neighbor groups, while only contextual interaction effects and association effects exist between neighboring village groups. The above conclusions provide a policy reference for rural household waste and environmental management. Full article

Law enforcement agencies continue to grow in the use of spatial analysis to assist in identifying patterns of outcomes. Despite the critical nature of proper resource allocation for mental health incidents, there has been little progress in statistical modeling of the geo-spatial nature of mental health events in Little Rock, Arkansas. In this article, we provide insights into the spatial nature of mental health data from Little Rock, Arkansas between 2015 and 2018, under a supervised spatial modeling framework. We provide evidence of spatial clustering and identify the important features influencing such heterogeneity via a spatially informed hierarchy of generalized linear, tree-based, and spatial regression models, viz. the Poisson regression model, the random forest model, the spatial Durbin error model, and the Manski model. The insights obtained from these different models are presented here along with their relative predictive performances. The inferential tools developed here can be used in a broad variety of spatial modeling contexts and have the potential to aid both law enforcement agencies and the city in properly allocating resources. We were able to identify several built-environment and socio-demographic measures related to mental health calls while noting that the results indicated that there are unmeasured factors that contribute to the number of events. Full article

The development of energy production from renewable sources includes the production of energy from photovoltaic installations by prosumers. In Europe, RES development is driven by political goals and requires subsidies during the deployment period, at least as long as the cost of renewable electricity does not reaches grid parity. The study attempts to determine the importance of factors in the development of energy production by prosumers from PV installations in Polish regions. In 2019, the ‘Moj Prad’ program was introduced, applying subsidies to investment costs and the settlement of energy production in the net-metering system. Almost 900 thousand prosumer PV installations were built by the end of 2021, with a total capacity of 5.9 GW. Solar energy share grew from 0.1 to 2.1%. Spatial econometrics models were use in research to determine factors of prosumer PV systems development in Poland (at NUTS-2). Spatial regimes were found in the studied regions, as indicated by a positive autocorrelation (0.75). Considering the pseudo-R-square co-efficient, we can conclude that the spatial error, i.e., factors not included in the GNS model, constitutes approximately 10%. The economic variables included in the Mansky model, i.e., level of salaries and GDP, explain 90% of the variability of installed PV capacity (Nagelkerke pseudo-R-squared value is 0.906). The level of development of prosumer photovoltaic installations (in W per capita) in regions depends primarily on economic factors represented by the level of salaries in a given region. With the increase in salaries by one unit, we also have an increase in installed power capacity in watts per person by 3.52. Surprisingly, the region’s overall wealth did not matter, as the relative number of installations in regions with lower GDP was higher than in others. One can explain that the individual income of households is more important for increasing the number of prosumer installations than the income of the regional economy. The increase in the number of installations in one region contributed to the subsequent increase in their number in neighboring regions. Full article

According to the literature, the path choice decision process of a user of a (road) transport network, named path choice problem (PCP), is composed of two levels/models: the definition of perceived alternative paths (choice set) and the choice of one path in the path choice set. The path choice probability can be estimated with two models: a choice model of the path choice set and a choice model of a path (Mansky paradigm). In this research, the paper’s contribution concerns two elements: extension of the PCP paradigm (two-level models) consolidated in the literature to the route choice decision process (vehicle routing problem (VRP)) and identification of common elements in the PCP and VRP concerning the criteria in the two decision levels and the procedure for route and path selection and choice. The experiment concerns the comparison of observed routes with simulated and optimized routes of commercial vehicles to analyse the level of similarity and coverage. The observed routes are extracted from floating car data (FCD) from commercial vehicles travelling inside a study area inside the Calabria Region (Southern Italy). The comparison is executed in terms of similarity of the sequences of nodes visited between observed routes and simulated/optimized routes. Full article

Sample selection models attempt to correct for non-randomly selected data in a two-model hierarchy where, on the first level, a binary selection equation determines whether a particular observation will be available for the second level (outcome equation). If the non-random selection mechanism induced by the selection equation is ignored, the coefficient estimates in the outcome equation may be severely biased. When the selection mechanism leads to many censored observations, few data are available for the estimation of the outcome equation parameters, giving rise to computational difficulties. In this context, the main reference is Greene (2008) who extends the results obtained by Manski and Lerman (1977), and develops an estimator which requires the knowledge of the true proportion of occurrences in the outcome equation. We develop a method that exploits the advantages of response-based sampling schemes in the context of binary response models with a sample selection, relaxing this assumption. Estimation is based on a weighted version of Heckman’s likelihood, where the weights take into account the sampling design. In a simulation study, we found that, for the outcome equation, the results obtained with our estimator are comparable to Greene’s in terms of mean square error. Moreover, in a real data application, it is preferable in terms of the percentage of correct predictions. Full article