Recursive Model Research Articles

Visiting parents in times of COVID-19: The impact of parent-adult child contacts on the psychological health of the elderly

Using the 8th wave of the SHARE and the SHARE Corona Survey, we investigated whether the disruption of parent–adult child contacts due to social distancing restrictions increased the symptoms of depression among old age individuals during the first wave of the COVID-19 pandemic. We model the relationship between the disruption of parent–adult child contacts and the mental health of the elderly using a recursive simultaneous equation model for binary variables. Our findings show that the likelihood of disruption of parent–adult child contacts was higher with adult children who do not live with or close to their parents (i.e., in the same household or in the same building) for whom contact disruption increases about 15 %. The duration of restrictions to movement and lockdowns also has a positive and significant effect on parent-child contact disruption: an additional week of lockdown significantly increases the probability of parent-child contact disruption, by about 1.5 %. The interventions deemed essential to reduce the spread of the pandemic, such as the “stay-at-home” order, necessarily disrupted personal parent–child contacts and the social processes that facilitate psychological well-being, increasing the probability of suffering from a deepening depressed mood by about 17 % for elderly parents.

Economic impact and energy transformation of different effort-sharing schemes to pursue 2 ℃ warming limit in China

Pursuing 2 ℃ warming limit, as a global climate target, would not be achieved without the mitigation contribution of each country. At present, possible ranges of regional impact of 2 ℃ climate target mostly focused on multi-model simulation results lack of the comparison of multiple effort-sharing schemes. This paper presents a reginal analysis framework introduced 10 different effort-sharing schemes and SSPs-RCPs mixed scenarios, and finds out how 2 ℃ climate target affects China in economy and energy transformation and balance the trade-offs of each effort-sharing scheme. To measure how to achieve 2 ℃ climate target in China, we developed an improved recursive dynamic CGE model including carbon taxes and CCS technologies. We find that carbon budgets of 2 ℃ climate target in China under various effort-sharing schemes would be 289.8–576.7 Gt during 2010–2050, accounting for 9.5–19.0% in the world. The mitigation cost would range from 0.2% to 2.95%, and the average cost would equal GDP level in 2018. The fossil energy consumption would decrease by 17.1–57.9%, equaling the consumption level before 2010. Differentiated emission pathway with similar the carbon budget, per capita convergence (PCC) would have less economic cost than grandfathering rule. We develop an evaluation algorithm to take economy, energy and emission reduction into account, and find that PCC and ability to pay (ATP) rules would be accepted for China.

Where to Invest Project Efforts for Greater Benefit: A Framework for Management Performance Mapping with Examples for Potato Seed Health

Policymakers and donors often need to identify the locations where technologies are most likely to have important effects, to increase the benefits from agricultural development or extension efforts. Higher-quality information may help to target the high-benefit locations, but often actions are needed with limited information. The value of information (VOI) in this context is formalized by evaluating the results of decision making guided by a set of specific information compared with the results of acting without considering that information. We present a framework for management performance mapping that includes evaluating the VOI for decision making about geographic priorities in regional intervention strategies, in case studies of Andean and Kenyan potato seed systems. We illustrate the use of recursive partitioning, XGBoost, and Bayesian network models to characterize the relationships among seed health and yield responses and environmental and management predictors used in studies of seed degeneration. These analyses address the expected performance of an intervention based on geographic predictor variables. In the Andean example, positive selection of seed from asymptomatic plants was more effective at high altitudes in Ecuador. In the Kenyan example, there was the potential to target locations with higher technology adoption rates and with higher potato cropland connectivity, i.e., a likely more important role in regional epidemics. Targeting training to high management performance areas would often provide more benefits than would random selection of target areas. We illustrate how assessing the VOI can contribute to targeted development programs and support a culture of continuous improvement for interventions.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Design of deep recursive CNN model for detecting and classifying peston plant

Automatic pest on plant detection in early stage is very essential for food quality control in the agriculture industry. However, the visual method to identify pest on every plant by human is a cumbersome process and cannot be well suited in the agriculture field, because it is time consuming, less accurate and labor intensive. Pest on Plant and plant leaf disease are the major factors responsible for reducing the quality and quantity of food production. Detection at the earlier stage of pest growth and its killing would result in reducing its effect on plant and enhance the quality of food production. Various existing ways have been used to identify and classify pest on plant, but issues have not been resolved, and there is still a scope for improvement. This paper proposes a Deep Recursive Convolutional Neural Networks (DR-CNN) to improve the average running time and achieve high accuracy. DR-CNN model is integrating the convolution, ReLU and Max pooling Layer into single unit and call recursively.

There and Back Again: Bleed from Extraordinary Experiences

Abstract From reenactments to pilgrimages, extraordinary experiences engage consumers with frames and roles that govern their actions for the duration of the experience. Exploring such extraordinary frames and roles, however, can make the act of returning to everyday life more difficult, a process prior research leaves implicit. The present ethnography of live action role-playing explains how consumers return from extraordinary experiences and how this process differs depending on consumers’ subjectivity. The emic term “bleed” captures the trace that extraordinary frames and roles leave in everyday life. The subjective tension between the extraordinary and the ordinary intensifies bleed. Consumers returning from the same experience can thus suffer different bleed intensities, charting four trajectories of return that differ in their potential for transformation: absent, compensatory, cathartic, and delayed. These findings lead to a transformative recursive process model of bleed that offers new insights into whether, how, and why consumers return transformed from extraordinary experiences with broader implications for experiential consumption and marketing.

Development of a recursive model for the calculation of transient processes in electric networks using the wavelet transformation

The digital transformation of the electric power industry is one of the priority tasks for the development of the industry. Wavelet transform is widely used in the electric power industry to analyze the dynamics of complex non-linear non-stationary processes. The article proposes a method for calculating transient processes in electrical networks based on a recursive algorithm. The approximating and detailing wavelet coefficients of the discrete wavelet transform are used as the voltage signal. To select the optimal wavelet function, it is proposed to use a criterion that takes into account the accuracy of the signal recovery as a result of the inverse wavelet transform. The nature of the change in the calculation result with an increase in the number of iterations is shown. The results of a numerical experiment for a 110 kV network when calculating a three-phase short circuit showed an acceptable accuracy of the developed technique. The proposed technique makes it possible to compress the volume of transmitted digital data on normal and emergency modes of electrical networks.

The energy, environment and economy impact of coal resource tax, renewable investment, and total factor productivity growth

China is the biggest emitter in the world, and coal is dominant in China's energy structure. At present, China has used many strategies to reduce coal use, but their cost-effectiveness is controversial. This paper applies the China-Energy-Environment-Economy Analysis (CEEEA) model, a dynamic recursive computable general equilibrium model with multi-sectors and multi-residents, to simulate progressive policy mix to reduce coal consumption and CO2 emission during 2020–2030, and presents the impact on energy, environment, and economy. The results show that the coal resource tax and the renewable investment can significantly reduce coal share and CO2 emissions to different degrees. The producer price index increases a lot in coal-related industries. However, the consumer price index only increases by no more than 1%. After all, energy products account for a small proportion of the supply chain of bulk commodities. Another, this paper tests the boundary of Porter Hypothesis of such policy mix: it only requires an additional 0.09% increase of total factor productivity every year to cover the negative economic impact of the taxes.

Encouraging research on recursive thinking through the lens of a model of the spread of contagious diseases

Recursive reasoning is a powerful tool used extensively in problem solving. For us, recursive reasoning includes iteration, sequences, difference equations, discrete dynamical systems, pattern identification, and mathematical induction; all of these can represent how things change, but in discrete jumps. Given the school mathematics curriculum’s later emphasis on calculus—the mathematics of change in continuous contexts—it is surprising that the curriculum seems to neglect recursive thinking after the early grades. Research shows that recursion supports the learning of algebra among younger students, but the lack of similar research with older students is concerning. In this paper we suggest possible affordances from teaching recursive modeling, including a basic model of the spread of contagious diseases. We also discuss different ways to present these models at various points in the curriculum that might develop connections between mathematics and the real world, and support students’ learning of mathematics. This leads to what we, as mathematicians, think would be interesting research questions for mathematical educators.

A Precise Medical Imaging Approach for Brain MRI Image Classification.

Magnetic resonance imaging (MRI) is an accurate and noninvasive method employed for the diagnosis of various kinds of diseases in medical imaging. Most of the existing systems showed significant performances on small MRI datasets, while their performances decrease against large MRI datasets. Hence, the goal was to design an efficient and robust classification system that sustains a high recognition rate against large MRI dataset. Accordingly, in this study, we have proposed the usage of a novel feature extraction technique that has the ability to extract and select the prominent feature from MRI image. The proposed algorithm selects the best features from the MRI images of various diseases. Further, this approach discriminates various classes based on recursive values such as partial Z-value. The proposed approach only extracts a minor feature set through, respectively, forward and backward recursion models. The most interrelated features are nominated in the forward regression model that depends on the values of partial Z-test, while the minimum interrelated features are diminished from the corresponding feature space under the presence of the backward model. In both cases, the values of Z-test are estimated through the defined labels of the diseases. The proposed model is efficiently looking the localized features, which is one of the benefits of this method. After extracting and selecting the best features, the model is trained by utilizing support vector machine (SVM) to provide the predicted labels to the corresponding MRI images. To show the significance of the proposed model, we utilized a publicly available standard dataset such as Harvard Medical School and Open Access Series of Imaging Studies (OASIS), which contains 24 various brain diseases including normal. The proposed approach achieved the best classification accuracy against existing state-of-the-art systems.

EXTREMUM SEEKING BASED COMPOSED RECURSIVE MODEL FREE CONTROL OF TWO-STAGE ANAEROBIC DIGESTION PROCESS

In this paper, a new structure of extremum seeking algorithm is applied to the two-stage anaerobic digestion process to maximize the outflow rate of both hydrogen and methane. The model of the two-stage AD process is presented, which provides the characteristics of the total gas production rate. Based on the original Extremum Seeking Control (ESC), a novel Composed Recursive Model Free Controller (CRMFC) is added for maximum tracking for the gas production in the bioreactors. The proposed controller comprises a recursive model free stabilization term and a recursive time delay compensation term. Standard ESC, Newton-based ESC and Kalman filter (KF) based ESC are respectively combined with the new model-free controller to verify the proposed structure. Numerical simulations illustrate the performance of the proposed controller.

Modelling and filtering for dynamic investment in the precious-metals market

ABSTRACT Taking financial positions on precious metals could serve as risk management mechanism against both inflation and economic downturn. The demand for precious metals depends on their consumption- and investment-asset role. We consider the accurate and efficient price modelling of gold, silver, platinum and palladium under a hidden Markov model (HMM) multivariate framework. The interplay of various computational implementation aspects is underscored. Recursive model parameter estimates are generated, which are then utilized to test several dynamic investment strategies. We consider the pure-switching, mixed-switching and mean-variance strategies. The pure switching strategy outperforms the other two strategies 61.76% of the time. Benchmarking against the asset-only strategy and within the 25-year data examined in our study, investing in palladium is found to be better than the above-mentioned strategies. A general mean-variance strategy for any number of assets is also presented in conjunction with a filtering-based parameter estimation for any number of HMM states.

Certification to compensate gender prejudice – Analysis on impact of management system certification on export

ABSTRACT Management system certification signals that the organization meets international standards, which provides a certain confidence in the company. This confidence is in particular needed for exporting companies in developing countries. Because the business world is dominated by men, female leadership might be another reason to have less confidence in a company. Women-led companies may therefore benefit more from certification. Therefore, this study empirically tests the impact of certification on export, and the moderating effect of female leadership. We use data from enterprise surveys conducted by the World Bank in 2013 that include 4111 firms from 25 Central and Eastern European countries in transition. We implement a recursive bivariate probit model and an extensive sensitivity analysis to account for endogeneity issues. Results confirm that certification and export are positively correlated. Firms managed by females benefit more from certification based on international standards than firms managed by men, especially in the service sector. This suggests that certification compensates for the possibly negative connotations of female leadership. Female managers may consider implementing a management system and get it certified, resulting in a competitive advantage in export markets. Our findings provide food for thought for purchase managers – are they free from prejudice?

Data-Driven State Prediction and Analysis of SOFC System Based on Deep Learning Method

A solid oxide fuel cell (SOFC) system is a kind of green chemical-energy–electric-energy conversion equipment with broad application prospects. In order to ensure the long-term stable operation of the SOFC power-generation system, prediction and evaluation of the system’s operating state are required. The mechanism of the SOFC system has not been fully revealed, and data-driven single-step prediction is of little value for practical applications. The state-prediction problem can be regarded as a time series prediction problem. Therefore, an innovative deep learning model for SOFC system state prediction is proposed in this study. The model uses a two-layer LSTM network structure that supports multiple sequence feature inputs and flexible multi-step prediction outputs, which allows multi-step prediction of system states using SOFC system experimental data. Comparing the proposed model with the traditional ARIMA model and LSTM recursive prediction model, it is shown that the multi-step LSTM prediction model performs better than the ARIMA and LSTM recursive prediction models in terms of two evaluation criteria: root mean square error and mean absolute error. Thus, the proposed multi-step LSTM prediction model can effectively and accurately predict and evaluate the SOFC system’s state.

Self-supervised representation learning for trip recommendation

Trip recommendation is a significant and engaging location-based service that can help new tourists make more customized travel plans. It often attempts to suggest a sequence of points of interest (POIs) for a user who requests a personalized travel demand. Conventional methods either leverage the heuristic algorithms (e.g., dynamic programming) or statistical analysis (e.g., Markov models) to search or rank a POI sequence. These procedures may fail to capture the diversity of human needs and transitional regularities. They even provide recommendations that deviate from tourists’ real travel intention when the trip data is sparse. Although recent deep recursive models (e.g., RNN) are capable of alleviating these concerns, existing solutions hardly recognize the practical reality, such as the diversity of tourist demands, uncertainties in the trip generation, and the complex visiting preference. Inspired by the advance in deep learning, we introduce a novel self-supervised representation learning framework for trip recommendation — SelfTrip, aiming at tackling the aforementioned challenges. Specifically, we propose a two-step contrastive learning mechanism concerning the POI representation, as well as trip representation. Furthermore, we present four trip augmentation methods to capture the visiting uncertainties in trip planning. We evaluate our SelfTrip on four real-world datasets, and extensive results demonstrate the promising gain compared with several cutting-edge benchmarks, e.g., up to 4% and 10% improvements on Osaka regarding F1 and pair-F1.

A novel recursive multi-scale modeling for predicting the burst pressure of filament wound composite pressure vessels

A novel recursive multi-scale modeling for predicting the burst pressure of filament wound composite pressure vessels

Is the value added tax reform a source of food insecurity and food poverty? The Burkina Faso case

To finance its ambitious development programme, Burkina Faso aims to pass tax reforms by introducing a minimum value added tax (VAT) of 5% on agricultural goods and increasing VAT on water and electricity to 18%. In a country where nearly 10% of the population is food insecure, this reform could worsen the situation. We built a dynamic and recursive computable general equilibrium model with microsimulation to evaluate the effects of these tax reforms on poverty and food security. Results show that both proposed VAT changes lead to increased poverty and food insecurity, especially with the minimum VAT on agricultural products.

METHODOLOGY FOR MANAGING INVESTMENT PROCESSES OF DIGITALIZATION OF THE ECONOMY

In the article the investigates the problems of the investment management digital transformations at the enterprise, where the instrumental basis based on the system economic theory and integrated IT risk management theory are allocated. The purpose of the study is to develop a recursive and convergence methodology of the investment management of the enterprise digitalization processes. 
 The components of the process of investment digitalization of enterprises are structurally reflected and a deterministic 5-component model of developing a recursive and convergence management methodology based on the digital economy is formed. It is determined that the recursive and conversion methodology is based on the understanding of investment management digital transformations at an enterprise as a complex system, characterized primarily by the diversity and heterogeneity of the constituent elements, numerous internal and external connections, which causes a variety of their interaction, changes in the composition and state of the system. 
 The recursive model provides management of the investment of digitalization in the enterprise as a sequential transition between processes of one level only after all the cycles provided for the current process are implemented. However, such a coherent sequence is possible at the expense of effective information support of each process, which should be implemented on a convergence basis. The precondition for its implementation in the field of digital technologies is civilizational development, consequences of globalization and digitalization. The focuses on the problems of reasonable and sustainable urban planning in the conditions of urbanization, which is the basis for the development and implementation of the «Smart City» concept. As a result, of theoretical analysis of the main definitions of «Smart City», the author's vision of this concept is proposed. The implementation of «Smart City» in the context of human rights protection, we will highlight the main tasks taking into account the development of cognitive technologies.

The role of residential choice on the travel behavior of young adults

Several studies have shown that the travel behavior of young adults in the United States in the past two decades differed from that of prior generations. On average, recent young adults drove fewer miles, owned fewer vehicles and made use of public transit more often. A higher share of young adults also chose to live in cities. This study examines the relationship between the location decision of young adults and their travel behavior. We examine how being a young adult and other socioeconomic variables are associated with residential location decisions, and how these in turn affect vehicle ownership, mode choice and travel distance. Our analysis uses household travel survey data from the Seattle regions collected in 2006 and 2017 and employs a recursive structural equation model to examine these questions. We find that young adult households were more likely to live closer to the city center and to have fewer vehicles than older ones. Fewer young adults also chose to own vehicles in 2017 than in 2006. While young adults made more use of non-automobile modes and had fewer person miles traveled, we find that these effects were more due to their residential location and vehicle ownership decisions than due to direct preferences about mode or distance traveled. These findings suggest that significant changes would be expected in the mode use and miles traveled among young adults if their residential location or vehicle ownership preferences change significantly due to life cycle or other factors.

Understanding the generation mechanism of BEV drivers' charging demand: An exploration of the relationship between charging choice and complexity of trip chaining patterns

In the context of the rapid popularization of battery electric vehicles (BEV), it has become a key problem to deeply understand the charging demand and reasonably configure charging facilities. Exploring the relationship between trip chaining patterns and charging choice of battery electric vehicle users may aid in understanding the mechanism of charging demand. Based on the recursive simultaneous bivariate probit model, we integrate the impact of BEV users’ risk aversion attitude and develop two causal structures: one is that the charging choice is determined first and influences trip chaining pattern, the other is that trip chaining pattern is determined first and influences charging choice. Next, a stated preference survey is conducted through online platform and field survey, and a total of 494 valid questionnaires are collected. The model results show that the fitting effect of the causal structure model where trip chaining pattern precedes charging choice is better than another causal structure model. Moreover, integrating the influence of risk aversion attitude in the form of latent variable into the RSBP model can significantly improve the fit goodness of the model. These findings will help to further understand the mechanism of charging demand and have some implications on estimating charging demand.

A Novel Adaptive Filtering for Cooperative Localization Under Compass Failure and Non-Gaussian Noise

Multi-autonomous underwater vehicles (AUVs) cooperative localization has become a research hotspot in the marine navigation field. In this paper, a filtering algorithm for slave AUV with compass failure is proposed based on ‘two-master-one slave’ cooperative localization model. In this algorithm, the course angle is unknown, and non-Gaussian measurement noise caused by measurement outliers is considered. The cooperative localization model is reconstructed, and a filtering algorithm is derived whose performance will not be affected by the unknown course angle. Moreover, the maximum information potential (MIP) criterion is introduced into a recursive model to deal with the non-Gaussian measurement noise in a complicated underwater environment. Simultaneously, the kernel bandwidth is adaptively adjusted by the innovation matrix and the measurement error covariance matrix. The proposed algorithm can accurately estimate the variation of the unknown course angle without expanding the dimension of state under the condition of non-Gaussian measurement noise. Finally, the proposed algorithm is verified through field experiments. The results show that the proposed algorithm has higher accuracy and robustness than other existing algorithms.