Suitability of Rural Living & Work Facilities, Spatial Behavior of Farmers and Family Income

In recent years, China has put forward policies to improve rural infrastructure and promote the development of rural industries causing to dramatic transition in the living and employment conditions. Moreover, under the pressure of the high expenditure of living and the difficulty of finding jobs, new changes have taken place in the spatial behavior pattern of farmers’ residence and employment. Based on a field survey of 839 observations in 123 villages in Poyang Lake basin, China, we developed indicators to evaluate the suitability of rural living facilities (SLF) and work facilities (SWF). Multinomial logit regression model was used to measure the relationship between SWF, SLF and the spatial behavior patterns of farmers. The results show that: (1) SWF development in rural areas lags behind SLF; (2) Higher SLF is most distributed in suburban areas ; (3) Higher SWF is mostly distributed in suburban towns and characteristic towns; (4) The effects of SLF and SWF on the spatial behavior patterns of farmers are significantly positive; (5) Farmers in the urban-rural amphibious pattern usually obtain a higher income level, and also bring vitality to the countryside.

Empirical Evidence on Factors Conditioning the Turning Point of the Public Debt–Growth Relationship

This paper contributes to the limited literature on the factors conditioning the turning point of the public debt–growth relationship. A decade after the global financial crisis, when the debt ratio in many countries was still above pre-crisis levels, the COVID-19 pandemic again increased the pressure on public finances. It revived the debate on the ability to promote economic recovery through debt-financed government expenditure. However, more intense government borrowing increases its costs and uncertainty about future taxation policy, thus potentially disturbing private consumption, investment, and economic growth. In this paper, we estimate the thresholds of indicators on which the expenditure multiplier depends, which may already imply a risk that public debt will dampen economic growth. We use a methodology of structural threshold regression to examine the varying effects that debt might have on growth using consumption, investment, taxes, and imports as threshold variables, as well as several other factors suggested by previous contributions. The applied methodology allows for the addressing of parameter heterogeneity and endogeneity to be accounted for at the same time. The main results suggest that a positive debt effect is more likely if the conditions for a high expenditure multiplier are met, that an increase in the public-debt-to-GDP ratio is not necessarily deleterious to growth if shares of private consumption and investment in GDP are high, while the tax-revenue-to-GDP ratio is low.

Hierarchical Coordinated Control Method for Multiload DC Microgrid Units

Electricity has become not merely a source of power but also a vital component of our lives in a rapidly changing world. However, according to a World Bank report, 17% of the globe’s population live without power. The key cause for such a huge population not being electrified is the proximity of the central electrical grid system or the high expenditure of installing the grid lines to such remote areas. The notion of a microgrid was first proposed more than a decade ago, but the numerous obstacles it entails have hampered its broad adoption and made it a research focus in recent years. A hierarchical control structure of the microgrid is designed, which is divided into layers according to the control objectives and control time scales of the microgrid, and the hierarchical control structure is realized by using multiagent technology. In the framework of hierarchical control, aiming at the demand of energy coordination and optimization of the microgrid, the operation strategy of the microgrid is proposed in grid-connected and/or off-grid mode. In the grid-connected mode, the large power grid is used for power supply and in the off-grid mode, the load bidding mechanism is introduced to ensure the power supply of important loads. Experiment results reveal the power quality and stability of the system.

The Heterogeneous Public Debt–Growth Relationship: The Role of the Expenditure Multiplier

Currently countries are facing a new crisis caused by the COVID-19, which leads to the rise of government expenditures and additional borrowing. This situation highlights the importance of examine factors which determine the level of public debt that still sustains economic growth. A growing body of research supports the idea of a non-linear debt–growth relationship and estimates the threshold level above which debt becomes unsustainable and has a negative effect on output. The empirical evidence points out that there is no single sustainable debt threshold level that holds for all countries. This research complements scarce empirical evidence on the heterogeneous debt–growth relationship and provides some insights on the publicly available statistical indicators that might signal a relatively low/high expenditure multiplier and, at the same time, potentially unsustainable/sustainable growth stimulus through the use of borrowed funds. We test the hypothesis that the expenditure multiplier is shaping the impact of public debt on growth. Our empirical examination is based on panel data analysis in the groups of countries with expected relatively high and low expenditure multiplier. Research results show that a statistically significant negative marginal effect of debt on growth starts to manifest at a lower debt-to-GDP ratio when the expenditure multiplier is lower and vice-versa. The study shed some light on the sources of heterogeneity in a debt–growth relationship. We can conclude that countries with a high expenditure multiplier level can borrow more and sustain growth. In contrast, in countries with a lower expenditure multiplier, a relatively low debt level becomes unsustainable for growth.

HUBUNGAN MIGRASI TERHADAP TINGKAT KUALITAS SARANA SANITASI RUMAH TANGGA DI JAKARTA: ANALISIS DATA MIKRO SUSENAS 2017

Jakarta's population is influenced by dynamic population growth regarding high migration flows. Migrants can be a resource in urban development and a burden to the environment concerning population density. For instance, problems of housing provision and basic sanitation services. This study aims to identify the relationship of migration on the quality household sanitation facilities level in Jakarta. This study uses multinomial logistic regression as an analytical method by applying three migration relationships models to the quality level of household sanitation facilities. The source for the analysis is Susenas microdata of 2017. Research findings specifically indicate a significant influence of recent migration on the quality level of household sanitation facilities. It confirmed that migrant households who have stayed longer (migrant lifetime and non-migrant recent) have the opportunity to safe sanitation facilities. Similar opportunities apply to recent migrants with characteristics of older age, higher education, and high expenditure. Conversely, this study found the percentage of households within the lowest category that is identical to the lease housing status with improper living conditions. To reduce the number of the latter category, the local government needs preventive planning in dealing with migration flow. Therefore, migrants can have a comfortable life in the city.

Elasticities of Food Demand in Germany – A Demand System Analysis Using Disaggregated Household Scanner Data

This paper presents price and income elasticities of food demand for Germany. Using disaggregated household scanner data and the Quadratic Almost Ideal Demand System (QUAIDS). The QUAIDS is modified to account for censoring and include household demographics. Furthermore, a two-stage budgeting approach is used to more accurately reflect households’ purchasing behaviour. Having disaggregated data also allowed to include convenience aspects into the demand system. High expenditure elasticities are found for fruits and nuts and meat, fish and eggs. The highest own-price elasticity is found for beverages. At the second stage, the bread toppings group reveals new insights into demand relations between cold cuts, cheese and other spreads. Cold cuts have both the highest expenditure and own-price elasticity. Cross-price elasticities indicate mostly complementary relations between cold cuts and other bread toppings. Comparing different income groups shows that expenditure elasticities of raw foods or basic ingredient foods tend to decrease as income increases, whereas expenditure elasticities of foods that require minimal or no preparation tend to increase with income. In conclusion, this study stresses the need for regularly updated elasticities of food demand that reflect up-to-date consumption behavior.

Impact of Covid-19 on tourism-based livelihood of people in Small Island Developing Nation: Case study of A & N Islands, India

In the past few decades, the tourism sector has emerged as a significant economic activity in island nations, particularly in tropical regions. However, most of the tropical islands face similar constraints. National and international tourists visit the SIDS including A & N Islands and contribute to the GDP to significant share. The Covid-19 outbreaks in SIDs including A & N islands shows that number of people infected were less as compared to metros or big cities. However, tourism activities completely stopped due to lockdown resulting in decreasing tourist’s arrival, declined GDP and per capita income of SIDs to greater extent. The information gathered from various sources, mass media and net analysed and interpreted in this chapter. Due to Covid-19 tourist’s arrival declined which has serious consequences on the livelihood of islander. Our analysis revealed A&N Islands deficit in energy available at from different sources by 18.26%. However, they are surplus in protein. The burden of high expenditure coupled with poor infrastructure makes them more vulnerable in the circumstance of pandemic outbreaks. This outbreak has created the question of survival due to loss of jobs, halted economic activates, psychological, health unrest and livelihood threats among the depending people of these Island nations. Therefore, government interventions and subsidized package is very much essential to revive the tourism industry.

Maschinelles Lernen zur Prognose von Auftragskennzahlen/Machine learning for the forecasting of key figures of customer orders

Prognosen bilden oft die Grundlage für Entscheidungen in der Produktion. Heute werden solche Voraussagen meist erfahrungs- oder modellbasiert getroffen. Bei komplexen Systemen stößt das an die Grenzen der Zuverlässigkeit oder ist mit hohem zeitlichen Aufwand verbunden. Klassierungsmethoden des Maschinellen Lernens versprechen dafür Lösungen. Automatisch erstellte Entscheidungsbäume können eine Möglichkeit sein, echtzeitnah Prognosen für Kennzahlen in der Produktion zu erstellen.   Forecasts often form the basis for decisions on the shop floor. Today, forecasts in production are mostly derived from personal experience or digital models. With complex systems, this approach reaches the limits of reliability or is associated with a high expenditure of time. Classification methods of machine learning promise solutions for this. Automatically generated decision trees can be a possibility to generate real-time forecasts for key figures in production.

In-Series U-Net Network to 3D Tumor Image Reconstruction for Liver Hepatocellular Carcinoma Recognition

Cancer is one of the common diseases. Quantitative biomarkers extracted from standard-of-care computed tomography (CT) scan can create a robust clinical decision tool for the diagnosis of hepatocellular carcinoma (HCC). According to the current clinical methods, the situation usually accounts for high expenditure of time and resources. To improve the current clinical diagnosis and therapeutic procedure, this paper proposes a deep learning-based approach, called Successive Encoder-Decoder (SED), to assist in the automatic interpretation of liver lesion/tumor segmentation through CT images. The SED framework consists of two different encoder-decoder networks connected in series. The first network aims to remove unwanted voxels and organs and to extract liver locations from CT images. The second network uses the results of the first network to further segment the lesions. For practical purpose, the predicted lesions on individual CTs were extracted and reconstructed on 3D images. The experiments conducted on 4300 CT images and LiTS dataset demonstrate that the liver segmentation and the tumor prediction achieved 0.92 and 0.75 in Dice score, respectively, by as-proposed SED method.