Supply-demand Relationship Research Articles

Bank Loan Strategy Based on Evaluation and Decision Model

In real life, in the face of small and medium-sized enterprises lacking mortgage assets, banks usually provide loans to enterprises with strong strength and stable supply-demand relationship according to credit policies, transaction notes information of enterprises and influence of upstream and downstream enterprises, and can give preferential interest rate to enterprises with high reputation and small credit risk. In order to better meet the application of the model in real life, the enterprise loan mode is introduced, and the risk assessment of each loan mode is carried out according to the actual situation. Finally, the fuzzy neural network model is introduced. The enterprise loan mode and three secondary indicators are taken as the input of the fuzzy neural network. Finally, a three-level index: loan risk assessment is obtained. Using Python program to preprocess the data, the original indicators in the given materials are extracted as the first level indicators, and then on the basis of the first level indicators, the main factor analysis and other methods are used to analyze the characteristics and scoring of the second level indicators. Then, the entropy weight is established to obtain the higher-level index loan tendency, and the loan propensity and enterprise reputation are used as parameters to establish the decision-making Model, step by step to get the bank’s quota allocation, interest rate, and finally get the bank allocation plan. In order to supplement the unknown data, this paper uses the known information to forecast the whole process of the unknown quantity, and then uses the data to forecast. First of all, it forecasts whether the default occurs, adds the predicted value to the whole eigenvalue, then forecasts the reputation rating, and finally completes the reasonable supplement of location data. TOPSIS model, SPSS software simulation machine learning, Fisher discriminant analysis

Identifying Hotspots of Overgrazing in Pastoral Areas: Livestock Mobility and Fodder Supply–Demand Balances in Nyangatom, Lower Omo Valley, Ethiopia

The Nyangatom, in South Omo, Ethiopia, are a group of agro-pastoralists whose access to land is affected by large-scale sugarcane development projects. An informed selection of appropriate livestock management measures by local communities requires a spatially explicit representation of prevailing and changing supply–demand relationships for livestock herds among the Nyangatom. This study addresses this caveat and identifies seasonal and location-specific ‘hotspots’ in Nyangatom, where fodder demand exceeds supply. Assessments of fodder production are based on primary data collected through focus group discussions, key informant interviews, and field observations. Overall, annual fodder availability is estimated at 508,967 tonnes against the requirement of 584,205 tons, resulting in a deficit of 12.9% annually after out-migration. Under the implementation of the Omo-V sugarcane project and climate change, the fodder supply deficit will worsen to 219,977 tonnes annually. The most critical dry matter hotspots are found in the western and central parts of Nyangatom near the Kibish River, which shows the highest livestock density. In contrast, better fodder supply is estimated around the southwestern and northeastern parts. Change in policy, the frequency of droughts, conflict, and the large-scale irrigation schemes-induced reduction of the Omo River floods are accounted for the changes. Thus, there are strong signals to the local community and government to collaborate to reduce the potential constraints that affect sustainable rangeland management and food security and ensure sufficient attention to the interest of the agro-pastoralists.

Measuring the Service Capacity of Public Facilities Based on a Dynamic Voronoi Diagram

The supply–demand relationship of urban public service facilities is the key to measuring a city’s service level and quality, and a balanced supply–demand relationship is an important indicator that reflects the optimal allocation of resources. To address the problem presented by the unbalanced distribution of educational resources, this paper proposes a dynamic Voronoi diagram algorithm with conditional constraints (CCDV). The CCDV method uses the Voronoi diagram to divide the plane so that the distance from any position in each polygon to the point is shorter than the distance from the polygon to the other points. In addition, it can overcome the disadvantage presented by the Voronoi diagram’s inability to use the nonspatial attributes of the point set to precisely constrain the boundary range; the CCDV method can dynamically plan and allocate according to the school’s capacity and the number of students in the coverage area to maintain a balance between supply and demand and achieve the optimal distribution effect. By taking the division of school districts in the Bao’an District, Shenzhen, as an example, the method is used to obtain a school district that matches the capacity of each school, and the relative error between supply and demand fluctuates only from −0.1~0.15. According to the spatial distribution relationship between schools and residential areas in the division results, the schools in the Bao’an District currently have an unbalanced distribution in some areas. A comparison with the existing school district division results shows that the school district division method proposed in this paper has advantages. Through a comprehensive analysis of the accessibility of public facilities and of the balance of supply and demand, it is shown that school districts based on the CCDV method can provide a reference for the optimal layout of schools and school districts.

Optimal pricing and production strategies for two manufacturers with industrial symbiosis

This paper focuses on studying how the industrial symbiosis members determine the optimal production plan after weighing the demand for core products and the supply and demand of waste. Considering the government waste disposal tax, optimal solutions of two manufacturers who have the potential in achieving waste symbiosis are derived from different supply-demand relations of waste. Then, we find that the relationship between supply and demand is mainly affected by the price sensitivity of the customer in the downstream market, and the optimal solutions of each manufacturer are also affected by the other's cost factors or price sensitivities under certain circumstances. Besides, by comparing the optimal solutions under the mode of zero waste reduction, mode of waste reduction, and mode of industrial symbiosis, we find that when the customer in the downstream market is relatively sensitive to the price, and the price of raw materials is relatively high, both manufacturers are willing to choose industrial symbiosis. Also, a numerical study is introduced to understand the impact of waste reduction and industrial symbiosis on the total social welfare and the environmental benefit from the perspective of the government.

Markets, Cultures, and the Politics of Value: The Case of Assisted Reproductive Technology

Assisted reproductive technology (ART) is a global market engaging a variety of local moral economies where the construction of the demand–supply relationship takes different forms through the operation of the politics of value. This paper analyzes how the market–culture relationship works in different settings, showing how power and resources determine what value will, or will not, accrue from that relationship. A commodity’s potential economic value can only be realized through the operation of the market if its cultural status is seen to be legitimate. At the same time, local moral economies and their associated social orders are potentially susceptible to the destabilizing implications of new commodities. The formal or informal organization of power relationships in the market–culture interaction can enable potential value to become manifest and tangible over time or block its path. The interaction is steered through national institutional sources of cultural authority embedded in state and religion, where the visible contest in the politics of value is conducted. Increasingly, that interaction finds its expression in transnational institutions of governance where the struggle for control of the cultural agenda reflects the global nature of the ART market.

Mathematical modeling and heuristic approaches to optimize shared parking resources: A case study of Beijing, China

Sharing unused private parking spaces with public travelers can improve parking resource utilization. However, a challenge lies in the approach to maximize the utilization of such parking resources without time colliding with the needs of these resources between public travelers and owners over time. To address this challenge, this study presents a methodology aiming at efficiently obtaining the optimum matches between parking supply and demand with time-window constraints. First, a framework for shared parking is proposed to guide the development of the computation algorithms. Based on this framework, a mixed-integer nonlinear programming model and its solving algorithms are then developed to maximize the spatial-temporal utilization. Further, the feasibility and validity of the proposed model and algorithms are tested by empirical data collected in our real-world. The results indicate that the non-increased demand durations and non-descending supply durations (NI-ND) heuristic performs the best under both small- and large-scale supply and demand settings with extremely high solution efficiency. Moreover, the solutions based on the NI-ND algorithm are very close to the optimal objective, implying that the proposed shared parking allocation method based on the dual bin-packing principle is an effective way to address the realistic supply–demand relationship of parking resources under time-window constraints. Finally, the potential of shared parking is estimated based on the statistics of the number of private parking spaces and parking behavior data in Beijing.

Bank Credit Strategy Model Based on AHP-Fuzzy Comprehensive Evaluation

Credit risk control and credit strategy formulation of medium and micro enterprises have always been important strategic issues faced by commercial banks. Banks usually make corporate loan policies based on the credit degree, the information of trading bills and the relationship of supply-demand chain of the enterprise. In this paper, we established the AHP-Fuzzy comprehensive evaluation model for quantifying enterprise credit risk. Based on the relevant data of 123 enterprises with credit records, the credit strategy is formulated according to the three indicators of enterprise strength, enterprise reputation and stability of supply-demand relationship. This paper also combines the credit reputation, credit risk and supply and demand stability rating in order to establish the bank credit strategic planning model to decide whether to lend or not and the lending order. The conclusion shows that, under the condition of constant total loan amount, the enterprises with the highest credit rating should be given priority. Then, combined with the change of customer turnover rate with interest rate, we take the bank's maximize expected income as objective to calculate the optimal loan interest rate of different customer groups.

N-GRAM KULLANILARAK HAMMADDE ARZ-TALEP İLİŞKİSİNİN ANALİZİ: KROM MADENİ ÖRNEĞİ

Depending on the strategic importance of the substance, the supply-demand relationship of the raw material has become remarkably important during the times of crisis. The type of raw material played an important role in this relationship from time to time. After the Industrial Revolution, some raw materials, especially in heavy industry and defense industry, became important due to their geography and the effect of the reserve amount. In some cases, issues such as supply security of these raw materials have caused serious security concerns and have even threatened the national survivability of some countries. Today, especially energy raw materials have undertaken such a mission, especially after the Industrial Revolution, industrial raw materials have been important during the two great war periods and the cold war period. One of these raw materials is chrome. In this study, the n-gram analysis of “chromium”, which is one of the important raw materials after its discovery in the 18th century, was carried out and its relationship with the important political events was analyzed with n-gram analysis. Using the interface provided by Google for the N-gram analysis, an analysis covering approximately the last hundred and fifty years was carried out in March 2020. The analysis was carried out within the scope of the corpus from 1850-2018, which is registered in the "Google books" database. As the browsing language, English, German, Russian, Italian and French languages were chosen, which were widely used in this period and available in the google n-gram analysis interface. 3 was chosen as the softening factor. As a result of the analysis, it was observed that the results of the n-gram analysis showed a high frequency of use in the two major world wars, especially where the raw material was an important factor. Chromium, which has an important place in the defense industry and has an important use in the steel industry and armored material production, has seen a considerable frequency of use in these periods. It was observed that the N-gram values also gave remarkable results for periods corresponding to important military-political events after the World War II. When all the findings are evaluated together, it is concluded that it is possible to investigate the relationship of raw material supply-demand relations with political events by n-gram analysis.

Research on operational optimization of urban multi-energy flow system considering seasonal peak shaving of natural gas

In order to alleviate the shortage of natural gas supply in winter, relevant policies have been issued to promote the construction of gas peak-shaving and storage facilities. Large-scale gas storage can transfer the supply-demand relationship of natural gas in time sequence, which has great potential in improving the economy and reliability of urban multi-energy flow system. Facing this issue, this paper proposes a mid- and long-term energy optimization method for urban multi-energy flow system that considers seasonal peak shaving of natural gas. Firstly, the energy supply and demand features of different energy subsystems are analyzed. Then, a network model of the electricity-gas-heat multi-energy flow system is established. Considering time-of-use electricity price mechanism and the seasonal fluctuations of natural gas price, a mid- and long-term energy optimization model maximizing the annual economic revenue is established. The alternative direction multiplier method with Gaussian back substitution (ADMM-GBS) algorithm is used to solve the optimal dispatch model. Finally, the proposed method is verified by employing the actual data of the demonstration zone in Yangzhong City, China. The simulation results show that the proposed method is effective.

Predicting Emergency Medical Service Demand With Bipartite Graph Convolutional Networks

Emergency medical service (EMS) plays an essential role in increasing survival rates as it provides first aid to victims of life-threatening emergencies. However, unbalanced EMS supply-demand distribution in the metropolis may cause a shortage of accessible EMS resources and delay the first aid treatment. There is an urgent need to discover the hidden EMS supply-demand relation, predict the incoming EMS demand, and take precautions against unexpected emergencies. This study assumes that EMS demand correlates with population demographic data, regional socioeconomic factors, and hospital conditions. To model these correlated factors, we represent Tokyo's ambulance record data as a hospital-region bipartite graph and propose a bipartite graph convolutional neural network model to predict the EMS demand between hospital-region pairs. Our approach achieves 77.3% - 87.7% accuracy in binary demand label prediction task. It significantly outperforms traditional machine learning algorithms, statistical models, and the latest graph-based methods. Finally, we use a case study to show the significance of EMS demand forecasting, proving that our approach can contribute to public health emergency management by making EMS predictions.

The role of China in the East Asian natural gas premium

East Asia is the world's main natural gas importing region, in which China became the world's largest gas importer in 2018. However, China's natural gas imports are subject to a high premium level, which increases the gas import cost and negatively impacts China's energy structure transformation and economic development. This paper analyses the East Asian natural gas premium (EANGP) and China's role in it. First, this study estimates the gas premium level in East Asia; second, it analyses the impact of five factors – the oil price, supply-demand relationship, supply risk, trade mode and transport mode – on EANGP in different periods. Third, it analyses the interaction between China and EANGP. Finally, it forecasts EANGP in 2040 and its impact on China's gas import price. The conclusions are as follows: EANGP in 2005–2016 showed a trend of rising first and then falling, with a peak in 2012 of approximately 15.4 $/MMBtu; the main influencing factors of EANGP are different in different periods; in general, the supply-demand relationship is one of the most important influencing factors for the premium; before 2010, China suppressed EANGP, after which it promoted EANGP; China's GDP (gross domestic product) loss caused by the premium in 2010–2016 was approximately 330 billion yuan; and the forecast results show that EANGP will rise by 0.54–0.86 $/kg in 2040. At last, some recommendations are provided as follows: promote the market-based pricing mechanism; accelerate the development of unconventional gas; balance the relationship between energy structure transformation and gas premium; improve gas import resilience; and promote the gas short-term and spot trade.

A retrospective study on prescription pattern of drugs used in myocardial infarction in a South Indian Tertiary Care Hospital

Introduction: Angina pectoris, the primary symptom of ischemic heart disease, is caused by transient episodes of myocardial ischemia that are due to imbalance in the myocardial oxygen supply-demand relationship. Various drugs are prescribed which keeps changing during every decade. This study tries to know the current trends in prescription in myocardial infarction. Material and Methods: About108 patients’ data were collected during the period year 2004-2008 admitted with acute myocardial infarction and data were entered in preformed proforma and were analysed by calculating percentage. Statistical Analysis:Data were interpreted in terms of percentage. Results: The percentage of prescription pattern of various classes of drugs was 91, 66.7, 63.9, 35.2, 31.5, 25, 15.7, 13.9, 12.9, 9.3 and 7.4 for Antiplatelet Agents, Antianginal drugs, Hypolipidemics, Beta receptor antagoist, Angiotensin Converting Enzyme Inhibitors (ACEI), Diuretics, Anticoagulants, Calcium Channel Blockers (CCBs), Angiotensin Receptor Blockers (ARBs), Fibrinolytics and Inotropic drugs respectively. Most of the patients received Aspirin as well as Clopidogrel. Conclusion: Antiplatelet drugs was the most commonly prescribed and alpha blockers were prescribed least as evident from our study. Keywords:Myocardial Infarction, Prescription Pattern, Retrospective Study, Tertiary Care Hospital.

Research on the Reasonability of IPO Pricing and the Causes of High Return under the Registration System of the STAR Market

The problems of inflated pricing and excessive returns of new shares on the Chinese stock market have been widely criticized by the market, and have been attributed to the imbalance between supply and demand caused by the control of the number and scale of issuing companies by the approval system. Therefore, with the establishment of the STAR Market, the registration system of the pilot is expected to improve the supply-demand relationship and the pricing efficiency. However, judging from the practice in the past year since the opening of the STAR Market, the IPO price, the initial P/E ratio and the return rate of the new shares have repeatedly set new highs on China’s stock market. The problem once again aroused the concern and worries of all parties. In order to investigate the rationality of IPO pricing of STAR companies under the registration system, this article adopts reverse engineering thinking first based on whether IPO pricing can effectively reflect the company’s intrinsic value, and uses the prospectus and the underwriter’s IPO data when the IPO price is known. Combining with the STAR Market attribute variables, we use the stochastic frontier model to calculate the potential effective frontier price. This price is an unbiased estimate of the intrinsic value, so as to determine the rationality of IPO pricing and its relationship with the effective frontier. Firstly, it is found that there is a significant lower boundary on IPO pricing, which indicates that there is no underpricing problem like mature markets but a significant overvaluation relative to its intrinsic value, with an overvaluation degree of 63.7%. A relatively high bubble appears in the issuance price of new shares. Therefore, it is not unreasonable to worry about the falsely high initial price of STAR companies. Secondly, we also test the cause of the higher return rate of new shares after the listing of companies. It is found that investors in the secondary market are obviously not paying enough attention to important indicators such as company fundamentals, technological innovation attributes and the other important indicators. The irrational speculation is still serious. This is the important reason for the stock price bubbles and the high returns on the secondary market. The research in this article shows that there are still major deficiencies on the pricing ability, self-restraint and the maturity level of investors for financial intermediaries and issuers. Therefore, the quality of research reports on the investment value to underwriters and the ethical restraint on issuers, underwriters and institutional investors should be the focus of the supervisory authorities.

Multi-Agent Based Optimal Scheduling and Trading for Multi-Microgrids Integrated With Urban Transportation Networks

This paper investigates multi-period optimal energy scheduling and trading for multi-microgrids (MMGs) integrated with an urban transportation network (UTN). Specifically, an optimization based multi-period traffic assignment model is built to characterize the vehicular flows considering rational drivers with time-flexible travel demand. Meanwhile, each microgrid (MG) is modeled to independently schedule its operation by trading energy with other MGs and charging electric vehicles (EVs) with its fast charging stations (FCSs). The EV charging prices could further influence traffic flow distribution of the UTN. To co-optimize the coupled system, a multi-agent based optimal scheduling and trading scheme is proposed, which is facilitated by peer-to-peer communication of transactive energy information among the agents, thus can preserve privacy of the market participants. Within the scheme, the MGs and EVs take their optimal decisions in response to MG energy trading and EV charging prices updated via multi-bilateral negotiation based on the demand-supply relationship. When an equilibrium of the coupled system is reached, each MG can minimize its operation cost, and the traffic condition could also be improved. Case studies of a MMG system coupled with a double-ring UTN are conducted to validate the effectiveness of the scheme.

Research on Bilateral Co-construction of Transportation Informationization Supply and Demand Based on Big Data Analysis

With the development of science and technology in our country, network information technology has been continuously integrated into transportation. Chinese traffic informatization has achieved great results, which provides better traffic services for people. However, compared with developed countries, Chinese transportation informatization still has many problems to be solved. Based on this, this article uses computer big data technology to first analyze the significance of traffic management informationization. Then, this paper analyses the unbalance of supply-demand relationship and its influencing factors. Finally, some suggestions are put forward.

Modeling and Simulation of a Multi-agent Green Supply Chain Management System for Retailers

In recent years, the agent technology has been successfully applied in supply chains, thanks to its excellent interactivity, proactivity, and autonomy. However, the existing research on multi-agent green supply chain (GSC) stops on the strategic and tactical levels, failing to implement the relevant supply chain models. To overcome the limitation, this paper designs a multi-agent GSC management system for retailers, with the aim to obtain scientific collaboration strategies among multiple agents and to make effective logistics decisions for the supply chain. Firstly, a 3-layer hierarchical evaluation index system (EIS) was established under the framework of the multi-agent GSC management system. Next, the authors modeled the supply-demand relationship and dynamic game of multi-agent GSC, and discussed the cooperation and negotiation models among multiple agents. Experimental results show that the proposed multi-agent GSC management model is highly effective. The research findings provide a reference for the application of multi-agent technology in other types of supply chain enterprises.

Blockchain and AI-Based Natural Gas Industrial IoT System: Architecture and Design Issues

With the development of energy interconnection and increasing consumption of natural gas, the distributed energy supply and trusted-transaction- based natural gas Industrial Internet of Things (IIoT) has become one of the hottest research spots in the energy industry. This article puts forward natural gas IIoT architecture based on blockchain and AI, in order to address the defects of centralized energy supply architecture. The proposed architecture is introduced in detail from three aspects of infrastructure, side-chain of natural gas block based on data dimension, and backbone of natural gas block based on value dimension. Then the design issues on the integration existence of blockchain and AI in an actual natural gas IIoT scenario are discussed, including data association and interaction of blockchains, trusted identity authentication and management, energy source and virtual currency transformation, and so on. Next, an LSTM-based natural gas load prediction model is proposed by virtue of AI technology. The transaction model based on natural gas value and supply-demand relationship is also proposed by choosing the common natural gas application scenarios and taking advantage of blockchain technology. Experiments show that the proposed models can predict demand and output load of natural gas, while achieving the balance of interests of natural gas suppliers, users, and market in the transaction scenarios.

A novel grey Riccati–Bernoulli model and its application for the clean energy consumption prediction

Accurately forecasting energy demand can better predict future changes in energy demand. This study is aimed to develop more accurate clean energy prediction model from two perspectives of the modeling mechanisms and improvements on the model structure. For this purpose, a novel Riccati–Bernoulli differential equation is established through social practice theory, supply–demand relationship, and the preference of consumption in the energy economy. Considering the limited information in observation data, this differential equation is then transformed into a grey Riccati–Bernoulli model (GRBM(1,1)) according to the differential information principle. With the response function solved on the basis of polynomial equation theory and the power exponent optimized combining the modified flower pollination algorithm, the main process of GRBM(1,1) can be summarized. The four validation examples are provided for confirming the effectiveness and reliability of the new model by comparing with other existing models. Finally, the proposed model is employed to estimate and forecast the clean energy consumption in China and India. The results show that the proposed model demonstrates better estimation in all cases and efficiency in short-term clean energy consumption forecasting. Therefore, by using this optimum model, China’s preference coefficient in total energy consumption is 0.7103, lower than that of India (0.8799), and China’s preference coefficient in clean energy consumption is 0.9665, higher than that of Indian (0.7155), which means China has less interest to increase total energy consumption but more interest in popularizing the clean energy.

Exploring Connections among Ecosystem Services Supply, Demand and Human Well-Being in a Mountain-Basin System, China.

Stakeholder perception and supply–demand relations are the main challenges and future directions for research on ecosystem services (ES). Based on spatial data and statistical data, we mapped eight key ES supply between 2005–2015 in the Huailai mountain-basin area. Using data from 507 survey questionnaires, we identified the ES demand and the public perceptions of the changes in ES. In addition, we also reveal the characteristics of the spatial distribution of ES demand, analyze the spatial supply–demand matching of ES, and explore the relationships between ES supply–demand and human well-being. From 2005 to 2015, a general upward trend was observed in the supply of four types of product provisioning services, which is different from the trend perceived by the stakeholders. An increasing trend was observed for carbon sequestration and forest recreation, which was in line with the perceptions of change. A spatial mismatch existed between the ES demand and supply, whereby the supply of carbon sequestration, soil conservation, habitat quality, and forest recreation services exceeded the demand in townships in the mountainous and hilly regions. On the other hand, townships located in the valley plains experienced a high imbalance between the demand and the supply. For the four types of product provisioning services, most towns and villages showed a balance in demand and supply. Linking ES supply–demand and human well-being showed that a threshold may exist in the supply–demand imbalance of regulating and supporting services before it begins to impact human well-being. Our study would enrich the theory and methodology research on relationships between ecosystem services and human well-being, and support knowledge to land allocation and management.

Spatial analysis of land-use management for gully land consolidation on the Loess Plateau in China

Abstract How to balance the relationship between man and land has always been an essential problem for human beings. In 2011, the Gully Land Consolidation (GLC) project was implemented on the Loess Plateau in China. The aim of this project was to improve the living conditions of the local people through land-use management while ensuring the integrity of the environment. As little is known about the GLC, the objective of this study is to provide insight into the effects and potential problems associated with this project. We carried out a polygon-based interpretation of five land types before and after this project was implemented using multiple high-resolution satellite images (0.46 m) and 0.1 m resolution UAV photographs. The ecological footprint method was applied to analyze the supply-demand relationship between human and the land to assess the impact of engineering. Both the ecological capacity value and the area of arable land have increased due to land-use management, suggesting that this project had a positive effect in the area studied. The correlation between the spatial distribution and the transformation of arable land types was also examined to evaluate the efficiency of this project. Six factors were statistically analyzed to identify the most typical characteristics of the unused land distribution role. The existence of unused arable land is concentrated at the head of each gully. It is mainly affected by engineering, but it is also affected by the scale of the project. As a result, we identified two effects of the GLC. Although there were problems after this project, for instance, the unused rate of land became higher as the arable land increased, this project can satisfy human needs by increasing the area and quality of the arable land while improving the carrying capacity of the ecological environment.