Macro Information Research Articles

A potential optical approach for diagnosis of the local magnetic field near the surface of the first wall/divertor tiles by Zeeman effect using polarization-resolved laser-induced breakdown spectroscopy

Magnetic field measurement is the basic diagnosis to obtain the physical engineering parameters of magnetic confinement fusion device and the macro information of plasma discharge. The real-time diagnosis of magnetic field distribution near the plasma-facing components (PFCs) surface provides the important information on the migration and transport model of key elements. In this work, a remote, in-situ approach for the magnetic field measurement near the surface of PFCs by the polarization-resolved laser-induced breakdown spectroscopy (LIBS) based on Zeeman effect is proposed and implemented. The Zeeman characteristics of the emission spectra of laser-induced W, Mo and C plasmas were verified in the laboratory by using different magnetic field configurations. According to the polarization characteristics of the Zeeman sublines of the LIBS spectrum, the intensity and direction of the external local magnetic field were successively identified by using a linear polarizer. Subsequently, a linear array fiber was utilized to determine the polarity of the external magnetic field. And finally, the magnetic field intensity near the lower edge surface of the tungsten baffle of the Experimental Advanced Superconducting Tokamak (EAST) upper divertor was measured when the field coils were demagnetized. This method can supplement the experimental data near the PFCs for the magnetic field configuration of the magnetic confinement fusion device and provide a reference for the wall element analysis model diagnosed by LIBS.

Asset allocation combining macro and micro information–Empirical test based on entropy pool model

Macroeconomic environment is an essential factor affecting asset return, but it is difficult to construct portfolios using macro information quantitatively in traditional models. In this paper, we extend the Black-Litterman framework to build an efficient portfolio by using views containing macro information and prior market distribution reflecting micro information. In order to enhance the model's flexibility, entropy pool method is used. Empirical evidence shows that with reasonable allocation across economic stages, the newly constructed portfolio enjoys a higher level of Sharpe ratio compared to classic allocation models, and the result is robust under different circumstance.

Coupled dynamics of information propagation and emotion influence: Emerging emotion clusters for public health emergency messages on the Chinese Sina Microblog

A major distinction between the COVID-19 pandemic and previous pandemics is the significant role of social media platforms in shaping public adherence to non-pharmaceutical interventions and vaccine acceptance. The information propagation and individual emotion communication relevant to public health messages have been promoted in the Chinese Sina Microblog, one of the most popular social platforms in China, through two major interaction mechanisms, “commenting” and “forwarding”. Here, we develop a hybrid system of macro information propagation dynamics Susceptible-Commenting-Forwarding-Immune model coupled with the micro emotion influence dynamics Hegselmann-Krause model, termed HK-SCFI coupled system, and reveal the mechanisms of individual emotion infection using the commenting and forwarding data (including time point and content) publicly available on public opinion events in the Chinese Sina Microblog. We demonstrate how our data-driven and model-informed analysis can provide quantitative insights into the co-evolution mechanism of individual emotion during information propagation, describe the emergency of emotion clusters, and predict individual emotion evolution trends. Our study shows that multiple interactive mechanisms can facilitate the propagation of public messages and shape public sentiment through online social networks. Our modeling framework can potentially be a tool for designing effective propagation strategies during public health emergencies.

Central bank communication and macro information in analyst forecasts: Evidence from Chinese listed firms

AbstractThe paper examines how central bank communication affects the macro information in analyst forecasts. Using quarterly data of Chinese‐listed firms from 2007 to 2018, we find that richer and more frequent central bank communication increases the macro information contained in analyst forecasts. This effect is realized through the employment of in‐house economists by security firms. In addition, we document that the effect of central bank communication on macroeconomic information in analyst forecasts is more salient under a contractionary monetary policy regime, during a bear market, or when the economic policy is more uncertain. We also show that analyst forecasts are more sensitive to central bank communication when firms that they follow are state‐owned enterprises, have larger leverage ratios, or are located in more developed regions. In addition, analyst forecasts are more susceptible to central bank communication when the communication is in an informal oral format, when the public has more trust in the credibility of the central bank communication, and when the central bank pays more attention to expectation management after 2010. Finally, we show that richer and more frequent central bank communication also improves the accuracy of analyst forecasts.

Resampling approach for one-Class classification

The performance of a classification model depends significantly on the degree to which the support of each data class overlaps. Successfully distinguishing between classes is difficult if the support is similar. In the one-class classification (OCC) problem, wherein the data comprise only a single class, the classifier performance is significantly degraded if the population support of each class is similar. In this study, we propose a resampling algorithm that enhances classifier performance by utilizing the macro information that is most easily obtainable in these two problem situations. The algorithm aims to improve classifier performance by reprocessing the given data into data with mitigated class imbalance through raking and sampling techniques. This performance improvement is demonstrated by comparing representative classifiers used in the existing OCC problem with traditional binary classifier models, which are unavailable on a single-class dataset.

Scientific basis for achievement of sustainable competitiveness of a tourist destination

The article examines the scientific basis for increasing the competitiveness of tourist regions (destinations) of Uzbekistan by ensuring sustainable development. Production and consumption models based on three principles of sustainable tourism development; “7M Concept” of competitiveness of a tourism region; three-dimensional system of internal tourism are scientifically substantiated. 5 models of the process of forming tourist flows in the tourism market of Uzbekistan from the point of view of demand are proposed. A forecast has been developed for the types of tourism services the need for will increase and decrease by 2030 in Uzbekistan. The macro information space of the country has been studied, which consists of 1) formal macro information spaces, 2) partially formal macro information spaces and 3) informal macro information spaces. A methodology has been developed for classifying tourism zone brands by regional scale, brand composition and logo shape, as well as a tourism region brand formula. Five CIS countries were comparatively analyzed, combining the methodology of brand classification by composition, the method of determining the quality of the offer in the slogan, the result of the regional brand strategy and the rating of national brands to determine the competitiveness of the tourism brand of Azerbaijan, Armenia, Kazakhstan, Kyrgyzstan, Tajikistan and Uzbekistan.

Limited investor attention and biased reactions to information: Evidence from the COVID-19 pandemic

We find that the COVID-19 pandemic increases (decreases) stock return sensitivity to market-wide (firm-specific) news, which is associated with return reversals (delayed reactions). These results are consistent with limited investor attention and investors paying heightened (reduced) attention to macro (micro) information after the outbreak. There are more biased reactions when the epidemic spread is higher, to good news than bad news, for firms headquartered in pandemic epicenters, and for larger stocks. We also find higher (lower) imbalanced trading, information flow, and price efficiency associated with market-wide (firm-specific) news during the pandemic.

An Efficient Parallel Framework for the Discrete Element Method Using GPU

The discrete element method (DEM), a discontinuum-based method to simulate the interaction between neighbouring particles of granular materials, suffers from intensive computational workload caused by massive particle numbers, irregular particle shapes, and complicated interaction modes from the meso-scale representation of the macro information. To promote the efficiency of the DEM and enlarge the modelling scales with a higher realism of the particle shapes, parallel computing on the graphics processing unit (GPU) is developed in this paper. The potential data race between the computing cores in the parallelisation is tackled by establishing the contact pair list with a hybrid technique. All the computations in the DEM are made on the GPU cores. Three benchmark cases, a triaxial test of a sand specimen, cone penetration test and granular flow due to a dam break, are used to evaluate the performance of the GPU parallel strategy. Acceleration of the GPU parallel simulations over the conventional CPU sequential counterparts is quantified in terms of speedup. The average speedups with the GPU parallelisation are 84, 73, and 60 for the benchmark cases.

Remote Sensing Colorization Based on Bidirectional Macro-Micro Adaptive Enhancement Network

The demand for re-colorization of remote sensing images is urgent since image quality is extremely deteriorated by haze or other noises occurring in the atmospheric layer. The most challenging issue is to restore the color information with respect to preserving spatial consistency as well as to obtain object salience in context with extremely imbalanced space structure, where the former requires learning stable macroscopic semantics while the latter needs to recover microscopic pixels. In this paper, we propose a Bidirectional Macro-Micro Adaptive Enhancement (BMMAEnet) framework by adopting three modules, i.e., the Downward Micro Enhancement (DME) module, the Upward Adaptive Macro Enhancement(UAME) module, and Macro-Micro Balance (MMB) module. Firstly, the DME module is designed by adding micro details as well as suppressing macro context during the multi-branch downsampling process to supplement missing pixel details. Secondly, UAME is proposed by adaptive selecting proper level of features during multi-branch upsampling process to strengthen macro semantic constraints. In addition, MMB is designed by embedding attention-guided local details and global semantics into the decoding features to balance micro and macro information within each branch. Comprehensive comparison and ablation experiments are implemented and verify the proposed method performs overpass SOTA methods not only in pixel color value restoration performance but also in human perceptive understanding.

FordNet: Recommending traditional Chinese medicine formula via deep neural network integrating phenotype and molecule

Traditional Chinese medicine (TCM) formula is widely used for thousands of years in clinical practice. With the development of artificial intelligence, deep learning models may help doctors prescribe reasonable formulas. Meanwhile, current studies of formula recommendation only focus on the observable clinical symptoms and lack of molecular information. Here, inspired by the theory of TCM network pharmacology, we propose an intelligent formula recommendation system based on deep learning (FordNet), fusing the information of phenotype and molecule. We collected more than 20,000 electronic health records from TCM Master Li Jiren's experience from 2013 to March 2020. In the FordNet system, the feature of diagnosis description is extracted by convolution neural network and the feature of TCM formula is extracted by network embedding, which fusing the molecular information. A hierarchical sampling strategy for data augmentation is designed to effectively learn training samples. Based on the expanded samples, a deep neural network based quantitative optimization model is developed for TCM formula recommendation. FordNet performs significantly better than baseline methods (hit ratio of top 10 improved by 46.9% compared with the best baseline random forest method). Moreover, the molecular information helps FordNet improve 17.3% hit ratio compared with the model using only macro information. Clinical evaluation shows that FordNet can well learn the effective experience of TCM Master and obtain excellent recommendation results. Our study, for the first time, proposes an intelligent recommendation system for TCM formula integrating phenotype and molecule information, which has potential to improve clinical diagnosis and treatment, and promote the shift of TCM research pattern from “experience based, macro” to “data based, macro-micro combined” as well as the development of TCM network pharmacology.

A robust photo‐based PM2.5$_{2.5}$ monitoring method by combining linear and non‐linear learning

Good health is pursued by people all over the world. However, the continual industrialisation has led to more and more atmospheric contamination, and PM 2.5 has caused serious harm to our life safety and living environment. Without increasing the cost of sustainable industrial production, more and more attention has been paid to the related researches on improving PM 2.5 monitoring, prevention and control level. Therefore, it is extremely urgent to establish a robust PM 2.5 monitoring model that can adapt to a variety of scenarios, not only in local places like campuses but also in wide area like city. Existing work has proven that PM 2.5 monitoring can be achieved by means of photos. But experiments show that the stated-of-the-art methods are far from ideal for PM 2.5 monitoring when the author tested the performance in two public datasets. To solve the aforesaid issue, this paper ulteriorly proposes a novel photo-based PM 2.5 monitoring model, which fuses the results of existing methods by firstly using the weighted average based on the least absolute shrinkage and selection operator regression for learning the basic linear component, secondly using the support vector regression for learning the non-linear residual component, and finally incorporating the above two outputs to infer the final PM 2.5 concentration. The main contributions and innovations of this paper are embodied in: (1) the innovative use of image quality assessment model to extract 9 features for PM 2.5 monitoring, (2) separately extract macro information and micro information from PM2.5 pictures, (3) two newly-established large-scaled datasets are applied to verify the effectiveness and robustness of the proposed PM 2.5 monitoring model. Experiments show that on the latest PM 2.5 datasets (local and wide), the proposed model has achieved high performance and demonstrated strong robustness.

Imbalanced Learning with Oversampling based on Classification Contribution Degree

AbstractImbalanced datasets exist commonly in the real world, which leads to poor performance of general machine learning models because of skewed class distribution. To address the data‐imbalance problem, a novel oversampling method based on classification contribution degree, called OS‐CCD is presented. First a new concept, classification contribution degree, is established based on micro and macro information extracted from raw datasets. With the classification contribution degree, OS‐CCD enables positive samples near the class boundary and located in an area with high density of positive samples to generate more synthetic samples than others. Furthermore, the neighbor selection for oversampling is no longer random but in the light of a selected probability. Experimental results on 12 benchmark datasets substantiate that four commonly used classifiers with the oversampling method outperform those with six popular oversampling methods in terms of accuracy, F1‐score and AUC.

Eye in the sky: Private satellites and government macro data

We develop an approach to identify whether recent technological advancements—such as the rise of commercial satellite-based macro estimates—can provide an alternative to government data. We measure the extent to which satellite estimates affect the value of a government macro announcement using its asset price impact. Our identification relies on cloud cover, which prevents satellites from observing economic activity at a few key hubs. Applying our approach, we find that some satellite estimates are now so effective that markets are no longer surprised by government announcements. Our results point to a future in which the resolution of macro uncertainty is smoother and governments have less control over macro information.

МЕХАНІЗМИ ТА РЕЖИМИ УПРАВЛІННЯ АГРОЕКОСИСТЕМНИМИ ПОСЛУГАМИ В БОЛГАРІЇ

The products and the variety of direct and indirect benefits that humans receive from nature and the various ecosystems are commonly known as ecosystem services. Agricultural ecosystems of different types and their specific “agro-ecosystem” services are among the most widespread in the world. In recent years increasing attention is given to the system of (“good”) governance as a key to achieving public, collective, corporate, and private goals in relation to conservation and improvement of (agro)ecosystem services. Nevertheless, in Bulgaria, like in many other countries, there are few studies on the specific mechanisms, modes, factors, and efficiency of agro-ecosystem survives governance. This article presents the initial results of a large-scale study on the governance of diverse ecosystem services in Bulgarian agriculture. Firstly, it identifies the type, amount, and importance of various ecosystem services maintained and “produced” by the Bulgarian farms. The study has found out that country’s farms provide a great number of essential ecosystem services among which provisioning food and feed, and conservation of elements of the natural environment prevail. Secondly, it identifies and assesses the efficiency and complementarities of specific modes and mechanisms of governance of ecosystem services used by agricultural holdings. The study has found out that a great variety of private, market, collective, public and hybrid modes of governance of farm activity related to agroecosystem services are applied. There is significant differentiation of employed managerial forms depending on the type of ecosystem services and the specialization of agricultural farms. Furthermore, the management of agroecosystem services is associated with a considerable increase in the production and transaction costs of participating farms as well as big socio-economic and environmental effects for agricultural holdings and other parties. The factors that mostly stimulate the activity of agricultural producers in Bulgaria for protection of (agro)ecosystems services are participation in public support programs, access to farmers’ advice, professional training, available information, and innovation received direct subsidies from the EU and national government, personal conviction and satisfaction, positive experience of others, long-term and immediate benefits for the farm, and integration with suppliers, buyers, and processors. The suggested holistic and interdisciplinary framework for analyzing the system of governance of agro-ecosystem services is to be further extended and improved, and more widely and periodically applied in the future. The latter requires systematic indepth multidisciplinary research in this new area, as well as the collection of original micro- and macro information on ecosystem survives, forms, efficiency, and factors of their management. The accuracy of analyzes is to be improved by increasing representativeness through enlarging the number of surveyed farms and related agents, applying statistical methods, special “training” of participants, etc. as well as improving the official system for collecting agricultural, agro-economic, and agrienvironmental information in the country.

Global Evidence on Unspanned Macro Risks in Dynamic Term Structure Models

There are mixed results on whether macro risks are spanned by the yield curve. This paper reviews the major arguments and takes a global perspective to obtain comprehensive evidence. We study a large cross-section of 22 countries, including both developed and emerging markets. Our regression evidence confirms that macro information provides explanatory power for bond excess returns on top of yield factors. This finding is particularly strong in emerging markets. However, from a mechanical perspective, discriminating between spanned and unspanned models when considering in-sample fit and term premium predictions makes no difference.

“DIGITAL DIVIDE” AS A FACTOR OF DESTINATION COMPETITIVENESS OF UZBEKISTAN’S TOURISM SPHERE

This study explains the concept of the "digital divide" as factor of competitiveness of the tourist regions of Uzbekistan, developed a map of the country's macro information space, and on the basis of the matrix of the tourist macro-information space, the directions of improving the tourist information space of the country are determined.

A GIS-based approach for high-level distribution networks expansion planning in normal and contingency operation considering reliability

This paper presents a novel methodology for the Distribution Networks expansion planning based on Geographic Information Systems. The proposed methodology combines Delaunay Triangulation with a Mixed-Integer Linear Programming model in a 2-steps approach. Secondary substations are grouped in primary substation clusters, considering both normal and contingency operation. Topological and electrical constraints have to be fulfilled as well as a given level of reliability; the mathematical formulation includes feeder and substation constraints. The proposed methodology is expected to be only the first step of the whole network expansion planning. Hence, it has to be able to give macro information that planners can refine in the following steps of the planning process. A numerical case study on a real network illustrates the effectiveness of the proposed approach for the Distribution Network expansion planning problem.

INFORMATION-ANALYTICAL SUPPORT OF THE MANAGEMENT PROCESS IN THE ENTERPRISE: STRUCTURE, ADVANTAGES AND DISADVANTAGES

In the process of modern enterprise management, information analysis support is an extremely important part. Information analysis support involves all aspects of enterprise management such as human resource, financial, production and sales. The purpose of this study is to explore the importance of information analysis in business management and study the information needs of enterprises in the current situation, summarize the types of information and study the latest information analysis methods and steps. The information should be used in the process of regulation can be classified into six types (macro information, industry information, competitor information, enterprise performance information, target consumer information and social humanities information). The main activities of the information analysis process consist of identifying information needs, collecting information, analyzing information, evaluating and improving the effectiveness of information analysis. Information analysis plays an important role in the management of a company, namely information analysis can reflect the objective situation of enterprise management; information analysis can help enterprise managers to make more reasonable decisions and plans; information analysis is conducive to the full play and utilization of information. Information analysis must be based on reliable numbers. Only accurate figures can draw the correct point of view, and provide a reliable basis for managers' decisions. Improving information quality is a long-term and arduous task. Only when the accuracy of the information is guaranteed can the information analysis be carried out effectively. The companies are gradually increasing the importance of information analysis. By using information analysis, they can guide the correct development direction of the enterprise and assist enterprise decision makers to make correct decisions. Data information is the core of enterprise value. Therefore, enterprises should conduct scientific and effective information analysis to lay the foundation for enterprise development.

Novel competitive information propagation macro mathematical model in online social network

This paper proposes a novel competitive information dissemination macro model CISIR (Competitive Information Susceptible Infected Recovered) in online social network (OSN). Firstly, the Markov chain theory is used to analyze the intrinsic relationship between the competition mechanism among different types of information on the network and the rules of node state transition and information propagation evolution. The macro probability model of node state transition is constructed from the perspective of probability, and the macro information diffusion of network system is constructed from the perspective of statistics. Secondly, the equilibrium point and stability of the proposed model are solved to ensure that the model is reasonable and meaningful. Finally, the relationship between the parameters in the model is analyzed by numerical simulation experiment, and the dynamic simulation is carried out. The process of information competition and dissemination is analyzed, and the degree of agreement between the simulation results and the real event statistics is researched through empirical comparative experiments. Experimental results show that the proposed CISIR model is reasonable and effective. It provides a new scientific method and research approach to solve the problem of competitive propagation of different information types in OSN, and it has high theoretical value and application value.

Real-Time Macro Information and Bond Return Predictability: Does Deep Learning Help?

This paper examines whether deep/machine learning can help find any statistical and/or economic evidence of out-of-sample bond return predictability when real-time, instead of fully-revised, macro variables are taken as predictors. First, when using pure real-time macro information alone, we find that deep learning cannot help find any statistical evidence for forecasting both non-overlapping and overlapping excess bond returns. In contrast, some machine learning models can help find some statistical evidence for forecasting overlapping excess bond returns. Second, when using both pure real-time macro information and yield curve information, we find that deep learning performs well for forecasting medium- and long-maturity overlapping excess bond returns, but such predictability is dominantly driven by yield curve information. Third, all statistical evidence of predictability is much weaker than that found from using fully-revised macro data and generates minimal economic gains for a mean-variance investor, regardless of her level of risk aversion and whether she can take short positions.