M2M消防远程监控系统的应用

文章把M2M的概念引入消防远程监控系统中应用,消防远程监控系统通过运营商网络将各建筑物内独立的火灾自动报警系统联网,在监控中心内对所有联网建筑物的火灾报警情况进行实时监测,对消防设施进行集中管理。     

Corporate Restructuring in Japan Part I: Can M‐Form Organization Manage Diverse Businesses?

The purpose of this paper is to examine the recent organizational restructuring in Japan in the framework of organizational economics, assuming that the product/market portfolio of the firm is fixed. How does a firm set about organizing its internal divisions? I first summarize some stylized facts on corporate diversification strategy and multi‐divisional (M‐form) organization in large Japanese firms from different perspectives. I then analyse the problem of choosing an organizational form. In particular, I argue that, precisely because of its related diversification, the multi‐business Japanese firm adopting the M‐form finds it difficult to differentiate its diverse businesses internally.     

When does FDI have positive spillovers? Evidence from 17 transition market economies

We use rich firm-level data and national input–output tables from 17 countries over the 2002–2005 period to test new and existing hypotheses about the impact of foreign direct investment (FDI) on the efficiency of domestic firms in the host country (i.e., spillovers). We document that backward linkages have a consistently positive effect on productivity of domestic firms while horizontal and forward linkages show no consistent effect. We also examine how the strength of spillovers varies by sector, FDI source, institutional environment (corruption, red tape, level of development), firm’s distance to the technological frontier, and other firm- and country-specific characteristics.     

Board internationalization and corporate social responsibility

This study examines the association between board internationalization and firms’ corporate social responsibility (CSR) performance in China during 2009–2019. Using a large dataset of director nationalities and CSR scores, we find that foreign directors promote CSR performance as evidenced by higher CSR scores. We further document that this relationship is more pronounced among government-controlled firms, firms with better corporate governance, and firms operating under stricter institutional environments. These findings remain valid after sequential robustness checks. Overall, our findings imply that foreign directors motivate the board of directors and their firms to actively pursue and practice social responsibility. Our study enriches the literature on the outcomes of board internationalization and determinants of CSR and provides the internationalization of corporate governance mechanisms a reasonable basis.     

Applicability of the M5 to Forecasting at Walmart

The M5 Forecasting Competition, the fifth in the series of forecasting competitions organized by Professor Spyros Makridakis and the Makridakis Open Forecasting Center at the University of Nicosia, was an extremely successful event. This competition focused on both the accuracy and uncertainty of forecasts and leveraged actual historical sales data provided by Walmart. This has led to the M5 being a unique competition that closely parallels the difficulties and challenges associated with industrial applications of forecasting. Like its precursor the M4, many interesting ideas came from the results of the M5 competition which will continue to push forecasting in new directions.In this article we discuss four topics around the practitioners view of the application of the competition and its results to the actual problems we face. First, we examine the data provided and how it relates to common difficulties practitioners must overcome. Secondly, we review the relevance of the accuracy and uncertainty metrics associated with the competition. Third, we discuss the leading solutions and their implications to forecasting at a company like Walmart. We then close with thoughts about a future M6 competition and further enhancements that can be explored.     

Exploring the representativeness of the M5 competition data

The main objective of the M5 competition, which focused on forecasting the hierarchical unit sales of Walmart, was to evaluate the accuracy and uncertainty of forecasting methods in the field to identify best practices and highlight their practical implications. However, can the findings of the M5 competition be generalized and exploited by retail firms to better support their decisions and operation? This depends on the extent to which M5 data is sufficiently similar to unit sales data of retailers operating in different regions selling different product types and considering different marketing strategies. To answer this question, we analyze the characteristics of the M5 time series and compare them with those of two grocery retailers, namely Corporación Favorita and a major Greek supermarket chain, using feature spaces. Our results suggest only minor discrepancies between the examined data sets, supporting the representativeness of the M5 data.     

M5 competition uncertainty: Overdispersion,distributional forecasting,GAMLSS, and beyond

The M5 competition uncertainty track aims for probabilistic forecasting of sales of thousands of Walmart retail goods. We show that the M5 competition data face strong overdispersion and sporadic demand, especially zero demand. We discuss modeling issues concerning adequate probabilistic forecasting of such count data processes. Unfortunately, the majority of popular prediction methods used in the M5 competition (e.g. lightgbm and xgboost GBMs) fail to address the data characteristics, due to the considered objective functions. Distributional forecasting provides a suitable modeling approach to overcome those problems. The GAMLSS framework allows for flexible probabilistic forecasting using low-dimensional distributions. We illustrate how the GAMLSS approach can be applied to M5 competition data by modeling the location and scale parameters of various distributions, e.g. the negative binomial distribution. Finally, we discuss software packages for distributional modeling and their drawbacks, like the R package gamlss with its package extensions, and (deep) distributional forecasting libraries such as TensorFlow Probability.     

Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities

Machine learning (ML) methods are gaining popularity in the forecasting field, as they have shown strong empirical performance in the recent M4 and M5 competitions, as well as in several Kaggle competitions. However, understanding why and how these methods work well for forecasting is still at a very early stage, partly due to their complexity. In this paper, I present a framework for regression-based ML that provides researchers with a common language and abstraction to aid in their study. To demonstrate the utility of the framework, I show how it can be used to map and compare ML methods used in the M5 Uncertainty competition. I then describe how the framework can be used together with ablation testing to systematically study their performance. Lastly, I use the framework to provide an overview of the solution space in regression-based ML forecasting, identifying areas for further research.     

Evaluating quantile forecasts in the M5 uncertainty competition

Probabilistic forecasts are necessary for robust decisions in the face of uncertainty. The M5 Uncertainty competition required participating teams to forecast nine quantiles for unit sales of various products at various aggregation levels and for different time horizons. This paper evaluates the forecasting performance of the quantile forecasts at different aggregation levels and at different quantile levels. We contrast this with some theoretical predictions, and discuss potential implications and promising future research directions for the practice of probabilistic forecasting.     

‘HAVE YOU FELT ANGRY LATELY?’: A NOTE ON UNFAIR WAGE PERCEPTIONS AND THE NEGATIVE EMOTION OF ANGER

The author analyses the nexus between unfair wage perceptions of workers and the frequency of the negative emotion of anger. For this purpose, German household panel data for the years 2007, 2009, 2011 and 2013 are used. Angry feelings in the last four weeks have occurred significantly more frequently for workers who perceive their wage as unfair, whereas the own absolute hourly wage is not significantly correlated with the frequency of having felt angry. The results further indicate that workers have felt more often angry if working hours are larger; but the economic significance seems rather small compared to unfair wage perceptions.