园林树木的养护管理措施

近年来,我国城市园林绿化事业蓬勃发展,园林树木的养护管理工作,在城市园林建设中,占有十分重要的地位。因此,"三分种,七分养",就是城市绿化施工与树木养护管理工作的关系。本文介绍了城市园林绿化树木的种类,并从多角度阐述了城市园林绿化树木的养护管理。     

Efficient Calibration of Trinomial Trees for One-Factor Short Rate Models

In this paper we propose a computationally efficient implementation of general one factor short rate models with a trinomial tree. We improve the Hull–Whites procedure to calibrate the tree to bond prices by circumventing the forward rate induction and numerical root search algorithms. Our calibration procedure is based on forward measure changes and is as general as the Hull–White procedure, but it offers a more efficient and flexible method of constructing a trinomial term structure model. It can be easily implemented and calibrated to both prices and volatilities. JEL classification G13, C6     

泰山的野生观赏植物资源调查

按一般的观赏植物分类体系将泰山的观赏植物资源分为古树名末,观花乔木和灌木,野生草本花齐、秋季观叶观果植物、常绿观赏植物,藤本植物,珍稀物人七厌烦,并对合理保护,开发观赏植物资源提出了几点建议。     

Using regression tree ensembles to model interaction effects: a graphical approach

Multiplicative interaction terms are widely used in economics to identify heterogeneous effects and to tailor policy recommendations. The execution of these models is often flawed due to specification and interpretation errors. This article introduces regression trees and regression tree ensembles to model and visualize interaction effects. Tree-based methods include interactions by construction and in a nonlinear manner. Visualizing nonlinear interaction effects in a way that can be easily read overcomes common interpretation errors. We apply the proposed approach to two different datasets to illustrate its usefulness.     

Forecasting with gradient boosted trees: augmentation,tuning, and cross-validation strategies: Winning solution to the M5 Uncertainty competition

Deep neural networks and gradient boosted tree models have swept across the field of machine learning over the past decade, producing across-the-board advances in performance. The ability of these methods to capture feature interactions and nonlinearities makes them exceptionally powerful and, at the same time, prone to overfitting, leakage, and a lack of generalization in domains with target non-stationarity and collinearity, such as time-series forecasting. We offer guidance to address these difficulties and provide a framework that maximizes the chances of predictions that generalize well and deliver state-of-the-art performance. The techniques we offer for cross-validation, augmentation, and parameter tuning have been used to win several major time-series forecasting competitions—including the M5 Forecasting Uncertainty competition and the Kaggle COVID19 Forecasting series—and, with the proper theoretical grounding, constitute the current best practices in time-series forecasting.     

Measuring customer equity and managing the customer asset: an explanatory study in the Australian hotels

Although there are a range of conceptual and theoretical studies about customer equity (CE) measurement and management in the literature, there are few empirical studies where these two concepts are examined together. This paper focuses on the need for a more synthesised approach and addresses calls for more research into this specific area of marketing management. A conceptual framework was developed that synthesises both customer lifetime value and customer relationship management from a customer equity management (CEM) perspective. The framework was used to empirically examine how four CE strategies, together with a firm’s use of the two types of customer data (aggregated and disaggregated), impact on CEM outcomes. Data were collected from 114 hotel managers in Australia and analysed through a decision tree methodology. The results show that hotels are utilising their CE strategies for acquisition purposes more than retention purposes (customer asset management), which overall appears to satisfice more than maximise their hotel’s capacity in achieving effective CE outcomes. The study’s contribution to theory is through empirically testing a framework that combines two discrete theoretical concepts in one study to provide insights to guide future research.     

Convergence rates of recombining trees for pricing options on stocks under GBM and regime-switching models with known cash dividends

In the literature there appear various kinds of binomial trees for pricing options on stocks under geometric Brownian motions (GBMs) with known cash dividends. The aim of this paper is to compare the performance of the existing binomial trees in aspect of the convergence rates, which are usually used to measure precisely how fast the approximate values converge to the exact one, and to give a theoretical proof of the convergence rates for the interpolation binomial trees which are based on a model that excludes the arbitrage possibilities. Also the paper extends the studies to the regime-switching models with known cash dividend payment.     

A feature engineering approach to wind power forecasting: GEFCom 2012

This paper provides detailed information about team Leustagos’ approach to the wind power forecasting track of GEFCom 2012. The task was to predict the hourly power generation at seven wind farms, 48 hours ahead. The problem was addressed by extracting time- and weather-related features, which were used to build gradient-boosted decision trees and linear regression models. This approach achieved first place in both the public and private leaderboards.     

鸡峰山的观赏植物

报道了鸡峰山森林公园50科96属202种主要观赏植物的形态和分布。     

Competitive Positioning in International Logistics: Identifying a System of Attributes Through Neural Networks and Decision Trees

Abstract

Firms involved in international logistics must develop a system of service attributes that give them a way to be profitable and to satisfy customers' needs at the same time. How customers trade-off these various attributes in forming satisfaction with competing international logistics providers has not been explored well in the literature. This study explores the ocean freight shipping sector to identify the system of attributes that maximizes customers' satisfaction. Data were collected from shipping managers in Singapore using personal interviews to identify the chief concerns in choosing and evaluating ocean freight services. The data were then examined using neural networks and decision trees, among other approaches to identify the system of attributes that is connected with customer satisfaction. The results illustrate the power of these methods in understanding how industrial customers with global operations process attributes to derive satisfaction. Implications are discussed.