Relationships between absenteeism,conservation group membership,and land management among family forest owners

Absentee landowners, or those who do not live on their forestland, own approximately 117 million acres of private forestland in the U.S. Thus, their land management decisions and activities influence the flow of forest-based goods and services. We explore the question of whether absentee family forest owners are less active land managers than resident landowners and whether membership in conservation organizations is associated with higher levels of land management activity by absentee owners. To examine these questions, we administered a mail survey to randomly-selected family forest landowners in Indiana. While we found some support for the contention that absentee owners are less active forestland managers than resident owners, we also found they are not necessarily inactive landowners. We found absentee owners were less likely to have: inspected their forestland for invasive plants, pulled or cut invasive plants, used herbicides to kill invasive plants, reduced fire hazard, or grazed livestock than resident owners. Absentee owners were more likely to be enrolled in the Indiana Classified Forest and Wildlands Program, a preferential forest property tax program. Absentee owners who are members of a conservation organization were more likely than absentee non-member owners to have undertaken a variety of land management activities, including: undertaking wildlife habitat improvement projects, inspecting their forestland for invasive plants, pulling or cutting invasive plants, enrolling in the Indiana Classified Forest and Wildlands program, and obtaining a management plan.     

High-frequency credit spread information and macroeconomic forecast revision

We examine whether professional forecasters incorporate high-frequency information about credit conditions when revising their economic forecasts. Using a mixed data sampling regression approach, we find that daily credit spreads have significant predictive ability for monthly forecast revisions of output growth, at both the aggregate and individual forecast levels. The relationships are shown to be notably strong during ‘bad’ economic conditions, which suggests that forecasters anticipate more pronounced effects of credit tightening during economic downturns, indicating an amplification effect of financial developments on macroeconomic aggregates. The forecasts do not incorporate all financial information received in equal measures, implying the presence of information rigidities in the incorporation of credit spread information.     

Forecasting value at risk and expected shortfall with mixed data sampling

I propose applying the Mixed Data Sampling (MIDAS) framework to forecast Value at Risk (VaR) and Expected shortfall (ES). The new methods exploit the serial dependence on short-horizon returns to directly forecast the tail dynamics of the desired horizon. I perform a comprehensive comparison of out-of-sample VaR and ES forecasts with established models for a wide range of financial assets and backtests. The MIDAS-based models significantly outperform traditional GARCH-based forecasts and alternative conditional quantile specifications, especially in terms of multi-day forecast horizons. My analysis advocates models that feature asymmetric conditional quantiles and the use of the Asymmetric Laplace density to jointly estimate VaR and ES.     

The effect of mergers and acquisitions on environmental,social and governance performance and market value: Evidence from EU acquirers

This study explores the effect of environmental, social, and governance (ESG) performance on market value and performance in the context of mergers and acquisitions. We examine whether acquisition of targets with better ESG performance can help acquirers to increase their own ESG performance and whether the market values the increased ESG performance positively. Moreover, we explore whether the acquisition of targets with better ESG performance affects the market value of acquirers. For this study, we utilize a sample of 100 European mergers and acquisitions between 2003 and 2017, for which matching data on the ESG performance of both the target and acquiring firms are available. Our results show that the postmerger ESG performance of the acquirer increases following the acquisition of a target that has higher ESG performance than that of the acquirer in the premerger stage, whereas the postmerger market value of the acquirer increases following an increase in the acquirer's postmerger ESG performance in relation to its premerger ESG performance. Finally, we provide partial evidence of a positive relationship between the postmerger market value of the acquirer and the acquisition of a target with higher ESG performance than itself in the premerger stage.     

Do institutional investors drive corporate transparency regarding business contribution to the sustainable development goals?

Institutional investors show increasing interest in how companies align their corporate social responsibility strategies with the sustainable development goals (SDGs) proposed by the United Nations (UN). The information disclosed in this regard is essential to know and monitor business contribution to the 2030 Agenda. In this paper, we analyze the influence that institutional investors have on the adoption of the disclosure strategy established by UN and the Global Reporting Initiative (GRI)—GRI‐SDG Compass. The results obtained for a sample of 989 international companies, which prepare their sustainability reports following the GRI guidelines, show that ownership by foreign investors, pension funds, and “other” investors boosts the relevance of the information disclosed in relation to the 2030 Agenda. On the contrary, government, financial institutions, and cross holdings have no impact on the information systems developed.     

利用ACE值构造QC-LDPC码

为进一步提升中短码长下准循环低密度奇偶校验(Quasi-cyclic Low-density Parity-check,QC-LDPC) 码的纠错性能，提出了一种综合短环数目和环连通性的QC-LDPC码构造方法。首先，采用Golomb规则构造QC-LDPC码，对基矩阵中的部分元素进行替换预处理，初步降低短环数目；其次，采用所提的利用近似环外信息度（Approximate Cycle Extrinsic message degree，ACE）的消环掩模算法来优化QC-LDPC码，使得掩模后的校验矩阵具有较大的ACE平均值，最终完成QC-LDPC码的构造。该构造方法简单、通用性强，在短环数目和连通性间进行了平衡。与只考虑减少短环数目、增大围长等方法相比，该方法构造的QC-LDPC码有更加优异的纠错性能。     

人工智能在我国公共卫生建设中的应用与思考

新冠疫情的暴发以及长期防治对中国的公共卫生体系造成深远影响，给我国突发公共卫生事件应急与防控机制带来巨大的考验。在疫情防控的过程中，信息化技术为传统医疗机构带来瞩目的帮助与改变，人工智能、大数据、5G高速网络等新兴高科技的蓬勃发展在直接提升医疗水平方面表现出巨大潜力。本文就人工智能在疫情期间的医疗辅助功能展开讨论，并对未来我国公共卫生体系的建设进行了思考。     

基于PRI信息的雷达脉冲信号分选技术研究综述

基于脉冲重复间隔（Pulse Repetition Interval，PRI）信息的雷达脉冲信号分选是电子侦察信号处理中的核心关键技术之一。以公开文献资料为来源，从直接搜索判定分选、直方图统计分选、PRI变换分选、平面变换分选、多方法综合应用等多个方面对该技术方向上的各种方法的原理、流程、特点、适用条件等进行了分类对比与归纳总结，指出了其所面临的巨大挑战，展现了其未来的发展趋势，为该方向上技术研究的深入推进与工程应用的优化推广提供了重要参考。     

基于ADS-B报文的“北斗”便携式终端实现

针对单个广播式自动相关监视（Automatic Dependent Surveillance-Broadcast，ADS-B）发射机传输距离受限、多地面站组网复杂以及成本较高等问题，在分析ADS-B报文编码与解码算法的基础上，探讨了基于无线网络传输的ADS-B报文信息技术，并设计了基于“北斗”卫星导航（BeiDou Satellite Navigation System，BDS）/嵌入式控制器以及无线网络的便携式监视终端系统。利用实测ADS-B数据联合搭建的监视中心对算法和系统进行测试，结果表明数据传输稳定，实现了低空空域通用航空飞机ADS-B态势信息的网络传输功能，丰富了监视信息的链路传输方式，为低空通航飞行器安全飞行和全面监视提供了新的方法。     

基于车辆数据的k近邻联合概率数据关联算法

在交通场景中采用一些预警措施能够有效地减少交通事故发生。例如，对车辆轨迹进行跟踪并预测车辆的驾驶行为，就是一个常用的预警方法。在对车辆进行跟踪的过程中，数据关联是很重要的部分，它可以对车辆的观测点和轨迹进行关联，从而更新车辆的轨迹，完成跟踪过程。在此背景下，提出了一种新的数据关联算法，即k近邻联合概率数据关联算法(k Nearest Neighbor-Joint Probability Data Association,kNN-JPDA)。实验结果表明，该算法能够较好地解决在交通场景下车辆数据的数据关联问题，在精度以及运行效率方面都有所提高。