Risk of second primary malignancies associated with radiotherapy in prostate cancer patients: competing risk analysis

Background: The effect of radiotherapy (RT) for second primary malignancies (SPMs) among prostate cancer survivors is controversial. Methods: Applying logistic regression, competing risk analysis and propensity score matching method, this study analyzed clinical data from the Surveillance, Epidemiology, and End Results program to compare the risk for SPMs between patients receiving RT and non-RT. Results: In this study, prostate cancer patients treated with RT developed more SPMs in the anus, bladder, rectum, liver, lung and bronchus and lymphoma than non-RT groups. Conclusion: More intensive surveillance should be adopted for these cancers among prostate cancer survivors.

Do Corporate Site Visits Constrain Real Earnings Management?

This study investigates the relationship between corporate site visits (CSVs) and firms’ real earnings management. Using a unique dataset of site visits to Chinese firms listed on the Shenzhen Stock Exchange from 2009 to 2016, we find that such visits are negatively associated with firms’ real earnings management. The results are robust to using alternative CSV measures, controlling for alternative communication channels, and using the propensity score matching method. In cross-sectional analyses, we find that the negative association between site visits and real earnings management is stronger for more complex firms and firms with greater information asymmetry. In addition, we find that CSVs are negatively associated with both management and corporate misconduct but not with accrual-based earnings management or restatements.

Map matching for circular road via contextual voting

Due to the limitation of positioning devices, there is a certain error between GPS positioning data and the real location on the map, and the positioning data needs to be processed to have better usability. For example, accurate location is needed for traffic flow control, automatic driving navigation, logistics tracking, etc. There are few studies specifically for circular road sections. In addition, many existing map matching methods based on Hidden Markov model (HMM) also have the problem that GPS points are easily to be matched to tangent or non-adjacent road sections at circular road sections. Therefore, the contextual voting map matching method for circular road sections (STDV-matching) is proposed. The method proposes multiple subsequent point direction analysis methods based on STD-matching to determine entry into the circular section, and adds candidate section frequency voting analysis to reduce matching errors. The effectiveness of the proposed method is verified at the circular section by comparing it with three existing HMM methods through experiments using two real map and trajectory datasets.

Analysis of Land Suitability for the Development of Porang (Amarphopallus ancophillus ) Plants in Lewa Sub-Regency, East Sumba Regency

Lewa sub-district is one of the sub-districts in East Sumba Regency, which has dry land that can be maximized for the development of porang plants and development purposes; information on the potential of porang plantations is needed. This study aimed to determine the biophysical characteristics of the land and the land suitability class of porang plants. The analytical method used was the matching method by comparing the land characteristics and plant growth requirements and the overlay method. The matching results show that the land characteristics in Lewa Subdistrict are class S1 (Very suitable), covering an area of 26.220,209 ha and Class S2 (quite suitable), covering an area of 3.608,523 ha. Limiting factors in this area are water availability (OA) such as drainage, nutrient retention (nr) such as CEC and pH, and erosion hazards (eh) such as slope.

Towards a Greenification

This study provides an empirical analysis on the existence of a green bond premium on the secondary market. The green bond premium is defined as the yield differential between a green and a comparable brown bond, while controlling for liquidity. The EUR-denominated green bonds are studied to determine if they diverge from comparable conventional bonds in terms of yields, during the period from January 2018 to December 2020. Through a matching method, a sample composed of 35 bond couples is obtained. On average, this study reports a negative greenium of -3.20 bps within the sample. The greenium differs across the sub-samples, being negative for green bonds issued by financial institutions, in domestic currency, by AA- and A-rated issuers, and for those issued by issuers with low or medium ESG risk levels. Finally, the ESG risk level has been found to be the driver of the negative green bond premium.

A Robust Invariant Local Feature Matching Method for Changing Scenes

The precise evaluation of camera position and orientation is a momentous procedure of most machine vision tasks, especially visual localization. Aiming at the shortcomings of local features of dealing with changing scenes and the problem of realizing a robust end-to-end network that worked from feature detection to matching, an invariant local feature matching method for changing scene image pairs is proposed, which is a network that integrates feature detection, descriptor constitution, and feature matching. In the feature point detection and descriptor construction stage, joint training is carried out based on a neural network. In the feature point extraction and descriptor construction stage, joint training is carried out based on a neural network. To obtain local features with solid robustness to viewpoint and illumination changes, the Vector of Locally Aggregated Descriptors based on Neural Network (NetVLAD) module is introduced to compute the degree of correlation of description vectors from one image to another counterpart. Then, to enhance the relationship between relevant local features of image pairs, the attentional graph neural network (AGNN) is introduced, and the Sinkhorn algorithm is used to match them; finally, the local feature matching results between image pairs are output. The experimental results show that, compared with the existed algorithms, the proposed method enhances the robustness of local features of varying sights, performs better in terms of homography estimation, matching precision, and recall, and when meeting the requirements of the visual localization system to the environment, the end-to-end network tasks can be realized.