Organization of disaster aid delivery: spending your donations

This paper examines how different organizational structures in disaster aid delivery affect house aid quality. We analyze three waves of survey data on fishermen and fishing villages in Aceh, Indonesia following the tsunami. We categorize four organizational structures based on whether and to whom donors contract aid implementation. Compared to bilateral contracting between donors and implementers, donors that vertically integrate and do their own implementation offer the highest quality housing as rated by village heads and have fewer counts of faults, such as leaky roofs and cracked walls, as reported by fishermen. However, they shade in quality as they lose dominance as the leading aid agency in a village. Domestic implementers and the government agency that was responsible for significant portions of aid delivery provide significantly lower quality aid. We also examine how the imposition of shared ownership, the primary social agenda for boat aid agencies, affects boat aid quality. We find that village and fishing leaders steer poor quality boats towards those whom shared ownership were imposed upon, often lower status fishermen

Physical and Radiobiological Evaluation of Radiotherapy Treatment Plan

Radiation treatment planning plays an important role in modern radiation therapy; it could simulate to plan the geometric, radiobiological, and dosimetric aspects of the therapy using radiation transport simulations and optimization. In this chapter, we have reviewed several quantitative methods used for evaluating radiation treatment plans and discussed some important considering points. For the purpose of quantitative plan evaluation, we reviewed dosimetrical indexes like PITV, CI, TCI, HI, MHI, CN, COSI, and QF. Furthermore, radiobiological indexes like Niemierko’s EUD-based TCP and NTCP were included for the purpose of radiobiological outcome modeling. Additionally, we have reviewed dose tolerance for critical organs including RTOG clinical trial results, QUENTEC data, Emami data, and Milano clinical trial results. For the purpose of clinical evaluation of radiation-induced organ toxicity, we have reviewed RTOG and EORTC toxicity criteria. Several programs could help for the easy calculation and analysis of dosimetrical plan indexes and biological results. We have reviewed the recent trend in this field and proposed further clinical use of such programs. Along this line, we have proposed clinically optimized plan comparison protocols and indicated further directions of such studies

Indentations on Air Plasma Sprayed Thermal Barrier Coatings Prepared by Different Starting Granules

The effect of starting granules on the indentation properties of air plasma sprayed thermal barrier coatings (TBCs) is investigated in this paper. Various kinds of spray-dried granules are prepared from different processing conditions, especially varying solvent and dispersant, showing a deformed hollow-typed and a filled spherical-typed granule. The similar coating thicknesses are prepared by adjusting process parameters during air plasma spray. All XRD peaks in phase analysis are tetragonal and cubic phases without any monoclinic phase after the starting granules were heat-treated. A relatively porous microstructure of the coating layer could be obtained from the monodisperse granules, while a relatively dense microstructure resulted from the hollow-typed granules. The morphology and distribution of the granules crucially affect the microstructure of thermal barrier coatings and thus have influences on indentation properties such as indentation stress-strain curves, contact damage, and hardness. The implication concerning microstructure design of TBCs for gas turbine applications is considered

Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges

Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important task in the analysis of complex systems. Graph anomalies are patterns in a graph that do not conform to normal patterns expected of the attributes and/or structures of the graph. In recent years, graph neural networks (GNNs) have been studied extensively and have successfully performed difficult machine learning tasks in node classification, link prediction, and graph classification thanks to the highly expressive capability via message passing in effectively learning graph representations. To solve the graph anomaly detection problem, GNN-based methods leverage information about the graph attributes (or features) and/or structures to learn to score anomalies appropriately. In this survey, we review the recent advances made in detecting graph anomalies using GNN models. Specifically, we summarize GNN-based methods according to the graph type (i.e., static and dynamic), the anomaly type (i.e., node, edge, subgraph, and whole graph), and the network architecture (e.g., graph autoencoder, graph convolutional network). To the best of our knowledge, this survey is the first comprehensive review of graph anomaly detection methods based on GNNs.Comment: 9 pages, 2 figures, 1 tables; to appear in the IEEE Access (Please cite our journal version.

Prediction of Cancer Patient Outcomes Based on Artificial Intelligence

Knowledge-based outcome predictions are common before radiotherapy. Because there are various treatment techniques, numerous factors must be considered in predicting cancer patient outcomes. As expectations surrounding personalized radiotherapy using complex data have increased, studies on outcome predictions using artificial intelligence have also increased. Representative artificial intelligence techniques used to predict the outcomes of cancer patients in the field of radiation oncology include collecting and processing big data, text mining of clinical literature, and machine learning for implementing prediction models. Here, methods of data preparation and model construction to predict rates of survival and toxicity using artificial intelligence are described

Effects of Textural Properties on the Response of a SnO2-Based Gas Sensor for the Detection of Chemical Warfare Agents

The sensing behavior of SnO2-based thick film gas sensors in a flow system in the presence of a very low concentration (ppb level) of chemical agent simulants such as acetonitrile, dipropylene glycol methyl ether (DPGME), dimethyl methylphosphonate (DMMP), and dichloromethane (DCM) was investigated. Commercial SnO2 [SnO2(C)] and nano-SnO2 prepared by the precipitation method [SnO2(P)] were used to prepare the SnO2 sensor in this study. In the case of DCM and acetonitrile, the SnO2(P) sensor showed higher sensor response as compared with the SnO2(C) sensors. In the case of DMMP and DPGME, however, the SnO2(C) sensor showed higher responses than those of the SnO2(P) sensors. In particular, the response of the SnO2(P) sensor increased as the calcination temperature increased from 400 °C to 800 °C. These results can be explained by the fact that the response of the SnO2-based gas sensor depends on the textural properties of tin oxide and the molecular size of the chemical agent simulant in the detection of the simulant gases (0.1–0.5 ppm)