Dark Matter directional detection: comparison of the track direction determination

Several directional techniques have been proposed for a directional detection of Dark matter, among others anisotropic crystal detectors, nuclear emulsion plates, and low-pressure gaseous TPCs. The key point is to get access to the initial direction of the nucleus recoiling due to the elastic scattering by a WIMP. In this article, we aim at estimating, for each method, how the information of the recoil track initial direction is preserved in different detector materials. We use the SRIM simulation code to emulate the motion of the first recoiling nucleus in each material. We propose the use of a new observable, D, to quantify the preservation of the initial direction of the recoiling nucleus in the detector. We show that in an emulsion mix and an anisotropic crystal, the initial direction is lost very early, while in a typical TPC gas mix, the direction is well preserved.Comment: 9 pages, 5 figure

The impact of banking regulations on banks' cost and profit efficiency: Cross-country evidence

This paper uses stochastic frontier analysis to provide international evidence on the impact of the regulatory and supervision framework on bank efficiency. Our dataset consists of 2853 observations from 615 publicly quoted commercial banks operating in 74 countries during the period 2000-2004. We investigate the impact of regulations related to the three pillars of Basel II (i.e. capital adequacy requirements, official supervisory power, and market discipline mechanisms), as well as restrictions on bank activities, on cost and profit efficiency of banks, while controlling for other country-specific characteristics. Our results suggest that banking regulations that enhance market discipline and empower the supervisory power of the authorities increase both cost and profit efficiency of banks. In contrast, stricter capital requirements improve cost efficiency but reduce profit efficiency, while restrictions on bank activities have the opposite effect, reducing cost efficiency but improving profit efficiency. © 2009 Elsevier Inc. All rights reserved

Exploring the Behavioral Intentions of Food Tourists Who Visit Crete

Food tourism has been growing globally in recent years. Food tourism is considered as special interest tourism, attracting tourists who have a great interest in food. Tourists spend a significant percentage of their budget on the purchase of local food products and related food activities, contributing to the sustainable development of the touristic destination in the process. This survey took place in Crete, Greece, throughout the touristic period of 2021, and 4268 valid questionnaires were completed by international tourists. For the data analysis, the Structural Equation Model and an extended Theory of Planned Behavior Model, based on subjective norms, attitudes, perceived behavioral control, and satisfaction, were used to better understand the consumers’ intentions to revisit and recommend the region of Crete. The outcomes of the research pinpointed that the perceived quality and perceived value of local foods positively influenced satisfaction, which, in turn, evoked favorable intentions to revisit and recommend Crete as a touristic destination. Moreover, while satisfaction, attitude, and subjective norms seem to be the most significant drivers affecting positive behavioral intentions, perceived behavior control seems to have had no significant impact. The implications and limitations of the survey, as well as future recommendations, are also discussed

Operational research and artificial intelligence methods in banking

Supplementary materials are available online at https://www.sciencedirect.com/science/article/pii/S037722172200337X?via%3Dihub#sec0031 .Copyright © 2022 The Authors. Banking is a popular topic for empirical and methodological research that applies operational research (OR) and artificial intelligence (AI) methods. This article provides a comprehensive and structured bibliographic survey of OR- and AI-based research devoted to the banking industry over the last decade. The article reviews the main topics of this research, including bank efficiency, risk assessment, bank performance, mergers and acquisitions, banking regulation, customer-related studies, and fintech in the banking industry. The survey results provide comprehensive insights into the contributions of OR and AI methods to banking. Finally, we propose several research directions for future studies that include emerging topics and methods based on the survey results

A detection algorithm for the first jump time in sample trajectories of jump-diffusions driven by α-stable white noise

The purpose of this paper is to develop a detection algorithm for the first jump point in sampling trajectories of jump-diffusions which are described as solutions of stochastic differential equations driven by $\alpha$-stable white noise. This is done by a multivariate Lagrange interpolation approach. To this end, we utilise computer simulation algorithm in MATLAB to visualise the sampling trajectories of the jump-diffusions for various combinations of parameters arising in the modelling structure of stochastic differential equations

Binary choice models for external auditors decisions in Asian banks

Summarization: The present study investigates the efficiency of four classification techniques, namely discriminant analysis, logit analysis, UTADIS multicriteria decision aid, and nearest neighbours, in the development of classification models that could assist auditors during the examination of Asian commercial banks. To develop the auditing models and examine their classification ability, the dataset is split into two distinct samples. The training sample consists of 1,701 unqualified financial statements and 146 ones that received a qualified opinion over the period 1996–2001. The models are tested in a holdout sample of 527 unqualified financial statements and 52 ones that received a qualified opinion over the period 2002–2004. The results show that the developed auditing models can discriminate between financial statements that should receive qualified opinions from the ones that should receive unqualified opinions with an out-of-sample accuracy around 60%. The highest classification accuracy is achieved by UTADIS, followed by logit analysis, nearest neighbours and discriminant analysis. Both financial variables and the environment in which banks operate appear to be important factors.Presented on: Operational Research, An International Journa

Decision process in large-scale crisis management

International audienceThis paper deals with the decision-aiding process in large-scale crisis such as natural disasters. It consists in four phases: decision context characterization, system modelling, aggregation and integration. The elements of the context, such as crisis level, risk situation, decision-maker problem issue are defined through the characterization phase. At the feared event occurrence, these elements will interact on a target system. Through the model on this system, the consequences to stakes could be assessed or estimated. The presented aggregation approaches will allow taking the right decisions. The architecture of a Decision Support System is presented in the integration phase

Combining machine learning and metaheuristics algorithms for classification method PROAFTN

© Crown 2019. The supervised learning classification algorithms are one of the most well known successful techniques for ambient assisted living environments. However the usual supervised learning classification approaches face issues that limit their application especially in dealing with the knowledge interpretation and with very large unbalanced labeled data set. To address these issues fuzzy classification method PROAFTN was proposed. PROAFTN is part of learning algorithms and enables to determine the fuzzy resemblance measures by generalizing the concordance and discordance indexes used in outranking methods. The main goal of this chapter is to show how the combined meta-heuristics with inductive learning techniques can improve performances of the PROAFTN classifier. The improved PROAFTN classifier is described and compared to well known classifiers, in terms of their learning methodology and classification accuracy. Through this chapter we have shown the ability of the metaheuristics when embedded to PROAFTN method to solve efficiency the classification problems