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

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

Nuclear Recoil Identification in a Scientific Charge-Coupled Device

Charge-coupled devices (CCDs) are a leading technology in direct dark matter searches because of their eV-scale energy threshold and high spatial resolution. The sensitivity of future CCD experiments could be enhanced by distinguishing nuclear recoil signals from electronic recoil backgrounds in the CCD silicon target. We present a technique for event-by-event identification of nuclear recoils based on the spatial correlation between the primary ionization event and the lattice defect left behind by the recoiling atom, later identified as a localized excess of leakage current under thermal stimulation. By irradiating a CCD with an $^{241}$Am$^{9}$Be neutron source, we demonstrate $>93\%$ identification efficiency for nuclear recoils with energies $>150$ keV, where the ionization events were confirmed to be nuclear recoils from topology. The technique remains fully efficient down to 90 keV, decreasing to 50$\%$ at 8 keV, and reaching ($6\pm2$)$\%$ at 1.5--3.5 keV. Irradiation with a $^{24}$Na $\gamma$-ray source shows no evidence of defect generation by electronic recoils, with the fraction of electronic recoils with energies $<85$ keV that are spatially correlated with defects $<0.1$$\%$.Comment: 9 pages, 7 figure

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

Application of Decision Theory methods for a Community of Madrid Soil classification case

A land classification method was designed for the Community of Madrid (CM), which has lands suitable for either agriculture use or natural spaces. The process started from an extensive previous CM study that contains sets of land attributes with data for 122 types and a minimum-requirements method providing a land quality classification (SQ) for each land. Borrowing some tools from Operations Research (OR) and from Decision Science, that SQ has been complemented by an additive valuation method that involves a more restricted set of 13 representative attributes analysed using Attribute Valuation Functions to obtain a quality index, QI, and by an original composite method that uses a fuzzy set procedure to obtain a combined quality index, CQI, that contains relevant information from both the SQ and the QI methods

Monitoring credit risk in the social economy sector by means of a binary goal programming model

The final publication is available at Springer via http://dx.doi.org/10.1007/s11628-012-0173-7Monitoring the credit risk of firms in the social economy sector presents a considerable challenge, since it is difficult to calculate ratings with traditional methods such as logit or discriminant analysis, due to the relatively small number of firms in the sector and the low default rate among cooperatives. This paper intro- duces a goal programming model to overcome such constraints and to successfully manage credit risk using economic and financial information, as well as expert advice. After introducing the model, its application to a set of Spanish cooperative societies is described.García García, F.; Guijarro Martínez, F.; Moya Clemente, I. (2013). Monitoring credit risk in the social economy sector by means of a binary goal programming model. 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Search for Daily Modulation of MeV Dark Matter Signals with DAMIC-M

Dark Matter (DM) particles with sufficiently large cross sections may scatter as they travel through Earth's bulk. The corresponding changes in the DM flux give rise to a characteristic daily modulation signal in detectors sensitive to DM-electron interactions. Here, we report results obtained from the first underground operation of the DAMIC-M prototype detector searching for such a signal from DM with MeV-scale mass. A model-independent analysis finds no modulation in the rate of 1$e^-$ events with periods in the range 1-48 h. We then use these data to place exclusion limits on DM in the mass range [0.53, 2.7] MeV/c$^2$ interacting with electrons via a dark photon mediator. Taking advantage of the time-dependent signal we improve by $\sim$2 orders of magnitude on our previous limit obtained from the total rate of 1$e^-$ events, using the same data set. This daily modulation search represents the current strongest limit on DM-electron scattering via ultralight mediators for DM masses around 1 MeV/c$^2$

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures

Search for inelastic scattering of WIMP dark matter in XENON1T

We report the results of a search for the inelastic scattering of weakly interacting massive particles (WIMPs) in the XENON1T dark matter experiment. Scattering off 129Xe is the most sensitive probe of inelastic WIMP interactions, with a signature of a 39.6 keV deexcitation photon detected simultaneously with the nuclear recoil. Using an exposure of 0.83 tonne-years, we find no evidence of inelastic WIMP scattering with a significance of more than 2σ. A profile-likelihood ratio analysis is used to set upper limits on the cross section of WIMP-nucleus interactions. We exclude new parameter space for WIMPs heavier than 100  GeV/c2, with the strongest upper limit of 3.3×10−39  cm2 for 130  GeV/c2 WIMPs at 90% confidence level