Purpose The present study aims to suggest a new approach to hotel quality rating, specifically designed for the business travel segment, where the evaluation of surveyed consumers (business travelers) does not necessarily reflect the priority of customers (corporate travel departments [CTDs]). Design/methodology/approach Preliminarily, the authors defined key areas (domains), exploring what was done by quality certifiers recognized worldwide. Then, each domain quality was considered as a latent variable measured by a set of observable attributes (sub-domains) surveyed by a professional assessor. A continuous, fine-grained, composite indicator (CI) for quality was finally obtained by a weighted average of the domain (latent) quality measures. Weights were endogenously determined by data envelopment analysis. Findings The suggested CI shows both the existence of large quality disparities within the same star rating and a relevant bias in the internet reviews. A “soundproofed” room, a front desk open 24 h with sufficient staff and an adequate urban context are necessary features of any business hotel. Research limitations/implications Data came from a professional assessor’s database; therefore, the authors could only consider a three-domains measurement model. The database is mainly composed of three- and four-star hotels in Italy; nonetheless, these accommodations are the most widespread in the Italian corporation hotel programs, preserving the practical utility of the results. Originality/value This study provides a transparent (replicable) evaluation protocol that is of potential use in the most popular models for quality measurement; any assessor can use it to underline its impartiality to CTD and assessed hotels.

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Although the literature demonstrates that cardiac autonomic control (CAC) might be impaired in patients with chronic pulmonary diseases, the interplay between CAC and disease severity in end-stage lung disease has not been studied yet. We investigated the effects of end-stage lung disease on CAC through the analysis of heart rate variability (HRV) among patients awaiting lung transplantation. Forty-nine patients on the waiting list for lung transplantation (LTx; 19 men, age 38 ± 15 years) and 49 healthy non-smoking controls (HC; 22 men, age 40 ± 16 years) were enrolled in a case–control study at Policlinico Hospital in Milan, Italy. LTx patients were divided into two groups, according to disease severity evaluated by the Lung Allocation Score (LAS). To assess CAC, electrocardiogram (ECG) and respiration were recorded at rest for 10 min in supine position and for 10 min during active standing. Spectral analysis identified low and high frequencies (LF, sympathetic, and HF, vagal). Symbolic analysis identified three patterns, i.e., 0V% (sympathetic) and 2UV% and 2LV% (vagal). Compared to HCs, LTx patients showed higher markers of sympathetic modulation and lower markers of vagal modulation. However, more severely affected LTx patients, compared to less severely affected ones, showed an autonomic profile characterized by loss of sympathetic modulation and predominant vagal modulation. This pattern can be due to a loss of sympathetic rhythmic oscillation and a subsequent prevalent respiratory modulation of heart rate in severely affected patients.

For the diagnosis of breast cancer using magnetic resonance imaging (MRI), one of the most important parameters is the analysis of contrast enhancement. A three-dimensional MR sequence is applied before and five times after bolus injection of paramagnetic contrast medium (Gd-DTPA). The dynamics of absorption are described by a time/intensity enhancement curve, which reports the mean intensity of the MR signal in a small region of interest (ROI) for about 8 minutes after contrast injection. The aim of our study was to use an artificial neural network to automatically classify the enhancement curves as "benign" or "malignant." We used a classic feed-forward back-propagation neural network, with three layers: five input nodes, two hidden nodes, and one output node. The network has been trained with 26 pathologic curves (10 invasive carcinoma [K], two carcinoma-in-situ [DCIS], and 14 benign lesion [B]). The trained network has been tested with 58 curves (36 K, one DCIS, 21 B). The network was able to correctly identify the test curves with a sensitivity of 76% and a specificity of 90%. For comparison, the same set of curves was analyzed separately by two radiologists (a breast MR expert and a resident radiologist). The first correctly interpreted the curves with a sensitivity of 76% and a specificity of 90%, while the second scored 59% for sensitivity and 90% for specificity. These results demonstrate that a trained neural network recognizes the pathologic curves at least as well as an expert radiologist. This algorithm can help the radiologist attain rapid and affordable screening of a large number of ROIs. A complete automatic computer-aided diagnosis support system should find a number of potentially interesting ROIs and automatically analyze the enhancement curves for each ROI by neural networks, reporting to the radiologist only the potentially pathologic ROIs for a more accurate, manual, repeated evaluation.