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Wheat Grain Yield (GY) and quality are particularly susceptible to nitrogen (N) fertilizer management. However, in rain-fed Mediterranean environments, crop N requirements might be variable due to the effects of water availability on crop growth. Therefore, in-season crop N status assessment is needed in order to apply N fertilizer in a cost-effective way while reducing environmental impacts. Remote sensing techniques might be useful at assessing in-season crop N status. In this study, we evaluated the capacity of vegetation indices formulated using blue (B), green (G), red (R) and near-infrared (NIR) bands obtained with a consumer-grade camera to assess wheat N status. Chlorophyll Content Index (CCI) and fractional intercepted PAR (fIPAR) were measured at three phenological stages and GY and biomass were determined at harvest. Indices formulated using RG bands and the normalized difference vegetation index (NDVI) were significantly correlated with both CCI and fIPAR at the different phenological stage (0.71 < r < 0.81, P < 0.01). Moreover, indices formulated using RG bands were capable at differentiating unfertilized and fertilized plots. In addition, RGB indices and NDVI were found to be related to both crop biomass and GY at harvest, particularly when data were obtained at initial grain filling stage (r > 0.80, P < 0.01). Finally, RGB indices and NDVI obtained with a consumer-grade camera showed comparable capacity at assessing chlorophyll content and predicting both crop biomass and GY at harvest than those obtained with a spectroradiometer. This study highlights the potential of standard and modified consumer-grade cameras at assessing canopy traits related to crop N status and GY in wheat under Mediterranean conditions.

Remote sensing methods are known to provide estimates of berry quality. However, previous studies have shown that the Normalized Difference Vegetation Index (NDVI) failed to predict berry quality attributes in rain-fed vineyards. This study explores the association of several reflectance indices with vine biophysical characteristics and berry yield and quality attributes and their temporal stability. The study was conducted in rain-fed Chardonnay vineyards located around Masquefa (Penedès region, Catalonia, Spain) over four years. Canopy reflectance, fractional Intercepted Photosynthetic Active Radiation, predawn water potential and canopy temperature at midday were measured at veraison whereas berry yield and quality attributes were determined at harvest. Water availability and vine biophysical attributes showed large temporal stability whereas berry quality attributes were not temporally stable. The capability of reflectance indices to estimate berry quality attributes was subject to the timing and extent of water deficits. The Photochemical Reflectance Index (PRI), the NDVI and the Water Index (WI) provided estimates of berry quality attributes under mild, moderate and severe water deficits, respectively. These results might have potential applications in precision viticulture activities such as selective harvesting according to grape quality attributes and the assessment of ripening.

PurposeThis research aims to answer two major research questions related to the COVID-19 crisis from a longitudinal approach: What is the revenue management (RM) role during the different periods subject to analysis? What are the RM strategies and measures implemented during this crisis in contrast with a non-crisis context? It also aims to propose an RM implementation model that provides a contingency plan to face future crises.Design/methodology/approachThis qualitative study, following a longitudinal approach, analyses three round-table discussions with 11 internationally renowned experts during three key scenarios of the COVID-19 crisis: the lockdown period (from March to June 2020) and the following two summer seasons (the post-lockdown period): Post-lockdown I (the summer campaign, 2020) and Post-lockdown II (the summer campaign, 2021). Based on a deductive approach, thematic analysis is conducted using NVivo.FindingsFurther professionalisation of revenue managers, which has enabled the correct application of strategies and measures, highlighting the importance of not lowering prices, the flexibility of booking conditions, the development of other sources of income and the increase in the value of services, amongst others, are key factors in managing this crisis. The longitudinal analysis carried out in three different periods of this crisis shows how these measures have evolved and the contrast with RM application in a non-crisis context. The revenue manager's leadership and proactivity, the holistic organisation of RM marketing, commercial and sales departments and the quick adaptation of RM systems (RMSs) by modifying their algorithms are essential to reducing the impact of COVID-19 on the hospitality industry. This crisis has led the industry to rethink processes and strategies and to increase digitalisation. The proposed model, which considers the various RM strategies and measures implemented during COVID-19 in contrast to a non-crisis context, is the cornerstone for developing a graded contingency plan to face future crises. This research sheds light on the widely discussed role of RM during this crisis.Research limitations/implicationsThis study has various limitations. First, the three round-table discussions were held online due to the health crisis, and the chosen webinar format may have biased the participants' answers due to its public nature. Second, the survey was carried out in Spanish. Despite the strong international profiles of the participants, cultural distortion may appear, suggesting that the research should possibly be extended to other cultural contexts in the future. Third, some of the participants were unable to attend all the round-table discussions due to their professional duties, so people with similar profiles were invited to the rest of the sessions.Practical implicationsThe revenue manager's leadership and proactivity, the holistic organisation of RM marketing, commercial and sales departments and the quick adaptation of RMSs by modifying their algorithms are essential to reducing the impact of COVID-19 on the hospitality industry. This crisis has led the industry to rethink processes and strategies and to increase digitalisation. The proposed model, which considers the various RM strategies and measures implemented during COVID-19 in contrast to a non-crisis context, is the cornerstone for developing a graded contingency plan to face future crises. This research sheds light on the widely discussed role of RM during this crisis.Originality/valueThis work contributes to the literature by providing a model that considers the various RM strategies and measures implemented during COVID-19 in contrast to a non-crisis context. The novelty of this research is mainly found in the conducting of a deductive and longitudinal study considering previous research focussed on RM strategies applied during the COVID-19 crisis and supplementing it with new measures by applying qualitative techniques.

During the COVID-19 pandemic, approximately 15% of patients with severe COVID-19 pneumonia were reported to have neutralizing anti-type I interferon (IFN) autoantibodies, which impaired the antiviral response and led to a poorer prognosis. However, the physiological impact of non-neutralizing autoantibodies remains unclear. In our cohort of COVID-19 patients admitted to intensive care, the presence of non-neutralizing anti-type I IFN autoantibodies increased the risk of thrombotic complications, likely via a cytokine carrier mechanism, prolonging the half-life of cytokines and dysregulating vascular endothelial function. Previous studies have associated non-neutralizing anti-type I IFN autoantibodies with an increased risk of cardiovascular complications in autoimmune diseases like systemic lupus erythematosus, but their relevance in infectious diseases remains uncertain. Stratifying anti-type I IFN autoantibodies based on their neutralizing capacity may have clinical significance not only in terms of susceptibility to infectious diseases but also in predicting cardiovascular and thrombotic events.

Purpose This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand. Design/methodology/approach A system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers. Findings The study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior. Research limitations/implications This study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications. Originality/value The value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.

COVID-19 has hit the hotel sector in a hitherto unknown way. This situation is producing a fundamental change in client behavior that makes crucial an adequate knowledge of their profile to overcome an uncertain environment. Customer Relationship Management (CRM) can provide key strategies in hospitality industry by generating a great amount of valuable information about clients, whereas Big Data tools are providing with unprecedented facilities to conduct massive analysis and to focus the client-to-business relationship. However, few instruments have been proposed to handle categorical features, which are the most usual in CRMs, aiming to adapt the statistical robustness with the best interpretability for the managers. Therefore, our aim was to identify the profiles of clients from an international hotel chain using the overall data in its CRM system. An analysis method was created involving three elements: First, Multiple Correspondence Analysis provides us with a statistical description of the interactions among categories and features. Second, bootstrap resampling techniques give us information about the statistical variability of the feature maps. Third, kernel methods provide easy-to-visualize domain descriptions based on confidence areas in the maps. The proposed methodology can provide an operative and statistically principled way to scrutinize the CRM profiles in hospitality.

This article aims to identify the measures to overcome the COVID-19 crisis proposed by revenue managers during the lockdown period. The comparison of such measures to others against previous crises and their development afterwards is valuable to future decision-making processes in the hospitality industry. A survey of 322 professionals linked to revenue management was undertaken. The holistic and innovative point of view, integrating RM implementation, operations, marketing, and communication following the Flywheel Model, led us to revenue managers’ viewpoints about the measures to overcome the lockdown phase of the COVID-19 crisis. It is, therefore, necessary to integrate them to improve the understanding of hospitality crisis management.

Customer Relationship Management (CRM) is a fundamental tool in the hospitality industry nowadays, which can be seen as a big-data scenario due to the large amount of recordings which are annually handled by managers. Data quality is crucial for the success of these systems, and one of the main issues to be solved by businesses in general and by hospitality businesses in particular in this setting is the identification of duplicated customers, which has not received much attention in recent literature, probably and partly because it is not an easy-to-state problem in statistical terms. In the present work, we address the problem statement of duplicated customer identification as a large-scale data analysis, and we propose and benchmark a general-purpose solution for it. Our system consists of four basic elements: (a) A generic feature representation for the customer fields in a simple table-shape database; (b) An efficient distance for comparison among feature values, in terms of the Wagner-Fischer algorithm to calculate the Levenshtein distance; (c) A big-data implementation using basic map-reduce techniques to readily support the comparison of strategies; (d) An X-from-M criterion to identify those possible neighbors to a duplicated-customer candidate. We analyze the mass density function of the distances in the CRM text-based fields and characterized their behavior and consistency in terms of the entropy and of the mutual information for these fields. Our experiments in a large CRM from a multinational hospitality chain show that the distance distributions are statistically consistent for each feature, and that neighbourhood thresholds are automatically adjusted by the system at a first step and they can be subsequently more-finely tuned according to the manager experience. The entropy distributions for the different variables, as well as the mutual information between pairs, are characterized by multimodal profiles, where a wide gap between close and far fields is often present. This motivates the proposal of the so-called X-from-M strategy, which is shown to be computationally affordable, and can provide the expert with a reduced number of duplicated candidates to supervise, with low X values being enough to warrant the sensitivity required at the automatic detection stage. The proposed system again encourages and supports the benefits of big-data technologies in CRM scenarios for hotel chains, and rather than the use of ad-hoc heuristic rules, it promotes the research and development of theoretically principled approaches.

Among the different biomarkers predicting response in chronic lymphocytic leukemia (CLL), the most influential parameters are the mutational status of the IGHV genes and the presence of TP53 gene disruptions. Nevertheless, these important assessments are not readily available in most centers dealing with CLL patients. To provide this molecular testing across the country, the Spanish Cooperative Group on CLL (GELLC) established a network of four analytical reference centers. A total of 2153 samples from 256 centers were analyzed over a period of 30 months. In 9% of the patients, we found pathological mutations in the TP53 gene, whereas 48.96% were classified as IGHV unmutated. Results of the satisfaction survey of the program showed a Net Promoter Score of 85.15. Building a national network for molecular testing in CLL allowed the CLL population a broad access to complex biomarkers analysis that should translate into a more accurate and informed therapeutic decision-making.

Despite the key role played by frequent consumers interacting on smart P2P (peer to peer) tourism platforms, there are hardly any studies on the explanatory variables of their frequency of use. This paper aims to understand the motivational and sociodemographic factors that bring about repeat consumers in collaborative accommodation and transport services. In order to test various assumptions, a set of logistic regressions were made where the dependent variable is the frequency of use and the independent variables are sociodemographic and motivational factors. The results suggest that many consumer attributes recognized as being typical of collaborative platforms, such as young people traveling with friends for leisure who are interested in low prices, have changed. We found that, due to the consolidation of these smart business models, the frequency of use increases with age and for those who travel for work reasons. In addition, it is worth noting the existence of a positive relationship between consumers who provide reviews on these platforms and their frequency of use.