An integrated fuzzy-stochastic model for revenue management: The hospitality industry case

Revenue management aims at improving the performance of an organization by selling the right product/service to the right customer at the right time. This task is very dependent on uncontrollable external factors. In the hospitality industry, rooms of the hotel represent perishable assets and fixed capacities at the same time. Therefore, in the case of a stochastic process for customers calling in reservations prior to a particular booking date, a common problem for hotels is to devise a policy for maximizing the total expected profit conditional on the set of bookings. We propose a fuzzy model for the hotel revenue management under an uncertain and vague environment. Fuzziness of objective and constraint functions have been incorporated into a stochastic booking model considering multiple-day stays to show the effect of uncertainty on the optimal demand. By changing the relaxation parameters of the objective function, we have found a set of optimal solutions with, in most of the cases, a value of the objective function equal to the optimal solution of the stochastic model, providing several alternative optimal room allocations

Mortality rates in transplant recipients and transplantation candidates in a high prevalence COVID-19 environment

Background: The risk of COVID-19 infection in transplant recipients (TRs) is unknown. Patients on dialysis may be exposed to greater risk of infection due to an inability to isolate. Consideration of these competing risks is important before restarting suspended transplant programs. This study compared outcomes in kidney and kidney/pancreas TRs with those on the waiting list, following admission with COVID-19 in a high-prevalence region. Methods: Audit data from all 6 London transplant centers were amalgamated. Demographic and laboratory data were collected and outcomes included mortality, intensive care (ITU) admission, and ventilation. Adult patients who had undergone a kidney or kidney/pancreas transplant, and those active on the transplant waiting list at the start of the pandemic were included. Results: One hundred twenty-one TRs and 52 waiting list patients (WL) were admitted to hospital with COVID-19. Thirty-six TR died (30%), while 14 WL patients died (27% P = 0.71). There was no difference in rates of admission to ITU or ventilation. Twenty-four percent of TR required renal replacement therapy, and 12% lost their grafts. Lymphocyte nadir and D-dimer peak showed no difference in those who did and did not die. No other comorbidities or demographic factors were associated with mortality, except for age (odds ratio of 4.3 [95% CI 1.8-10.2] for mortality if aged over 60 y) in TR. Conclusions: TRs and waiting list patients have similar mortality rates after hospital admission with COVID-19. Mortality was higher in older TRs. These data should inform decisions about transplantation in the COVID era

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV

The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8  TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum