Exclusive semileptonic $b$ hadron decays ($b \to u \ell \nu$) serve as a sandbox for probing strong and electroweak interactions and for extracting the CKM element $V_{ub}$. Instead, this work investigates their underexplored potential to reveal new short-distance physics. Utilizing SMEFT as a conduit to chart territory beyond the SM, we demonstrate that substantive new physics contributions in $b \to u \ell \nu$ are necessarily linked to correlated effects in rare neutral-current $b$ decays, neutral $B$ meson mixing or high-mass Drell-Yan tails. We find that measurements of the latter processes strongly restrict the allowed deviations in the former. A complete set of tree-level mediators, originating from a perturbative ultraviolet model and matching at dimension 6, is thoroughly explored to support this assertion. As a showcase application, we examine the feasibility of a new physics interpretation of the recent tension in exclusive $|V_{ub}|$ extraction from $B \to V \ell \nu$ where $V=(\rho,\omega)$.

O sistema circadiano tem papel fundamental na saúde humana, podendo influenciá-la tanto de forma positiva, quanto negativa, visto que governa os ritmos biológicos diários. Disrupções nesse sistema acabam impactando o metabolismo e provocando desordens que vão desde o ganho de peso corporal ao desenvolvimento de resistência à insulina, doenças crônicas, cardiovasculares e câncer. A crononutrição tem despontado como uma opção terapêutica interessante, pois tem como foco a relação entre padrões alimentares, ritmo circadiano e saúde metabólica. Sendo assim, o objetivo deste trabalho foi avaliar o impacto do ritmo biológico nos processos fisiológicos do corpo, bem como averiguar o potencial da crononutrição na prevenção e tratamento de indivíduos em risco. Como metodologia para desenvolvimento deste trabalho foi feita uma revisão de literatura em que foram selecionados artigos científicos publicados em periódicos internacionais de 2012 a 2023. Utilizou-se para consultas às bases de dados PubMed e MDPI. Em conclusão, as pesquisas, em geral, salientam ser possível conter os efeitos da cronodisrupção e a evolução de doenças crônicas por meio de estratégias baseadas na estimulação dos sincronizadores circadianos, bem como na supressão dos fatores que desregulam o ritmo biológico. Para tanto, diversas estratégias são propostas com foco na alimentação, sono, atividade física e exposição à luz artificial.

The risk assessments during the COVID‐19 pandemic were primarily based on dose–response models derived from the pooled datasets for infection of animals susceptible to SARS‐CoV. Despite similarities, differences in susceptibility between animals and humans exist for respiratory viruses. The two most commonly used dose–response models for calculating the infection risk of respiratory viruses are the exponential and the Stirling approximated β‐Poisson (BP) models. The modified version of the one‐parameter exponential model or the Wells–Riley model was almost solely used for infection risk assessments during the pandemic. Still, the two‐parameter (α and β) Stirling approximated BP model is often recommended compared to the exponential dose–response model due to its flexibility. However, the Stirling approximation restricts this model to the general rules of β ≫ 1 and α ≪ β, and these conditions are very often violated. To refrain from these requirements, we tested a novel BP model by using the Laplace approximation of the Kummer hypergeometric function instead of the conservative Stirling approximation. The datasets of human respiratory airborne viruses available in the literature for human coronavirus (HCoV‐229E) and human rhinovirus (HRV‐16 and HRV‐39) are used to compare the four dose–response models. Based on goodness‐of‐fit criteria, the exponential model was the best fitting model for the HCoV‐229E (k = 0.054) and for HRV‐39 datasets (k = 1.0), whereas the Laplace approximated BP model followed by the exact and Stirling approximated BP models are preferred for both the HRV‐16 (α = 0.152 and β = 0.021 for Laplace BP) and the HRV‐16 and HRV‐39 pooled datasets (α = 0.2247 and β = 0.0215 for Laplace BP).

: Ischemic coronary disease is one of the leading causes of death in Portugal. As a chronic disease, it is imperative to develop skills for effective behavioural change and a higher level of acceptance and adherence to the recommendations provided

Landmines and explosive remnants of war are still present in the Western Balkans and remain a deadly legacy of the hostilities at the end of the 20th century. Over the years, several incidents have occurred in Bosnia and Herzegovina, in Serbia, and in Croatia where intact ordnance has caused injuries and fatalities. Floods, torrential flows, and gravitational mass movements pose a particular threat. Landmines and explosive remnants of war are mobilized and displaced into previously uncontaminated areas. We first discuss the historical and technical background of this hazardous situation. We then show which hydro-morphological processes are responsible for the mobilization and displacement. We then illustrate how a prediction of the likely contaminated areas can be obtained. We show that the problem can only be tackled using a stochastic-deterministic model. However, for the eventual development of risk-hazard maps, preliminary work using laboratory experiments and field surveys is required. The article, therefore, proposes a novel approach to the problem in an international research project. The aim would be to produce risk-hazard maps that can be used by elected decision-makers, administrative authorities, and emergency personnel in affected municipalities.

Heat transfer through building envelopes is a crucial aspect of energy efficiency in construction. Masonry walls, being a commonly used building material, have a significant impact on thermal performance. In recent years, green roofs and walls have gained popularity as a means of improving energy efficiency, reducing urban heat islands, and enhancing building aesthetics. This study aims to investigate the effect of ivy (Hedera helix) greening on heat transfer through masonry walls and their corresponding surface temperatures. Ivy was chosen as a model plant due to its widespread use and ability to cover large surface areas. The results of this study suggest that ivy greening can have a significant impact on the thermal performance of masonry walls. During winter, the heat transfer coefficient of greened walls was found to be up to 30% lower compared to non-greened walls. This indicates that ivy greening can help reduce energy consumption for heating and thus improve the energy efficiency of buildings. In addition, the surface temperature under the ivy was found to be significantly higher than on the bare wall during winter. However, during summer, the surface temperature under the ivy was lower than on the bare wall, which may help reduce cooling energy consumption. The results of this study are consistent with previous research in the field. Overall, this study provides valuable insights into the potential benefits of ivy greening on the thermal performance of masonry walls.

Deep Operator Network (DeepONet), a recently introduced deep learning operator network, approximates linear and nonlinear solution operators by taking parametric functions (infinite-dimensional objects) as inputs and mapping them to solution functions in contrast to classical neural networks that need re-training for every new set of parametric inputs. In this work, we have extended the classical formulation of DeepONets by introducing sequential learning models like the gated recurrent unit (GRU) and long short-term memory (LSTM) in the branch network to allow for accurate predictions of the solution contour plots under parametric and time-dependent loading histories. Two example problems, one on transient heat transfer and the other on path-dependent plastic loading, were shown to demonstrate the capabilities of the new architectures compared to the benchmark DeepONet model with a feed-forward neural network (FNN) in the branch. Despite being more computationally expensive, the GRU- and LSTM-DeepONets lowered the prediction error by half (0.06\% vs. 0.12\%) compared to FNN-DeepONet in the heat transfer problem, and by 2.5 times (0.85\% vs. 3\%) in the plasticity problem. In all cases, the proposed DeepONets achieved a prediction $R^2$ value of above 0.995, indicating superior accuracy. Results show that once trained, the proposed DeepONets can accurately predict the final full-field solution over the entire domain and are at least two orders of magnitude faster than direct finite element simulations, rendering it an accurate and robust surrogate model for rapid preliminary evaluations.

: The intensity of convective electric heating of the fluid is mainly determined by its volumetric flow, the installed power of the heater and the geometric characteristics of the channel through which it flows. The temperature of the surface of the heating source, and its power is limited by the maximum allowed value. The constant convective surface of the electric heating source, with the above limitations, results in a wide range of electric convective heaters. The thermal efficiency of these heaters depends on a case-by-case basis, while the temperature of the fluid varies in some intervals in relation to the required temperature that needs to be achieved. During fast transient fluid heating processes, convective electric heaters are thermally inert, low efficiency, while in some cases their application is unjustified. Therefore, the thermally generated entropy of the described convective heaters and fluids increases, from case to case, while their energy efficiency is minimized.

Solid-state light-emitting diodes (LEDs) emit nearly monochromatic light, yet seamless tuning of emission color throughout the visible region remains elusive. Color-converting powder phosphors are therefore used for making LEDs with a bespoke emission spectrum, yet broad emission lines and low absorption coefficients compromise the formation of small-footprint monochromatic LEDs. Color conversion by quantum dots (QDs) can address these issues, but high-performance monochromatic LEDs made using QDs free of restricted, hazardous elements remain to be demonstrated. Here, we show green, amber, and red LEDs formed using InP-based QDs as on-chip color convertor for blue LEDs. Implementing QDs with near-unity photoluminescence efficiency yields a color conversion efficiency over 50% with little intensity roll-off and nearly complete blue light rejection. Moreover, as the conversion efficiency is mostly limited by package losses, we conclude that on-chip color conversion using InP-based QDs can provide spectrum-on-demand LEDs, including monochromatic LEDs that bridge the green gap.

O trabalho tem como objetivo analisar o nível de conhecimento dos gestores do ramo de Farmácia que utilizam o ERP “InovaFarma” e sua percepção de como os dados retornados podem ser utilizados para obter maiores informações administrativas para apoio na tomada de decisões. Tem também como objetivo verificar se o sistema é “alimentado” de forma correta para que possam obter informações precisas e certeiras.  Por meio de questionário será observado até que ponto os gestores buscam informações nas ferramentas que possuem a disposição e como podem utilizá-las para auxiliar no dia a dia. O método de pesquisa utilizado foi a pesquisa descritiva, onde utilizou-se a técnicas de pesquisa bibliográfica, coleta de dados, análise e interpretação dos resultados. Os gestores que utilizam de forma frequente e muito frequente as ferramentas disponíveis encontram-se mais aptos a realizarem investimentos mais assertivos, e conseguem tomar decisões de forma mais facilitada, além de que ao realizarem uma “alimentação” mais constante das informações financeiras da empresa, conseguem disponibilizar aos demais participantes das tomadas de decisão informações mais transparentes e que podem ser comprovadas de acordo com os dados inseridos no sistema. Palavras-chave: ERP. Gestores. Informações. Tomada de decisão.