Locating Ruelle–Pollicott resonances*

We study the spectrum of transfer operators associated to various dynamical systems. Our aim is to obtain precise information on the discrete spectrum. To this end we propose a unitary approach. We consider various settings where new information can be obtained following different branches along the proposed path. These settings include affine expanding Markov maps, uniformly expanding Markov maps, non-uniformly expanding or simply monotone maps, hyperbolic diffeomorphisms. We believe this approach could be greatly generalised.

Study the $$B^{0}_{(s)}$$ decays into $$\phi $$ and a scalar or vector meson

In the present work, we investigate the decays of $$B^{0}_{(s)} \rightarrow \phi \pi ^{+}\pi ^{-}$$ B ( s ) 0 → ϕ π + π - with the final state interaction based on the chiral unitary approach. In the final state interaction of the $$\pi ^+\pi ^-$$ π + π - with its coupled channels, we study the effects of the $$\eta \eta $$ η η channel in the two-body interaction for the reproduction of the $$f_{0}(980)$$ f 0 ( 980 ) state, where its contribution to the $$f_{0}(980)$$ f 0 ( 980 ) state can be ignored. Our results for the $$\pi ^+\pi ^-$$ π + π - invariant mass distributions of the decay $$B^{0}_{s} \rightarrow \phi \pi ^{+}\pi ^{-}$$ B s 0 → ϕ π + π - describe the experimental data up to 1 GeV well, with the resonance contributions from the $$f_{0}(980)$$ f 0 ( 980 ) and $$\rho $$ ρ . For the predicted invariant mass distributions of the $$B^{0} \rightarrow \phi \pi ^{+}\pi ^{-}$$ B 0 → ϕ π + π - decay, we find that the contributions from the $$f_{0}(500)$$ f 0 ( 500 ) are significant, but not from the $$f_{0}(980)$$ f 0 ( 980 ) state. With some experimental branching ratios to determine the production vertex factors, we make predictions for the branching ratios of the other decay channels, including the vector mesons, in the $$B^0_{(s)}$$ B ( s ) 0 decays, some of which are consistent with the experimental measurements within the uncertainties.

Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images

The need for time and attention, given by the doctor to the patient, due to the increased volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes has encouraged the development of the option to support, constructively and effectively, deep learning models. Deep learning (DL) has experienced an exponential development in recent years, with a major impact on interpretations of the medical image. This has influenced the development, diversification and increase of the quality of scientific data, the development of knowledge construction methods and the improvement of DL models used in medical applications. All research papers focus on description, highlighting, classification of one of the constituent elements of deep learning models (DL), used in the interpretation of medical images and do not provide a unified picture of the importance and impact of each constituent in the performance of DL models. The novelty in our paper consists primarily in the unitary approach, of the constituent elements of DL models, namely, data, tools used by DL architectures or specifically constructed DL architecture combinations and highlighting their “key” features, for completion of tasks in current applications in the interpretation of medical images. The use of “key” characteristics specific to each constituent of DL models and the correct determination of their correlations, may be the subject of future research, with the aim of increasing the performance of DL models in the interpretation of medical images.

An Analysis of the Determinants of Household Expenditures in Rwanda

Economists use two different approaches, unitary and collective, to analyze household decisions. The unitary approach ignores the differences between single-person and multi-person households, whereas the collective approach states that each person in the household must be characterized by specific preferences. The household’s decisions concern mainly the allocation of their income to current consumption or for savings and future consumer expenditures. This study uses the Comprehensive Food Security and Vulnerability Analysis (CFSVA) data collected from a random sample in 2015 in Rwanda. The ordinary least squares (OLS) method was applied to a linear regression model to estimate the household demand functions (total household consumption expenditures, household food consumption expenditures and household nonfood consumption expenditures). The results show that the socioeconomic characteristics of the household, the possession of productive assets and wealth conditions as well as the household locational controls are among the primary drivers of its consumption expenditures. The findings highlight the policy efforts that improve household human capital (education, health), access to and capitalization of productive assets and financial capital, continuous urbanization of rural areas, and sustained provision of quality infrastructure, to achieve high standards of household welfare.

CYBER SECURITY MANAGEMENT MODEL FOR CRITICAL INFRASTRUCTURE PROTECTION

Purpose – in this article, the authors propose a management model for Critical Infrastructure cybersecurity, further development of a model developed by Limba, Plėta, Agafonov, and Damkus (2017). Research methodology – methodology consists of researching the best practices in cybersecurity management for Critical Infrastructures and evaluating the best element to be included. The article offers an overview of the model, including structure and objectives, and further analysis that focuses on pre-existing CI management frameworks. Findings – main results show that, although previously published protocols and models contain valuable elements, there is still the need to implement a comprehensive model which can be applied to every type of CI. Research limitations – research might have been limited due to the lack of a unitary approach to cybersecurity management for CI, meaning the lack of possibility of reference to a similar model and approach. Practical implications – model which is presented in the article could offer a new approach to CI protection strategies and could be the beginning of a more structured approach towards their protection. Originality/Value – model was created by the authors with references to past published protocols and models, which are present in the quotation in the text as well as the bibliography.

Excessive Worrying as the Driving Force of Anxiety During the First COVID-19 Lockdown-Phase in Belgium

Since the WHO declared the COVID-19 pandemic on March 11, 2020, the novel coronavirus, SARS-CoV-2, has profoundly impacted public health and the economy worldwide. But there are not the only ones to be hit. The COVID-19 pandemic has also substantially altered mental health, with anxiety symptoms being one of the most frequently reported problems. Especially, the number of people reporting anxiety symptoms increased significantly during the first lockdown-phase compared to similar data collected before the pandemic. Yet, most of these studies relied on a unitary approach to anxiety, wherein its different constitutive features (i.e., symptoms) were tallied into one sum-score, thus ignoring any possibility of interactions between them. Therefore, in this study, we seek to map the associations between the core features of anxiety during the first weeks of the first Belgian COVID-19 lockdown-phase (n = 2,829). To do so, we implemented, in a preregistered fashion, two distinct computational network approaches: a Gaussian graphical model (GGM) and a directed acyclic graph (DAG). Despite their varying assumptions, constraints, and computational methods to determine nodes (i.e., the variables) and edges (i.e., the relations between them), both GGM and DAG pointed to excessive worrying as a node playing an especially influential role in the network system of the anxiety features. Altogether, our findings offer novel data-driven clues for the ongoing field's larger quest to elucidate, and eventually alleviate, the mental health consequences of the COVID-19 pandemic.

Theoretical study of the $$D^0 \rightarrow K^- \pi ^+ \eta $$ reaction

We develop a model to study the $$D^0 \rightarrow K^- \pi ^+ \eta $$ D 0 → K - π + η weak decay, starting with the color favored external emission and Cabibbo favored mode at the quark level. A less favored internal emission decay mode is also studied as a source of small corrections. Some pairs of quarks are allowed to hadronize producing two pseudoscalar mesons, which posteriorly are allowed to interact to finally provide the $$K^- \pi ^+ \eta $$ K - π + η state. The chiral unitary approach is used to take into account the final state interaction of pairs of mesons, which has as a consequence the production of the $$\kappa $$ κ ($$K^*_0(700)$$ K 0 ∗ ( 700 ) ) and the $$a_0(980)$$ a 0 ( 980 ) resonances, well visible in the invariant mass distributions. We also introduce the $$\bar{K}^{*0} \eta $$ K ¯ ∗ 0 η production in a phenomenological way and show that the s-wave pseudoscalar interaction together with this vector excitation mode are sufficient to provide a fair reproduction of the experimental data. The model provides the relative weight of the $$a_0(980)$$ a 0 ( 980 ) to the $$\kappa $$ κ excitation, and their strength is clearly visible in the low energy part of the $$K \pi $$ K π spectrum.