We introduce the boundary reproduction number, adapted from the basic reproduction number in mathematical epidemiology, to assess whether an infusion of species will persist or become exhausted in a chemical reaction system. Our main contributions are as follows: (a) we show how the concept of a siphon, prevalent in Petri nets and chemical reaction network theory, identifies sets of species that may become depleted at steady state, analogous to a disease-free boundary steady state; (b) we develop an approach for incorporating biochemically motivated conservation laws, which allows the stability of boundary steady states to be determined within specific compatibility classes; and (c) we present an effective heuristic for decomposing the Jacobian of the system that reduces the computational complexity required to compute the stability domain of a boundary steady state. The boundary reproduction number approach significantly simplifies existing parameter-dependent methods for determining the stability of boundary steady states in chemical reaction systems and has implications for the capacity of critical metabolites and substrates in metabolic pathways to become exhausted.
Herein, we comprehensively investigated the structural, electronic, optical, and photocatalytic properties of van der Waals heterostructure (vdWHs) MASnBr3/MoS2 (MA: CH3NH3). Monolayer MASnBr3 exhibits dynamical stability, as confirmed by phonon spectrum analysis, but suffers from a wide direct bandgap (2.72 eV at the HSE06-SOC (Heyd-Scuseria-Ernzerhof 2006 - Spin Orbit Coupling) level), limiting its photovoltaic efficiency. The formation of heterostructure with MoS2 results in type-II band alignment that facilitates efficient carrier separation, with HSE06-SOC band gaps of 1.89 eV (for AA-configuration) and 1.36 eV (for AB-configuration), aligning optimally with the solar spectrum, while strain engineering further tunes the band gap, extending light absorption into the near-infrared region. The heterostructure exhibits remarkable optoelectronic performance, including a high optical absorption coefficient (8 x 105 cm- 1) and a Spectroscopic Limited Maximum Efficiency (SLME) of up to 30 %, exceeding that of conventional lead-based perovskites and monolayer MASnBr3. Favorable valence and conduction band offsets (VBO = 0.48 eV, CBO = 1.6 eV) ensure rapid electron-hole separation, while robust mechanical stability (Young's modulus approximate to 80 N & sdot;m- 1) underscores practical viability. These attributes, combined with its potential for photocatalytic hydrogen evolution, position the vdWHs MASnBr3/MoS2 as a promising candidate for sustainable photovoltaics and photocatalysis, offering tunable optoelectronic properties with robust structural stability.
Recently, a fit of charmless B→ PP decays (B ∈{B^0, B^+, B_s^0}, P ∈{ π, K, η, η' }) to the latest data was performed under the assumption of flavour SU(3) symmetry [SU(3)_F]. It was found that there is a 4.1σ disagreement with the SU(3)_F limit of the Standard Model [SM_SU(3)_F]. In this paper, we extend this analysis to charmless B → VV decays (V ∈{ρ, K^*, ϕ, ω}). The fit examining B → ρK^* decays, assuming only isospin symmetry, is found to be acceptable. When we fit to B → VV decays with V ∈{ ρ, K^* } within SU(3)_F, we find a 5.2σ discrepancy with SM_SU(3)_F. Finally, when B → VV decays with V ∈{ ρ, K^*, ϕ, ω} are considered, the discrepancy grows to >7σ. The theoretical input in this analysis is modest, so our results are quite rigorous, group theoretically, and hold almost exactly in the SU(3)_F limit. Although it seems unlikely that the introduction of ∼ 30
Cardiovascular disease is the leading cause of death worldwide. There is a need for advanced, precise, and scalable diagnostic procedures to identify and address it promptly. This study uses clinical and demographic data from the Cleveland Heart Disease dataset to develop a new deep learning method for predicting heart disease. The approach facilitates transfer learning and tabular transformer architectures, such as SAINT and FT-Transformer. Our approach distinguishes itself from conventional machine learning methodologies by utilizing pretrained representations and attention mechanisms to identify intricate relationships between samples and attributes. Traditional machine learning algorithms depend on a limited set of fabricated features and interactions. Our systematic methodology incorporates principal component analysis (PCA) for dimensionality reduction, domain-specific feature engineering, modifications to the transformer layer for binary classification, and improved data preparation. Using a fixed base encoder significantly reduces training time and improves generalization when learning a specific classification head. Extensive testing revealed that the SAINT-based model outperformed random forests, logistic regression, and CNN-MLP models. The study’s findings suggest that tabular converters can accurately interpret structured clinical data and that transfer learning is a beneficial approach for medical predictive analytics. The proposed method is a versatile and scalable solution for various diagnostic applications in healthcare AI, facilitating the prediction of heart disease.
In the face of lockdowns promulgated in response to COVID-19, the effects on local planning are diverse. One of the effects is planners’ use of tactical urbanism in their efforts to improve public health. Tactical urbanism is characterized as temporary, and it is appropriate to ask whether tactical strategies help keep communities healthy in times of pandemic. We address that question by examining several examples of a tactical program called Slow Streets. This program provides temporary public spaces in the streets for exercise and interaction. We review literature to understand the relevance of the program to tactical urbanism, and we investigate how successful it has been. Survey questionnaires complement the research. The study outcomes suggest that the pandemic forced planners to act swiftly, but the Slow Streets program was limited in improving the health of the underserved population because it lacks comprehensive community engagement and alignment with longer term plans.
