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Quantum mechanical electron tunneling is explored as the mediator of chemical bonding. Three topics are addressed. First, a baseball game-of-catch metaphor is employed to elucidate the physics of chemical bonding. According to this metaphor, a baseball player, a baseball, and the act of throwing a baseball are analogous to an atom, an electron, and the act of electron tunneling. Using this framework, the tunneling physics of covalent, polar covalent, ionic, and hydrogen bonding is clarified. Second, gas-state energies associated with frontier orbital positioning are established and are used in the chemical bond tunneling assessment of four homopolar bonds, H–H, NN, OO, and F–F, and four heteropolar bonds, H–F, H–Cl, H–Br, and H–I. Third, a new procedure for estimating solid-state tunneling parameters is introduced, involving quantum confinement of gas-state energy frontier orbitals.

In Cyber-Physical Systems (CPS), sensor data integrity is crucial since acting on malicious sensor data can cause serious consequences, given the tight coupling between cyber components and physical systems. While extensive works focus on sensor attack detection, attack diagnosis that aims to find out when the attack starts has not been well studied yet. This temporal sensor attack diagnosis problem is equally important because many recovery methods rely on the accurate determination of trustworthy historical data. To address this problem, we propose a lightweight data-driven solution to achieve real-time sensor attack diagnosis. Our novel solution consists of five modules, with the attention and diagnosis ones as the core. The attention module not only helps accurately predict future sensor measurements but also computes statistical attention scores for the diagnosis module. Based on our unique observation that the score fluctuates sharply once an attack launches, the diagnosis module determines the onset of an attack through monitoring the fluctuation. Evaluated on high-dimensional high-fidelity simulators and a testbed, our solution demonstrates robust and accurate temporal diagnosis results while incurring millisecond-level computational overhead on Raspberry Pi.

The Matrix Profile is a data structure that annotates a time series by recording each subsequence’s Euclidean distance to its nearest neighbor. In recent years the community has shown that using the Matrix Profile it is possible to discover many useful properties of a time series, including repeated behaviors, anomalies, evolving patterns, regimes, etc. However, the Matrix Profile is limited to representing the relationship between the subsequence’s shapes. It is known that, for some domains, useful information is conserved not in the subsequence’s shapes, but in the subsequence’s features. In recent years a new set of features for time series called catch22 has revolutionized feature-based mining of time series. Combining these two ideas seems to offer many possibilities for novel data mining applications, however, there are two difficulties in attempting this. A direct application of the Matrix Profile with the catch22 features would be prohibitively slow. Less obviously, as we will demonstrate, in almost all domains, using all twenty-two of the catch22 features produces poor results, and we must somehow select the subset appropriate for the domain. In this work we introduce novel algorithms to solve both problems and demonstrate that for most domains, the proposed $\mathrm{C}^{22}$MP is a state-of-the-art anomaly detector.

Our study investigates the economic growth and catch-up of the NUTS3 regions of 6 Central and Eastern European (CEE) member states of the European Union (EU), 4 countries acceding in 2004 (Czechia, Poland, Hungary, and Slovakia) and further two admitted in 2007 (Bulgaria and Romania), compared to the average of 14 older members of the EU between 2000 and 2019. We based our analysis on the urban–rural region types of the EU in the case of 185 regions, identifying predominantly urban, intermediate, and predominantly rural types. We apply Theil Index to examine the development of disparities and test the phenomena with unconditional β-convergence hypothesis. The analysis indicates that the growth of all CEE countries and their regions is faster than the EU14 average; the capitals considerably exceed it, the catch-up of other urban regions is also relatively fast, while it is very slow in the case of other regions. The convergence between the 185 regions is weak, based on the EU region typology it was initially strong between the capitals, moderate in the case of intermediate and rural types, while divergence can be observed in the urban types. The catch-up of less developed regions is very slow despite EU cohesion funding, even though 80% of the population live here. The stagnation of regional disparities and slow catch-up of less developed regions indicate the poor efficiency of the EU cohesion policy.

A minority of children born small for gestational age (SGA) may experience catch-up growth failure and remain short in adulthood. However, the underlying causes and mechanisms of this phenomenon are not yet fully comprehended. We reviewed the present state of research concerning the growth hormone-insulin-like growth factor axis and growth plate in SGA children who fail to achieve catch-up growth. Additionally, we explored the factors influencing catch-up growth in SGA children and potential molecular mechanisms involved. Furthermore, we considered the potential benefits of supplementary nutrition, specific dietary patterns, probiotics and drug therapy in facilitating catch-up growth.

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