The Deterministic Nature of Sensor-Based Information for Condition Monitoring of the Cutting Process

Condition monitoring of the cutting process is a core function of autonomous machining and its success strongly relies on sensed data. Despite the enormous amount of research conducted so far into condition monitoring of the cutting process, there are still limitations given the complexity underlining tool wear; hence, a clearer understanding of sensed data and its dynamical behavior is fundamental to sustain the development of more robust condition monitoring systems. The dependence of these systems on acquired data is critical and determines the success of such systems. In this study, data is acquired from an experimental setup using some of the commonly used sensors for condition monitoring, reproducing realistic cutting operations, and then analyzed upon their deterministic nature using different techniques, such as the Lyapunov exponent, mutual information, attractor dimension, and recurrence plots. The overall results demonstrate the existence of low dimensional chaos in both new and worn tools, defining a deterministic nature of cutting dynamics and, hence, broadening the available approaches to tool wear monitoring based on the theory of chaos. In addition, recurrence plots depict a clear relationship to tool condition and may be quantified considering a two-dimensional structural measure, such as the semivariance. This exploratory study unveils the potential of non-linear dynamics indicators in validating information strength potentiating other uses and applications.

A Structural Measure of the Shadow Federal Funds Rate

We propose a shadow policy interest rate based on an estimated structural model that accounts for the zero lower bound. The lower bound constraint, if expected to bind, is contractionary and increases the shadow rate compared to an unconstrained systematic policy response. By contrast, forward guidance and other unconventional policies that extend the expected duration of zero-interest-rate policy are expansionary and decrease the shadow rate. By quantifying these distinct effects, our structural shadow federal funds rate better captures the stance of monetary policy given economic conditions than a shadow rate based only on the term structure of interest rates.

Does Market Power Affect Banking Dividend Policy? Evidence from Nepal

We attempt to explain how market power impacts bank dividend payment behaviors in Nepal by taking the sample from the commercial banking sector employing a panel data regression model. Using the Lerner Index (LI), a non-structural measure of market power or lack of competition, we found that market power inversely but statistically insignificantly affect dividend payment. This finding leads us to conclude that market power-a proxy of more or less competition is not an important and influencing factor to the dividend decisions in commercial banking sectors signifying that competition does not seem helpful in mitigating agency conflicts. It is also concluded that banking dividend payouts are not the result of the punitive influence of product market antagonism. Further, among other firm-specific determinants, bank size and leverage significantly positively whereas asset growth significantly negatively affect the dividend decision. However, profitability is found insignificant determinant of dividend payment. The paper enriches and contributes to the literature on banking dividend payout and helps to identify the key factors that affect banking dividend decision-making. Keywords : Banks, Market competition, Market power, Lerner Index, Nepal

Nonlinear Simulation and Vulnerability Analysis of Masonry Structures Impacted by Flash Floods

The vulnerability of buildings impacted by flash floods is often assessed by empirical approaches. However, the damage-developing mechanisms of buildings remain unclear. This study presented a nonlinear numerical analysis for the interaction between flash floods and masonry structures. A new Structured ALE (S-ALE) solver in an explicit finite element platform LS-DYNA was applied. Thirty different scenarios, including two different FE models, three different water depths, and five different flow velocities, were studied. Nonlinear structural responses and failure mechanisms of masonry structures were analyzed and compared. Results showed that due to the consideration of wall damage, the time evolution of the impact force on the entire building was distributed in a multipeak pattern. Under the impact of flash floods, the building was in a complex bending-shear state, and the overturning moment was the principal reason for the building damage. The critical role played by the structural measure was reaffirmed. Moreover, the physical vulnerability was quantified through a macroscopic damage index, the lateral drift ratio of the ground floor. It can be concluded that the physical vulnerability depends on both the local structural strength (local view) and the structural resistance hierarchies (global view).

Prioritizing allocation of COVID-19 vaccines based on social contacts increases vaccination effectiveness

We study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. Even optimistic estimates suggest that most countries will likely take 6 to 24 months to vaccinate their citizens. These time estimates and the emergence of new viral strains urge us to find quick and effective ways to allocate the vaccines and contain the pandemic. While current approaches use combinations of age-based and occupation-based prioritizations, our strategy marks a departure from such largely aggregate vaccine allocation strategies. We propose a novel agent-based modeling approach motivated by recent advances in (i) science of real-world networks that point to efficacy of certain vaccination strategies and (ii) digital technologies that improve our ability to estimate some of these structural properties. Using a realistic representation of a social contact network for the Commonwealth of Virginia, combined with accurate surveillance data on spatio-temporal cases and currently accepted models of within- and between-host disease dynamics, we study how a limited number of vaccine doses can be strategically distributed to individuals to reduce the overall burden of the pandemic. We show that allocation of vaccines based on individuals’ degree (number of social contacts) and total social proximity time is significantly more effective than the currently used age-based allocation strategy in terms of number of infections, hospitalizations and deaths. Our results suggest that in just two months, by March 31, 2021, compared to age-based allocation, the proposed degree-based strategy can result in reducing an additional 56–110k infections, 3.2–5.4k hospitalizations, and 700–900 deaths just in the Commonwealth of Virginia. Extrapolating these results for the entire US, this strategy can lead to 3–6 million fewer infections, 181–306k fewer hospitalizations, and 51–62k fewer deaths compared to age-based allocation. The overall strategy is robust even: (i) if the social contacts are not estimated correctly; (ii) if the vaccine efficacy is lower than expected or only a single dose is given; (iii) if there is a delay in vaccine production and deployment; and (iv) whether or not non-pharmaceutical interventions continue as vaccines are deployed. For reasons of implementability, we have used degree, which is a simple structural measure and can be easily estimated using several methods, including the digital technology available today. These results are significant, especially for resource-poor countries, where vaccines are less available, have lower efficacy, and are more slowly distributed.

Inverse Percolation to Quantify Robustness in Multiplex Networks

Inverse percolation is known as the problem of finding the minimum set of nodes whose elimination of their links causes the rupture of the network. Inverse percolation has been widely used in various studies of single-layer networks. However, the use and generalization of multiplex networks have been little considered. In this work, we propose a methodology based on inverse percolation to quantify the robustness of multiplex networks. Specifically, we present a modified version of the mathematical model for the multiplex-vertex separator problem (m-VSP). By solving the m-VSP, we can find nodes that cause the rupture of the mutually connected giant component (MCGC) and the large viable cluster (LVC) when their links are removed from the network. The methodology presented in this work was tested in a set of benchmark networks, and as case study, we present an analysis using a set of multiplex social networks modeled with information about the main characteristics of the best universities in the world and the universities in Mexico. The results show that the methodology presented in this work can work in different models and types of 2- and 3-layer multiplex networks without dividing the entire multiplex network into single-layer as some techniques described in the specific literature. Furthermore, thanks to the fact that the technique does not require the calculation of some structural measure or centrality metric, and it is easy to scale for networks of different sizes.

Combined multi-modal assessment of glaucomatous damage with electroretinography and optical coherence tomography/angiography

PurposeTo compare the diagnostic performance and to evaluate the interrelationship of electroretinographical and structural and vascular measures in glaucoma.MethodsFor 14 eyes of 14 healthy controls and 15 eyes of 12 patients with glaucoma ranging from preperimetric to advanced stages OCT, OCT-A and electrophysiological measures [multifocal photopic negative response ratio (mfPhNR) and steady state pattern electroretinogram (ssPERG)] were applied to assess changes in retinal structure, microvasculature, and function, respectively. The diagnostic performance was assessed via area-under-curve (AUC) measures obtained from ROC analyses. The interrelation of the different measures was assessed with correlation analyses.ResultsmfPhNR and ssPERG amplitudes, parafoveal (pfVD) and peripapillary vessel density (pVD), macular ganglion cell inner plexiform layer thickness (mGCIPL) and peripapillary retinal nerve fibre layer thickness (pRNFL) were significantly reduced in glaucoma. The AUC for mfPhNR was highest among diagnostic modalities (AUC: 0.88, 95%-CI: 0.75-1.0, P< 0.001), albeit not statistically different from that for macular (mGCIPL: 0.76, 0.58-0.94, P< 0.05; pfVD: 0.81, .65-.97, P< 0.01) or peripapillary imaging (pRNFL: 0.85, 0.70-1.0, P< 0.01; pVD: 0.82, 0.68-0.97, P < 0.01). Combined functional/vascular measures yielded the highest AUC (mfPhNR-pfVD: 0.94, 0.85-1.0, P<0.001). The functional/structural measure correlation (mfPhNR-mGCIPL correlation coefficient (rs): 0.58, P = 0.001; mfPhNR-pRNFL rs: 0.66, P < 0.0001) was stronger than the functional-vascular correlation (mfPhNR-pfVD rs: 0.29, P = 0.13; mfPhNR-pVD rs: 0.54, P = 0.003).ConclusionsThe combination of ERG measures and OCT-A improved diagnostic performance in glaucoma. Combing ERG, structural and OCT-A parameters provides an enhanced understanding of the pathophysiology of glaucoma.

Structure prediction algorithm for protein complexes based on gene ontology

We propose an algorithm for comparing protein-protein complexes based on their functional properties in terms of Gene Ontology. The proposed measure of a functional similarity between complexes is combined with a structural measure to find templates for the template-based docking of protein complexes. We present the results on the modeling of protein complexes based on this algorithm.