The ongoing COVID-19 pandemic has led to a serious health crisis, and information obtained from disease transmission models fitted observed data is needed inform containment strategies. As the of virus varies city in different countries, we use two-level individual-level model analyze spatiotemporal SARS-CoV-2 spread. However, inference procedures such as Bayesian Markov chain Monte Carlo, whic...

Geographically dependent individual level models (GD-ILMs) are a class of statistical that can be used to study the spread infectious disease through population in discrete-time which covariates measured both at and area levels. The typical ILMs illustrate spatial data based on distance between susceptible individuals. A key feature GD-ILMs is they take into account location individuals additio...

This paper aims to study the open source integrated library management systems,,i.e. Koha and NewGenLib, to inform librarians about what considerations to make when choosing an open source integrated library management system (ILMS) for their library. The paper provides a detailed comparative analysis of both types of software, i.e. Koha and NewGenLib which are undertaken in the study. Both typ...

Both bright and dark traveling, locked, intrinsic localized modes (ILMs) have been generated with a spatially uniform driver at a frequency in the acoustic spectrum of a nonlinear micromechanical cantilever array. Complementary numerical simulations show that a minimum density of modes, hence array size, is required for the formation of such locked smoothly running excitations. Additional simul...

The past decade has seen great progress in research on large scale mapping and perception in static environments. Real world perception requires handling uncertain situations with multiple possible interpretations: e.g. changing appearances, dynamic objects, and varying motion models. These aspects of perception have been largely avoided through the use of heuristics and preprocessing. This the...

Appropriate bias is widely viewed as the key to efficient learning and generalization. I present a new algorithm, the Incremental Delta-Bar-Delta (IDBD) algorithm, for the learning of appropriate biases based on previous learning experience. The IDBD algorithm is developed for the case of a simple, linear learning system—the LMS or delta rule with a separate learning-rate parameter for each inp...

In this paper we consider the problem of distributed adaptive estimation in wireless sensor networks for two different observation noise conditions. In the first case, we assume that there are some sensors with high observation noise variance (noisy sensors) in the network. In the second case, different variance for observation noise is assumed among the sensors which is more close to real scen...

This paper describes the R package EpiILMCT, which allows users to study spread of infectious disease using continuous time individual level models (ILMs). The provides tools for simulation from ILMs that are based on either spatial demographic, contact network, or a combination both them, and graphical summarization epidemics. Model fitting is carried out within Bayesian Markov Chain Monte Car...