Updated projections for COVID-19 omicron wave in Florida

In this report, we use a detailed simulation model to assess and project the COVID-19 epidemic in Florida. The model is a data-driven, stochastic, discrete-time, agent based model with an explicit representation of people and places. Using the model, we find that the omicron variant wave in Florida is likely to cause many more infections than occurred during the delta variant wave. Due to testing limitations and often mild symptoms, however, we anticipate that omicron infections will be underreported compared to delta. We project that reported cases of COVID-19 will continue to grow significantly and peak in early January 2022, and that the number of reported COVID-19 deaths due to omicron may be 1/3 of the total caused by the delta wave.

A review of methodologies for performance evaluation of automated construction processes

PurposeThe aim of this paper is to synthesize knowledge related to performance evaluation of automated construction processes during the planning and execution phases through a theme-based literature classification. The primary research question that is addressed is “How to quantify the performance improvement in automated construction processes?”Design/methodology/approachA systematic literature review of papers on automated construction was conducted involving three stages-planning, conducting and reporting. In the planning stage, the purpose of the review is established through key research questions. Then, a four-step process is employed consisting of identification, screening, shortlisting and inclusion of papers. For reporting, observations were critically analysed and categorized according to themes.FindingsThe primary conclusion from this study is that the effectiveness of construction processes can only be benchmarked using realistic simulations. Simulations help to pinpoint the root causes of success or failure of projects that are either already completed or under execution. In automated construction, there are many complex interactions between humans and machines; therefore, detailed simulation models are needed for accurate predictions. One key requirement for simulation is the calibration of the models using real data from construction sites.Research limitations/implicationsThis study is based on a review of 169 papers from a database of peer-reviewed journals, within a time span of 50 years.Originality/valueGap in research in the area of performance evaluation of automated construction is brought out. The importance of simulation models calibrated with on-site data within a methodology for performance evaluation is highlighted.

Application of the space-based optical interferometer towards measuring cosmological distances of quasars.

Measuring the quasar distance through joint analysis of spectroastrometry (SA) and reverberation mapping (RM) observations is a new method for driving the development of cosmology. In this paper, we carry out detailed simulation and analysis to study the effect of four basic observational parameters (baseline length, exposure time, equivalent diameter and spectral resolution) on the data quality of differential phase curves (DPCs), furthermore on the accuracy of distance measurement. In our simulation, we adopt an axis symmetrical disc model of broad line region (BLR) to generate differential phase signals. We find that the differential phases and their Poisson errors could be amplified by extending the baseline, while the influence of OPD errors can be reduced during fitting the BLR model. Longer exposure time or larger equivalent diameter helps reduce the absolute Poisson error. Therefore, the relative error of DPCs could be reduce by increasing any of the above three parameters, then the the accuracy of distance measurement could be improved. In contrast, the uncertainty of $D_{\rm{A}}$ could be improved with higher spectral resolution, although the relative error of DPCs would be amplified. We show how the uncertainty of distance measurement varies with the relative error of DPCs. It is found that the relative error of DPCs $<$ 20$\%$ is a limit for accurate distance measurement. As any of the basic observational parameters become larger, the relative error of DPCs have a lower limit (roughly 5$\%$) and the uncertainty of distance measurement can be better than 2$\%$.

Human-Centered Applications in Sustainable Smart City Development: A Qualitative Survey

Corporate Industries apply new technologies for manufacturing and production applications. This makes the process depend upon multiple computers, robotic applications, and varying specifications, which reduces efficiency and speed. These technological challenges are incredibly diverse since an extensive range of processing technologies is available. When it comes to human-centered automation, it’s all about the scientific knowledge and data encapsulation for robots to interact within this field. Robots must operate in various ecosystems and interact closely with non-professional customers. Current technology is not well adapted to this scenario, requiring sustainable management separated from available technology. In this article, the human-centered Industrial Robot using Artificial Intelligence (AI-HCIR) is suggested as a tool to resolve such issues. Several manufacturing protocols are presented to efficiently produce products that simplify human work and involve different needs. The HCIR scrutinizes it for time allocation and manufacturing time allocation. For this, a suitable processing environment and a detailed simulation is conducted. The proposed AI-HCIR achieves a 33.68% error rate, 24% throughput, and 18.7% efficiency in the smart city environment. In contrast with conventional methods, the proposed method obtains a better result efficiently.

Simplified calculation of friction mean effective pressure for fast simulation of fuel consumption

An environmentally friendly and economical ship operation can be accomplished through many different methods. Most of these approaches focus on technological solutions, e.g. internal engine measures in order to make the engine more eco-friendly, by changing engine control parameters for a better connection between propulsion system and ship, by usage of different fuels or fuel supplements or installation of exhaust gas-treatment systems. For many ships, it is neither efficient nor economically viable to replace or improve existing power generation or propulsion systems in order to improve efficiency or reduce emissions. Some of the internal measures used to reduce NOx-emissions like exhaust gas recirculation even lead to a higher fuel consumption. The vessel itself is still controlled by a crew and they should be kept in the loop to improve efficiency. Therefore optimal operational procedures for handling ships and specifically the outcome of engine manoeuvres is a substantial source for eco-friendly ship operations. The German research project MEmBran (Modelling Emissions and Fuel Consumption during Ship Manoeuvres) addresses especially the basis for optimising ship engine manoeuvres. It focusses on very detailed simulation of the processes of currently existing ship diesel engines, especially in a first step 4-stroke engines in order to implement models in wider comprehensive ship handling simulation software. As part of an existing planning and prediction software that can be used on board, it enables the watch keeping nautical officer and the shipping company to forecast and compare the fuel consumption of the ship for each manoeuvre. In order to reach this goal it is necessary to use fast calculating and stable methods that can be used to forecast the power output of the engine and the fuel consumption. This paper discusses an approach to calculate friction mean effective pressure.

A Dynamic Holding Approach to Stabilizing a Bus Line Based on the Q-Learning Algorithm with Multistage Look-Ahead

The unreliable service and the unstable operation of a high-frequency bus line are shown as bus bunching and the uneven distribution of headways along the bus line. Although many control strategies, such as the static and dynamic holding strategies, have been proposed to solve the above problems, many of them take on some oversimplified assumptions about the real bus line operation. So it is hard for them to continuously adapt to the evolving complex system. In view of this dynamic setting, we present an adaptive holding method that combines the classic approximate dynamic programming (ADP) with the multistage look-ahead mechanism. The holding time, the only control means used in this study, will be determined by estimating its impact on the operation stability of the bus line system in the remaining observation period. The multistage look-ahead mechanism introduced into the classic Q-learning algorithm of the ADP model makes it easy that the algorithm gets through its earlier unstable phase more quickly and easily. During the implementation of the new holding approach, the past experiences of holding operations can be cumulated effectively into an artificial neural network used to approximate the unavailable Q-factor. The use of a detailed simulation system in the new approach makes it possible to take into account most of the possible causes of instability. The numerical experiments show that the new holding approach can stabilize the system by producing evenly distributed headway and removing bus bunching thoroughly. Compared with the terminal station holding strategies, the new method brings a more reliable bus line with shorter waiting times for passengers.

Properties and Applications of the Modified Kies–Lomax Distribution with Estimation Methods

The present study introduces a new three-parameter model called the modified Kies–Lomax (MKL) distribution to extend the Lomax distribution and increase its flexibility in modeling real-life data. The MKL distribution, due to its flexibility, provides left-skewed, symmetrical, right-skewed, and reversed-J shaped densities and increasing, unimodal, decreasing, and bathtub hazard rate shapes. The MKF density can be expressed as a linear mixture of Lomax densities. Some basic mathematical properties of the MKF model are derived. Its parameters are estimated via six estimation algorithms. We explore their performances using detailed simulation results, and the partial and overall ranks are provided for the measures of absolute biases, mean square errors, and mean relative errors to determine the best estimation method. The results show that the maximum product of spacings and maximum likelihood approaches are recommended to estimate the MKL parameters. Finally, the flexibility of the MKL distribution is checked using two real datasets, showing that it can provide close fit to both datasets as compared with other competing Lomax models. The three-parameter MKL model outperforms some four-parameter and five-parameter rival models.

Single Level Tunneling Model for Molecular Junctions: Evaluating the Simulation Methods

Single level tunneling model has been the most popular model system in both the experimental and theoretical study of molecular junctions. We performed a detailed simulation study on the performance of the single level tunneling model in analyzing the charge transport mechanism of molecular junctions. Three different modeling methods, including the numerical integration of the Landauer formula and two approximated analytical formulas that are extensively used for extracting key transport parameters from current–voltage (I-V) characteristics, i.e. the energy offset and the coupling between molecule and electrode, were compared and evaluated for their applicability. The simulation of I-V plots shows that the applicability of the two approximated analytical models is energy offset and coupling strength dependent. Model fitting based on the three methods performed on experimental data attained from representative literature papers revealed that the two approximated analytical methods are neither suitable for the situation of small coupling strength and low energy offset, and they also deviated from the exact results at high bias. We finally provided a phase map of the applicability of different modeling methods as a guide for their proper usage in charge transport study in molecular devices.

Single Level Tunneling Model for Molecular Junctions: Evaluating the Simulation Methods

Single level tunneling model has been the most popular model system in both the experimental and theoretical study of molecular junctions. We performed a detailed simulation study on the performance of the single level tunneling model in analyzing the charge transport mechanism of molecular junctions. Three different modeling methods, including the numerical integration of the Landauer formula and two approximated analytical formulas that are extensively used for extracting key transport parameters from current–voltage (I-V) characteristics, i.e. the energy offset and the coupling between molecule and electrode, were compared and evaluated for their applicability. The simulation of I-V plots shows that the applicability of the two approximated analytical models is energy offset and coupling strength dependent. Model fitting based on the three methods performed on experimental data attained from representative literature papers revealed that the two approximated analytical methods are neither suitable for the situation of small coupling strength and low energy offset, and they also deviated from the exact results at high bias. We finally provided a phase map of the applicability of different modeling methods as a guide for their proper usage in charge transport study in molecular devices.