Low energy exciton states in a nanoscopic semiconducting ring

We consider an effective mass model for an electron-hole pair in a simplified confinement potential, which is applicable to both a nanoscopic self-assembled semiconducting InAs ring and a quantum dot. The linear optical susceptibility, proportional to the absorption intensity of near-infrared transmission, is calculated as a function of the ring radius $$. Compared with the properties of the quantum dot corresponding to the model with a very small radius $R_0$, our results are in qualitative agreement with the recent experimental measurements by Pettersson {\it et al}.Comment: 4 pages, 4 figures, revised and accepted by Phys. Rev.

Process nano scale mechanical properties measurement of thin metal films using a novel paddle cantilever test structure

A new technique was developed for studying the mechanical behavior of nano-scale thin metal films on substrate is presented. The test structure was designed on a novel "paddle" cantilever beam specimens with dimensions as few hundred nanometers to less than 10 nanometers. This beam is in triangle shape in order to provide uniform plane strain distribution. Standard clean room processing was used to prepare the paddle sample. The experiment can be operated by using the electrostatic deflection on the paddle uniform distributed stress cantilever beam and then measure the deposited thin metal film materials on top of it. A capacitance technique was used to measurement on the other side of the deflected plate to measure its deflection with respect to the force. The measured strain was converted through the capacitance measurement for the deflection of the cantilever. System performance on the residual stress measurement of thin films are calculated with three different forces on the "paddle" cantilever beam, including the force due to the film, compliance force and electrostatic force.Comment: Submitted on behalf of EDA Publishing Association (http://irevues.inist.fr/handle/2042/16838

Exploiting the Power of Human-Robot Collaboration: Coupling and Scale Effects in Bricklaying

As an important contributor to GDP growth, the construction industry is suffering from labor shortage due to population ageing, COVID-19 pandemic, and harsh environments. Considering the complexity and dynamics of construction environment, it is still challenging to develop fully automated robots. For a long time in the future, workers and robots will coexist and collaborate with each other to build or maintain a facility efficiently. As an emerging field, human-robot collaboration (HRC) still faces various open problems. To this end, this pioneer research introduces an agent-based modeling approach to investigate the coupling effect and scale effect of HRC in the bricklaying process. With multiple experiments based on simulation, the dynamic and complex nature of HRC is illustrated in two folds: 1) agents in HRC are interdependent due to human factors of workers, features of robots, and their collaboration behaviors; 2) different parameters of HRC are correlated and have significant impacts on construction productivity (CP). Accidentally and interestingly, it is discovered that HRC has a scale effect on CP, which means increasing the number of collaborated human-robot teams will lead to higher CP even if the human-robot ratio keeps unchanged. Overall, it is argued that more investigations in HRC are needed for efficient construction, occupational safety, etc.; and this research can be taken as a stepstone for developing and evaluating new robots, optimizing HRC processes, and even training future industrial workers in the construction industry

NP-Free: A Real-Time Normalization-free and Parameter-tuning-free Representation Approach for Open-ended Time Series

As more connected devices are implemented in a cyber-physical world and data is expected to be collected and processed in real time, the ability to handle time series data has become increasingly significant. To help analyze time series in data mining applications, many time series representation approaches have been proposed to convert a raw time series into another series for representing the original time series. However, existing approaches are not designed for open-ended time series (which is a sequence of data points being continuously collected at a fixed interval without any length limit) because these approaches need to know the total length of the target time series in advance and pre-process the entire time series using normalization methods. Furthermore, many representation approaches require users to configure and tune some parameters beforehand in order to achieve satisfactory representation results. In this paper, we propose NP-Free, a real-time Normalization-free and Parameter-tuning-free representation approach for open-ended time series. Without needing to use any normalization method or tune any parameter, NP-Free can generate a representation for a raw time series on the fly by converting each data point of the time series into a root-mean-square error (RMSE) value based on Long Short-Term Memory (LSTM) and a Look-Back and Predict-Forward strategy. To demonstrate the capability of NP-Free in representing time series, we conducted several experiments based on real-world open-source time series datasets. We also evaluated the time consumption of NP-Free in generating representations.Comment: 9 pages, 12 figures, 9 tables, and this paper was accepted by 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC 2023

Hybrid Job-driven Scheduling for Virtual MapReduce Clusters

It is cost-efficient for a tenant with a limited budget to establish a virtual MapReduce cluster by renting multiple virtual private servers (VPSs) from a VPS provider. To provide an appropriate scheduling scheme for this type of computing environment, we propose in this paper a hybrid job-driven scheduling scheme (JoSS for short) from a tenant's perspective. JoSS provides not only job level scheduling, but also map-task level scheduling and reduce-task level scheduling. JoSS classifies MapReduce jobs based on job scale and job type and designs an appropriate scheduling policy to schedule each class of jobs. The goal is to improve data locality for both map tasks and reduce tasks, avoid job starvation, and improve job execution performance. Two variations of JoSS are further introduced to separately achieve a better map-data locality and a faster task assignment. We conduct extensive experiments to evaluate and compare the two variations with current scheduling algorithms supported by Hadoop. The results show that the two variations outperform the other tested algorithms in terms of map-data locality, reduce-data locality, and network overhead without incurring significant overhead. In addition, the two variations are separately suitable for different MapReduce-workload scenarios and provide the best job performance among all tested algorithms.Comment: 13 pages and 17 figure