Scattering Analysis of Electromagnetic Materials Using Fast Dipole Method Based on Volume Integral Equation

The fast dipole method (FDM) is extended to analyze the scattering of dielectric and magnetic materials by solving the volume integral equation (VIE). The FDM is based on the equivalent dipole method (EDM) and can achieve the separation of the field dipole and source dipole, which reduces the complexity of interactions between two far groups (such as group i and group j) from O(NiNj) to O(Ni+Nj), where Ni and Nj are the numbers of dipoles in group i and group j, respectively. Targets including left-handed materials (LHMs), which are a kind of dielectric and magnetic materials, are calculated to demonstrate the merits of the FDM. Furthermore, in this study we find that the convergence may become much slower when the targets include LHMs compared with conventional electromagnetic materials. Numerical results about convergence characteristics are presented to show this property

Online and semi-online scheduling on two hierarchical machines with a common due date to maximize the total early work

In this study, we investigated several online and semi-online scheduling problems on two hierarchical machines with a common due date to maximize the total early work. For the pure online case, we designed an optimal online algorithm with a competitive ratio of $\sqrt 2$. For the case when the total processing time is known, we proposed an optimal semi-online algorithm with a competitive ratio of $\frac{4}{3}$. Additionally, for the cases when the largest processing time is known, we gave optimal algorithms with a competitive ratio of $\frac{6}{5}$ if the largest job is a lower hierarchy one, and of $\sqrt 5-1$ if the largest job is a higher hierarchy one, respectively

The response of sea ice and high-salinity shelf water in the Ross Ice Shelf Polynya to cyclonic atmosphere circulations

Coastal polynyas in the Ross Sea are important source regions of high-salinity shelf water (HSSW) - the precursor of Antarctic Bottom Water thatsupplies the lower limb of the thermohaline circulation. Here, the responseof sea ice production and HSSW formation to synoptic-scale and mesoscalecyclones was investigated for the Ross Ice Shelf Polynya (RISP) using acoupled ocean-sea ice-ice shelf model targeted on the Ross Sea. Whensynoptic-scale cyclones prevailed over RISP, sea ice production (SIP)increased rapidly by 20 %-30 % over the entire RISP. During the passage of mesoscale cyclones, SIP increased by about 2 times over the western RISP but decreased over the eastern RISP, resulting respectively from enhancement inthe offshore and onshore winds. HSSW formation mainly occurred in thewestern RISP and was enhanced responding to the SIP increase under bothtypes of cyclones. Promoted HSSW formation could persist for 12-60 h after the decay of the cyclones. The HSSW exports across the DrygalskiTrough and the Glomar Challenger Trough were positively correlated with themeridional wind. Such correlations are mainly controlled by variations ingeostrophic ocean currents that result from sea surface elevation change and density differences.Peer reviewe

WebLab: a data-centric, knowledge-sharing bioinformatic platform

With the rapid progress of biological research, great demands are proposed for integrative knowledge-sharing systems to efficiently support collaboration of biological researchers from various fields. To fulfill such requirements, we have developed a data-centric knowledge-sharing platform WebLab for biologists to fetch, analyze, manipulate and share data under an intuitive web interface. Dedicated space is provided for users to store their input data and analysis results. Users can upload local data or fetch public data from remote databases, and then perform analysis using more than 260 integrated bioinformatic tools. These tools can be further organized as customized analysis workflows to accomplish complex tasks automatically. In addition to conventional biological data, WebLab also provides rich supports for scientific literatures, such as searching against full text of uploaded literatures and exporting citations into various well-known citation managers such as EndNote and BibTex. To facilitate team work among colleagues, WebLab provides a powerful and flexible sharing mechanism, which allows users to share input data, analysis results, scientific literatures and customized workflows to specified users or groups with sophisticated privilege settings. WebLab is publicly available at http://weblab.cbi.pku.edu.cn, with all source code released as Free Software

Efficient resource provisioning in compute clouds via VM multiplexing

Resource provisioning in compute clouds often requires an estimate of the capacity needs of Virtual Machines (VMs). The estimated VM size is the basis for allocating resources commensurate with demand. In contrast to the traditional practice of estimating the size of VMs individually, we pro-pose a joint-VM provisioning approach in which multiple VMs are consolidated and provisioned together, based on an estimate of their aggregate capacity needs. This new ap-proach exploits statistical multiplexing among the workload patterns of multiple VMs, i.e., the peaks and valleys in one workload pattern do not necessarily coincide with the others. Thus, the unused resources of a low utilized VM can be bor-rowed by the other co-located VMs with high utilization. Compared to individual-VM based provisioning, joint-VM provisioning could lead to much higher resource utilization. This paper presents three design modules to enable such a concept in practice. Specifically, a performance constraint describing the capacity need of a VM for achieving a certain level of application performance; an algorithm for estimat-ing the aggregate size of multiplexed VMs; a VM selection algorithm that seeks to find those VM combinations with complementary workload patterns. We showcase that the proposed three modules can be seamlessly plugged into ap-plications such as resource provisioning, and providing re-source guarantees for VMs. The proposed method and ap-plications are evaluated by performance data collected from about 16 thousand VMs in commercial data centers. The results demonstrate more than 45 % improvements in terms of the overall resource utilization

Construction and validation of a musculoskeletal disease risk prediction model for underground coal miners

ObjectiveTo understand the prevalence among underground coal miners of musculoskeletal disorders (MSDs), analyze the risk factors affecting MSDs, and develop and validate a risk prediction model for the development of MSDs.Materials and methodsMSD questionnaires were used to investigate the prevalence of work-related musculoskeletal disorders among 860 underground coal miners in Xinjiang. The Chinese versions of the Effort-Reward Imbalance Questionnaire (ERI), the Burnout Scale (MBI), and the Self-Rating Depression Inventory (SDS) were used to investigate the occupational mental health status of underground coal miners. The R4.1.3 software cart installation package was applied to randomly divide the study subjects into a 1:1 training set and validation set, screen independent predictors using single- and multi-factor regression analysis, and draw personalized nomogram graph prediction models based on regression coefficients. Subject work characteristic (ROC) curves, calibration (Calibrate) curves, and decision curves (DCA) were used to analyze the predictive value of each variable on MSDs and the net benefit.Results(1) The prevalence of MSDs was 55.3%, 51.2%, and 41.9% since joining the workforce, in the past year, and in the past week, respectively; the highest prevalence was in the lower back (45.8% vs. 38.8% vs. 33.7%) and the lowest prevalence was in the hips and buttocks (13.3% vs. 11.4% vs. 9.1%) under different periods. (2) Underground coal miners: the mean total scores of occupational stress, burnout, and depression were 1.55 ± 0.64, 51.52 ± 11.53, and 13.83 ± 14.27, respectively. (3) Univariate regression revealed a higher prevalence of MSDs in those older than 45 years (49.5%), length of service > 15 years (56.4%), annual income <$60,000 (79.1$60,000 (OR = 1.742, 95% CI: 1.100–2.759), and severe burnout (OR = 0.284, 95% CI: 0.109–0.739), and that these were independent predictors of the occurrence of MSDs among workers in underground coal mine operations (p <  0.05). (5) The areas under the ROC curve for the training and validation sets were 0.665 (95% CI: 0.615–0.716) and 0.630 (95% CI: 0.578–0.682), respectively, indicating that the model has good predictive ability; the calibration plots showed good agreement between the predicted and actual prevalence of the model; and the DCA curves suggested that the predictive value of this nomogram model for MSDs was good.ConclusionThe prevalence of MSDs among workers working underground in coal mines was high, and the constructed nomogram showed good discriminatory ability and optimal accuracy

Why cannot investors hold Leveraged Exchanged Traded Funds as a long-term investment instrument given the mathematical structure of LETFs?

Investments in Leveraged Exchange-Traded Funds have increased recently. Leverage is a key feature of these funds, and they're utilized mostly by short-term traders. They are also popular with individual investors who want to make leveraged or hedging investments in their portfolios. Critics, however, have charged that LETFs are unsuitable for longer-term investors, due to volatility decay. Volatility decay occurs when the returns of Leveraged Exchange Traded Funds diverge from the multiple returns of their underlying assets as the holding period lengthens. Volatility decay is demonstrated to be accelerated by increasing the leverage multiplier algebraically. In this paper, I will describe the mathematical structures of Leveraged Exchange-Traded Funds and Exchange-Traded Funds in terms of their performance in terms of arithmetic return, geometric return, rebalancing activity, and diffusion process. Using their mathematical structures, it is straightforward to demonstrate the long-term impact of volatility decay on these funds. In addition, I will use historical data to validate the influence of volatility decay, and I will examine the performance of LETFs on the assumption that the portfolio is not subject to volatility decay