Assessment of torque ripple minimization techniques for aircraft switched reluctance machine starter/generator

This paper presents an assessment of different torque ripple minimization techniques for more electric aircraft Switched Reluctance Machine (SRM) starter/generator (S/G). SRM is one of the most popular potential candidates for future aircraft S/G. SRM is a type of electric machine that features simple structure hence it is cheap to manufacture, also it is very robust. However, one of the major disadvantages of SRM is that, due to its structural nature, its highly nonlinear characteristics would result in unwanted torque ripple. In order to eliminate/minimise the torque ripple, some techniques were proposed over the last decades. In this paper, several techniques of Torque ripples minimization (TRM) are introduced and then it focuses on the assessment of two techniques suitable for the proposed 45 kW SRM as S/G, namely torque sharing function (TSF) technique and a newly proposed closed-loop torque control (CLTC) technique. An analytical modelling method is also proposed in order to apply the proposed TRM techniques. Conclusions and future works are presented at the end of this paper

Probabilistic Skyline Queries over Uncertain Moving Objects

Data uncertainty inherently exists in a large number of applications due to factors such as limitations of measuring equipments, update delay, and network bandwidth. Recently, modeling and querying uncertain data have attracted considerable attention from the database community. However, how to perform advanced analysis on uncertain data remains an interesting question. In this paper, we focus on the execution of skyline computation over uncertain moving objects. We propose a novel probabilistic skyline model where an uncertain object may take a probability to be in the skyline at a certain time point, therefore a p-t-skyline contains those moving objects whose skyline probabilities are at least p at time point t. Computing probabilistic skyline over a large number of uncertain moving objects is a daunting task in practice. In order to efficiently compute the probabilistic skyline query, we propose a discrete-and-conquer strategy, which follows the sampling-bounding-pruning-refining procedure. To further reduce the skyline computation cost, we propose an enhanced framework that is based on a multi-dimensional indexing structure combined with the discrete-and-conquer strategy. Through extensive experiments with synthetic datasets, we show that the framework can efficiently support skyline queries over uncertain moving object and is scalable on large data sets

Immunotherapy for Renal Cell Carcinoma

Despite the rapid development of therapeutic modalities for advanced or metastatic renal cell carcinoma (mRCC) over the past decade to include traditional immunotherapy, such as high-dose interleukin-2 and interferon-α, as well as a number of targeted antiangiogenic therapies, mRCC continues to be associated with poor prognosis. Currently, immunotherapy has seen tremendous development in the form of immune checkpoint inhibition and vaccines at a dizzying pace, which are being studied in mRCC and are showing promise as important steps in the management of this disease. With so many drugs available to clinicians and patients, properly integrating immunotherapy especially immune checkpoint blockade (ICB) into the treatment paradigm is challenging. Emerging research with additional ICB agents and novel combination strategies is likely to further impact clinical decision-making. The further development of biomarkers for predicting a response is required to achieve optimal efficacy with these therapeutic interventions. This chapter summarizes the current landscape of standard and emerging immune therapeutics and other modalities for mRCC

Magnetic field-modulated exciton generation in organic semiconductors: an intermolecular quantum correlation effect

Magnetoelectroluminescence (MEL) of organic semiconductor has been experimentally tuned by adopting blended emitting layer consisting of both hole and electron transporting materials. A theoretical model considering intermolecular quantum correlation is proposed to demonstrate two fundamental issues: (1) two mechanisms, spin scattering and spin mixing, dominate the two different steps respectively in the process of the magnetic field modulated generation of exciton; (2) the hopping rate of carriers determines the intensity of MEL. Calculation successfully predicts the increase of singlet excitons in low field with little change of triplet exciton population.Comment: 16 pages, 4 figure

Risk Assessment and Prediction of Aflatoxin in Agro-Products

Aflatoxin (AFT), highly toxic and carcinogenic to humans, seriously threatens consumption safety of agro-products. It is necessary to conduct risk assessment of aflatoxin contamination in agro-food products to find out critical control points (CCPs) and develop prediction, prevention and control theories and technologies. In this chapter, risk assessment and prediction of aflatoxin contamination in peanut were taken as an example. The values under the limit of detection (LOD) were replaced by zero, 1/2 LOD or LOD according to their respective proportion, and the distribution of values higher than the LOD was fitted by @RISK software. AFB1 dietary exposure was evaluated based on non-parametric probability risk assessment and margin of exposure (MOE). A risk ranking method was adopted for mycotoxins based on food risk expectation ranking. Spatial analysis of AFB1 contamination was conducted using geographic information system (GIS). Average climatic conditions were calculated by Thiessen polygon method and the relationship between AFB1 concentration and average pre-harvest climatic conditions was obtained through multiple regression. To fulfill the purposes of reducing cost, increasing efficiency, maximizing the role of risk assessment and prediction, and improving the quality and safety of agricultural products, we will continuously focus on developing advanced and integrated technologies and solutions

Endoscopic rhizotomy for chronic lumbar zygapophysial joint pain.

BACKGROUND: Chronic lumbar zygapophysial joint pain is a common cause of chronic low back pain. Percutaneous radiofrequency ablation (RFA) is one of the effective management options; however, the results from the traditional RFA need to be improved in certain cases. The aim of this study is to investigate the effect of percutaneous radiofrequency ablation under endoscopic guidance (ERFA) for chronic low back pain secondary to facet joint arthritis. METHODS: This is a prospective study enrolled 60 patients. The cases were randomized into two groups: 30 patients in the control group underwent traditional percutaneous radiofrequency ablation, others underwent ERFA. The lumbar visual analog scale (VAS), MacNab score, and postoperative complications were used to evaluate the outcomes. All outcome assessments were performed at postoperative 1 day, 1 month, 3 months, 6 months, and 12 months. RESULTS: There was no difference between the two groups in preoperative VAS (P \u3e 0.05). VAS scores, except the postoperative first day, in all other postoperative time points were significantly lower than preoperative values each in both groups (P \u3c 0.05). There was no significant difference between the two groups in VAS at 1 day, 1 month, and 3 months after surgery (P \u3e 0.05). However, the EFRA demonstrated significant benefits at the time points of 3 months and 6 months (P \u3e 0.05). The MacNab scores of 1-year follow-up in the ERFA group were higher than that in the control group (P \u3c 0.05). The incidence of complications in the ERFA group was significantly less than that in the control group (P \u3c 0.05). CONCLUSIONS: ERFA may achieve more accurate and definite denervation on the nerves, which leads to longer lasting pain relief

Use of the D-R Model to Define Trends in the Emergence of Ceftazidime-Resistant Escherichia coli in China

OBJECTIVE: To assess the efficacy of the D-R model for defining trends in the appearance of Ceftazidime-resistant Escherichia coli. METHODS: Actual data related to the manifestation of Ceftazidime-resistant E. coli spanning years 1996-2009 were collected from the China National Knowledge Internet. These data originated from 430 publications encompassing 1004 citations of resistance. The GM(1,1) and the novel D-R models were used to fit current data and from this, predict trends in the appearance of the drug-resistant phenotype. The results were evaluated by Relative Standard Error (RSE), Mean Absolute Deviation (MAD) and Mean Absolute Error (MAE). RESULTS: Results from the D-R model showed a rapid increase in the appearance of Ceftazidime-resistant E. coli in this region of the world. These results were considered accurate based upon the minor values calculated for RSE, MAD and MAE, and were equivalent to or better than those generated by the GM(1,1) model. CONCLUSION: The D-R model which was originally created to define trends in the transmission of swine viral diseases can be adapted to evaluating trends in the appearance of Ceftazidime-resistant E. coli. Using only a limited amount of data to initiate the study, our predictions closely mirrored the changes in drug resistance rates which showed a steady increase through 2005, a decrease between 2005 and 2008, and a dramatic inflection point and abrupt increase beginning in 2008. This is consistent with a resistance profile where changes in drug intervention temporarily delayed the upward trend in the appearance of the resistant phenotype; however, resistance quickly resumed its upward momentum in 2008 and this change was better predicted using the D-R model. Additional work is needed to determine if this pattern of "increase-control-increase" is indicative of Ceftazidime-resistant E. coli or can be generally ascribed to bacteria acquiring resistance to drugs in the absence of alternative intervention

Segmentation of the kidney and its tumor

At present, computer-aided diagnosis and treatment has become a hot research direction. The segmentation of 3D medical images is an important part of computer-aided diagnosis and treatment. This paper uses a two-stage approach to achieve segmentation of kidney and kidney tumors from 3D CT image