Evolutionary L∞ identification and model reduction for robust control

An evolutionary approach for modern robust control oriented system identification and model reduction in the frequency domain is proposed. The technique provides both an optimized nominal model and a 'worst-case' additive or multiplicative uncertainty bounding function which is compatible with robust control design methodologies. In addition, the evolutionary approach is applicable to both continuous- and discrete-time systems without the need for linear parametrization or a confined problem domain for deterministic convex optimization. The proposed method is validated against a laboratory multiple-input multiple-output (MIMO) test rig and benchmark problems, which show a higher fitting accuracy and provides a tighter L�¢���� error bound than existing methods in the literature do

An integrated wind risk warning model for urban rail transport in Shanghai, China

The integrated wind risk warning model for rail transport presented has four elements: Background wind data, a wind field model, a vulnerability model, and a risk model. Background wind data uses observations in this study. Using the wind field model with effective surface roughness lengths, the background wind data are interpolated to a 30-m resolution grid. In the vulnerability model, the aerodynamic characteristics of railway vehicles are analyzed with CFD (Computational Fluid Dynamics) modelling. In the risk model, the maximum value of three aerodynamic forces is used as the criteria to evaluate rail safety and to quantify the risk level under extremely windy weather. The full model is tested for the Shanghai Metro Line 16 using wind conditions during Typhoon Chan-hom. The proposed approach enables quick quantification of real- time safety risk levels during typhoon landfall, providing sophisticated warning information for rail vehicle operation safety

A new dromaeosaurid (Dinosauria: Theropoda) from the Upper Cretaceous Wulansuhai Formation of Inner Mongolia, China

We describe a new dromaeosaurid theropod from the Upper Cretaceous Wulansuhai Formation of Bayan Mandahu, Inner Mongolia. The new taxon, Linheraptor exquisitus gen. et sp. nov., is based on an exceptionally well-preserved, nearly complete skeleton. This specimen represents the fifth dromaeosaurid taxon recovered from the Upper Cretaceous Djadokhta Formation and its laterally equivalent strata, which include the Wulansuhai Formation, and adds to the known diversity of Late Cretaceous dromaeosaurids. Linheraptor exquisitus closely resembles the recently reported Tsaagan mangas. Uniquely among dromaeosaurids, the two taxa share a large, anteriorly located maxillary fenestra and a contact between the jugal and the squamosal that excludes the postorbital from the infratemporal fenestra. These features suggest a sister-taxon relationship between L. exquisitus and T. mangas, which indicates the presence of a unique dromaeosaurid lineage in the Late Cretaceous of Asia. A number of cranial and dental features seen in L. exquisitus and T. mangas, and particularly some postcranial features of L. exquisitus, suggest that these two taxa are probably intermediate in systematic position between known basal and derived dromaeosaurids. The discovery of Linheraptor exquisitus is thus important for understanding the evolution of some salient features seen in the derived dromaeosaurids

ZIKV infection activates the IRE1-XBP1 and ATF6 pathways of unfolded protein response in neural cells.

BACKGROUND: Many viruses depend on the extensive membranous network of the endoplasmic reticulum (ER) for their translation, replication, and packaging. Certain membrane modifications of the ER can be a trigger for ER stress, as well as the accumulation of viral protein in the ER by viral infection. Then, unfolded protein response (UPR) is activated to alleviate the stress. Zika virus (ZIKV) is a mosquito-borne flavivirus and its infection causes microcephaly in newborns and serious neurological complications in adults. Here, we investigated ER stress and the regulating model of UPR in ZIKV-infected neural cells in vitro and in vivo. METHODS: Mice deficient in type I and II IFN receptors were infected with ZIKV via intraperitoneal injection and the nervous tissues of the mice were assayed at 5 days post-infection. The expression of phospho-IRE1, XBP1, and ATF6 which were the key markers of ER stress were analyzed by immunohistochemistry assay in vivo. Additionally, the nuclear localization of XBP1s and ATF6n were analyzed by immunohistofluorescence. Furthermore, two representative neural cells, neuroblastoma cell line (SK-N-SH) and astrocytoma cell line (CCF-STTG1), were selected to verify the ER stress in vitro. The expression of BIP, phospho-elF2α, phospho-IRE1, and ATF6 were analyzed through western blot and the nuclear localization of XBP1s was performed by confocal immunofluorescence microscopy. RT-qPCR was also used to quantify the mRNA level of the UPR downstream genes in vitro and in vivo. RESULTS: ZIKV infection significantly upregulated the expression of ER stress markers in vitro and in vivo. Phospho-IRE1 and XBP1 expression significantly increased in the cerebellum and mesocephalon, while ATF6 expression significantly increased in the mesocephalon. ATF6n and XBP1s were translocated into the cell nucleus. The levels of BIP, ATF6, phospho-elf2α, and spliced xbp1 also significantly increased in vitro. Furthermore, the downstream genes of UPR were detected to investigate the regulating model of the UPR during ZIKV infection in vitro and in vivo. The transcriptional levels of atf4, gadd34, chop, and edem-1 in vivo and that of gadd34 and chop in vitro significantly increased. CONCLUSION: Findings in this study demonstrated that ZIKV infection activates ER stress in neural cells. The results offer clues to further study the mechanism of neuropathogenesis caused by ZIKV infection

Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge.

Artificial intelligence has rapidly evolved from the experimental phase to the implementation phase in many image-driven clinical disciplines, including ophthalmology. A combination of the increasing availability of large datasets and computing power with revolutionary progress in deep learning has created unprecedented opportunities for major breakthrough improvements in the performance and accuracy of automated diagnoses that primarily focus on image recognition and feature detection. Such an automated disease classification would significantly improve the accessibility, efficiency, and cost-effectiveness of eye care systems where it is less dependent on human input, potentially enabling diagnosis to be cheaper, quicker, and more consistent. Although this technology will have a profound impact on clinical flow and practice patterns sooner or later, translating such a technology into clinical practice is challenging and requires similar levels of accountability and effectiveness as any new medication or medical device due to the potential problems of bias, and ethical, medical, and legal issues that might arise. The objective of this review is to summarize the opportunities and challenges of this transition and to facilitate the integration of artificial intelligence (AI) into routine clinical practice based on our best understanding and experience in this area

Predictable Disruption Tolerant Networks and Delivery Guarantees

This article studies disruption tolerant networks (DTNs) where each node knows the probabilistic distribution of contacts with other nodes. It proposes a framework that allows one to formalize the behaviour of such a network. It generalizes extreme cases that have been studied before where (a) either nodes only know their contact frequency with each other or (b) they have a perfect knowledge of who meets who and when. This paper then gives an example of how this framework can be used; it shows how one can find a packet forwarding algorithm optimized to meet the 'delay/bandwidth consumption' trade-off: packets are duplicated so as to (statistically) guarantee a given delay or delivery probability, but not too much so as to reduce the bandwidth, energy, and memory consumption.Comment: 9 page