Time Series Outlier Detection Based on Sliding Window Prediction

In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI), which can be calculated by the predicted value and confidence coefficient. The use of PCI as threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis

Extension of the Lower Load Limit in Dieseline Compression Ignition Mode

AbstractA study to extend the low load limit of the mixture of commercial gasoline and diesel in the compression mode is performed on a single cylinder diesel engine. The additional measures, like intake heating, rebreathing, negative valve overlap, are not employed. By adopting boosting, sweeping the injection pressure and varying the fuel octane number, the minimum fuelling rate and the minimum IMEP gained is compared. Besides, the thermal efficiency and emission results at these operation points are also discussed.The results illustrate that the high intake pressure, the low injection pressure and the low fuel octane number are very effective to extend low load limit. With these strategies, gasoline-type fuels can get the lowest load 0.07MPa IMEP (0.14MPa intake pressure and 20MPa injection pressure) and successfully replace diesel at low load operation points in the compression mode. Increasing the intake pressure and reducing the injection pressure can significantly reduce the minimum fuelling rate and then the minimum IMEP. The minimum IMEP (0.34MPa) of the calibration point on the original engine at test speed (1600rpm) can be achieved by G80 without boosting.The combustion efficiency is influenced by the intake pressure and the injection pressure, however, the ISFC is more dependent on the engine load rather than other factors. If there is more over-lean mixture in cylinder when adjusting the experimental conditions, CO and HC emissions are higher. To satisfy the Euro VI regulation on NOx (<0.4g/kWh), a small amount of EGR is needed to control NOx emission

Zinc inhibits TRPV1 to alleviate chemotherapy-induced neuropathic pain

Zinc is a transition metal that has a long history of use as an anti-inflammatory agent. It also soothes pain sensations in a number of animal models. However, the effects and mechanisms of zinc on chemotherapy-induced peripheral neuropathy remain unknown. Here we show that locally injected zinc markedly reduces neuropathic pain in male and female mice induced by paclitaxel, a chemotherapy drug, in a TRPV1-dependent manner. Extracellularly applied zinc also inhibits the function of TRPV1 expressed in HEK293 cells and mouse DRG neurons, which requires the presence of zinc-permeable TRPA1 to mediate entry of zinc into the cytoplasm. Moreover, TRPA1 is required for zinc-induced inhibition of TRPV1-mediated acute nociception. Unexpectedly, zinc transporters, but not TRPA1, are required for zinc-induced inhibition of TRPV1-dependent chronic neuropathic pain produced by paclitaxel. Together, our study demonstrates a novel mechanism underlying the analgesic effect of zinc on paclitaxel-induced neuropathic pain that relies on the function of TRPV1

Neural Cognitive Diagnosis for Intelligent Education Systems

Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover the proficiency level of students on specific knowledge concepts. Existing approaches usually mine linear interactions of student exercising process by manual-designed function (e.g., logistic function), which is not sufficient for capturing complex relations between students and exercises. In this paper, we propose a general Neural Cognitive Diagnosis (NeuralCD) framework, which incorporates neural networks to learn the complex exercising interactions, for getting both accurate and interpretable diagnosis results. Specifically, we project students and exercises to factor vectors and leverage multi neural layers for modeling their interactions, where the monotonicity assumption is applied to ensure the interpretability of both factors. Furthermore, we propose two implementations of NeuralCD by specializing the required concepts of each exercise, i.e., the NeuralCDM with traditional Q-matrix and the improved NeuralCDM+ exploring the rich text content. Extensive experimental results on real-world datasets show the effectiveness of NeuralCD framework with both accuracy and interpretability

The immunosuppressive effects and mechanisms of loureirin B on collagen-induced arthritis in rats

IntroductionRheumatoid arthritis (RA) is a common disease mainly affecting joints of the hands and wrists. The discovery of autoantibodies in the serum of patients revealed that RA belonged to the autoimmune diseases and laid a theoretical basis for its immunosuppressive therapy. The pathogenesis of autoimmune diseases mainly involves abnormal activation and proliferation of effector memory T cells, which is closely related to the elevated expression of Kv1.3, a voltage-gated potassium (Kv) channel on the effector memory T cell membrane. Drugs blocking the Kv1.3 channel showed a strong protective effect in RA model animals, suggesting that Kv1.3 is a target for the discovery of specific RA immunosuppressive drugs.MethodsIn the present study, we synthesized LrB and studied the effects of LrB on collagen- induced arthritis (CIA) in rats. The clinical score, paw volume and joint morphology of CIA model rats were compared. The percentage of CD3+, CD4+ and CD8+ T cells in rat peripheral blood mononuclear and spleen were analyzed with flow cytometry. The concentrations of inflammatory cytokines interleukin (IL)-1b, IL-2, IL-4, IL-6, IL-10 and IL-17 in the serum of CIA rats were analyzed with enzyme-linked immunosorbent assay. The IL-1b and IL-6 expression in joints and the Kv1.3 expression in peripheral blood mononuclear cells (PBMCs) were quantified by qPCR. To further study the mechanisms of immunosuppressive effects of LrB, western blot and immunofluorescence were utilized to study the expression of Kv1.3 and Nuclear Factor of Activated T Cells 1 (NFAT1) in two cell models - Jurkat T cell line and extracted PBMCs.ResultsLrB effectively reduced the clinical score and relieved joint swelling. LrB could also decrease the percentage of CD4+ T cells, while increase the percentage of CD8+ T cells in peripheral blood mononuclear and spleen of rats with CIA. The concentrations of inflammatory cytokines interleukin (IL)-1b, IL-2, IL-6, IL-10 and IL-17 in the serum of CIA rats were significantly reduced by LrB. The results of qPCR showed that Kv1.3 mRNA in the PBMCs of CIA rats was significantly higher than that of the control and significantly decreased in the LrB treatment groups. In addition, we confirmed in cell models that LrB significantly decreased Kv1.3 protein on the cell membrane and inhibited the activation of Nuclear Factor of Activated T Cells 1 (NFAT1) with immune stimulus.ConclusionIn summary, this study revealed that LrB could block NFAT1 activation and reduce Kv1.3 expression in activated T cells, thus inhibiting the proliferation of lymphocytes and the release of inflammatory cytokines, thereby effectively weakening the autoimmune responses in CIA rats. The effects of immunosuppression due to LrB revealed its potential medicinal value in the treatment of RA

Bifurcation on boundary data for linear broadwell model with conservative boundary condition

10.1142/S0219891614500179Journal of Hyperbolic Differential Equations113603-61

Identical parallel machine scheduling with assurance of maximum waiting time for an emergency job

International audienceCurrently, customer satisfaction is playing an increasingly vital role in both manufacturing and service industries. Assuring an acceptable waiting time to customers is considered as an effective approach to improve customer satisfaction. In this study, an identical parallel machine scheduling problem assuring the maximum waiting time for an emergency job which arrives at any time is investigated. A mixed integer programming model is formulated, based on which a variant formulation is generated. The formulations are further enhanced by various techniques, which forms two formulation-based methods. Two objectives, makespan and total completion time, are considered separately. Regarding the makespan, the worst-case approximation ratios of the classical heuristic rules are deduced. For the total completion time, efficient bounds are provided and the NP-hardness of the problem is proved. Heuristic methods based on the classical dispatch rules are developed, for both the cases. Extensive computational experiments are conducted, based on which the performances of the formulation-based methods and heuristics are compared, the relationship between the objective values and the assured maximum waiting time for an emergency job is explored, and a few observations and managerial insights are obtained

A relax-and-fix method for clothes inventory balancing scheduling problem

International audienceThe clothes inventory balancing scheduling problem (CIBSP) among branch stores with the allowance of lateral transshipments has gained increasing attentions in fast-fashion apparel industry, especially for the trial sale of new products. To solve the CIBSP faced by a leading apparel company in China, a mixed integer linear programming (MILP) model is first formulated, based on which a relax-and-fix (R&F) method is developed. Several heuristic cuts based on practical experience and observations are further integrated into the R&F method to speed up the searching. The effectiveness of the method is demonstrated through extensive computational experiments: it is able to provide near-optimal solutions with average optimality gap 1.15% with less computation time, compared to solving the MILP model directly in a commercial solver. Case studies also demonstrate that the developed R&F method can obtain high-quality solutions with average optimality gap 0.09% with much less computation time

The interval min–max regret knapsack packing-delivery problem

International audienceThis paper studies an interval data min–max regret (IDMR) version of the packing-delivery problem, in which a 0-1 knapsack problem is for parcel packing and a capacitated travelling salesman problem is for parcel delivery. The parcel profits for the courier and the tour costs are uncertain and they can take any value from a specific interval with lower and upper bound values. The problem is how to select and deliver a subset of parcels to minimise the maximum regret of net profit which is the difference between the total profits of the selected parcels and the total delivery costs, to deal with the trade-off of the solution robustness and performance. To tackle the problem effectively, we first prove the worst-case scenario of a solution to the problem, based on which, a mixed integer linear programming is formulated. A Benders-like decomposition algorithm is then developed to solve small-scale problems to optimality within the manageable computation time. For medium- and large-scale problems, a simulated-annealing-based heuristic method with a local search procedure is designed. Extensive computational experiments show the efficiency and effectiveness of the proposed methods