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Complex Event Processing (CEP) is a powerful paradigm for scalable data management that is employed in many real-world scenarios such as detecting credit card fraud in banks. The so-called complex events are expressed using a specification language that is typically implemented and executed on a specific runtime system. While the tight coupling of these two components has been regarded as the key for supporting CEP at high performance, such dependencies pose several inherent challenges as follows. (1) Application development atop a CEP system requires extensive knowledge of how the runtime system operates, which is typically highly complex in nature. (2) The specification language dependence requires the need of domain experts and further restricts and steepens the learning curve for application developers. In this paper, we propose CEPLESS, a scalable data management system that decouples the specification from the runtime system by building on the principles of serverless computing. CEPLESS provides operator as a service and offers flexibility by enabling the development of CEP application in any specification language while abstracting away the complexity of the CEP runtime system. As part of CEPLESS, we designed and evaluated novel mechanisms for in-memory processing and batching that enables the stateful processing of CEP operators even under high rates of ingested events. Our evaluation demonstrates that CEPLESS can be easily integrated into existing CEP systems like Apache Flink while attaining similar throughput under a high scale of events (up to 100K events per second) and dynamic operator update in up to 238 ms., 10 pages, Published in the Proceedings of the IEEE International Conference on Big Data

Discriminating the source of water inrush accurately and efficiently is necessary for water control in the coal mining industry. We combined the Fisher feature extraction and support vector machine (SVM) methods and applied this new model to the Wuhai mining area. The method extracts features from the raw data and integrated SVM, and synthetically considers the influence of geographical factors. Cross-analysis was tested 100 times, which arbitrarily selected 12 samples for the prediction and discrimination process. The results indicate that this new combined model of linear dimension reduction and non-linear dimension elevation was more accurate and efficient in discriminating water inrush sources than the traditional SVM model. Moreover, by reducing the penalty term of SVM model, we analyzed the correlation among the aquifers. We concluded that aquifers II and IV correlated strongly with each other, and that aquifer III was poorly connected with the other aquifers.

The COVID-19 pandemic has generated an overall slowdown in hospital activities that might lead to delays in healthcare interventions, and the scarcity of resources can raise concerns about ventilators allocation criteria. These circumstances could lead to lawsuits against hospitals and healthcare professionals: together with Regions and States, they may be vulnerable to legal actions, due to the breach of right to health, to physical integrity and right to life, to the manifestation of the informed consent in the medical field or on the basis of contractual or Aquilian obligations. In this context, predicting the litigation rate could be useful to assess the economic impact of a dispute at a local and national level, so that hospital managers and public institutions can perform multi-dimensional and cost/benefit evaluations to decide whether to invest resources to increase critical care surge capacity. In this work we present CLIP (COVID-19 LItigation Prediction), a modeling approach supported by swarm intelligence designed to forecast the occupancy of intensive care units using COVID-19 time-series. CLIP fits a logistic model of COVID-19 patients admission in order to estimate the future number of patients, and then exploits a probabilistic model to predict the number of occupied intensive care beds, whose parameters are calibrated by means of Fuzzy Self-Tuning Particle Swarm Optimization. We assume that each individual rejected from an intensive care unit due to the lack of resources should be considered a potential plaintiff. The development and the availability of such a predictive model, that could further be used within other clinical conditions and important diseases, could help policy-makers in taking decisions under conditions of uncertainty.

BackgroundCardiopulmonary bypass (CPB) is an important cause of significant systemic inflammatory response syndrome (SIRS) in the surgical treatment of acute type A aortic dissection (ATAAD). In patients with arch vessel involvement, extensive surgical repairs often necessitate prolonged use of CPB and results in extensive inflammatory responses. Cytokines and chemokines released during CPB contribute to the progression of SIRS, increase perioperative complications, and negatively impact surgical outcomes. A cytokine adsorber (HA380) is expected to reduce the level of cytokines during CPB, which may decrease both intraoperative and postoperative inflammation. The purpose of this study is to investigate if HA380 is able to reduce the levels of inflammatory cytokines and decrease perioperative complications in ATAAD patients undergoing CPB and deep hypothermic circulatory arrest (DHCA).MethodsThis study is a single-center, randomized, controlled, double-blind clinical trial. The study aims to recruit 88 patients with ATAAD and aortic arch involvement who will undergo CPB and DHCA to repair the dissected aorta. Patients will be randomized equally into the CPB/DHCA only group (control group) and the CPB/DHCA + HA380 hemoperfusion group (intervention group), with 44 patients each. Patients in the control group will undergo CPB and DHCA only, while patients in the intervention group will undergo continuous hemoperfusion with HA380, in addition to CPB and DHCA. The primary outcome is a composite of major perioperative complications. The secondary outcomes include related inflammatory markers, coagulation parameters, and minor perioperative complications. To comprehensively evaluate the effect of hemoperfusion on the perioperative outcomes, we will also determine if there are differences in perioperative all-cause mortality, length of ICU stay, and total hospitalization costs.DiscussionIn the current trial, hemoperfusion will be applied in patients undergoing CPB and DHCA for repair of the aorta involving the aortic arch. This trial aims to test the safety and efficacy of our hemoperfusion device (HA380) in such settings. Upon completion of the trial, we will determine if HA380 is effective in reducing perioperative proinflammatory cytokine levels. Further, we will also verify if reduction in the proinflammatory cytokine levels, if present, translates to improvement in patient outcomes.Trial registrationClinicalTrials.govNCT04007484. Registered on 1 July 2019 (retrospectively registered).

Integrated energy system(IES) refers to a variety of energy in the coupling of electricity, gas, heat energy in the form of management, distribution and application of the system, is the expression of energy in the Internet area, this paper constructs the IES structure of the park, using the energy hub model to analyze the cell transformation relationship, and constructed the mathematical model of the structure of the unit, and presents a total cost as the objective function, considering the photovoltaic output and the user can use kinds of uncertainty planning method. The example is solved by using BONMIN, and in a 3 energy unit planning examples verily the effectiveness of the proposed method.

Perfluoroalkyl acids (PFAAs) have been suspected to act as endocrine disruptors and adversely affect human reproductive health. We aimed to investigate the association between PFAAs in blood and semen, explore a potential link between PFAAs exposure and semen quality in the population of the Pearl River Delta (PRD) region in China, one of the “world factories”. The monitoring results demonstrated that the population (103 male participants) from the PRD region in this study had higher PFAAs levels in blood and semen than some other areas in China. PFOS was found at the highest mean concentrations of 118.16 ng/mL in blood and 5.31 ng/mL in semen among the nine PFAAs. Significant associations were found between concentrations of several analytes in blood and semen, including Σ9 PFAAs (r = 0.475, P

How to correctly stress hard samples in metric learning is critical for visual recognition tasks, especially in challenging person re-ID applications. Pedestrians across cameras with significant appearance variations are easily confused, which could bias the learned metric and slow down the convergence rate. In this paper, we propose a novel weighted complete bipartite graph based maximum-value perfect (MVP) matching for mining the hard samples from a batch of samples. It can emphasize the hard positive and negative sample pairs respectively, and thus relieve adverse optimization and sample imbalance problems. We then develop a new batch-wise MVP matching based loss objective and combine it in an end-to-end deep metric learning manner. It leads to significant improvements in both convergence rate and recognition performance. Extensive empirical results on five person re-ID benchmark datasets, i.e., Market-1501, CUHK03-Detected, CUHK03-Labeled, Duke-MTMC, and MSMT17, demonstrate the superiority of the proposed method. It can accelerate the convergence rate significantly while achieving state-of-the-art performance. The source code of our method is available at \url{https://github.com/IAAI-CVResearchGroup/MVP-metric}.

The traditional satellite prognostic and health management (PHM) mostly relies on the satellite downlink telemetry data. Nowadays, the application of satellite becomes more diversification and its function becomes much more complicated. As a result, the telemetry data cannot meet the data requirements for satellite PHM due to the limited resources of telemetry channels and low sampling rate. Therefore, satellite in-orbit embedded testing was proposed. In order to satisfy the requirement of large volumes of data acquisition and transmission in satellite in-orbit embedded testing, this article proposes a new data agreement by the modification of the Modbus protocol to unified control 5-lane MLVDS bus. Therefore, a multi-channel parallel MLVDS data transmission bus is proposed to realise the data transmission from the data collection end to the information processing end of the PHM system on-board. The bus protocol designed here is implemented by using the Programmable Logic Part on the Xilinx XC7Z045.

After large-scale access to distributed generation of new energy generation in urban AC/DC distribution network, its own randomness and uncertainty affect the supply-demand balance mechanism of distribution network. Therefore, this paper studies the power exchange flexibility optimization method of urban AC/DC distribution network to provide reference for the operation and scheduling of distribution network and main network. First, the active power exchange model of distribution network is established, the extreme value of power exchange of distribution network is obtained, and the flexible range of exchange power is calculated. Next, the flexibility evaluation index is established, and the operation characteristics of energy storage are transformed to complete the research on power exchange flexibility optimization of urban AC/DC distribution network. Through the actual example analysis, the results show that after the optimization of the design method, the net load curve is smoother, the effect of reducing the peak valley difference is more significant, and the overall curve is relatively smooth, which shows that the design method has certain effectiveness.

The contribution of time series forecasting (TSF) on various aspects from economic to engineering has yielded its importance. Lot of recent studies concentrated on applying and modifying artificial neural network (ANN) to improve forecasting accuracy and achieved promising results. However, the selection of proper set from historical data for forecasting still has limited consideration. In addition, the selection of network structure as well as initial weights in ANN has been proved to have significant impact on the performance. This paper aims to propose a hybrid model that takes advantages of optimization algorithm: differential evolution (DE) in combine with ANN. The DE operates as features selection process that evaluates useful historical data known as lag to involve in learning process. Besides, DE will perform pre-calculation to determine the set of weight use for ANN. This proposed model is examined on agricultural commodity’s price to evaluate its accuracy. The experimental results is compared and surpassed the popular TSF technique autoregressive integrated moving average (ARIMA) and traditional multilayer perceptron (MLP).

This study is the first one investigating the correlation between the concentration of polycyclic aromatic hydrocarbon (PAHs) in blood and semen qualities for residents in the Pearl River Delta (PRD) region in China. Blood samples from 53 infertile volunteers were studied for measures of semen quality and 16 PAHs. Information on the study subjects' living habits (such as smoking, drinking and preference of consumption for food) and general information (age, body-mass-index (BMI) and educational background) were also collected. Statistical results showed that age and BMI were significantly and negatively related to semen motilities. The total concentrations of PAHs (∑16 PAHs) in the blood were 12,010, 7493, 9105 and 8647ng/g for factory workers, office workers, technicians and salespersons, respectively. In addition, ∑16 PAHs in the blood of smokers, drinkers and heavy-taste food consumers were 11,950, 11,266 and 12,141 ng/g, which were higher than those observed in nonsmokers (10,457 ng/g), nondrinkers (10,920 ng/g) and light-taste food consumers (9202 ng/g), individually. Furthermore, the Pearson correlation analysis results showed significant positive correlations between BMI and ∑16 PAHs in the blood. Statistically significant correlations were observed between semen motilities and ∑16 PAHs in the blood as well. Logistic regression results showed that for each 1 ng/g increase in ∑16 PAHs in blood samples, the log odds of experiencing a pregnancy decrease by 0.039 on average. However, more evidences are needed to clarify the impact of PAHs in the blood to male infertility. Keywords: Polycyclic aromatic hydrocarbons, Whole blood, Semen quality, Male infertility, Pearl River Delta

The damping characteristics of the traditional dual mass flywheel (DMF) cannot be changed and can only meet one of the damping requirements. Given that the traditional DMF cannot avoid the resonance interval in start/stop conditions, it tends to generate high-resonance amplitude, which reduces the lifetime of a vehicle’s parts and leads to vehicle vibration and noise. The problems associated with the traditional DMF can be solved through the magnetorheological fluid dual mass flywheel (MRF-DMF), which was designed in this study with adjustable damping performance under different conditions. The MRF-DMF is designed based on the rheological behavior of the magnetorheological fluid (MRF), which can be changed by magnetic field strength. The damping torque of the MRF-DMF, which is generated by the MRF effect, is derived in detail. Thus, the cosimulation between the drivetrain model built in AMESim and the control system model developed in Simulink is conducted. The controller of MRF-DMF is developed, after which the torsional vibration control test of drivetrain is carried out. The cosimulation and test results indicate that MRF-DMF with the controller effectively isolates torque fluctuation of the engine in the driving condition and exhibits high performance in suppressing the resonance amplitude in the start/stop conditions.

Time series classification (TSC) is home to a number of algorithm groups that utilise different kinds of discriminatory patterns. One of these groups describes classifiers that predict using phase dependant intervals. The time series forest (TSF) classifier is one of the most well known interval methods, and has demonstrated strong performance as well as relative speed in training and predictions. However, recent advances in other approaches have left TSF behind. TSF originally summarises intervals using three simple summary statistics. The ‘catch22' feature set of 22 time series features was recently proposed to aid time series analysis through a concise set of diverse and informative descriptive characteristics. We propose combining TSF and catch22 to form a new classifier, the Canonical Interval Forest (CIF). We outline additional enhancements to the training procedure, and extend the classifier to include multivariate classification capabilities. We demonstrate a large and significant improvement in accuracy over both TSF and catch22, and show it to be on par with top performers from other algorithmic classes. By upgrading the interval-based component from TSF to CIF, we also demonstrate a significant improvement in the hierarchical vote collective of transformation-based ensembles (HIVE-COTE) that combines different time series representations. HIVE-COTE using CIF is significantly more accurate on the UCR archive than any other classifier we are aware of and represents a new state of the art for TSC.

Road damages are of great interest for federal road authorities and their infrastructure management as well as the automated driving task and thus safety and comfort of vehicle occupants. Therefore, we are investigating the automatic detection of different types of road damages by images from a front-facing camera in the vehicle. The data basis of our work is provided by the ’IEEE BigData Cup Challenge’ and its dataset ’RDD-2020’ with a large number of labelled images from Japan, India and the Czech Republic. Our Deep Learning approach utilizes the pre-trained Faster Region Based Convolutional Neural Networks (R-CNN). In the first step, we classify the destination of the image followed by expert networks for each region. Between the explanation of our applied Deep Learning methodology, some remaining sources of errors are discussed and further, partly failed approaches during our development period are displayed, which could be of interest for future work. Our results are convincing and we are able to achieve an F1 score of 0 . 487 across all regions for longitudinal and lateral cracks, alligator cracks and potholes.

Deep learning-based technology is a good key to unlock the object detection tasks in our real world. By using deep neural networks, we could break a problem that is dangerous and very time-consuming but has to be done every day like detecting the road state. This paper describes the solution using YOLO to detect the various types of road damage in the IEEE BigData Cup Challenge 2020. Our YOLOv5x based-solution is light-weight and fast, even it has good accuracy. We achieved an F1 score of 0.58 using our ensemble model with TTA, and it could be an adequate candidate for detecting real road damage in real-time.