An executable interface specification for industrial embedded system design.

Nowadays, designers resort to abstraction techniques to conquer the complexity of industrial embedded systems during the design process. However, due to the large semantic gap between the abstractions and the implementation, the designers often fails to apply the abstraction techniques. In this paper, an EIS-based (executable interface specification) approach is proposed for the embedded system design.The proposed approach starts with using interface state diagrams to specify system architectures. A set of rules is introduced to transfer these diagrams into an executable model (EIS model) consistently. By making use of simulation/verification techniques, many architectural design errors can be detected in the EIS model at an early design stage. In the end, the EIS model can be systematically transferred into an interpreted implementation or a compiled implementation based on the constraints of the embedded platform. In this way, the inconsistencies between the high-level abstractions and the implementation can largely be reduced

Rule-based classification approach for railway wagon health monitoring

Modern machine learning techniques have encouraged interest in the development of vehicle health monitoring systems that ensure secure and reliable operations of rail vehicles. In an earlier study, an energy-efficient data acquisition method was investigated to develop a monitoring system for railway applications using modern machine learning techniques, more specific classification algorithms. A suitable classifier was proposed for railway monitoring based on relative weighted performance metrics. To improve the performance of the existing approach, a rule-based learning method using statistical analysis has been proposed in this paper to select a unique classifier for the same application. This selected algorithm works more efficiently and improves the overall performance of the railway monitoring systems. This study has been conducted using six classifiers, namely REPTree, J48, Decision Stump, IBK, PART and OneR, with twenty-five datasets. The Waikato Environment for Knowledge Analysis (WEKA) learning tool has been used in this study to develop the prediction models

Predicting vertical acceleration of railway wagons using regression algorithms

The performance of rail vehicles running on railway tracks is governed by the dynamic behaviors of railway bogies, particularly in cases of lateral instability and track irregularities. To ensure reliable, safe, and secure operation of railway systems, it is desirable to adopt intelligent monitoring systems for railway wagons. In this paper, a forecasting model is developed to investigate the vertical-acceleration behavior of railway wagons that are attached to a moving locomotive using modern machine-learning techniques. Both front- and rear-body vertical-acceleration conditions are predicted using popular regression algorithms. Different types of models can be built using a uniform platform to evaluate their performance. The estimation techniques' performance has been measured using a set of attributes' correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE), and computational complexity for each of the algorithms. Statistical hypothesis analysis is applied to determine the most suitable regression algorithm for this application. Finally, spectral analysis of the front- and rear-body vertical condition is produced from the predicted data using the fast Fourier transform (FFT) and is used to generate precautionary signals and system status that can be used by a locomotive driver for necessary actions

Application of machine learning techniques for railway health monitoring

Emerging wireless sensor networking (WSN) and modern machine learning techniques have encouraged interest in the development of vehicle health monitoring (VHM) systems that ensure secure and reliable operation of the rail vehicle. The performance of rail vehicles running on railway tracks is governed by the dynamic behaviours of railway bogies especially in the cases of lateral instability and track irregularities. In order to ensure safety and reliability of railway in this chapter, a forecasting model has been developed to investigate vertical acceleration behaviour of railway wagons attached to a moving locomotive using modern machine learning techniques. Initially, an energy-efficient data acquisition model has been proposed for WSN applications using popular learning algorithms. Later, a prediction model has been developed to investigate both front and rear body vertical acceleration behaviour. Different types of models can be built using a uniform platform to evaluate their performances and estimate different attributes’ correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE) and computation complexity for each of the algorithm. Finally, spectral analysis of front and rear body vertical condition is produced from the predicted data using Fast Fourier Transform (FFT) and used to generate precautionary signals and system status which can be used by the locomotive driver for deciding upon necessary actions

High energy solar neutrinos and p-wave contributions to ^3He(p,\nue^+)^4He

High energy solar neutrinos can come from the hep reaction ^3He(p,\nue^+)^4He with a large end point energy of 18.8 MeV. Understanding the hep reaction may be important for interpreting solar neutrino spectra. We calculate the contribution of the axial charge transition $^3P_0\to ^1S_0$ to the hep thermonuclear S factor using a one-body reaction model involving a nucleon moving in optical potentials. Our result is comparable to or larger than previous calculations of the s-wave Gamow Teller contribution. This indicates that the hep reaction may have p-wave strength leading to an enhancement of the S factor.Comment: 4 pages, 1 ps figure, very minor changes, Phys. Rev. C in pres

Clinical Pharmacokinetics of Triazoles in Pediatric Patients

Triazoles represent an important class of antifungal drugs in the prophylaxis and treatment of invasive fungal disease in pediatric patients. Understanding the pharmacokinetics of triazoles in children is crucial to providing optimal care for this vulnerable population. While the pharmacokinetics is extensively studied in adult populations, knowledge on pharmacokinetics of triazoles in children is limited. New data are still emerging despite drugs already going off patent. This review aims to provide readers with the most current knowledge on the pharmacokinetics of the triazoles: fluconazole, itraconazole, voriconazole, posaconazole, and isavuconazole. In addition, factors that have to be taken into account to select the optimal dose are summarized and knowledge gaps are identified that require further research. We hope it will provide clinicians guidance to optimally deploy these drugs in the setting of a life-threatening disease in pediatric patients

Effect of Antibacterial Prophylaxis on Febrile Neutropenic Episodes and Bacterial Bloodstream Infections in Dutch Pediatric Patients with Acute Myeloid Leukemia:A Two-Center Retrospective Study

Bloodstream infections (BSIs), especially those caused by Gram-negative rods (GNR) and viridans group streptococci (VGS), are common and potentially life-threatening complications of pediatric acute myeloid leukemia (AML) treatment. Limited literature is available on prophylactic regimens. We retrospectively evaluated the effect of different antibacterial prophylaxis regimens on the incidence of febrile neutropenic (FN) episodes and bacterial BSIs. Medical records of children (0–18 years) diagnosed with de novo AML and treated at two Dutch centers from May 1998 to March 2021 were studied. Data were analyzed per chemotherapy course and consecutive neutropenic period. A total of 82 patients had 316 evaluable courses: 92 were given with single-agent ciprofloxacin, 138 with penicillin plus ciprofloxacin, and 51 with teicoplanin plus ciprofloxacin. The remaining 35 courses with various other prophylaxis regimens were not statistically compared. During courses with teicoplanin plus ciprofloxacin, significantly fewer FN episodes (43% vs. 90% and 75%; p < 0.0001) and bacterial BSIs (4% vs. 63% and 33%; p < 0.0001) occurred than with single-agent ciprofloxacin and penicillin plus ciprofloxacin, respectively. GNR and VGS BSIs did not occur with teicoplanin plus ciprofloxacin and no bacterial BSI-related pediatric intensive care unit (PICU) admissions were required, whereas, with single-agent ciprofloxacin and penicillin plus ciprofloxacin, GNR BSIs occurred in 8% and 1% (p = 0.004), VGS BSIs in 24% and 14% (p = 0.0005), and BSI-related PICU admissions were required in 8% and 2% of the courses (p = 0.029), respectively. Teicoplanin plus ciprofloxacin as antibacterial prophylaxis is associated with a lower incidence of FN episodes and bacterial BSIs. This may be a good prophylactic regimen for pediatric AML patients during treatment