Generating a Performance Stochastic Model from UML Specifications

Since its initiation by Connie Smith, the process of Software Performance Engineering (SPE) is becoming a growing concern. The idea is to bring performance evaluation into the software design process. This suitable methodology allows software designers to determine the performance of software during design. Several approaches have been proposed to provide such techniques. Some of them propose to derive from a UML (Unified Modeling Language) model a performance model such as Stochastic Petri Net (SPN) or Stochastic process Algebra (SPA) models. Our work belongs to the same category. We propose to derive from a UML model a Stochastic Automata Network (SAN) in order to obtain performance predictions. Our approach is more flexible due to the SAN modularity and its high resemblance to UML' state-chart diagram

The Effect of Homogeneous Grouping versus Heterogeneous Grouping on High School Students’ EFL Writing Achievement

This study is an attempt to investigate the effectiveness of homogeneous grouping versus heterogeneous grouping on students’ EFL achievement in writing. A pretest posttest design was used to answer the research questions about the effectiveness of grouping students homogeneously versus heterogeneously. Two classes were assigned for the study. One class was assigned for heterogeneous grouping in which high achievers were in group of four or less and low achievers were in group of four or less. The second classes was assigned for heterogeneous grouping where students of different abilities high and low achievers were in groups of four or less. The findings of the study suggested that there is a difference between homogeneous grouping and heterogeneous grouping. The analysis of the results of the study showed that there was a significant difference between the scores of the students in homogeneous group and heterogeneous group in favour of the homogeneous group. However, there was no significant in the achievement of between high achievers and low achievers in the two groups. Based on the conclusions and discussions of the study it was recommended that teachers may group students homogeneously based on students’ level and according to their needs. Finally recommendations and suggestions for future research were made

Immunological characterization of diphtheria toxin recovered from Corynebacterium pseudotuberculosis

AbstractDiphtheria toxin (DT) is a potent toxin produced by the so-called diphtheria group which includes Corynebacterium diphtheriae (C. diphtheriae), Corynebacterium ulcerans (C. ulcerans), and Corynebacterium pseudotuberculosis (C. pseudotuberculosis). The present investigation is aimed to study in detail the production of DT by C. pseudotuberculosis. Twenty isolates were obtained from sheep diseased with caseous lymphadenitis (CLA) and twenty-six isolates were obtained from 26 buffaloes diseased with oedematous skin disease (OSD). All isolates were identified by standard microbiological and DT production was assayed serologically by modified Elek test and immunoblotting. All sheep isolates were nitrate negative, failed to hydrolyze starch and could not produce DT, while all buffalo isolates (biotype II) revealed positive results and a specific band of 62kDa, specific to DT, was resulted in all concentrated cell fractions (CF), but was absent from non-toxigenic biotype I isolates. At the same time, another band of 31kDa specific to the PLD gene was obtained with all isolates of biotype I and II. Moreover, all isolates showed positive synergistic hemolytic activity and antagonistic hemolysis with β-hemolytic Staphylococci. The obtained results also indicated that C. pseudotuberculosis could be classified into two strains; non-toxigenic biotype I strain, which failed to produce DT as well as being negative to nitrate and starch hydrolysis, and toxigenic biotype II strain, which can reduce nitrate, hydrolyze starch as well as produce DT

Model Predictive Path Integral Control Framework for Partially Observable Navigation: A Quadrotor Case Study

Recently, Model Predictive Path Integral (MPPI) control algorithm has been extensively applied to autonomous navigation tasks, where the cost map is mostly assumed to be known and the 2D navigation tasks are only performed. In this paper, we propose a generic MPPI control framework that can be used for 2D or 3D autonomous navigation tasks in either fully or partially observable environments, which are the most prevalent in robotics applications. This framework exploits directly the 3D-voxel grid acquired from an on-board sensing system for performing collision-free navigation. We test the framework, in realistic RotorS-based simulation, on goal-oriented quadrotor navigation tasks in a cluttered environment, for both fully and partially observable scenarios. Preliminary results demonstrate that the proposed framework works perfectly, under partial observability, in 2D and 3D cluttered environments.Comment: The withdrawal reason is that the co-authors do not want to associate their name to the article on arXi

GP-guided MPPI for Efficient Navigation in Complex Unknown Cluttered Environments

Robotic navigation in unknown, cluttered environments with limited sensing capabilities poses significant challenges in robotics. Local trajectory optimization methods, such as Model Predictive Path Intergal (MPPI), are a promising solution to this challenge. However, global guidance is required to ensure effective navigation, especially when encountering challenging environmental conditions or navigating beyond the planning horizon. This study presents the GP-MPPI, an online learning-based control strategy that integrates MPPI with a local perception model based on Sparse Gaussian Process (SGP). The key idea is to leverage the learning capability of SGP to construct a variance (uncertainty) surface, which enables the robot to learn about the navigable space surrounding it, identify a set of suggested subgoals, and ultimately recommend the optimal subgoal that minimizes a predefined cost function to the local MPPI planner. Afterward, MPPI computes the optimal control sequence that satisfies the robot and collision avoidance constraints. Such an approach eliminates the necessity of a global map of the environment or an offline training process. We validate the efficiency and robustness of our proposed control strategy through both simulated and real-world experiments of 2D autonomous navigation tasks in complex unknown environments, demonstrating its superiority in guiding the robot safely towards its desired goal while avoiding obstacles and escaping entrapment in local minima. The GPU implementation of GP-MPPI, including the supplementary video, is available at https://github.com/IhabMohamed/GP-MPPI.Comment: This paper has 8 pages, 6 figures, 2 tables. It has been accepted for publication at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, Michigan, USA, 202

An Affordable Custom-Built Negative Pressure Wound Therapy

Negative pressure wound therapy (NPWT) is a wellestablished and effective method for treating complex wounds. However, this modality of treatment may not be available in limited resource countries due to the high cost. We describe a simple and cheap method of NPWT using gauze swabs, a naso-gastric tube, adhesive occlusive drape and a central or portable suction machine.Key words: Negative Pressure, Wound Therap

Aortic stiffness and microalbuminuria in patients with chronic obstructive pulmonary disease

AbstractBackgroundCardiovascular diseases (CVDs) remain a major leading cause of morbidity and mortality in patients with chronic obstructive pulmonary disease (COPD). Increased aortic stiffness is an independent predictor of cardiovascular disease. Microalbuminuria (MAU) reflects increased permeability of the glomerulus, usually due to microvascular damage and suggested as an early prognostic cardiovascular marker. So this study was done to determine the prevalence of aortic stiffness in patients with COPD and to evaluate the relationship of MAU levels with the degree of aortic stiffness.Subjects and methodsThis study was done at Respirology, Cardiology, Internal Medicine, and Clinical Pathology Departments, Farwaniya Hospital, Ministry of Health, State of Kuwait in the period between July 2013 and October 2014. A total 60 patients was distributed into 38 patients with COPD (group 1) and 22 control subjects (group 2). Patients with COPD and controls underwent spirometry, blood pressure, aortic stiffness assessment using aortic pulse wave velocity (aPWV) study and provided a spot urine sample for MAU measurement.ResultsPatients with COPD (group 1) had increased aortic stiffness compared with matched controls (group 2) 11.2±2.3 vs. 7.8±1.5m/s, P<0.05. Patients with GOLD III and IV had significant higher aPWV values as compared to patients with GOLD I and II (P<0.05). Multiple logistic regression analyses revealed that the adjusted odds ratios of having MAU for aPWV quartile III and IV were 6.38 (95% confidence interval: 2.37–13.2) and 6.58 (95% confidence interval: 1.59–22.0) respectively, P<0.05.ConclusionsCOPD is associated with increased aortic stiffness. MAU is independently related to aortic stiffness in patients with COPD. Further studies are necessary to investigate whether MAU could be an effective biomarker of aortic stiffness and potential cardiovascular compromise in patients with COPD

Towards Efficient MPPI Trajectory Generation with Unscented Guidance: U-MPPI Control Strategy

The classical Model Predictive Path Integral (MPPI) control framework lacks reliable safety guarantees since it relies on a risk-neutral trajectory evaluation technique, which can present challenges for safety-critical applications such as autonomous driving. Additionally, if the majority of MPPI sampled trajectories concentrate in high-cost regions, it may generate an infeasible control sequence. To address this challenge, we propose the U-MPPI control strategy, a novel methodology that can effectively manage system uncertainties while integrating a more efficient trajectory sampling strategy. The core concept is to leverage the Unscented Transform (UT) to propagate not only the mean but also the covariance of the system dynamics, going beyond the traditional MPPI method. As a result, it introduces a novel and more efficient trajectory sampling strategy, significantly enhancing state-space exploration and ultimately reducing the risk of being trapped in local minima. Furthermore, by leveraging the uncertainty information provided by UT, we incorporate a risk-sensitive cost function that explicitly accounts for risk or uncertainty throughout the trajectory evaluation process, resulting in a more resilient control system capable of handling uncertain conditions. By conducting extensive simulations of 2D aggressive autonomous navigation in both known and unknown cluttered environments, we verify the efficiency and robustness of our proposed U-MPPI control strategy compared to the baseline MPPI. We further validate the practicality of U-MPPI through real-world demonstrations in unknown cluttered environments, showcasing its superior ability to incorporate both the UT and local costmap into the optimization problem without introducing additional complexity.Comment: This paper has 15 pages, 10 figures, 4 table