Design Space Exploration in Cyber-Physical Systems

Cyber physical systems (CPS) integrate a variety of engineering areas such as control, mechanical and computer engineering in a holistic design effort. While interdependencies between the different disciplines are key attributes of CPS design science, little is known about the impact of design decisions of the cyber part on the overall system qualities. To investigate these interdependencies, this paper proposes a simulation-based Design Space Exploration (DSE) framework that considers detailed cyber system parameters such as cache size, bus width, and voltage levels in addition to physical and control parameters of the CPS. We propose an exploration algorithm that surfs the parameter configurations in the cyber physical sub-systems, in order to approximate the Pareto-optimal design points with regards to the trade-os among the design objectives, such as energy consumption and control stability. We apply the proposed framework to a network control system for an inverted-pendulum application. The presented holistic evaluation of the identified Pareto-points reveals the presence of non-trivial trade-os, which are imposed by the control, physical, and detailed cyber parameters. For instance the identified energy and control optimal design points comprise configurations with a wide range of CPU speeds, sample times and cache configuration following non-trivial zig-zag patterns. The proposed framework could identify and manage those trade-os and, as a result, is an imperative rst step to automate the search for superior CSP configurations

Resource-Aware Predictive Models in Cyber-Physical Systems

Cyber-Physical Systems (CPS) are composed of computing devices interacting with physical systems. Model-based design is a powerful methodology in CPS design in the implementation of control systems. For instance, Model Predictive Control (MPC) is typically implemented in CPS applications, e.g., in path tracking of autonomous vehicles. MPC deploys a model to estimate the behavior of the physical system at future time instants for a specific time horizon. Ordinary Differential Equations (ODE) are the most commonly used models to emulate the behavior of continuous-time (non-)linear dynamical systems. A complex physical model may comprise thousands of ODEs that pose scalability, performance and power consumption challenges. One approach to address these model complexity challenges are frameworks that automate the development of model-to-model transformation. In this dissertation, a state-based model with tunable parameters is proposed to operate as a reconfigurable predictive model of the physical system. Moreover, we propose a run-time switching algorithm that selects the best model using machine learning. We employed a metric that formulates the trade-off between the error and computational savings due to model reduction. Building statistical models are constrained to having expert knowledge and an actual understanding of the modeled phenomenon or process. Also, statistical models may not produce solutions that are as robust in a real-world context as factors outside the model, like disruptions would not be taken into account. Machine learning models have emerged as a solution to account for the dynamic behavior of the environment and automate intelligence acquisition and refinement. Neural networks are machine learning models, well-known to have the ability to learn linear and nonlinear relations between input and output variables without prior knowledge. However, the ability to efficiently exploit resource-hungry neural networks in embedded resource-bound settings is a major challenge.Here, we proposed Priority Neuron Network (PNN), a resource-aware neural networks model that can be reconfigured into smaller sub-networks at runtime. This approach enables a trade-off between the model's computation time and accuracy based on available resources. The PNN model is memory efficient since it stores only one set of parameters to account for various sub-network sizes. We propose a training algorithm that applies regularization techniques to constrain the activation value of neurons and assigns a priority to each one. We consider the neuron's ordinal number as our priority criteria in that the priority of the neuron is inversely proportional to its ordinal number in the layer. This imposes a relatively sorted order on the activation values. We conduct experiments to employ our PNN as the predictive model in a CPS application. We can see that not only our technique will resolve the memory overhead of DNN architectures but it also reduces the computation overhead for the training process substantially. The training time is a critical matter especially in embedded systems where many NN models are trained on the fly

Sustainable cultural heritage landscape: an imaginary journey inside the veins of deserts

IntroductionTravel narratives are very attractive sources that examine the destination from the point of view of tourists with different attitudes. A group of studies has been conducted on travel narratives, but this type of analysis has been done very little in Iran despite their potential.Methods11 Persian qanats (PQs) have been recognized as UNESCO World Heritage sites. Visitors who explore the qanats as conduits of the desert have extraordinary experiences that can seem like captivating fiction. The main focus of the current research is on the narratives of tourists who have visited these PQs. Qualitative and narrative analysis methods were used to avoid relying on numerical data.ResultsThe study includes 30 participants who had visited the qanats and shared their travel stories. The study findings show that Persian qanats are a distinctive draw for foreign tourists that can leave a lasting impression.DiscussionThe PQs listed as a UNESCO World Heritage property are one of the unique attractions of Iran’s tourism, so far its tourism potential has been less noticed, and qanat tourism is a nascent branch of tourism in Iran. The narratives of travelers can be instrumental in promoting this invaluable groundwater engineering heritage

Clinical Significance and Different Expression of Dipeptidyl Peptidase IV and Procalcitonin in Mild and Severe COVID-19

Background: Coronavirus has become a global concern in 2019-20. The virus belongs to the coronavirus family, which has been able to infect many patients and victims around the world. The virus originated in the Chinese city of Wuhan, which eventually spread around the world and became a pandemic. Materials and Methods: A total of 60 Patients with severe (n=30) and mild (n=30) symptoms of COIVD-19 were included in this study. Peripheral blood samples were collected from the patients. Real-time PCR was used to compare the relative expression levels of Procalcitonin and dipeptidyl peptidase IV (DPPIV) in a patient with severe and mild Covid-19 infection. Results: Procalcitonin and dipeptidyl peptidase IV markers in the peripheral blood of patients with severe symptoms, were positive in 29 (96.60%) and 26 (86.60%), respectively (n=30); however, positive rates in the mild symptoms patients group were 27 (90%) and 25 (83.30%), respectively. There was a statistically significant difference between these two groups in terms of DDPIV and Procalcitonin (p<0.001). Conclusion: Procalcitonin and DPPIV increase in patients with COVID-19 infection, significantly higher in the patients with more severe clinical symptoms than those with milder ones. More studies will be needed to verify the reliability of the current findings. Keywords: Procalcitonin, DPPIV, Severe symptoms, Mild symptoms, COVID-1

Non-alcoholic fatty liver disease is not independent risk factor for cardiovascular disease event : a cohort study

There are no consistent results between previous studies for an independent association between non-alcoholic fatty liver disease (NAFLD) and cardiovascular disease (CVD) events. To determine if there is an independent association between NAFLD and CVD events. In the present study, valid outcome data of 4808 subjects were available for phase 2 of our cohort study. These subjects had been followed up for seven years from phase 1, beginning in 2009-2010 to phase 2 during 2016-2017. Simple and multiple Cox proportional models were used to determine the association between NAFLD in the primary phase of the cohort and subsequent fatal and non-fatal CVD events during follow-up. The incidence of non-fatal CVD events in males with NAFLD was significantly higher ( = 0.004) than in males without NAFLD. A positive association was demonstrated between NAFLD and non-fatal CVD events in males (Hazard ratio = 1.606; 95%CI: 1.166-2.212; = 0.004) by the simple Cox proportional hazard model, but no independent association was detected between these in the multiple Cox models. No independent association was detected between NAFLD and CVD. It is likely that diabetes mellitus and age may be the principle mediators in this regard. [Abstract copyright: ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.

The emerging role of regulatory cell-based therapy in autoimmune disease

Autoimmune disease, caused by unwanted immune responses to self-antigens, affects millions of people each year and poses a great social and economic burden to individuals and communities. In the course of autoimmune disorders, including rheumatoid arthritis, systemic lupus erythematosus, type 1 diabetes mellitus, and multiple sclerosis, disturbances in the balance between the immune response against harmful agents and tolerance towards self-antigens lead to an immune response against self-tissues. In recent years, various regulatory immune cells have been identified. Disruptions in the quality, quantity, and function of these cells have been implicated in autoimmune disease development. Therefore, targeting or engineering these cells is a promising therapeutic for different autoimmune diseases. Regulatory T cells, regulatory B cells, regulatory dendritic cells, myeloid suppressor cells, and some subsets of innate lymphoid cells are arising as important players among this class of cells. Here, we review the roles of each suppressive cell type in the immune system during homeostasis and in the development of autoimmunity. Moreover, we discuss the current and future therapeutic potential of each one of these cell types for autoimmune diseases

Sex difference and intra-operative tidal volume: Insights from the LAS VEGAS study

BACKGROUND: One key element of lung-protective ventilation is the use of a low tidal volume (VT). A sex difference in use of low tidal volume ventilation (LTVV) has been described in critically ill ICU patients.OBJECTIVES: The aim of this study was to determine whether a sex difference in use of LTVV also exists in operating room patients, and if present what factors drive this difference.DESIGN, PATIENTS AND SETTING: This is a posthoc analysis of LAS VEGAS, a 1-week worldwide observational study in adults requiring intra-operative ventilation during general anaesthesia for surgery in 146 hospitals in 29 countries.MAIN OUTCOME MEASURES: Women and men were compared with respect to use of LTVV, defined as VT of 8 ml kg-1 or less predicted bodyweight (PBW). A VT was deemed 'default' if the set VT was a round number. A mediation analysis assessed which factors may explain the sex difference in use of LTVV during intra-operative ventilation.RESULTS: This analysis includes 9864 patients, of whom 5425 (55%) were women. A default VT was often set, both in women and men; mode VT was 500 ml. Median [IQR] VT was higher in women than in men (8.6 [7.7 to 9.6] vs. 7.6 [6.8 to 8.4] ml kg-1 PBW, P < 0.001). Compared with men, women were twice as likely not to receive LTVV [68.8 vs. 36.0%; relative risk ratio 2.1 (95% CI 1.9 to 2.1), P < 0.001]. In the mediation analysis, patients' height and actual body weight (ABW) explained 81 and 18% of the sex difference in use of LTVV, respectively; it was not explained by the use of a default VT.CONCLUSION: In this worldwide cohort of patients receiving intra-operative ventilation during general anaesthesia for surgery, women received a higher VT than men during intra-operative ventilation. The risk for a female not to receive LTVV during surgery was double that of males. Height and ABW were the two mediators of the sex difference in use of LTVV.TRIAL REGISTRATION: The study was registered at Clinicaltrials.gov, NCT01601223

Resource-Aware Predictive Models in Cyber-Physical Systems

Cyber-Physical Systems (CPS) are composed of computing devices interacting with physical systems. Model-based design is a powerful methodology in CPS design in the implementation of control systems. For instance, Model Predictive Control (MPC) is typically implemented in CPS applications, e.g., in path tracking of autonomous vehicles. MPC deploys a model to estimate the behavior of the physical system at future time instants for a specific time horizon. Ordinary Differential Equations (ODE) are the most commonly used models to emulate the behavior of continuous-time (non-)linear dynamical systems. A complex physical model may comprise thousands of ODEs that pose scalability, performance and power consumption challenges. One approach to address these model complexity challenges are frameworks that automate the development of model-to-model transformation. In this dissertation, a state-based model with tunable parameters is proposed to operate as a reconfigurable predictive model of the physical system. Moreover, we propose a run-time switching algorithm that selects the best model using machine learning. We employed a metric that formulates the trade-off between the error and computational savings due to model reduction. Building statistical models are constrained to having expert knowledge and an actual understanding of the modeled phenomenon or process. Also, statistical models may not produce solutions that are as robust in a real-world context as factors outside the model, like disruptions would not be taken into account. Machine learning models have emerged as a solution to account for the dynamic behavior of the environment and automate intelligence acquisition and refinement. Neural networks are machine learning models, well-known to have the ability to learn linear and nonlinear relations between input and output variables without prior knowledge. However, the ability to efficiently exploit resource-hungry neural networks in embedded resource-bound settings is a major challenge.Here, we proposed Priority Neuron Network (PNN), a resource-aware neural networks model that can be reconfigured into smaller sub-networks at runtime. This approach enables a trade-off between the model's computation time and accuracy based on available resources. The PNN model is memory efficient since it stores only one set of parameters to account for various sub-network sizes. We propose a training algorithm that applies regularization techniques to constrain the activation value of neurons and assigns a priority to each one. We consider the neuron's ordinal number as our priority criteria in that the priority of the neuron is inversely proportional to its ordinal number in the layer. This imposes a relatively sorted order on the activation values. We conduct experiments to employ our PNN as the predictive model in a CPS application. We can see that not only our technique will resolve the memory overhead of DNN architectures but it also reduces the computation overhead for the training process substantially. The training time is a critical matter especially in embedded systems where many NN models are trained on the fly

Agronomic yield and essential oil properties of purple coneflower (echinacea purpurea l. moench) with different nutrient applications

Echinacea purpurea is cultivated around the world due to its unique pharmacological effects. The aerial parts of the plant, especially its flowers, contain a wide variety of beneficial bioactive substances. The objective of this study was to evaluate the effect of different nutrient applications on the growth parameters, essential oil yield, and compounds of Echinacea purpurea L. Moench in Afyonkarahisar/Turkey. The experiment was conducted over a 3-year period (2016-18), including four experimental treatments (F0: control, F1: 75 kg ha-1 N; F2: 150 kg ha-1 N; and F3: 75 kg ha-1 N + foliar fertilizer). As a general result of five cuttings, F2 and F3 had a positive effect on agronomic yield. F2 and F3 recorded the highest plant height (91 and 90 cm, respectively) and yields for fresh bud (578 and 543 kg ha-1), dry bud (118 and 112 kg ha-1), fresh flower (8,595 and 7,449 kg ha-1), dry flower (2,021 and 1,745 kg ha-1), fresh herb (32,645 and 29,291 kg ha-1), dry herb (8,746 and 7,745 kg ha-1) and essential oil (4.55 and 3.57 L ha-1). Sesquiterpene hydrocarbons were the most abundant chemical group compound of E. purpurea essential oil. Germacrene D (20.4-50.6%) was the predominant constituent, recording its maximum level in F1. Other major compounds were β-pinene, β-myrcene, α-humulene, δ-cadinene, spathulenol, and α-cadinol. The application of 150 kg ha-1 N as well as the combined use of 75 kg ha-1 N and foliar application of macro and micro elements resulted in the highest agronomic yield and essential oil production. © 2022 Universidad de Concepcion. All rights reserved