The application of scheduler agents in time-triggered embedded systems

This thesis is concerned with the monitoring of embedded systems in which timing behaviour is the key concern. The focus of this work is on the development of a “scheduler agent” (SA) which is used to monitor the temporal behaviour of embedded systems. It is assumed that the system to be monitored employs a time-triggered software architecture. This thesis begins by providing a review of timing issues in embedded systems, and followed by a review of previous research on runtime monitoring techniques (including hardware, software and “hybrid” approaches). The SA is then introduced. It consists of two parts: an internal monitor (IM) and an external analyzer (EA). Both of the IM and the EA have to work cooperatively in order to obtain information from the target system. The communication between them relies on a GPIO interface. However, an encoding technique is required since modern microcontrollers may not have enough GPIO port pins to represent all tasks in the target system. A simple and effective encoding technique has been introduced in this thesis to address this issue. Two versions of the SA – Passive SA (PSA) and Active SA (ASA) – are implemented. PSA retrieves task information from the “instrumented” target system passively. ASA takes advantages from the TTC architecture employed by the target system, in which the monitoring process collects information from the target system at the time when a task is due to start and end. We also developed a SA automation tool which can automatically generate the SA code for the external analyzer and the target system. This tool is used in the case study to generate the source code for both PSA and ASA. In the case study presented in this thesis, we confirm that the functionality of SA has in line with its requirements, since it is capable to measure task execution time and detect temporal errors in the target system. Finally, the conclusions of this thesis with a discussion of the results and some suggestions for further work in this important area are presented

DataSheet_1_Development and validation a nomogram prediction model for early diagnosis of bloodstream infections in the intensive care unit.csv

PurposeThis study aims to develop and validate a nomogram for predicting the risk of bloodstream infections (BSI) in critically ill patients based on their admission status to the Intensive Care Unit (ICU).Patients and methodsPatients’ data were extracted from the Medical Information Mart for Intensive Care−IV (MIMIC−IV) database (training set), the Beijing Friendship Hospital (BFH) database (validation set) and the eICU Collaborative Research Database (eICU−CRD) (validation set). Univariate logistic regression analyses were used to analyze the influencing factors, and lasso regression was used to select the predictive factors. Model performance was assessed using area under receiver operating characteristic curve (AUROC) and Presented as a Nomogram. Various aspects of the established predictive nomogram were evaluated, including discrimination, calibration, and clinical utility.ResultsThe model dataset consisted of 14930 patients (1444 BSI patients) from the MIMIC-IV database, divided into the training and internal validation datasets in a 7:3 ratio. The eICU dataset included 2100 patients (100 with BSI) as the eICU validation dataset, and the BFH dataset included 419 patients (21 with BSI) as the BFH validation dataset. The nomogram was constructed based on Glasgow Coma Scale (GCS), sepsis related organ failure assessment (SOFA) score, temperature, heart rate, respiratory rate, white blood cell (WBC), red width of distribution (RDW), renal replacement therapy and presence of liver disease on their admission status to the ICU. The AUROCs were 0.83 (CI 95%:0.81-0.84) in the training dataset, 0.88 (CI 95%:0.88-0.96) in the BFH validation dataset, and 0.75 (95%CI 0.70-0.79) in the eICU validation dataset. The clinical effect curve and decision curve showed that most areas of the decision curve of this model were greater than 0, indicating that this model has a certain clinical effectiveness.ConclusionThe nomogram developed in this study provides a valuable tool for clinicians and nurses to assess individual risk, enabling them to identify patients at a high risk of bloodstream infections in the ICU.</p

Improving the performance of time-triggered embedded systems by means of a scheduler agent.

Knowledge of task execution time is a key requirement when determining the most appropriate scheduler algorithm (and scheduler parameters) for use with embedded systems. Unfortunately, determining task execution times (ETs) can be a challenging process. This paper introduces a novel system architecture which is based on two components (i) the main processor (MP) platform, containing the time-triggered (cooperative) scheduler and task code, and (ii) a second processor, executing a “scheduler agent” (SA). In the experiments described in this paper, the MP contains an instrumented scheduler and, during a “tuning” phase, the SA measures – on line – the ET of each task as it runs. The measured values are then used to fine tune the task schedule in an attempt to ensure that (i) all task constraints - such as deadline and jitter - are met (ii) power consumption is reduced. After the tuning phase is completed the SA continues to monitor the MP and can take appropriate action in case of errors. In the paper, the effectiveness of the proposed architecture is demonstrated empirically by applying it to a set of tasks that represent a typical embedded control system

Room-Temperature Intercalation and ∼1000-Fold Chemical Expansion for Scalable Preparation of High-Quality Graphene

Low-cost, scalable preparation of high-quality graphene has been a critical challenge that hampers its large-scale application. We here propose a novel, scalable liquidphase exfoliation method in which the intercalation, expansion, and exfoliation of graphite are achieved all under ambient conditions, not involving any heating or high-temperature treatment. We demonstrate that such room-temperature liquid-phase intercalation and expansion allow graphite flakes to expand up to 1000 times. Significantly different from thermally expanded graphite, the resulting chemically expanded graphite (CEG) exhibits a uniform, open, porous structure with a specific surface area (847 m²/g) comparable to the theoretical value of three-layer graphene. The CEG obtained is able to be exfoliated under mild conditions to give high-quality graphene with a yield of 70% relative to the starting graphite. The exfoliated graphene sheets have very few defects, with an atomic ratio of carbon to oxygen (C/O ratio) of 28. The as-prepared graphene exhibits an electrical conductivity of 1.17 × 10⁵ S/m and the corresponding transparent films also reveal superior optical and electrical performance

DataSheet_2_Development and validation a nomogram prediction model for early diagnosis of bloodstream infections in the intensive care unit.docx

PurposeThis study aims to develop and validate a nomogram for predicting the risk of bloodstream infections (BSI) in critically ill patients based on their admission status to the Intensive Care Unit (ICU).Patients and methodsPatients’ data were extracted from the Medical Information Mart for Intensive Care−IV (MIMIC−IV) database (training set), the Beijing Friendship Hospital (BFH) database (validation set) and the eICU Collaborative Research Database (eICU−CRD) (validation set). Univariate logistic regression analyses were used to analyze the influencing factors, and lasso regression was used to select the predictive factors. Model performance was assessed using area under receiver operating characteristic curve (AUROC) and Presented as a Nomogram. Various aspects of the established predictive nomogram were evaluated, including discrimination, calibration, and clinical utility.ResultsThe model dataset consisted of 14930 patients (1444 BSI patients) from the MIMIC-IV database, divided into the training and internal validation datasets in a 7:3 ratio. The eICU dataset included 2100 patients (100 with BSI) as the eICU validation dataset, and the BFH dataset included 419 patients (21 with BSI) as the BFH validation dataset. The nomogram was constructed based on Glasgow Coma Scale (GCS), sepsis related organ failure assessment (SOFA) score, temperature, heart rate, respiratory rate, white blood cell (WBC), red width of distribution (RDW), renal replacement therapy and presence of liver disease on their admission status to the ICU. The AUROCs were 0.83 (CI 95%:0.81-0.84) in the training dataset, 0.88 (CI 95%:0.88-0.96) in the BFH validation dataset, and 0.75 (95%CI 0.70-0.79) in the eICU validation dataset. The clinical effect curve and decision curve showed that most areas of the decision curve of this model were greater than 0, indicating that this model has a certain clinical effectiveness.ConclusionThe nomogram developed in this study provides a valuable tool for clinicians and nurses to assess individual risk, enabling them to identify patients at a high risk of bloodstream infections in the ICU.</p

High-Performance All-Solid-State Supercapacitor Based on the Assembly of Graphene and Manganese(II) Phosphate Nanosheets

Manganese phosphate nanosheets (Mn₃(PO₄)₂·3H₂O NSs) with ∼2 nm thickness were prepared by exfoliating the bulk material in dimethylformamide (DMF) under ultrasonication. They can spontaneously form face-to-face stacked assemblies with exfoliated graphene NSs in DMF. The assemblied Mn₃(PO₄)₂·3H₂O and graphene NSs at the mass ratio of 1:10 (M₁G₁₀) revealed a specific capacitance of 2086 F g^–1 at 1 mV s^–1. These M₁G₁₀ assemblies were used to fabricate all-solid-state supercapacitor (M₁G₁₀-ASSS) on the basis of PVA/KOH solid polymer electrolytes, which exhibited a specific capacitance of 152 F g^–1 (or 40 mF cm^–2) at 0.5 A g^–1, an energy density of 0.17 μWh cm^–2 at 0.5 A g^–1 (1.3 A m^–2) and a power density of 46 μW cm^–2 at 2 A g^–1 (5.3 A m^–2). M₁G₁₀-ASSS also showed excellent cycling stability and nearly 100% capacitance retention was achieved after 2000 galvanostatic charge–discharge cycles at 2 A g^–1. Such extraordinary properties were attributed to the synergistic effect of high pseudocapacitance of Mn₃(PO₄)₂·3H₂O NSs, high conductivity and surface areas of graphene NSs

Room-Temperature Intercalation and ∼1000-Fold Chemical Expansion for Scalable Preparation of High-Quality Graphene

Low-cost, scalable preparation of high-quality graphene has been a critical challenge that hampers its large-scale application. We here propose a novel, scalable liquidphase exfoliation method in which the intercalation, expansion, and exfoliation of graphite are achieved all under ambient conditions, not involving any heating or high-temperature treatment. We demonstrate that such room-temperature liquid-phase intercalation and expansion allow graphite flakes to expand up to 1000 times. Significantly different from thermally expanded graphite, the resulting chemically expanded graphite (CEG) exhibits a uniform, open, porous structure with a specific surface area (847 m²/g) comparable to the theoretical value of three-layer graphene. The CEG obtained is able to be exfoliated under mild conditions to give high-quality graphene with a yield of 70% relative to the starting graphite. The exfoliated graphene sheets have very few defects, with an atomic ratio of carbon to oxygen (C/O ratio) of 28. The as-prepared graphene exhibits an electrical conductivity of 1.17 × 10⁵ S/m and the corresponding transparent films also reveal superior optical and electrical performance

Room-Temperature Intercalation and ∼1000-Fold Chemical Expansion for Scalable Preparation of High-Quality Graphene

Low-cost, scalable preparation of high-quality graphene has been a critical challenge that hampers its large-scale application. We here propose a novel, scalable liquidphase exfoliation method in which the intercalation, expansion, and exfoliation of graphite are achieved all under ambient conditions, not involving any heating or high-temperature treatment. We demonstrate that such room-temperature liquid-phase intercalation and expansion allow graphite flakes to expand up to 1000 times. Significantly different from thermally expanded graphite, the resulting chemically expanded graphite (CEG) exhibits a uniform, open, porous structure with a specific surface area (847 m²/g) comparable to the theoretical value of three-layer graphene. The CEG obtained is able to be exfoliated under mild conditions to give high-quality graphene with a yield of 70% relative to the starting graphite. The exfoliated graphene sheets have very few defects, with an atomic ratio of carbon to oxygen (C/O ratio) of 28. The as-prepared graphene exhibits an electrical conductivity of 1.17 × 10⁵ S/m and the corresponding transparent films also reveal superior optical and electrical performance

Facile Processing of Free-Standing Polyaniline/SWCNT Film as an Integrated Electrode for Flexible Supercapacitor Application

Flexible supercapacitors (SCs) with compact configuration are ideal energy storage devices for portable electronics, owing to their original advantages (e.g., fast charging/discharging). To effectively reduce the volume of SCs, an integrated electrode of free-standing polyaniline (PANI)/single-wall carbon nanotube (SWCNT) film with high performance has been developed via a facile solution deposition method, which can be employed as current collector and active material in the meantime. Thanks to the strong π–π interactions between PANI and CNTs, an efficient conductive network with ordered PANI molecular chains is formed in this hybrid film electrode, which is beneficial for the ion diffusion process and fast redox reaction resulting in a high capacitance of 446 F g^–1 and outstanding cycling stability, achieving 98% retention over 13 000 cycles. Predictably, solid-state SCs constructed by this free-standing PANI/SWCNT film electrode exhibited remarkable mechanical stability and flexibility in a compact configuration, let alone its excellent capacitive performance (218 F g^–1). Moreover, the highest energy density of flexible solid-state SC reached 19.45 Wh kg^–1 at a power density of 320.5 W kg^–1, further indicating a good potential as an energy storage device. This work would inspire other simple process techniques for high-performance flexible SCs, catering to the demand of portable electronic devices

Defect Engineering of MoS₂ Nanosheets by Heavy-Ion Irradiation for Hydrogen Evolution

Molybdenum disulfide (MoS2), a very promising nonprecious catalyst with high hydrogen evolution reaction (HER) catalytic activity, has been extensively studied for its excellent properties. Nonmetal doping and defects can effectively improve the electrocatalytic activity of MoS2 electrocatalysts, but a lack of systematic studies on nonmetal doping and defect engineering hinders further design and improvement of the MoS2 electrocatalysts. Herein, a novel strategy is proposed to improve the HER performance of MoS2 via heavy-ion irradiation. Theoretical calculations show that fluorine (F), as a doping element, exhibits an excellent HER performance of MoS2. Doping and defect engineering are combined by irradiating MoS2 using an F– ion beam with controllable fluence to verify whether F– ions can effectively regulate the electrical structure of MoS2 and contribute to its HER efficiency. This work provides a strong theoretical basis and experimental support for the rational design of the MoS2 electrocatalysts and expands the understanding of the optimization of the electrocatalytic activity of other catalysts