A Machine Learning and Compiler-based Approach to Automatically Parallelize Serial Programs Using OpenMP

Single core designs and architectures have reached their limits due to heat and power walls. In order to continue to increase hardware performance, hardware industries have moved forward to multi-core designs and implementations which introduces a new paradigm in parallel computing. As a result, software programmers must be able to explicitly write or produce parallel programs to fully exploit the potential computing power of parallel processing in the underlying multi-core architectures. Since the hardware solution directly exposes parallelism to software designers, different approaches have been investigated to help the programmers to implement software parallelism at different levels. One of the approaches is to dynamically parallelize serial programs at the binary level. Another approach is to use automatic parallelizing compilers. Yet another common approach is to manually insert parallel directives into serial codes to achieve parallelism. This writing project presents a machine learning and compiler-based approach to design and implement a system to automatically parallelize serial C programs via OpenMP directives. The system is able to learn and analyze source code parallelization mechanisms from a training set containing pre-parallelized programs with OpenMP constructs. It then automatically applies the knowledge learned onto serial programs to achieve parallelism. This automatic parallelizing approach can be used to target certain common parallel constructs or directives, and its results when combined with a manual parallelizing technique can achieve maximum or better parallelism in complex serial programs. Furthermore, the approach can also be used as part of compiler design to help improve both the speed and performance of a parallel compiler

Detection of Trace Heavy Metals in Water: Development of Electrochemical Sensors

The presence of heavy metals in our ecosystem poses significant ecological and physiological consequences. As a result, numerous techniques are developed for the detection of contaminants in aqueous solutions. However, early and trace detection of such contaminants still remains a challenge. Amongst many techniques, electrochemistry driven sensors have shown promise due to their possibility of miniaturization and low-cost. Our research investigates the use of electrically conducting polymer and atomically thin carbon materials as electrodes towards the development of electrochemical sensor. Nanocomposite electrode films have been synthesized and fabricated using in-situ polymerization technique and the relationship between number of cycles of deposition and its electrochemical performance is analyzed. Capacitive microcomb sensor device has been fabricated. Understanding of device performance using electrochemical, chemical, and morphological tests on electrodes will be conducted and generation of ohmic and non-ohmic resistance as a function of deposition, electrolyte solutions will be highlighted. Future work on fabrication of nanofibers using electrospinning and selective deposition on sensor platform will also be discussed

IS014001 and OSHAS 18001, Effective EHS Management Tools for Shoe Factories in Vietnam Case Study

With growing demand from stakeholders, Vietnamese shoe factories desperately need to effectively implement sound EHS management systems. The ISO14001 and OSHAS 18001 standards ( the Standards ) have emerged as effective EHS management tools to serve those needs. However, the Vietnamese shoe industry has very limited experience in implementing those standards. They also do not understand the concept of cost-effectiveness and the challenges of implementation. Small medium enterprises anticipated difficulties in technology improvement required by the ISO 14001 due to resources limitation and all local companies concern about cost prohibited certification and no tangible market benefits (Greening Trade in Vietnam, 16). The purpose of this study was to identify the drivers, the values added, the challenges and the success factors of implementing the Standards at three shoe factories in Vietnam. The results tend to indicate that: 1) the key drivers of implementing the Standards were from stakeholder chain of actions, i.e. corporate policy, multinationals, customers, NGOs and labor groups. There was a shift from external directed to internal values regarding implementing standards at the studied factories; 2) implementing the Standards was value added. The benefits outweigh the costs. The key benefits include reduced injury, reduced waste handling costs and improved multinationals (clients) satisfaction; 3) key challenges include the workforce\u27s lack of knowledge of EHS Standards, no existing trade specific ISO 14001 and 18001 model at the time of implementing the Standards and the lack of standardized waste treatment and disposal facilities; and 4) key success factors include leadership\u27s commitment and employees\u27 involvement, top-down management , employee empowerment, and training. Most importantly, the study demonstrates evidence of successful implementing the Standards in shoe factories in Vietnam with improved EHS results

I am what I am – Convergence Behaviors on Online Discussion About the Safety of COVID-19 Vaccines

Using data from an online discussion on the risk of getting blood clot from Johnson & Johnson vaccine moderated by the New York Times Facebook page, we investigated the presence of eleven convergence behaviors, and the interaction between them. While recent research focuses on misinformation or fake news as the object of analysis, we argue in this exploratory research that it is equally important to analyze who and, whenever possible, why people engage in information exchange given a particular crisis, hence their convergence behaviors. Mapping the types of postings to their authors would be an additional step to design, develop, implement, and possibly, regulate online discussions for a more effective and just civic engagement. As we witness a mass manipulation of public opinion, our findings suggest that the number of netizens that seek to correct misinformation is growing. If the society goal is to swiftly rebut as many conspiracy theories as possible, we advocate for a dual social media control strategy: restrain as much as possible the misinformation spreaders/manipulators and encourage correctors to help propagate countervailing facts

Development of Electrochemical Sensors for the Detection of Trace Contaminants

Several industrial processes, such as stainless steel fabrication and textiles, produce heavy metal byproducts such as chromium. These heavy metals have detrimental effects on the surrounding environments and humans. Recently, electrochemical-driven sensors have been studied and show great potential in miniaturization while still providing measurements at a low cost. In addition, atomically thin allotropes of carbon, graphene, and graphene oxide have shown remarkable results in producing a highly responsive and selective sensor platform. These results are due to their excellent electrical conductivity, high surface area for utility, and physicochemical stability. The existing challenge for electrochemical-driven sensors is understanding the molecular level\u27s relationship between microstructures and chemical affinities. In this work, the research efforts are to understand the relation structure-property-function to comprehend reaction kinetics better and identify the rate-limiting steps. Experimental results from interdigitated micro-comb chips deposited with nanocomposite electrodes will be presented under different electrolytes and varying concentrations. In addition, we will display governing mechanisms of charge transfer relating to sensor performance

Calculation of loading-induced tendon slip in beams prestressed with external tendons. Part I: Experiment

This study consists of two parts, in which both experimental and numerical studies on externally prestressed concrete beams were investigated. In Part I, three identical beams of T-shaped section prestressed with external tendons have been tested to failure to investigate the effects of geometry of the applied load on flexural behavior of externally prestressed concrete beams. The tendon slip at deviators was also monitored in order to examine the evolution of stress in the external tendons. Test results were presented with emphasis on the effects of geometry of applied load and tendon slip at deviators

Attitude determination using an adaptive multiple model filtering Scheme

Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown quantities such as spacecraft dynamics parameters, gyro biases, dynamic disturbances, or environment variations

Frequency robust control in stand-alone microgrids with PV sources : design and sensitivity analysis

International audienceIn this paper, a robust H-infinity control strategy for frequency regulation is proposed in isolated microgrids (MGs) composed of diesel engine generators, photovoltaic (PV) sources, and storage units. First, the linear matrix inequalities (LMI) method is adopted to design a multi-variable H-infinity controller which ensures given specifications. In a second step, uncertainties in the storage device state of charge (SoC) are considered and a sensitivity analysis is carried out in order to determine the maximum variation range of SoC for which the dynamic performances are respected. The controller's robustness and performance in the presence of various load disturbances, PV output power variations, and the SoC uncertainty are validated through a series of nonlinear time-domain simulations performed with MATLAB/Simulink.</p

Wasser qualität Bewertung und Verwaltung von Flachland Fluss Einzugsgebiet

This dissertation describes at first the hydrology and the long-term impact of point and diffuse source pollution on nutrient loads based on the current agricultural practices and sewage disposals in rural lowland catchments. The evaluations of Best Management Practices (BMPs) for water quality improvement was then implemented aiming at controlling and reducing pollution from point and diffuse sources in the entire catchments.Diese Dissertation beschreibt zunächst die Hydrologie und die langfristigen Auswirkungen von Punkt- und diffusen Quellen auf die Nährstoffbelastungen, die auf den derzeitigen landwirtschaftlichen Praktiken und Abwassereinleitungen in ländlichen Tiefland-Einzugsgebieten basieren. Bewertungen von Best Management Practices (BMPs) für die Verbesserung der Wasserqualität wurden dann mit dem Ziel der Kontrolle und Verringerung der Umweltverschmutzung aus Punkt- und diffusen Quellen in gesamten Einzugsgebieten durchgeführt

Optimization of network traffic anomaly detection using machine learning

In this paper, to optimize the process of detecting cyber-attacks, we choose to propose 2 main optimization solutions: Optimizing the detection method and optimizing features. Both of these two optimization solutions are to ensure the aim is to increase accuracy and reduce the time for analysis and detection. Accordingly, for the detection method, we recommend using the Random Forest supervised classification algorithm. The experimental results in section 4.1 have proven that our proposal that use the Random Forest algorithm for abnormal behavior detection is completely correct because the results of this algorithm are much better than some other detection algorithms on all measures. For the feature optimization solution, we propose to use some data dimensional reduction techniques such as information gain, principal component analysis, and correlation coefficient method. The results of the research proposed in our paper have proven that to optimize the cyber-attack detection process, it is not necessary to use advanced algorithms with complex and cumbersome computational requirements, it must depend on the monitoring data for selecting the reasonable feature extraction and optimization algorithm as well as the appropriate attack classification and detection algorithms