The Highly Effective Cobalt Based Metal–Organic Frameworks Catalyst for One Pot Oxidative Esterification Under Mild Conditions

Catalysis Letters - The cobalt-based metal organic frameworks (Co-MOFs) catalyst has been prepared with using terephthalic acid and 4,4′-bipyridine as organic linkers by facile solvothermal...     

Fault-tolerant embedded system design and optimization considering reliability estimation uncertainty

In this paper, we model embedded system design and optimization, considering component redundancy and uncertainty in the component reliability estimates. The systems being studied consist of software embedded in associated hardware components. Very often, component reliability values are not known exactly. Therefore, for reliability analysis studies and system optimization, it is meaningful to consider component reliability estimates as random variables with associated estimation uncertainty. In this new research, the system design process is formulated as a multiple-objective optimization problem to maximize an estimate of system reliability, and also, to minimize the variance of the reliability estimate. The two objectives are combined by penalizing the variance for prospective solutions. The two most common fault-tolerant embedded system architectures, N-Version Programming and Recovery Block, are considered as strategies to improve system reliability by providing system redundancy. Four distinct models are presented to demonstrate the proposed optimization techniques with or without redundancy. For many design problems, multiple functionally equivalent software versions have failure correlation even if they have been independently developed. The failure correlation may result from faults in the software specification, faults from a voting algorithm, and/or related faults from any two software versions. Our approach considers this correlation in formulating practical optimization models. Genetic algorithms with a dynamic penalty function are applied in solving this optimization problem, and reasonable and interesting results are obtained and discussed.     

System optimization with component reliability estimation uncertainty: a multi-criteria approach

Summary & Conclusions-This paper addresses system reliability optimization when component reliability estimates are treated as random variables with estimation uncertainty. System reliability optimization algorithms generally assume that component reliability values are known exactly, i.e., they are deterministic. In practice, that is rarely the case. For risk-averse system design, the estimation uncertainty, propagated from the component estimates, may result in unacceptable estimation uncertainty at the system-level. The system design problem is thus formulated with multiple objectives: (1) to maximize the system reliability estimate, and (2) to minimize its associated variance. This formulation of the reliability optimization is new, and the resulting solutions offer a unique perspective on system design. Once formulated in this manner, standard multiple objective concepts, including Pareto optimality, were used to determine solutions. Pareto optimality is an attractive alternative for this type of problem. It provides decision-makers the flexibility to choose the best-compromise solution. Pareto optimal solutions were found by solving a series of weighted objective problems with incrementally varied weights. Several sample systems are solved to demonstrate the approach presented in this paper. The first example is a hypothetical series-parallel system, and the second example is the fault tolerant distributed system architecture for a voice recognition system. The results indicate that significantly different designs are obtained when the formulation incorporates estimation uncertainty. If decision-makers are risk averse, and wish to consider estimation uncertainty, previously available methodologies are likely to be inadequate.     

Effects of handicraft sitting postures on lower trunk muscle fatigue

Fifteen male subjects were exposed to a primary tracking task and a variety of subsidiary tasks for a total of 15 hours. Tracking performance was assessed through the use of two error measures, amount of time off the target track, and number of times off target. Subsidiary performance tasks included: a vigilance task requiring subjects to respond to the deflection of the noodle of a small meter; two reaction time tasks requiring response to the onset of one or the other of two lights; mental multiplication, which required the solution of simple multiplication problems; and digit span, in which the subject was required to repeat as rapidly and as accurately as possible a sot of digits of either five, six or seven numbers in length. In addition, three physiological measures were obtained.

A significant decrement in trucking performance was obtained for both measures utilized. This decrement was not particularly abrupt in its occurrence but rather took place cumulatively over the entire course of the experiment. There was no clearly established performance decrement on the subsidiary tasks utilized in this investigation. A marked variability in performance over the course of the experimental session was characteristic of performance on these tasks. Performance on the vigilance task, and one of the reaction time tasks improved during the 15-hour test session.

The 17-Kotosteroid and 17-Hydroxyeortieoid values increased during the session but only in the case of the latter was the increase significant. The eosinophil count of subjects exposed to the test conditions decreased steadily throughout the experimental session. However, eosinophil measures obtained from control subjects increased during a similar time period.     

Oxidation Kinetics of Boronized Low Carbon Steel AISI 1018

The high-temperature oxidation behavior of boron coating on plain low-carbon steel AISI 1018 was studied at 500, 600, 700, 800 and 900 °C in air. The oxidation resistance of unboronized (uncoated) and boronized (boron-coated) specimens was studied isothermally in a thermogravimetric analyzer. The oxidation-rate constant represented as a parabolic rate constant (k _p ) was evaluated with the parabolic rate law. The activation energy of oxidation on unboronized and boronized steel specimens was determined by Arrhenius law. Optical microscopy, XRD and SEM were used for surface characterizations. The experimental results show that boronized coating increases the oxidation resistance of plain low carbon steel AISI 1018 about five times and prevents oxygen from penetrating into the steel substrate at temperatures below 900 °C.     

Reliability optimization models for embedded systems with multiple applications

Summary and Conclusions-This paper presents four models for optimizing the reliability of embedded systems considering both software and hardware reliability under cost constraints, and one model to optimize system cost under multiple reliability constraints. Previously, most optimization models have been developed for hardware-only or software-only systems by assuming the hardware, if any, has perfect reliability. In addition, they assume that failures for each hardware or software unit are statistically independent. In other words, none of the existing optimization models were developed for embedded systems (hardware and software) with failure dependencies. For our work, each of our models is suitable for a distinct set of conditions or situations. The first four models maximize reliability while meeting cost constraints, and the fifth model minimizes system cost under multiple reliability constraints. This is the first time that optimization of these kinds of models has been performed on this type of system. We demonstrate and validate our models for an embedded system with multiple applications sharing multiple resources. We use a Simulated Annealing optimization algorithm to demonstrate our system reliability optimization techniques for distributed systems, because of its flexibility for various problem types with various constraints. It is efficient, and provides satisfactory optimization results while meeting difficult-to-satisfy constraints.     

The Effect of Boronizing on Metallic Alloys for Automotive Applications

In this study the wear resistance, corrosion resistance, and oxidation resistance of boronized metallic alloys were investigated. Thermochemical treatment was performed by powder pack boronizing process at temperature 850-950 °C for 4 h. Saw-tooth morphology and smooth interface microstructures were observed with an optical microscope; microhardness was measured across the coating depth. The phases present in the boron coatings depend on the substrate material. High-temperature oxidation resistance was investigated and it was found that boron coating on ferrous alloys can resist temperatures up to 800 °C. The corrosion resistance of the boronized samples was improved and the corrosion rate was calculated for boronized and plain specimens. Wear testing was conducted by following the procedures of ASTM G99, ASTM D2526, and ASTM D4060. The obtained experimental results revealed that boronizing significantly improves the wear-resistance, corrosion-resistance, and oxidation resistance of metallic alloys. This article was presented at Materials Science & Technology 2007, Automotive and Ground Vehicles symposium held September 16-20, 2007, in Detroit, MI.     

Practical real-time intrusion detection using machine learning approaches

The growing prevalence of network attacks is a well-known problem which can impact the availability, confidentiality, and integrity of critical information for both individuals and enterprises. In this paper, we propose a real-time intrusion detection approach using a supervised machine learning technique. Our approach is simple and efficient, and can be used with many machine learning techniques. We applied different well-known machine learning techniques to evaluate the performance of our IDS approach. Our experimental results show that the Decision Tree technique can outperform the other techniques. Therefore, we further developed a real-time intrusion detection system (RT-IDS) using the Decision Tree technique to classify on-line network data as normal or attack data. We also identified 12 essential features of network data which are relevant to detecting network attacks using the information gain as our feature selection criterions. Our RT-IDS can distinguish normal network activities from main attack types (Probe and Denial of Service (DoS)) with a detection rate higher than 98% within 2 s. We also developed a new post-processing procedure to reduce the false-alarm rate as well as increase the reliability and detection accuracy of the intrusion detection system.