The optimal fiber volume fraction and fiber-matrix property compatibility in fiber reinforced composites

Although the question of minimum or critical fiber volume fraction beyond which a composite can then be strengthened due to addition of fibers has been dealt with by several investigators for both continuous and short fiber composites, a study of maximum or optimal fiber volume fraction at which the composite reaches its highest strength has not been reported yet. The present analysis has investigated this issue for short fiber case based on the well-known shear lag (the elastic stress transfer) theory as the first step. Using the relationships obtained, the minimum spacing between fibers is determined upon which the maximum fiber volume fraction can be calculated, depending on the fiber packing forms within the composites. The effects on the value of this maximum fiber volume fraction due to such factors as fiber and matrix properties, fiber aspect ratio and fiber packing forms are discussed. Furthermore, combined with the previous analysis on the minimum fiber volume fraction, this maximum fiber volume fraction can be used to examine the property compatibility of fiber and matrix in forming a composite. This is deemed to be useful for composite design. Finally some examples are provided to illustrate the results

Wideband DOA Estimation via Sparse Bayesian Learning over a Khatri-Rao Dictionary

This paper deals with the wideband direction-of-arrival (DOA) estimation by exploiting the multiple measurement vectors (MMV) based sparse Bayesian learning (SBL) framework. First, the array covariance matrices at different frequency bins are focused to the reference frequency by the conventional focusing technique and then transformed into the vector form. Then a matrix called the Khatri-Rao dictionary is constructed by using the Khatri-Rao product and the multiple focused array covariance vectors are set as the new observations. DOA estimation is to find the sparsest representations of the new observations over the Khatri-Rao dictionary via SBL. The performance of the proposed method is compared with other well-known focusing based wideband algorithms and the Cramer-Rao lower bound (CRLB). The results show that it achieves higher resolution and accuracy and can reach the CRLB under relative demanding conditions. Moreover, the method imposes no restriction on the pattern of signal power spectral density and due to the increased number of rows of the dictionary, it can resolve more sources than sensors

Knowledge engineering complex decision support system in managing rheumatoid arthritis.

Background: The management of rheumatoid arthritis (RA) involves partially recursive attempts to make optimal treatment decisions that balance the risks of the treatment to the patient against the benefits of the treatment, while monitoring the patient closely for clinical response, as inferred from prior and residual disease activity, and unwanted drug effects, including abnormal laboratory findings. To the extent that this process is logical, based on best available evidence and determined by considered opinion, it should be amenable to capture within a Clinical Decision Support Systems (CDSSs). The formalisation of logical transformations and their execution by computer tools at point of patient encounter holds the promise of more efficient and consistent use of treatment rules and more reliable clinical decision making. Research Setting: The early Rheumatoid Arthritis (eRA) clinic of the Royal Adelaide Hospital (RAH) with approximately 20 RA patient visits per week, and involving 160 patients with a median duration of treatment of more than 4.5 years. Methods: The study applied a Knowledge Engineering approach to interpret the complexities of RA management, in order to implement a knowledge-based CDSS. The study utilised Knowledge Acquisition processes to elicit and explicitly define the RA management rules underpinning the development of the CDSS; the processes were (1) conducting a comprehensive literature review of RA management, (2) observing clinic consultations and (3) consulting with local clinical experts/leaders. Bayes’ Theorem and Bayes Net were used to generate models for assessing contingent probabilities of unwanted events. A questionnaire based on 16 real patient cases was developed to test the concordance agreement between CDSS generated guidance in response to real-life clinical scenarios and decisions of rheumatologists in response to the scenarios. Results: (1) Complex RA management rules were established which included (a) Rules for Changes in Dose/Agent and (b) Drug Toxicity Monitoring Rules. (2) A computer interpretable dynamic model for implementing the complex clinical guidance was found to be applicable. (3) A framework for a methotrexate (MTX) toxicity prediction model was developed, thereby allowing missing risk ratios (probabilities) to be identified. (4) Clinical decision-making processes and workflows were described. Finally, (5) a preliminary version of the CDSS which computed Rules for Changes in Dose/Agent and Drug Toxicity Monitoring Rules was implemented and tested. One hundred and twenty-eight decisions collected from the 8 participating rheumatologists established the ability of the CDSS to match decisions of clinicians accustomed to application of Rules for Changes in Dose/Agent; rheumatologists unfamiliar with the rules displayed lower concordance (0.7857 vs. 0.3929, P = 0.0027). Neither group of rheumatologists matched the performance of the CDSS in making decisions based on highly complex Drug Toxicity Monitoring Rules (0.3611 vs. 0.4167, P = 0.7215). Conclusion: The study has made important contributions to the development of a CDSS suitable for routine use in the eRA clinic setting. Knowledge Acquisition processes were used to elicit domain knowledge, and to refine, validate and articulate eRA management rules, that came to form the knowledge base of the CDSS. The development of computer interpretable guideline models underpinned the CDSS development. The alignment of CDSS guidance in response to clinical scenarios with questionnaire responses of rheumatologists familiar with and accepting of the management rules (and divergence with responses by rheumatologists not familiar with the rules) indicates that the CDSS can be used to guide toward evidence-based considered opinion. The poor correlation between CDSS generated guidance regarding out of range blood results and response of rheumatologists to questions regarding toxicity scenarios, underlines the value of computer aided guidance when decisions involve greater complexity. It also suggests the need for attention to rule development and considered opinion in this area. Discussion: Effective utilisation of extant knowledge is fundamental to knowledgebased systems in healthcare. CDSSs development for chronic disease management is a complex undertaking which is tractable using Knowledge Engineering and Knowledge Acquisition approaches coupled with modelling into computer interpretable algorithms. Complexities of drug toxicity monitoring were addressed using Bayes’ Theorem and Bayes Net for making probability based decisions under conditions of uncertainty. While for logistic reasons the system could not be developed to full implementation, preliminary analyses support the utility of the approach, both for intensifying treatment on a response contingent basis and also for complex drug toxicity monitoring. CDSSs are inherently suited to iterative refinements based on new knowledge including that arising from analyses of the data they capture during their use. This study has achieved important steps toward implementation and refinement.Thesis (Ph.D.) -- University of Adelaide, School of Medicine, 201

Integration of RFID and Industrial WSNs to Create A Smart Industrial Environment

A smart environment is a physical space that is seamlessly embedded with sensors, actuators, displays, and computing devices, connected through communication networks for data collection, to enable various pervasive applications. Radio frequency identification (RFID) and Wireless Sensor Networks (WSNs) can be used to create such smart environments, performing sensing, data acquisition, and communication functions, and thus connecting physical devices together to form a smart environment. This thesis first examines the features and requirements a smart industrial environment. It then focuses on the realization of such an environment by integrating RFID and industrial WSNs. ISA100.11a protocol is considered in particular for WSNs, while High Frequency RFID is considered for this thesis. This thesis describes designs and implementation of the hardware and software architecture necessary for proper integration of RFID and WSN systems. The hardware architecture focuses on communication interface and AI/AO interface circuit design; while the driver of the interface is implemented through embedded software. Through Web-based Human Machine Interface (HMI), the industrial users can monitor the process parameters, as well as send any necessary alarm information. In addition, a standard Mongo database is designed, allowing access to historical and current data to gain a more in-depth understanding of the environment being created. The information can therefore be uploaded to an IoT Cloud platform for easy access and storage. Four scenarios for smart industrial environments are mimicked and tested in a laboratory to demonstrate the proposed integrated system. The experimental results have showed that the communication from RFID reader to WSN node and the real-time wireless transmission of the integrated system meet design requirements. In addition, compared to a traditional wired PLC system where measurement error of the integrated system is less than 1%. The experimental results are thus satisfactory, and the design specifications have been achieved

From Petrov-Einstein-Dilaton-Axion to Navier-Stokes equation in anisotropic model

In this paper we generalize the previous works to the case that the near-horizon dynamics of the Einstein-Dilaton-Axion theory can be governed by the incompressible Navier-Stokes equation via imposing the Petrov-like boundary condition on hypersurfaces in the non-relativistic and near-horizon limit. The dynamical shear viscosity $\eta$ of such dual horizon fluid in our scenario, which isotropically saturates the Kovtun-Son-Starinet (KSS) bound, is independent of both the dilaton field and axion field in that limit.Comment: 13 pages,no figures; v2: 15 page, Equation.(33), some discussions and references added, minor corrections , Version accepted for publication in Physics Letters