Static and vibration analysis of functionally graded beams using refined shear deformation theory

Static and vibration analysis of functionally graded beams using refined shear deformation theory is presented. The developed theory, which does not require shear correction factor, accounts for shear deformation effect and coupling coming from the material anisotropy. Governing equations of motion are derived from the Hamilton's principle. The resulting coupling is referred to as triply coupled axial-flexural response. A two-noded Hermite-cubic element with five degree-of-freedom per node is developed to solve the problem. Numerical results are obtained for functionally graded beams with simply-supported, cantilever-free and clamped-clamped boundary conditions to investigate effects of the power-law exponent and modulus ratio on the displacements, natural frequencies and corresponding mode shapes

Time-varying Learning and Content Analytics via Sparse Factor Analysis

We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) analyzes learner concept knowledge state transitions (induced by interacting with learning resources, such as textbook sections, lecture videos, etc, or the forgetting effect), and (iii) estimates the content organization and intrinsic difficulty of the assessment questions. These quantities are estimated solely from binary-valued (correct/incorrect) graded learner response data and a summary of the specific actions each learner performs (e.g., answering a question or studying a learning resource) at each time instance. Experimental results on two online course datasets demonstrate that SPARFA-Trace is capable of tracing each learner's concept knowledge evolution over time, as well as analyzing the quality and content organization of learning resources, the question-concept associations, and the question intrinsic difficulties. Moreover, we show that SPARFA-Trace achieves comparable or better performance in predicting unobserved learner responses than existing collaborative filtering and knowledge tracing approaches for personalized education

Prototype edge-grown nanowire sensor array for the real-time monitoring and classification of multiple gases

The monitoring and classification of different gases using a single resistive semiconductor sensor are challenging because of the similar response characteristics. An array of separated sensors can be used as an electronic nose, but such arrays have a bulky structure and complex fabrication processes. Herein, we easily fabricated a gas-sensor array based on edge-grown SnO2 nanowires for the real-time monitoring and classification of multiple gases. The array comprised four sensors and was designed on a glass substrate. SnO2 nanowires were grown on-chip from the edge of electrodes, made contact together, and acted as sensing elements. This method was advantageous over the post-synthesis technique because the SnO2 nanowires were directly grown from the edge of the electrodes rather than on the surface. Accordingly, damage to the electrode was avoided by alloying Sn with Pt at a high growth temperature. The sensing characteristics of the sensor array were further examined for different gases, including methanol, isopropanol, ethanol, ammonia, hydrogen sulphide and hydrogen. Radar plots were used to improve the selective detection of different gases and enable effective classification

Miniaturized multisensor system with a thermal gradient: Performance beyond the calibration range

Two microchips, each with four identical microstructured sensors using SnO2 nanowires as sensing material (one chip decorated with Ag nanoparticles, the other with Pt nanoparticles), were used as a nano-electronic nose to distinguish five different gases and estimate their concentrations. This innovative approach uses identical sensors working at different operating temperatures thanks to the thermal gradient created by an integrated microheater. A system with in-house developed hardware and software was used to collect signals from the eight sensors and combine them into eight-dimensional data vectors. These vectors were processed with a support vector machine allowing for qualitative and quantitative discrimination of all gases after calibration. The system worked perfectly within the calibrated range (100% correct classification, 6.9% average error on concentration value). This work focuses on minimizing the number of points needed for calibration while maintaining good sensor performance, both for classification and error in estimating concentration. Therefore, the calibration range (in terms of gas concentration) was gradually reduced and further tests were performed with concentrations outside these new reduced limits. Although with only a few training points, down to just two per gas, the system performed well with 96% correct classifications and 31.7% average error for the gases at concentrations up to 25 times higher than its calibration range. At very low concentrations, down to 20 times lower than the calibration range, the system worked less well, with 93% correct classifications and 38.6% average error, probably due to proximity to the limit of detection of the sensors

Multi Visualization and Dynamic Query for Effective Exploration of Semantic Data

Semantic formalisms represent content in a uniform way according to ontologies. This enables manipulation and reasoning via automated means (e.g. Semantic Web services), but limits the user’s ability to explore the semantic data from a point of view that originates from knowledge representation motivations. We show how, for user consumption, a visualization of semantic data according to some easily graspable dimensions (e.g. space and time) provides effective sense-making of data. In this paper, we look holistically at the interaction between users and semantic data, and propose multiple visualization strategies and dynamic filters to support the exploration of semantic-rich data. We discuss a user evaluation and how interaction challenges could be overcome to create an effective user-centred framework for the visualization and manipulation of semantic data. The approach has been implemented and evaluated on a real company archive

Commune agroecosystem analysis to support decision making for water allocation for fisheries and agriculture in the Tonle Sap wetland system

The Project on Commune Agroecosystem Analysis to Support Decision Making for Water Allocation for Fisheries and Agriculture in the Tonle Sap Wetland System was undertaken with the aim of improving fisheries considerations in the Commune Agroecosystem Analysis (CAEA) process undertaken in Cambodia, to facilitate better planning at the commune level. Under this project a number of changes were made to the CAEA tools and process and pilot tested in an adaptive, iterative manner in four communes – two that had conducted a CAEA previously and two that had not. Results and analyses indicated that the project had significantly strengthened the manner in which livelihoods, water resources and fisheries are now addressed by CAEA. The revised CAEA guidance manual has also shown potential for having wider uptake, and a number of tools have been used by several other projects within Cambodia

A case of hepatic cyst-induced internal jugular venous thrombosis

• Echocardiography can demonstrate hepatic cyst–induced right atrial compression. • Hepatic cyst–induced blood flow stasis can cause internal jugular venous thrombus. • Laparoscopic deroofing of hepatic cysts is a safe and effective treatment