Assessment of Speech Dialog Systems using Multi-Modal Cognitive Load Analysis and Driving Performance Metrics

In this paper, cognitive load analysis via acoustic- and CAN-Bus-based driver performance metrics is employed to assess two different commercial speech dialog systems (SDS) during in-vehicle use. Several metrics are proposed to measure increases in stress, distraction and cognitive load and we compare these measures with statistical analysis of the speech recognition component of each SDS. It is found that care must be taken when designing an SDS as it may increase cognitive load which can be observed through increased speech response delay (SRD), changes in speech production due to negative emotion towards the SDS, and decreased driving performance on lateral control tasks. From this study, guidelines are presented for designing systems which are to be used in vehicular environments.

Utilizing robustness of positive buck-boost converter against input voltage and load current disturbances

A Positive Buck- Boost (PBB) converter is a known DC-DC converter that can operate in step up and step down modes. Unlike Buck, Boost, and Inverting Buck Boost converters, the inductor current of a PBB can be controlled independently of its voltage conversion ratio. In other words, the inductor of PBB can be utilised as an energy storage unit in addition to its main function of energy transfer. In this paper, the capability of PBB to store energy has been utilised to achieve robustness against input voltage fluctuations and output current changes. The control strategy has been developed to keep accuracy, affordability, and simplicity acceptable. To improve the efficiency of the system a Smart Load Controller (SLC) has been suggested. Applying SLC extra current storage occurs when there is sudden loads change otherwise little extra current is stored.

QoS-Based Web Service Scheduling Accommodating Inter-Service Dependencies Using Minimal-Conflict Hill-Climbing Repair Genetic Algorithm

In the filed of semantic grid, QoS-based Web service scheduling for workflow optimization is an important problem.However, in semantic and service rich environment like semantic grid, the emergence of context constraints on Web services is very common making the scheduling consider not only quality properties of Web services, but also inter service dependencies which are formed due to the context constraints imposed on Web services. In this paper, we present a repair genetic algorithm, namely minimal-conflict hill-climbing repair genetic algorithm, to address scheduling optimization problems in workflow applications in the presence of domain constraints and inter service dependencies. Experimental results demonstrate the scalability and effectiveness of the genetic algorithm.

Comparison of 3D finite element analysis derived stiffness and BMD to determine the failure load of the excised proximal femur

Bone mineral density (BMD) is currently the preferred surrogate for bone strength in clinical practice. Finite element analysis (FEA) is a computer simulation technique that can predict the deformation of a structure when a load is applied, providing a measure of stiffness (N mm− 1). Finite element analysis of X-ray images (3D-FEXI) is a FEA technique whose analysis is derived from a single 2D radiographic image. This ex-vivo study demonstrates that 3D-FEXI derived from a conventional 2D radiographic image has the potential to significantly increase the accuracy of failure load assessment of the proximal femur compared with that currently achieved with BMD.

Scheduling trains as a blocking parallel-machine job shop scheduling problem

In this paper, the train scheduling problem is modelled as a blocking parallel-machine job shop scheduling (BPMJSS) problem. In the model, trains, single-track sections and multiple-track sections, respectively, are synonymous with jobs, single machines and parallel machines, and an operation is regarded as the movement/traversal of a train across a section. Due to the lack of buffer space, the real-life case should consider blocking or hold-while-wait constraints, which means that a track section cannot release and must hold the train until next section on the routing becomes available. Based on literature review and our analysis, it is very hard to find a feasible complete schedule directly for BPMJSS problems. Firstly, a parallel-machine job-shop-scheduling (PMJSS) problem is solved by an improved shifting bottleneck procedure (SBP) algorithm without considering blocking conditions. Inspired by the proposed SBP algorithm, feasibility satisfaction procedure (FSP) algorithm is developed to solve and analyse the BPMJSS problem, by an alternative graph model that is an extension of the classical disjunctive graph models. The proposed algorithms have been implemented and validated using real-world data from Queensland Rail. Sensitivity analysis has been applied by considering train length, upgrading track sections, increasing train speed and changing bottleneck sections. The outcomes show that the proposed methodology would be a very useful tool for the real-life train scheduling problems

Online scheduling in the operating theatre

Online scheduling is considered in this paper for the Operating Theatre. Robust elective schedules are determined in the offline environment prior to the day of surgery for the online environment. Changes to the offline schedule during project implementation are minimized using an online scheduling model that operates in real-time. The model aims to minimise cancellations of pre-scheduled elective patients whilst also allowing for additional scheduling of emergency cases, time permitting, which may arise during the schedules implementation. Surgical durations are modelled with a lognormal distribution. The single theatre case is solved and the computationally complex multiple theatre case, which is left for future work, is discussed.

Glycaemic index and glycaemic load intake patterns in older Australian women

Aims: Dietary glycemic index (GI) and glycemic load (GL) have been associated with risk of chronic diseases, yet limited research exists on patterns of consumption in Australia. Our aims were to investigate glycemic carbohydrate in a population of older women, identify major contributing food sources, and determine low, moderate and high ranges. Methods: Subjects were 459 Brisbane women aged 42-81 years participating in the Longitudinal Assessment of Ageing in Women. Diet history interviews were used to assess usual diet and results were analysed into energy and macronutrients using the FoodWorks dietary analysis program combined with a customised GI database. Results: Mean±SD dietary GI was 55.6±4.4% and mean dietary GL was 115±25. A low GI in this population was ≤52.0, corresponding to the lowest quintile of dietary GI, and a low GL was ≤95. GI showed a quadratic relationship with age (P=0.01), with a slight decrease observed in women aged in their 60’s relative to younger or older women. GL decreased linearly with age (P<0.001). Bread was the main contributor to carbohydrate and dietary GL (17.1% and 20.8%, respectively), followed by fruit (15.5% and 14.2%), and dairy for carbohydrate (9.0%) or breakfast cereals for GL (8.9%). Conclusions: In this population, dietary GL decreased with increasing age, however this was likely to be a result of higher energy intakes in younger women. Focus on careful selection of lower GI items within bread and breakfast cereal food groups would be an effective strategy for decreasing dietary GL in this population of older women.

Glycaemic load is associated with insulin resistance in older Australian women

Background: Diets with a high postprandial glycemic response may contribute to long-term development of insulin resistance and diabetes, however previous epidemiological studies are conflicting on whether glycemic index (GI) or glycemic load (GL) are dietary factors associated with the progression. Our objectives were to estimate GI and GL in a group of older women, and evaluate cross-sectional associations with insulin resistance. Subjects and Methods: Subjects were 329 Australian women aged 42-81 years participating in year three of the Longitudinal Assessment of Ageing in Women (LAW). Dietary intakes were assessed by diet history interviews and analysed using a customised GI database. Insulin resistance was defined as a homeostasis model assessment (HOMA) value of >3.99, based on fasting blood glucose and insulin concentrations. Results: GL was significantly higher in the 26 subjects who were classified as insulin resistant compared to subjects who were not (134±33 versus 114±24, P<0.001). In a logistic regression model, an increment of 15 GL units increased the odds of insulin resistance by 2.09 (95%CI 1.55, 2.80, P<0.001) independently of potential confounding variables. No significant associations were found when insulin resistance was assessed as a continuous variable. Conclusions: Results of this cross-sectional study support the concept that diets with a higher GL are associated with increased risk of insulin resistance. Further studies are required to investigate whether reducing glycemic intake, by either consuming lower GI foods and/or smaller serves of carbohydrate, can contribute to a reduction in development of insulin resistance and long-term risk of type 2 diabetes.