A generic design environment for the rural industry knowledge acquisition

This paper describes a new knowledge acquisition method using a generic design environment where context-sensitive knowledge is used to build specific DSS for rural business. Although standard knowledge acquisition methods have been applied in rural business applications, uptake remains low and familiar weaknesses such as obsolescence and brittleness apply. We describe a decision support system (DSS) building environment where contextual factors relevant to the end users are directly taken into consideration. This "end user enabled design environment" (EUEDE) engages both domain experts in creating an expert knowledge base and business operators/end users (such as farmers) in using this knowledge for building their specific DSS. We document the knowledge organisation for the problem domain, namely a dairy industry application. This development involved a case-study research approach used to explore dairy operational knowledge. In this system end users can tailor their decision-making requirements using their own judgement to build specific DSSs. In a specific end user's farming context, each specific DSS provides expert suggestions to assist farmers in improving their farming practice. The paper also shows the environment's generic capability.

Comparison of nitrogen fertilization methods and rates for subsurface drip irrigated corn in the semi-arid Great Plains

In semi-arid areas such as western Nebraska, interest in subsurface drip irrigation (SDI) for corn is increasing due to restricted irrigation allocations. However, crop response quantification to nitrogen (N) applications with SDI and the environmental benefits of multiple in-season (IS) SDI N applications instead of a single early-season (ES) surface application are lacking. The study was conducted in 2004, 2005, and 2006 at the University of Nebraska-Lincoln West Central Research and Extension Center in North Platte, Nebraska, comparing two N application methods (IS and ES) and three N rates (128, 186, and 278 kg N ha(-1)) using a randomized complete block design with four replications. No grain yield or biomass response was observed in 2004. In 2005 and 2006, corn grain yield and biomass production increased with increasing N rates, and the IS treatment increased grain yield, total N uptake, and gross return after N application costs (GRN) compared to the ES treatment. Chlorophyll meter readings taken at the R3 corn growth stage in 2006 showed that less N was supplied to the plant with ES compared to the IS treatment. At the end of the study, soil NO3-N masses in the 0.9 to 1.8 m depth were greater under the IS treatment compared to the ES treatment. Results suggested that greater losses of NO3-N below the root zone under the ES treatment may have had a negative effect on corn production. Under SDI systems, fertigating a recommended N rate at various corn growth stages can increase yields, GRN, and reduce NO3-N leaching in soils compared to concentrated early-season applications.

Comparison Of If Estimation Methods For Noise Robustness

Non-stationary signal modeling is a well addressed problem in the literature. Many methods have been proposed to model non-stationary signals such as time varying linear prediction and AM-FM modeling, the later being more popular. Estimation techniques to determine the AM-FM components of narrow-band signal, such as Hilbert transform, DESA1, DESA2, auditory processing approach, ZC approach, etc., are prevalent but their robustness to noise is not clearly addressed in the literature. This is critical for most practical applications, such as in communications. We explore the robustness of different AM-FM estimators in the presence of white Gaussian noise. Also, we have proposed three new methods for IF estimation based on non-uniform samples of the signal and multi-resolution analysis. Experimental results show that ZC based methods give better results than the popular methods such as DESA in clean condition as well as noisy condition.

A comparative study of speaker adaptation methods

For the problem of speaker adaptation in speech recognition, the performance depends on the availability of adaptation data. In this paper, we have compared several existing speaker adaptation methods, viz. maximum likelihood linear regression (MLLR), eigenvoice (EV), eigenspace-based MLLR (EMLLR), segmental eigenvoice (SEV) and hierarchical eigenvoice (HEV) based methods. We also develop a new method by modifying the existing HEV method for achieving further performance improvement in a limited available data scenario. In the sense of availability of adaptation data, the new modified HEV (MHEV) method is shown to perform better than all the existing methods throughout the range of operation except the case of MLLR at the availability of more adaptation data.

Indian developments in ultrasonics and acoustic emission methods

Fundamental investigations in ultrasonics in India date back to the early 20th century. But, fundamental and applied research in the field of nondestructive evaluation (NDE) came much later. In the last four decades it has grown steadily in academic institutions, national laboratories and industry. Currently, commensurate with rapid industrial growth and realisation of the benefits of NDE, the activity is becoming much stronger, deeper, broader and very wide spread. Acoustic Emission (AE) is a recent entry into the field of nondestructive evaluation. Pioneering efforts in India in AE were carried out at the Indian Institute of Science in the early 1970s. The nuclear industry was the first to utilise it. Current activity in AE in the country spans materials research, incipient failure detection, integrity evaluation of structures, fracture mechanics studies and rock mechanics. In this paper, we attempt to project the current scenario in ultrasonics and acoustic emission research in India.

Various Methods of Designing Spillway Capacity of Dams and Estimating Flood Discharges

Abstract is not available.

Bottleneck assignment problems under categorization

This paper considers two special cases of bottleneck grouped assignment problems when n jobs belong to m distinct categories (m < n). Solving these special problems through the available branch and bound algorithms will result in a heavy computational burden. Sequentially identifying nonopitmal variables, this paper provides more efficient methods for those cases. Propositions leading to the algorithms have been established. Numerical examples illustrate the respective algorithms.

Multi-Hypotheses Tracking using the Dempster–Shafer Theory, application to ambiguous road context

This paper presents a Multi-Hypotheses Tracking (MHT) approach that allows solving ambiguities that arise with previous methods of associating targets and tracks within a highly volatile vehicular environment. The previous approach based on the Dempster–Shafer Theory assumes that associations between tracks and targets are unique; this was shown to allow the formation of ghost tracks when there was too much ambiguity or conflict for the system to take a meaningful decision. The MHT algorithm described in this paper removes this uniqueness condition, allowing the system to include ambiguity and even to prevent making any decision if available data are poor. We provide a general introduction to the Dempster–Shafer Theory and present the previously used approach. Then, we explain our MHT mechanism and provide evidence of its increased performance in reducing the amount of ghost tracks and false positive processed by the tracking system.

Mathematics, programming, and STEM

Learning mathematics is a complex and dynamic process. In this paper, the authors adopt a semiotic framework (Yeh & Nason, 2004) and highlight programming as one of the main aspects of the semiosis or meaning-making for the learning of mathematics. During a 10-week teaching experiment, mathematical meaning-making was enriched when primary students wrote Logo programs to create 3D virtual worlds. The analysis of results found deep learning in mathematics, as well as in technology and engineering areas. This prompted a rethinking about the nature of learning mathematics and a need to employ and examine a more holistic learning approach for the learning in science, technology, engineering, and mathematics (STEM) areas.

A comparison of training methods to increase neck muscle strength

Objective To compare two neck strength training modalities. Background Neck injury in pilots flying high performance aircraft is a concern in aviation medicine. Strength training may be an effective means to strengthen the neck and decrease injury risk. Methods The cohort consisted of 32 age-height-weight matched participants, divided into two experimental groups; the Multi-Cervical Unit (MCU) and Thera-Band tubing groups (THER), and a control (CTRL) group. Ten weeks of training were undertaken and pre-and post isometric strength testing for all groups was performed on the MCU. Comparisons between the three groups were made using a Kruskal-Wallis test and effect sizes between the MCU and the THER groups and the THER and CTRL groups were also calculated. Results The MCU group displayed the greatest increase in isometric strength (flexion 64.4%, extension 62.9%, left lateral flexion 53.3%, right lateral flexion 49.1%) and differences were only statistically significant (p<0.05) when compared to the CTRL group. Increases in neck strength for the THER group were lower than that shown in the MCU group (flexion 42.0%, extension 29.9%, left lateral flexion 26.7%, right lateral flexion 24.1%). Moderate to large effect sizes were found between the MCU and THER as well as the THER and CTRL groups. Conclusions This study demonstrated that the MCU was the most effective training modality to increase isometric cervical muscle strength. Thera-Band tubing did however, produce moderate gains in isometric neck strength

Assessing university students' prior knowledge

The aim of this dissertation was to explore how different types of prior knowledge influence student achievement and how different assessment methods influence the observed effect of prior knowledge. The project started by creating a model of prior knowledge which was tested in various science disciplines. Study I explored the contribution of different components of prior knowledge on student achievement in two different mathematics courses. The results showed that the procedural knowledge components which require higher-order cognitive skills predicted the final grades best and were also highly related to previous study success. The same pattern regarding the influence of prior knowledge was also seen in Study III which was a longitudinal study of the accumulation of prior knowledge in the context of pharmacy. The study analysed how prior knowledge from previous courses was related to student achievement in the target course. The results implied that students who possessed higher-level prior knowledge, that is, procedural knowledge, from previous courses also obtained higher grades in the more advanced target course. Study IV explored the impact of different types of prior knowledge on students’ readiness to drop out from the course, on the pace of completing the course and on the final grade. The study was conducted in the context of chemistry. The results revealed again that students who performed well in the procedural prior-knowledge tasks were also likely to complete the course in pre-scheduled time and get higher final grades. On the other hand, students whose performance was weak in the procedural prior-knowledge tasks were more likely to drop out or take a longer time to complete the course. Study II explored the issue of prior knowledge from another perspective. Study II aimed to analyse the interrelations between academic self-beliefs, prior knowledge and student achievement in the context of mathematics. The results revealed that prior knowledge was more predictive of student achievement than were other variables included in the study. Self-beliefs were also strongly related to student achievement, but the predictive power of prior knowledge overruled the influence of self-beliefs when they were included in the same model. There was also a strong correlation between academic self-beliefs and prior-knowledge performance. The results of all the four studies were consistent with each other indicating that the model of prior knowledge may be used as a potential tool for prior knowledge assessment. It is useful to make a distinction between different types of prior knowledge in assessment since the type of prior knowledge students possess appears to make a difference. The results implied that there indeed is variation between students’ prior knowledge and academic self-beliefs which influences student achievement. This should be taken into account in instruction.

Comparison of decision tree, support vector machines, and Bayesian network approaches for classification of falls in Parkinson’s disease

Being able to accurately predict the risk of falling is crucial in patients with Parkinson’s dis- ease (PD). This is due to the unfavorable effect of falls, which can lower the quality of life as well as directly impact on survival. Three methods considered for predicting falls are decision trees (DT), Bayesian networks (BN), and support vector machines (SVM). Data on a 1-year prospective study conducted at IHBI, Australia, for 51 people with PD are used. Data processing are conducted using rpart and e1071 packages in R for DT and SVM, con- secutively; and Bayes Server 5.5 for the BN. The results show that BN and SVM produce consistently higher accuracy over the 12 months evaluation time points (average sensitivity and specificity > 92%) than DT (average sensitivity 88%, average specificity 72%). DT is prone to imbalanced data so needs to adjust for the misclassification cost. However, DT provides a straightforward, interpretable result and thus is appealing for helping to identify important items related to falls and to generate fallers’ profiles.

Evaluation of an intervention for patients with alcohol-related injuries: results of a mixed methods study

Objective: To explore the effect of education and training on the delivery of alcohol screening and brief intervention and referral to high-risk patients in a hospital setting. Main outcome measures included; delivery of training; practice change in relation to staff performing alcohol screening, brief intervention and referrals. Methods: Observational study design using mixed methods set in a tertiary referral hospital. Pre-post assessment of medical records and semi-structured interviews with key informants. Results: Routine screening for substance misuse (9% pre / 71.4% post) and wellbeing concerns (6.6% pre / 15 % post) was more frequent following the introduction of resources and staff participation in educational workshops. There was no evidence of a concomitant increase in delivery of brief intervention or referrals to services. Implementation challenges, including time constraints and staff attitudes, and enablers such as collaboration and visible pathways, were identified. Conclusion: Rates of patient screening increased, however barriers to delivery of brief intervention and referrals remained. Implementation strategies targeting specific barriers and enablers to introducing interventions are both required to improve the application of secondary prevention for patients in acute settings. Implications: Educational training, formalised liaison between services, systematised early intervention protocols, and continuous quality improvement processes will progress service delivery in this area.