Environmental factors that influence the community participation of adults with aphasia: The perspective of service industry workers

Background: The loss of language and the inability to communicate effectively as a result of aphasia often affects community participation. Within the World Health Organisation International Classification of Functioning, Disability and Health, disability is recognised as a dynamic interaction between the individual's health condition, such as aphasia, and his or her personal and environmental factors. There has been little research identifying the environmental facilitators and barriers to participation for people with aphasia in the community, and no research focusing on the perspective of service industry workers. Aims: This study aimed to identify barriers and facilitators to community participation for adults with aphasia from the perspective of service industry workers. Methods & Procedures: Eight focus groups were conducted with 24 service industry employees. Transcripts of the focus group discussions were analysed using qualitative content analysis procedures, and barriers to and facilitators for participation of people with aphasia were identified. Outcomes & Results: Results revealed that the participation of people with aphasia in the community can be affected by many environmental factors within three broad categories: (1) people environmental factors, (2) physical environmental factors, and (3) business or organisational environmental factors. Conclusions: Service industry employees were able to identify a range of factors that would act as barriers and facilitators for people with aphasia. Some of the more significant findings include the lack of other people's awareness about aphasia, the willingness of service industry workers at the individual level to accommodate people with aphasia, and the difficulty in making the necessary system, policy, and procedural changes at the organisational level. Speech pathologists are encouraged to assist service industry providers to be more aphasia-friendly through education and training, in addition to assisting people with aphasia to become self-advocates.

Using machine vision classification to control access of animals to water

Invasive vertebrate pests together with overabundant native species cause significant economic and environmental damage in the Australian rangelands. Access to artificial watering points, created for the pastoral industry, has been a major factor in the spread and survival of these pests. Existing methods of controlling watering points are mechanical and cannot discriminate between target species. This paper describes an intelligent system of controlling watering points based on machine vision technology. Initial test results clearly demonstrate proof of concept for machine vision in this application. These initial experiments were carried out as part of a 3-year project using machine vision software to manage all large vertebrates in the Australian rangelands. Concurrent work is testing the use of automated gates and innovative laneway and enclosure design. The system will have application in any habitat throughout the world where a resource is limited and can be enclosed for the management of livestock or wildlife.

Sterilization of allograft bone: is 25 kGy the gold standard for gamma irradiation?

For several decades, a dose of 25 kGy of gamma irradiation has been recommended for terminal sterilization of medical products, including bone allografts. Practically, the application of a given gamma dose varies from tissue bank to tissue bank. While many banks use 25 kGy, some have adopted a higher dose, while some choose lower doses, and others do not use irradiation for terminal sterilization. A revolution in quality control in the tissue banking industry has occurred in line with development of quality assurance standards. These have resulted in significant reductions in the risk of contamination by microorganisms of final graft products. In light of these developments, there is sufficient rationale to re-establish a new standard dose, sufficient enough to sterilize allograft bone, while minimizing the adverse effects of gamma radiation on tissue properties. Using valid modifications, several authors have applied ISO standards to establish a radiation dose for bone allografts that is specific to systems employed in bone banking. These standards, and their verification, suggest that the actual dose could be significantly reduced from 25 kGy, while maintaining a valid sterility assurance level (SAL) of 10−6. The current paper reviews the methods that have been used to develop radiation doses for terminal sterilization of medical products, and the current trend for selection of a specific dose for tissue banks.

Unicellular pheromone glands of the pentatomid bug Nezara viridula (Heteroptera : Insecta): Ultrastructure, classification, and proposed function

Male Nezara viridula produce sex pheromones from many independent single cells, each with a duct that opens onto the ventral abdominal surface. Despite the presence of along duct and an associated end complex (in the form of a cupule and microvillus saccule), the structural organization of the cells that comprise the gland conform to Class 1 epidermal gland cell classification : a single cell surrounds the entire secretory complex. Each cuticular cupule contains a central bed of filaments and opens into a narrow tubular ductule that leads from the base of the cupule through the epidermis to the cuticle to open externally as a pore. The cuticle of the cupule is continuous with that of the ductule and has the appearance of three layers, although the inner (middle) layer may be a gap formed during construction of the complex. In young adult males, just molted, the ultrastructure of the cells and their inclusions indicate that they are not active. The region of the cell that is distal to the abdominal cuticle is reduced and the proximal region, surrounding the duct, is enlarged when compared with sexually mature (3-4 weeks old) adult males. At maturity the pheromone cells are enlarged distally around the cupule, but are reduced to a narrow sleeve proximally, around the ductule. Two characteristic cell profiles are evident, based on the shape of the cupule and the organelle content. Type A shows a broad opening to the cupule, an abundance of mitochondria, and few vesicular bodies. Type B has an elongated, narrow, vase-like opening to the cupule, few mitochondria, and numerous vesicular bodies. Type B cells are smaller and more abundant than Type A. Distribution within the epidermal layer also differs. It is likely that the different types represent cells producing different secretion profiles. However, the secretions retained by the standard fixation protocol within mature cells of both types look similar and appear to collect as crystalline bodies within the lumen. This may represent a common storage mechanism.

Molecular classification of cutaneous malignant melanoma by gene expression profiling

The most common human cancers are malignant neoplasms of the skin(1,2). Incidence of cutaneous melanoma is rising especially steeply, with minimal progress in non-surgical treatment of advanced disease(3,4). Despite significant effort to identify independent predictors of melanoma outcome, no accepted histopathological, molecular or immunohistochemical marker defines subsets of this neoplasm(2,3). Accordingly, though melanoma is thought to present with different 'taxonomic' forms, these are considered part of a continuous spectrum rather than discrete entities(2). Here we report the discovery of a subset of melanomas identified by mathematical analysis of gene expression in a series of samples. Remarkably, many genes underlying the classification of this subset are differentially regulated in invasive melanomas that form primitive tubular networks in vitro, a feature of some highly aggressive metastatic melanomas(5). Global transcript analysis can identify unrecognized subtypes of cutaneous melanoma and predict experimentally verifiable phenotypic characteristics that may be of importance to disease progression.

Quantification of geological uncertainty and risk using stochastic simulation and applications in the coal mining industry

Stochastic simulation is a recognised tool for quantifying the spatial distribution of geological uncertainty and risk in earth science and engineering. Metals mining is an area where simulation technologies are extensively used; however, applications in the coal mining industry have been limited. This is particularly due to the lack of a systematic demonstration illustrating the capabilities these techniques have in problem solving in coal mining. This paper presents two broad and technically distinct areas of applications in coal mining. The first deals with the use of simulation in the quantification of uncertainty in coal seam attributes and risk assessment to assist coal resource classification, and drillhole spacing optimisation to meet pre-specified risk levels at a required confidence. The second application presents the use of stochastic simulation in the quantification of fault risk, an area of particular interest to underground coal mining, and documents the performance of the approach. The examples presented demonstrate the advantages and positive contribution stochastic simulation approaches bring to the coal mining industry

Multisensorial obstacles detection and classification for ADAS and autonomous driving

A reliable perception of the real world is a key-feature for an autonomous vehicle and the Advanced Driver Assistance Systems (ADAS). Obstacles detection (OD) is one of the main components for the correct reconstruction of the dynamic world. Historical approaches based on stereo vision and other 3D perception technologies (e.g. LIDAR) have been adapted to the ADAS first and autonomous ground vehicles, after, providing excellent results. The obstacles detection is a very broad field and this domain counts a lot of works in the last years. In academic research, it has been clearly established the essential role of these systems to realize active safety systems for accident prevention, reflecting also the innovative systems introduced by industry. These systems need to accurately assess situational criticalities and simultaneously assess awareness of these criticalities by the driver; it requires that the obstacles detection algorithms must be reliable and accurate, providing: a real-time output, a stable and robust representation of the environment and an estimation independent from lighting and weather conditions. Initial systems relied on only one exteroceptive sensor (e.g. radar or laser for ACC and camera for LDW) in addition to proprioceptive sensors such as wheel speed and yaw rate sensors. But, current systems, such as ACC operating at the entire speed range or autonomous braking for collision avoidance, require the use of multiple sensors since individually they can not meet these requirements. It has led the community to move towards the use of a combination of them in order to exploit the benefits of each one. Pedestrians and vehicles detection are ones of the major thrusts in situational criticalities assessment, still remaining an active area of research. ADASs are the most prominent use case of pedestrians and vehicles detection. Vehicles should be equipped with sensing capabilities able to detect and act on objects in dangerous situations, where the driver would not be able to avoid a collision. A full ADAS or autonomous vehicle, with regard to pedestrians and vehicles, would not only include detection but also tracking, orientation, intent analysis, and collision prediction. The system detects obstacles using a probabilistic occupancy grid built from a multi-resolution disparity map. Obstacles classification is based on an AdaBoost SoftCascade trained on Aggregate Channel Features. A final stage of tracking and fusion guarantees stability and robustness to the result.

Blood type classification using computer vision and machine learning

In emergency situations, where time for blood transfusion is reduced, the O negative blood type (the universal donor) is administrated. However, sometimes even the universal donor can cause transfusion reactions that can be fatal to the patient. As commercial systems do not allow fast results and are not suitable for emergency situations, this paper presents the steps considered for the development and validation of a prototype, able to determine blood type compatibilities, even in emergency situations. Thus it is possible, using the developed system, to administer a compatible blood type, since the first blood unit transfused. In order to increase the system’s reliability, this prototype uses different approaches to classify blood types, the first of which is based on Decision Trees and the second one based on support vector machines. The features used to evaluate these classifiers are the standard deviation values, histogram, Histogram of Oriented Gradients and fast Fourier transform, computed on different regions of interest. The main characteristics of the presented prototype are small size, lightweight, easy transportation, ease of use, fast results, high reliability and low cost. These features are perfectly suited for emergency scenarios, where the prototype is expected to be used.

Local skirmishes in the contruction of (global) digital business communication technologies

This paper applies Latour’s 1992 translation map as a device to explore the development of and recent conflict between two data standards for the exchange of business information – EDIFACT and XBRL. Our research is focussed in France, where EDIFACT is well established and XBRL is just emerging. The alliances supporting both standards are local and global. The French/European EDIFACT is promulgated through the United Nations while a consortium of national jurisdictions and companies has coalesced around the US initiated XBRL International (XII). We suggest cultural differences pose a barrier to co-operation between the two networks. Competing data standards create the risk of switching costs. The different technical characteristics of the standards are identified as raising implications for regulators and users. A key concern is the lack of co-ordination of data standard production and the mechanisms regulatory agencies use to choose platforms for electronic data submission.

Topographic mappings and feed-forward neural networks

This thesis is a study of the generation of topographic mappings - dimension reducing transformations of data that preserve some element of geometric structure - with feed-forward neural networks. As an alternative to established methods, a transformational variant of Sammon's method is proposed, where the projection is effected by a radial basis function neural network. This approach is related to the statistical field of multidimensional scaling, and from that the concept of a 'subjective metric' is defined, which permits the exploitation of additional prior knowledge concerning the data in the mapping process. This then enables the generation of more appropriate feature spaces for the purposes of enhanced visualisation or subsequent classification. A comparison with established methods for feature extraction is given for data taken from the 1992 Research Assessment Exercise for higher educational institutions in the United Kingdom. This is a difficult high-dimensional dataset, and illustrates well the benefit of the new topographic technique. A generalisation of the proposed model is considered for implementation of the classical multidimensional scaling (¸mds}) routine. This is related to Oja's principal subspace neural network, whose learning rule is shown to descend the error surface of the proposed ¸mds model. Some of the technical issues concerning the design and training of topographic neural networks are investigated. It is shown that neural network models can be less sensitive to entrapment in the sub-optimal global minima that badly affect the standard Sammon algorithm, and tend to exhibit good generalisation as a result of implicit weight decay in the training process. It is further argued that for ideal structure retention, the network transformation should be perfectly smooth for all inter-data directions in input space. Finally, there is a critique of optimisation techniques for topographic mappings, and a new training algorithm is proposed. A convergence proof is given, and the method is shown to produce lower-error mappings more rapidly than previous algorithms.

Efficient training of RBF networks for classification.

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. We show how RBFs with logistic and softmax outputs can be trained efficiently using the Fisher scoring algorithm. This approach can be used with any model which consists of a generalised linear output function applied to a model which is linear in its parameters. We compare this approach with standard non-linear optimisation algorithms on a number of datasets.

Efficient training of RBF networks for classification

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. In this paper we show how RBFs with logistic and softmax outputs can be trained efficiently using algorithms derived from Generalised Linear Models. This approach is compared with standard non-linear optimisation algorithms on a number of datasets.

Brand equity and purchase intention in fashion industry:a cross-cultural study in Asia and Europe

This research is the leading brand for purchase of assets, and analyzing the factors based on brand asset components and the relationship between the brand and brand assets assets impact factors and purchase intent on uncovering the relationship between components and trademarks centered on South Korea and the United Kingdom, by comparing the asset management plan would generate. The study, information navigation product knowledge affects of constant (+), brand attitudes and knowledge of the brand loyalty and brand value to the constant trademark (+). Brand value and brand loyalty and purchase intent-(+) in the United Kingdom, on the other hand, of the impact that do not affect that.