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Correctional facilities host a disproportionately high prevalence of HBV, HCV and HIV infection. We evaluated the prevalence of HBV and/or HCV co‐infection among HIV‐infected inmates entering our correctional facility. Over a 30‐month period, 173 consecutive HIV‐infected inmates entered our institution and were evaluated. HCV co‐infection was observed in more than 90% of the tested HIV‐infected inmates, past HBV infection in 77.4% and active HBV co‐infection in 6.7%; triple coinfection (HIV, HCV and HBs‐Ag positivity) was seen in 6.1% of them. Given the observed high prevalence of co‐infection, testing for HBV and HCV in all HIV‐infected inmates at entry in any correctional system is recommended to identify those in need of specific care and/or preventing interventions.

The purpose of this paper is to present a fresh approach to stimulate individual creativity. It introduces a mathematical representation for creative ideas, six creativity operators and methods of matrix-algebra to evaluate, improve and stimulate creative ideas. Creativity begins with ideas to resolve a problem or tackle an opportunity. By definition, a creative idea must be simultaneously novel and useful. To inject analytic rigor into these concepts of creative ideas, the author introduces a feature-attribute matrix-construct to represent ideas, creativity operators that use ideas as operands and methods of matrix algebra. It is demonstrated that it is now possible to analytically and quantitatively evaluate the intensity of the variables that make an idea more, equal or less, creative than another. The six creativity operators are illustrated with detailed multi-disciplinary real-world examples. The mathematics and working principles of each creativity operator are discussed.

The unit of analysis is ideas, not theory. Ideas are man-made artifacts. They are represented by an original feature-attribute matrix construct. Using matrix algebra, idea matrices can be manipulated to improve their creative intensity, which are now quantitatively measurable. Unlike atoms and cute rabbits, creative ideas, do not occur in nature. Only people can conceive and develop creative ideas for embodiment in physical, non-physical forms, or in a mix of both. For example, as widgets, abstract theorems, business processes, symphonies, organization structures, and so on. The feature-attribute matrix construct is used to represent novelty and usefulness. The multiplicative product of these two matrices forms the creativity matrix. Six creativity operators and matrix algebra are introduced to stimulate and measure creative ideas. Creativity operators use idea matrices as operands. Uses of the six operators are demonstrated using multi-disciplinary real-world examples. Metrics for novelty, usefulness and creativity are in ratio scales, grounded on the Weber–Fechner Law. This law is about persons’ ability to discern differences in the intensity of stimuli.

Ideas are represented using feature-attribute matrices. This construct is used to represent novel, useful and creative ideas with more clarity and precision than before. Using matrices, it is shown how to unambiguously and clearly represent creative ideas endowed with novelty and usefulness. It is shown that using matrix algebra, on idea matrices, makes it possible to analyze multi-disciplinary, real-world cases of creative ideas, with clarity and discriminatory power, to uncover insights about novelty and usefulness. Idea-matrices and the methods of matrix algebra have strong explanatory and predictive power. Using of matrix algebra and eigenvalue analyses, of idea-matrices, it is demonstrated how to quantitatively rank ideas, features and attributes of creative ideas. Matrix methods operationalize and quantitatively measure creativity, novelty and usefulness. The specific elementary variables that characterize creativity, novelty and usefulness factors, can now be quantitatively ranked. Creativity, novelty and usefulness factors are not considered as monolithic, irreducible factors, vague “lumpy” qualitative factors, but as explicit sets of elementary, specific and measurable variables in ratio scales. This significantly improves the acuity and discriminatory power in the analyses of creative ideas. The feature-attribute matrix approach and its matrix operators are conceptually consistent and complementary with key extant theories engineering design and creativity.

First to define and specify ideas as feature-attribute matrices. It is demonstrated that creative ideas, novel ideas and useful ideas can be analytically and unambiguously specified and measured for creativity. It is significant that verbose qualitative narratives will no longer be the exclusive means to specify creative ideas. Rather, qualitative narratives will be used to complement the matrix specifications of creative ideas. First to specify six creativity operators enabling matrix algebra to operate on idea-matrices as operands to generate new ideas. This capability informs and guides a person’s intuition. The myth and dependency, on non-repeatable or non-reproducible serendipity, flashes of “eureka” moments or divine inspiration, can now be vacated. Though their existence cannot be ruled out. First to specify matrix algebra and eigen-value methods of quantitative analyses of feature-attribute matrices to rank the importance of elementary variables that characterize factors of novelty, usefulness and creativity. Use of verbose qualitative narratives of novelty, usefulness and creativity as monolithic “lumpy” factors can now be vacated. Such lumpy narratives risk being ambiguous, imprecise, unreliable and non-reproducible, Analytic and quantitative methods are more reliable and consistent. First to define and specify a method of “attacking the negatives” to systematically pinpoint the improvements of an idea’s novelty, usefulness and creativity. This procedure informs and methodically guides the improvements of deficient ideas.

The purpose of this paper is to provide an overview of the wide range of biomimetic sensor technology and innovations.

The reader is introduced to biomimetic sensors, their types, their advantages and how they are different from traditional sensors. Background information is also provided regarding sensor design, inspiration and innovation.

There are two approaches to sensor design, which lead to diverse advantages and innovations. Classification of biomimetic sensors indicated which natural senses are underutilized by sensor designers and researchers.

The paper provides information of value for those seeking innovative sensor designs and research information for those who want to research in this area.

Presents an inductive machine learning algorithm called CLILP2 that learns multiple covers for a concept from positive and negative examples. Although inductive learning is an error‐prone process, multiple meaning interpretation of the examples is utilized by CLILP2 to compensate for the narrowness of induction. The algorithm is tested on data sets representing three different domains. Analyses the complexity of the algorithm and compares the results with those obtained by others. Employs measures of specificity, sensitivity, and predictive accuracy which are not usually used in presenting machine learning results, and shows that they evaluate better the “correctness” of the learned concepts. The study is published in two parts: I – the CLILP2 algorithm; II – experimental results and conclusions.

The aims of this article are to introduce a modularization framework and a method for the formation of modules.

A methodological framework is presented to guide designers and engineers in the formation and selection of suitable modules in developing customized products. Detailed explanations of the framework are presented theoretically. This framework interacts with different product development participants such as resources, customers' preferences, design architecture for planning, and scheduling a custom‐built product. A new method is proposed with a case example to facilitate the formation of modules.

This paper investigates the potential of the modularization framework, usable for prioritizing the components dependencies and creating required number of modules. It also explains the overall concept, usability and rules/methods for the module formation applied to product design, to allow a greater degree of freedom for the designer, and the opportunity to reduce development time and increase customer satisfaction. A method, based on the rules for modularity concept is proposed within the scope of this paper.

The framework and the method of modularization as illustrated in this article are based on a theoretical hypothesis. Both the approaches require implementing in a real industrial environment in order to generalize their effectiveness, applicability and consistency in the manufacturing arena.

Since product architecture is an important element in determining the value and flexibility of the product development process, the relationship pattern between the architecture and productivity is therefore worthy of careful investigation. The aims of modular framework and rules for modularity are to incorporate design variables and dependency structure with a view to enhancing product development lead time and will contribute to the exploitation of overall bottlenecks of manufacturing systems.

The implementation framework for modular product architecture seems unique as its potential value could be applied in the industrial environment for production flexibility and reducing bottlenecks. Along with the framework, the presented modularity rule or method will contribute to business architecture with a view to providing more reliable operation, easier maintenance and faster product development time.

The recent shift towards accommodating flexibility in manufacturing companies and the complexity resulting from product variety highlight the significance of flexible assembly systems and designing products for them. The purpose of this paper is to provide insight into the requirements of a flexible assembly system for product design from the assembly system’s standpoint.

To fulfil the purpose of the paper, a literature review and a case study were performed. The case study was conducted with an interactive research approach in a global market leader company within the heavy vehicle manufacturing industry.

The findings indicate that common assembly sequence, similar assembly interfaces, and common parts are the main requirements of a flexible assembly system for product design which reduce complexity and facilitate various flexibility dimensions. Accordingly, a model is proposed to broaden the understanding of these requirements from the assembly system’s standpoint.

This study contributes to the overlapping research area of flexible assembly systems and product design.

The proposed model is largely based on practical data and clarifies the role of product design in facilitating flexibility in an assembly system. It can be used by assembly managers, assembly engineers, and product designers.

The key originality of this paper compared to the previous studies lies in presenting a novel assembly-oriented design model. The model enhances understanding of a flexible assembly system’s requirements for product design with regard to reducing complexity and managing variation in a flexible assembly system. These requirements can be applied to product design across various product families within a company’s product portfolio.

Three-dimensional computer-aided design (CAD) assembly model has become important resource for design reuse in enterprises, which implicates plenty of design intent, assembly intent, design experience knowledge and functional structures. To acquire quickly CAD assembly models associated with specific functions by using product function requirement information in the product conceptual design phase for model reuse, this paper aims to find an approach for structure-function correlations analysis and functional semantic annotation of mechanical CAD assembly model before functional semantic-based assembly retrieval.

An approach for structure-function correlations analysis and functional semantic annotation of CAD assembly model is proposed. First, the product knowledge model is constructed based on ontology including design knowledge and function knowledge. Then, CAD assembly model is represented by part attributed adjacency graph and partitioned into multiple functional regions. Assembly region and flow-activity region are defined for structure-function correlations analysis of CAD assembly model. Meanwhile, the extraction process of assembly region and flow-activity region is given in detail. Furthermore, structure-function correlations analysis and functional semantic annotation are achieved by considering comprehensively assembly structure and assembled part shape structure in CAD assembly model. After that, a structure-function relation model is established based on polychromatic sets for expressing explicitly and formally relationships between functional structures, assembled parts and functional semantics.

The correlation between structure and function is analyzed effectively, and functional semantics corresponding to structures in CAD assembly model are labeled. Additionally, the relationships between functional structures, assembled parts and functional semantics can be described explicitly and formally.

The approach can be used to help designers accomplish functional semantic annotation of CAD assembly models in model repository, which provides support for functional semantic-based CAD assembly retrieval in the product conceptual design phase. These assembly models can be reused for product structure design and assembly process design.

The paper proposes a novel approach for structure-function correlations analysis and functional semantic annotation of mechanical CAD assembly model. Functional structures in assembly model are extracted and analyzed from the point of view of assembly structure and function part structure. Furthermore, the correlation relation between structures, assembled parts and functional semantics is expressed explicitly and formally based on polychromatic sets.

Previous studies on hospital supply chain performance have attempted to measure the performance of the hospital supply chain either by the measurement of performance indicators or the performance of specific activities. This paper attempts to measure the internal hospital supply chain's performance indicators to find their interdependencies to understand the relationship among them and identify the key performance indicators for each of those aspects of the logistics process toward improvement.

In this research, a systematic assessment and analysis method under vagueness is proposed to assess, analyze and measure the internal health care performance aspects (HCPA). The proposed method combines the group Decision-Making and Trial Evaluation Laboratory (DEMATEL) method and rough set theory.

The study results indicate that the most critical aspects of hospital supply chain performance are completeness of treatment, clinical care process time and no delay in treatment.

The causal relationship from rough-DEMATEL can advise management officials that to improve the completeness of treatment toward patient safety, clinical care process time should be addressed initially and with it, patient safety aspects such as free from error, clinical care productivity, etc. should be improved as well. Improvement of these aspects will improve the other aspects they are related to.

A brief comparison of tooling techniques in the automotive and aircraft industries. One problem arising where the same tooling shop is used lies in the difference between the types of engineering drawings. The aircraft drawings are complicated by the fact that engineering changes are not included in the up‐to‐date working drawings.

Presents an inductive machine learning algorithm, CLILP2 that learns multiple covers for a concept from positive and negative examples. Although inductive learning is an error‐prone process, multiple meaning interpretation of the examples is utilized by CLILP2 to compensate for the narrowness of induction. The algorithm is tested on data sets representing three different domains. The complexity of the algorithm is analysed and the results are compared with those obtained by others. Employs measures of specificity, sensitivity, and predictive accuracy not usually used in presenting machine learning results, and shows that they evaluate better the “correctness” of the learned concepts. Published in two parts: I – The CLILP2 algorithm; II – Experimental results and conclusions.