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Faced with fierce competition in marketplaces, manufacturers need to determine the appropriate settings of engineering characteristics of the new products so that the best customer preferences of the products can be obtained. To achieve this, functional models relating customer preferences to engineering characteristics need to be developed. As information regarding functional relationships between customer preferences are generally subjective or heuristic in nature, development of the customer preference models involve two uncertainties, namely fuzziness and randomness. Existing approaches use only fuzzy-based technologies to address the uncertainty caused by fuzziness. They are not designed to address the randomness of the observed data which is caused by a limited knowledge of the variability of influences between customer preferences and engineering characteristics. In this article, a fuzzy ordinary regression method is proposed to develop the customer preference models which are capable of addressing the two uncertainties of crispness and fuzziness of the customer preferences. A case study of a tea maker design which involves both uncertainties is used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Market demand estimation is an important process to assess the financial feasibility of new product development (NPD) projects. The development of models for market demand estimation involves market potential estimation and choice modeling. Previous studies commonly used conjoint analysis to develop utility functions which were then used in discrete choice models to generate market share models. The jury of executive opinion method is commonly used in industries wherein a number of experts and/or consultants are always involved in the market potential estimation. However, a high degree of fuzziness always exists in the data obtained from conjoint surveys and the market potential estimation because of the subjective judgments of respondents and experts. However, ignorance of the fuzziness would lead to the over-estimation of market demands. This research aims to tackle the fuzziness associated with market potential estimation and survey data in the development of market demand models. In this paper, a new methodology of developing fuzzy market demand models for NPD is proposed to address the fuzziness by which market demands can be estimated for the worst, normal, and best scenarios. The proposed methodology involves fuzzy choice modeling based on fuzzy regression and discrete choice analysis, and fuzzy estimate generation of market potential. To evaluate the effectiveness of the proposed methodology, a case study of market demand estimation of a new tablet PC is conducted based on the proposed methodology. The results of the implementation are compared with those based on a popular multinominal logit (MNL) based demand model. From the comparison, it can be noted that the estimated market demand based on the MNL model is very close to that for the normal scenario based on the proposed fuzzy demand model. However, the MNL model cannot provide estimates for other scenarios. [ABSTRACT FROM AUTHOR]

Plasma treatment is becoming increasingly popular in enhancing the hydrophobicity of synthetic textiles. In recent years, the study of surface hydrophobisation by means of plasma treatment, under low or atmospheric pressure, has drawn a great deal of attention. A large amount of research has reported on the possibility of applying this technique with merits that include a dry process, reduced pollution, a single step treatment, etc. In this regard, this paper reviews recent approaches on enhancing the hydrophobicity of synthetic textiles by means of plasma treatment. The basic working principle of generating plasma to enhance hydrophobicity is explained. Both low and atmospheric plasma treatments are introduced. A higher cost is usually required for low pressure plasma because of the investment on a vacuum chamber. On the other hand, carrier gas is required for atmospheric plasma treatment, which is not the case for low pressure plasma. The experimental set up and the chemicals involved in the processes are discussed. In order to enhance surface hydrophobicity, fluorocarbons are always applied, such as perfluoroalkylacrylate, perfluorodecaline and tetrafluoroethylene. [ABSTRACT FROM AUTHOR]

Purpose: There is a major clinical need to devise an optimal treatment sequence for the multiple therapy options available for patients with metastatic castration-resistant prostate cancer (mCRPC). In the absence of prospective clinical trials, sequencing information can be derived from large, real-world registry studies. Patients and Methods: PROXIMA (Treatment Patterns in Patients With Metastatic Castration-Resistant Prostate Cancer Previously Treated With Docetaxel-Based Chemotherapy) is a large, global, prospective registry study evaluating real-world treatment patterns of patients with mCRPC who experience disease progression during or after docetaxel therapy. Patients were enrolled worldwide between 2011 and 2014. Treatments were determined by the treating physicians and recorded in categories of chemotherapy, hormonal therapy, targeted therapy, immunotherapy, and palliative therapy. Treatment sequencing patterns, response to treatment, and types of progression were recorded and analyzed. Progression-free survival and overall survival with different treatment modalities were analyzed using Kaplan–Meier method. Results: Treatment patterns were evaluated in 903 patients. Therapy selection was influenced by region. Hormonal therapy (57.5%) and taxane chemotherapy (26.4%) were the most frequently administered first subsequent treatments after docetaxel. Tumor responses to first subsequent treatment were observed in 22.6% of evaluable patients. Overall survival and progression-free survival did not differ significantly across different treatment modalities. Conclusion: Identifying an optimal treatment sequence is vital for improving the care of patients with mCRPC. The PROXIMA registry provided a representative sample of global data on real-world treatment patterns for patients with mCRPC previously treated with docetaxel. These data can be used to devise optimal therapy sequences and inform treatment decisions.

Atmospheric pressure plasma (APP) technology has been proven to be an effective method for improving the hydrophobicity of fabric surface. While many researchers have worked on apparel fabrics, polyester and synthetic leather fabrics, popular in the luxury textile market, have been examined rarely. In this regard, this paper reports the effects of APP treatment using tetrafluoromethane (CF 4) as the polymerizing monomer on hydrophobicity of polyester synthetic leather. The polyester synthetic leather samples were treated under various parameters and hydrophobicity was found to be greatly enhanced under a certain range of treatment conditions. An optimization of the treatment parameters was conducted and the results showed that a hydrophobic surface with a contact angle of 106 ° was achieved, compared to the untreated sample’s 0 °. [ABSTRACT FROM AUTHOR]

Objective: Carotid endarterectomy (CEA) reduces the risk of cerebrovascular events due to the presence of atherosclerotic plaque in the internal carotid artery. Arterial stiffness is an indicator of cardiovascular risk and strongly associates with the development of atherosclerosis. This study aims to assess the short-term effect of CEA on arterial stiffness and haemodynamics. Design: Prospective observational study. Methods: Measurements of arterial stiffness and haemodynamics, including carotid-femoral pulse wave velocity (cfPWV), carotid-radial PWV (crPWV), augmentation pressure, augmentation index, subendocardial viability ratio, central pressures and pulse pressure amplification, were performed pre- and 6 weeks post-CEA on both surgical and non-surgical sides. Results: Fifty-nine patients completed the study (n = 46 men, age 68.9 ± 10.1 years). crPWV was decreased after CEA on the surgical (P = 0.01) and non-surgical side (P = 0.0008), AIx75 tended to decrease only on the surgical side (P = 0.06). cfPWV did not change significantly on either side. Conclusion: We assessed, for the first time, the short-term effect of CEA on arterial stiffness and haemodynamics. CEA improved peripheral but not central arterial stiffness. This study provides evidence for significant changes in certain arterial stiffness and haemodynamic parameters. Longer-term follow-up will assess whether these changes are sustained and whether CEA is associated with further haemodynamic benefits. [Copyright &y& Elsevier]

In the semiconductor manufacturing industry, epoxy dispensing is a popular process commonly used in die-bonding as well as in microchip encapsulation for electronic packaging. Modeling the epoxy dispensing process is important because it enables us to understand the process behavior, as well as determine the optimum operating conditions of the process for a high yield, low cost, and robust operation. Previous studies of epoxy dispensing have mainly focused on the development of analytical models. However, an analytical model for epoxy dispensing is difficult to develop because of its complex behavior and high degree of uncertainty associated with the process in a real-world environment. Previous studies of modeling the epoxy dispensing process have not addressed the development of explicit models involving high-order and interaction terms, as well as fuzziness between process parameters. In this paper, a hybrid fuzzy regression (HFR) method integrating fuzzy regression with genetic programming is proposed to make up the deficiency. Two process models are generated for the two quality characteristics of the process, encapsulation weight and encapsulation thickness based on the HFR, respectively. Validation tests are performed. The performance of the models developed based on the HFR outperforms the performance of those based on statistical regression and fuzzy regression. [ABSTRACT FROM AUTHOR]

Family mould layout design (FMLD) is demanding and experience-dependent. It involves many sub-design tasks that determine the cost and performance of a family mould in the early conceptual mould layout design phase. This paper conducts a comprehensive literature review in the field of FMLD. The review ascertains that family mould cavity and runner layout design (FMCRLD) automation and optimisation is the critical knowledge gap in FMLD, and it should be viewed as a complex combinatorial layout design optimisation problem. The second part of this paper extends the review of the literature regarding possible computational techniques for supporting FMCRLD automation and optimisation. The review discovers that the evolutionary design approach using genetic algorithms reveals a new opportunity to automate and optimise such complex FMCRLD with an explorative and generative design process embodied in a stochastic evolutionary search. However, implementation of this innovative approach is full of challenges. Further research on this topic is urgently needed. [ABSTRACT FROM AUTHOR]