A hybrid CFGTSA based approach for scheduling problem: a case study of an automobile industry

In the global competitive world swift, reliable and cost effective production subject to uncertain situations, through an appropriate management of the available resources, has turned out to be the necessity for surviving in the market. This inspired the development of the more efficient and robust methods to counteract the existing complexities prevailing in the market. The present paper proposes a hybrid CFGTSA algorithm inheriting the salient features of GA, TS, SA, and chaotic theory to solve the complex scheduling problems commonly faced by most of the manufacturing industries. The proposed CFGTSA algorithm has been tested on a scheduling problem of an automobile industry, and its efficacy has been shown by comparing the results with GA, SA, TS, GTS, and hybrid TSA algorithms

A stepwise based fuzzy regression procedure for developing customer preference models in new product development

Fuzzy regression methods have commonly been used to develop consumer preferences models which correlate the engineering characteristics with consumer preferences regarding a new product; the consumer preference models provide a platform whereby product developers can decide the engineering characteristics in order to satisfy consumer preferences prior to developing the products. Recent research shows that these fuzzy regression methods are commonly used to model customer preferences. However, these approaches have a common limitation in that they do not investigate the appropriate polynomial structure which includes significant regressors with only significant engineering characteristics; also, they cannot generate interaction or high-order regressors in the models. The inclusion of insignificant regressors is not an effective approach when developing the models. Exclusion of significant regressors may affect the generalization capability of the consumer preference models. In this paper, a novel fuzzy modelling method is proposed, namely fuzzy stepwise regression (F-SR), in order to develop a customer preference model which is structured with an appropriate polynomial which includes only significant regressors.Based on the appropriate polynomial structure, the fuzzy coefficients are determined using the fuzzy least square regression. The developed fuzzy regression model attempts to obtain a better generalization capability using a smaller number of regressors. The effectiveness of the F-SR is evaluated based on two design problems, namely a tea maker design and a solder paste dispenser design. Results show that better generalization capabilities can be obtained compared with the fuzzy regression methods commonly-used for new product development. Also, smaller-scale consumer preference models with fewer engineering characteristics can be obtained. Hence, a simpler and more effective product development platform can be provided

Audience Reactions to Repeating A Piece on A Concert Programme

Repetition of a piece on a concert programme is a well-established, but uncommon performance practice. Musicians have presumed that repetition benefits audience enjoyment and understanding but no research has examined this. In two naturalistic and one lab study, we examined audience reaction to repeated live performances of contemporary pieces played by the same ensemble. In all studies, we asked listeners to rate their enjoyment and willingness to hear the piece again (Affective), and perceived understanding and predicted memory of the piece (Cognitive). In Study 3, we assessed immediate recognition memory of each excerpt. In all studies, Cognitive variables increased significantly. Affective reaction also increased except for one piece that was well liked at first hearing. Memory performance was low and not related to predicted memory, nor increased after a second hearing. Being informed or not had no systematic effect on reaction. Audience and performer reaction was mixed. We discuss the implications for musical directors when considering repeat performances

CD4+T cells do not mediate within-host competition between genetically diverse malaria parasites

Ecological interactions between microparasite populations in the same host are an important source of selection on pathogen traits such as virulence and drug resistance. In the rodent malaria model Plasmodium chabaudi in laboratory mice, parasites that are more virulent can competitively suppress less virulent parasites in mixed infections. There is evidence that some of this suppression is due to immune-mediated apparent competition, where an immune response elicited by one parasite population suppress the population density of another. This raises the question whether enhanced immunity following vaccination would intensify competitive interactions, thus strengthening selection for virulence in Plasmodium populations. Using the P. chabaudi model, we studied mixed infections of virulent and avirulent genotypes in CD4+T cell-depleted mice. Enhanced efficacy of CD4+T cell-dependent responses is the aim of several candidate malaria vaccines. We hypothesized that if immune-mediated interactions were involved in competition, removal of the CD4+T cells would alleviate competitive suppression of the avirulent parasite. Instead, we found no alleviation of competition in the acute phase, and significant enhancement of competitive suppression after parasite densities had peaked. Thus, the host immune response may actually be alleviating other forms of competition, such as that over red blood cells. Our results suggest that the CD4+-dependent immune response, and mechanisms that act to enhance it such as vaccination, may not have the undesirable affect of exacerbating within-host competition and hence the strength of this source of selection for virulence

Variable weight neural networks and their applications on material surface and epilepsy seizure phase classifications

This paper presents a novel neural network having variable weights, which is able to improve its learning and generalization capabilities, to deal with classification problems. The variable weight neural network (VWNN) allows its weights to be changed in operation according to the characteristic of the network inputs so that it demonstrates the ability to adapt to different characteristics of input data resulting in better performance compared with ordinary neural networks with fixed weights. The effectiveness of the VWNN is tested with the consideration of two real-life applications. The first application is on the classification of materials using the data collected by a robot finger with tactile sensors sliding along the surface of a given material. The second application considers the classification of seizure phases of epilepsy (seizure-free, pre-seizure and seizure phases) using real clinical data. Comparisons are performed with some traditional classification methods including neural network, k-nearest neighbors and naive Bayes classification techniques. It is shown that the VWNN classifier outperforms the traditional methods in terms of classification accuracy and robustness property when input datais contaminated by noise

Unpacking the impact of social media analytics on customer satisfaction : do external stakeholder characteristics matter?

Purpose Underpinned by the lens of Contingency Theory (CT), the purpose of this paper is to empirically evaluate whether the impact of social media analytics (SMA) on customer satisfaction (CS) is contingent on the characteristics of different external stakeholders, including business partners (i.e. partner diversity), competitors (i.e. localised competition) and customers (i.e. customer engagement). Design/methodology/approach Using both subjective and objective measures from multiple sources, we collected primary data from 141 hotels operating in Greece and their archival data from TripAdvisor and the Hellenic Chamber of Hotels (HCH) database to test the hypothesised relationships. Data were analysed through structural equation modelling. Findings This study confirms the positive association between SMA and CS, but it remains subject to the varied characteristics of external stakeholders. We find that an increase in CS due to the implementation of SMA is more pronounced for firms that (1) adopt a selective distribution strategy where a limited number of business partners are chosen for collaboration or (2) operate in a highly competitive local environment. The results further indicate that high level of customer engagement amplifies the moderating effect of partner diversity (when it is low) and localised competition (when it is high) on the SMA–CS relationship. Originality/value The study provides novel insights for managers on the need to consider external stakeholder characteristics when implementing SMA to enhance firms' CS, and for researchers on the value of studying SMA implementation from the CT perspective

Contact tracing over uncertain indoor positioning data

Pandemics often cause dramatic losses of human lives and impact our societies in many aspects such as public health, tourism, and economy. To contain the spread of an epidemic like COVID-19, efficient and effective contact tracing is important, especially in indoor venues where the risk of infection is higher. In this work, we formulate and study a novel query called Indoor Contact Query (ICQ) over raw, uncertain indoor positioning data that digitalizes people's movements indoors. Given a query object o, e.g., a person confirmed to be a virus carrier, an ICQ analyzes uncertain indoor positioning data to find objects that most likely had close contact with o for a long period of time. To process ICQ, we propose a set of techniques. First, we design an enhanced indoor graph model to organize different types of data necessary for ICQ. Second, for indoor moving objects, we devise methods to determine uncertain regions and to derive positioning samples missing in the raw data. Third, we propose a query processing framework with a close contact determination method, a search algorithm, and the acceleration strategies. We conduct extensive experiments on synthetic and real datasets to evaluate our proposals. The results demonstrate the efficiency and effectiveness of our proposals

Elevated plasma TGF-β1 levels in patients with chronic obstructive pulmonary disease

SummaryBackgroundTransforming growth factor-β1 (TGF-β1), a multifunctional cytokine, has been implicated to be responsible for the increased deposition of extracellular matrix in the airways, and increased submucosal collagen expression in chronic obstructive pulmonary disease (COPD). We determined plasma TGF-β1 levels in patients with COPD and explored its association with common functional polymorphisms of TGF-β1 gene at C-509T and T869C in the development of COPD in a case–control study.MethodsStable COPD patients who were ever smokers, and age and pack-years smoked matched healthy controls (n = 205 in each group) were recruited for measurement of plasma TGF-β1 levels using commercially available ELISA kit, and genotyped at C-509T and T869C functional polymorphisms of TGF-β1 gene using polymerase chain reaction and restriction fragment length polymorphism (PCR–RFLP).ResultsCOPD patients had significantly elevated plasma TGF-β1 levels in comparison to healthy controls irrespective of the genotypes. Allele frequencies and genotype distributions at both polymorphic sites were not different among COPD patients or controls. TGF-β1 levels were inversely correlated (Pearson's correlation analysis) with FEV1 (% predicted) (p < 0.001) and FVC (% predicted) (p < 0.001).ConclusionThe findings of elevated plasma TGF-β1 levels in patients with COPD suggest that TGF-β1 may play a role in COPD pathogenesis. The C-509T and T869C functional polymorphisms of TGF-β1 gene do not represent a genetic predisposition to COPD susceptibility in Hong Kong Chinese patients

Recent advance in high manufacturing readiness level and high temperature CMOS mixed-signal integrated circuits on silicon carbide

A high manufacturing readiness level silicon carbide (SiC) CMOS technology is presented. The unique process flow enables the monolithic integration of pMOS and nMOS transistors with passive circuit elements capable of operation at temperatures of 300 °C and beyond. Critical to this functionality is the behaviour of the gate dielectric and data for high temperature capacitance–voltage measurements are reported for SiO2/4H-SiC (n and p type) MOS structures. In addition, a summary of the long term reliability for a range of structures including contact chains to both n-type and p-type SiC, as well as simple logic circuits is presented, showing function after 2000 h at 300 °C. Circuit data is also presented for the performance of digital logic devices, a 4 to 1 analogue multiplexer and a configurable timer operating over a wide temperature range. A high temperature micro-oven system has been utilised to enable the high temperature testing and stressing of units assembled in ceramic dual in line packages, including a high temperature small form-factor SiC based bridge leg power module prototype, operated for over 1000 h at 300 °C. The data presented show that SiC CMOS is a key enabling technology in high temperature integrated circuit design. In particular it provides the ability to realise sensor interface circuits capable of operating above 300 °C, accommodate shifts in key parameters enabling deployment in applications including automotive, aerospace and deep well drilling

Fraction-score: a generalized support measure for weighted and maximal co-location pattern mining

Co-location patterns, which capture the phenomenon that objects with certain labels are often located in close geographic proximity, are defined based on a support measure which quantifies the prevalence of a pattern candidate in the form of a label set. Existing support measures share the idea of counting the number of instances of a given label set C as its support, where an instance of C is an object set whose objects collectively carry all labels in C and are located close to one another. However, they suffer from various weaknesses, e.g., fail to capture all possible instances, or overlook the cases when multiple instances overlap. In this paper, we propose a new measure called Fraction-Score which counts instances fractionally if they overlap. Fraction-Score captures all possible instances, and handles the cases where instances overlap appropriately (so that the supports defined are more meaningful and anti-monotonic). We develop efficient algorithms to solve the co-location pattern mining problem defined with Fraction-Score. Furthermore, to obtain representative patterns, we develop an efficient algorithm for mining the maximal co-location patterns, which are those patterns without proper superset patterns. We conduct extensive experiments using real and synthetic datasets, which verified the superiority of our proposals