PRODUCT FEATURES AND QUALITY IN BRAND SWITCHING BEHAVIOR OF IMPLORA EYELINER ON SUMBAWA

Indonesia’s economy has grown, which has led to the growth of a business. According to BPS Indonesia, there were 26 million more businesses in Indonesia in 2016. One of them is the cosmetics industry, which the minister of the industry says is important. Because there are so many different types of cosmetic industries and so much competition, manufacturers have to change their products to meet the needs of their customers. Eyeliner is a popular product in the cosmetics industry that many people use. Following the widening of Eyeliner products, many different brands came out with different levels of quality, features, and quantity, as well as other features. Consumers may switch brands because there are so many different brands for the same thing, along with their desire to find new products. Consumers look at the quality and features of a product, as well as other product attributes when they decide to switch brands. According to the results of data processing on hypothesis testing, Implora brand eyeliner has qualities and features that make people switch brands. This is based on validity, reliability, and classical assumption tests, as well as coefficients of determination. The sampling of 100 people was used in this study, which shows that product features and product quality have a big and positive impact on brand switching. According to the results of data processing on hypothesis testing, Implora brand eyeliner has qualities and features that make people switch brands. KEYWORDS: Product Features, Product Quality, Brand Switching, and Implora Eyeliner

MAFFIN: Metabolomics Sample Normalization Using Maximal Density Fold Change with High-Quality Metabolic Features and Corrected Signal Intensities

Sample normalization is a critical step in metabolomics to remove differences in total sample amount or concentration of metabolites between biological samples. Here, we present MAFFIN, an accurate and robust post-acquisition sample normalization workflow that works universally for metabolomics data collected by mass spectrometry (MS)-based platforms. The most important design of MAFFIN is the calculation of normalization factor using maximal density fold change (MDFC) value computed by a kernel density-based approach. MDFC is more accurate than traditional median FC-based normalization, especially when the numbers of up- and down-regulated metabolic features are different. In addition, we showcase two essential steps that are overlooked by conventional normalization methods, and incorporated them into MAFFIN. First, instead of using all detected metabolic features, MAFFIN automatically extracts and uses only the high-quality features to calculate FCs and determine the normalization factor. In particular, multiple orthogonal criteria are proposed to pick up the high-quality features. Second, to guarantee the accuracy of the FCs, the MS signal intensities of the high-quality features are corrected using serial quality control (QC) samples. Using simulated data and urine metabolomics datasets, we demonstrated the critical need of high-quality feature selection, MS signal correction, and MDFC. We also show the superior performance of MAFFIN over other commonly used post-acquisition sample normalization methods. Finally, a biological application on a human saliva metabolomics study shows that MAFFIN provides robust sample normalization, leading to better data separation in principal component analysis (PCA) and the identification of more significantly altered metabolic features.

Optimize the Fluctuations on Iraqi Economic Growth based on the oil prices using linear programming approach

This study discus the Fluctuations on Iraqi Economic Growth using linear programming approach. This article proposes an operational approach for research to maximize customer satisfaction by optimizing the quality levels of numerous quality attributes of a physical product within the limits of the manufacturer's requirements. The linear model of programming has been shown using the data acquired for an example bicycle product. The optimal values for the quality of selected bicycle quality features have been calculated using optimization software. A cost-benefit analysis for some quality features of the crude oil was conducted to validate the proposed model. The model discussed in this article considers not only pleasure of customers but also satisfaction of the producer with the costs associated with the product

An evaluation of the evidence submitted to Australian alcohol advertising policy consultations

Background Industry self-regulation is the dominant approach to managing alcohol advertising in Australia and many other countries. There is a need to explore the barriers to government adoption of more effective regulatory approaches. This study examined relevance and quality features of evidence cited by industry and non-industry actors in their submissions to Australian alcohol advertising policy consultations. Methods Submissions to two public consultations with a primary focus on alcohol advertising policy were analysed. Submissions (n = 71) were classified into their actor type (industry or non-industry) and according to their expressed support for, or opposition to, increased regulation of alcohol advertising. Details of cited evidence were extracted and coded against a framework adapted from previous research (primary codes: subject matter relevance, type of publication, time since publication, and independence from industry). Evidence was also classified as featuring indicators of higher quality if it was either published in a peer-reviewed journal or academic source, published within 10 years of the consultation, and/or had no apparent industry connection. Results Almost two-thirds of submissions were from industry actors (n = 45 submissions from alcohol, advertising, or sporting industries). With few exceptions, industry actor submissions opposed increased regulation of alcohol advertising and non-industry actor submissions supported increased regulation. Industry actors cited substantially less evidence than non-industry actors, both per submission and in total. Only 27% of evidence cited by industry actors was highly relevant and featured at least two indicators of higher quality compared to 58% of evidence cited by non-industry actors. Conclusions Evaluation of the value of the evidentiary contribution of industry actors to consultations on alcohol advertising policy appears to be limited. Modifications to consultation processes, such as exclusion of industry actors, quality requirements for submitted evidence, minimum standards for referencing evidence, and requirements to declare potential conflicts, may improve the public health outcomes of policy consultations.

Hierarchical Boosting Dual-Stage Feature Reduction Ensemble Model for Parkinson’s Disease Speech Data

As a neurodegenerative disease, Parkinson’s disease (PD) is hard to identify at the early stage, while using speech data to build a machine learning diagnosis model has proved effective in its early diagnosis. However, speech data show high degrees of redundancy, repetition, and unnecessary noise, which influence the accuracy of diagnosis results. Although feature reduction (FR) could alleviate this issue, the traditional FR is one-sided (traditional feature extraction could construct high-quality features without feature preference, while traditional feature selection could achieve feature preference but could not construct high-quality features). To address this issue, the Hierarchical Boosting Dual-Stage Feature Reduction Ensemble Model (HBD-SFREM) is proposed in this paper. The major contributions of HBD-SFREM are as follows: (1) The instance space of the deep hierarchy is built by an iterative deep extraction mechanism. (2) The manifold features extraction method embeds the nearest neighbor feature preference method to form the dual-stage feature reduction pair. (3) The dual-stage feature reduction pair is iteratively performed by the AdaBoost mechanism to obtain instances features with higher quality, thus achieving a substantial improvement in model recognition accuracy. (4) The deep hierarchy instance space is integrated into the original instance space to improve the generalization of the algorithm. Three PD speech datasets and a self-collected dataset are used to test HBD-SFREM in this paper. Compared with other FR algorithms and deep learning algorithms, the accuracy of HBD-SFREM in PD speech recognition is improved significantly and would not be affected by a small sample dataset. Thus, HBD-SFREM could give a reference for other related studies.

The effect of photocopier types on line quality features of handwriting in multi-generational photocopies

The present research was aimed at finding out the relative effect of advancement in photocopier technology on the analysis of handwriting line quality features in multi-generational photocopies. Five subsequent multi-generational photocopies of 50 signature samples from different individuals which were produced by 75 black and white photocopiers were used for line quality assessments. Variations in line quality features in different photocopy generations, such as: smooth edges or ragged edges, broken or continuous stroke, fine tapering changes, and some identification details of features like pen lifts, retouching, and hesitation were carefully observed in sample photocopies, which were recorded according to the brand and model of photocopier machines, as well as their size and speed. The results of the study revealed that raggedness in line was often observed in third generation (G3) and higher generations, in samples produced by some low speed copiers (copying speed < 30 cpm) or very high speed copiers (copying speed ≤ 80 cpm). However, their line quality was still found to be good enough for handwriting analysis up to the fifth generation (G5) in photocopy samples produced by some medium speed copiers (copying speed 30 to 50 cpm). It was, however, observed that, usually, continuity of line and smoothness of line were appreciably lost in G3 and higher generations in photocopies which were produced by portable desktop printers. Exceptionally, certain artifacts that are usually found in some higher generation copies were conspicuously absent in all photocopy generations (G1–G5) which were produced by some advanced third generation photocopiers. Hopefully, the information obtained from the study will be found useful to document examiners when examining certain cases involving multi-generations of photocopier reproductions.

Forensic examination of electronic signatures: A comparative study

The writing of names or signatures has great importance due to its use in authentication, validation, and authorization of documents. Moreover, handwritten signatures present an aura of personality and make an impression on many people. But now, with the emergence of new technologies, a variety of electronic writing media such as digital tablets and pens are being used to produce writings and signatures; and the conventional way to produce the writing using pen and paper is waning. With the changing ways of writing and signature production, the means of producing forged writing or signature are also bound to change. This has brought new challenges for handwriting examiners. In the present study, a comparative analysis of electronically captured signatures with pen-paper signatures has been performed to study the effect of changes in writing media. Signature samples were taken from the same subjects on paper and electronic pads. The similarities and differences with respect to class and line quality features between both signatures were analysed. It was observed that despite differences between the signatures produced by the same author, it is still possible to establish the authorship of signatures in the case of electronic signatures.