Contemporary customized clothes using folk motifs

More and more consumers are attracted to fashion brands that make an extra effort to offer them personalized experiences. The unique details, as well as the complexity of the decorative elements of the folk costume inspire the fashion designers to return to the folk motifs, which they reinterpret and resize while integrating them in the contemporary space, offering models adapted to the customers’ tastes, sizes and preferences and at the same time to make mass customization a profitable production. This study addresses the issue of personalizing clothing items with folk motifs. In order to collect information on consumer satisfaction regarding the use of folk motifs in contemporary clothing, an online survey about the clothing available on the market and about personalized clothes with folk motifs was developed and applied. The survey was applied to a number of 548 respondents from Romania, Bulgaria, Canada and Spain. To determine the correlation between the answers to the questions for the four countries and to analyse the answers in each country, the PCA method was used. Based on the answers to the survey, certain motifs from the folk costumes were selected, reinterpreted in a modern way and inserted in two fabric patterns. The fabrics were produced on a Loom Jacquard SMIT Textile GS900.

Machine Learning of Medical Applications Involving Complicated Proteins and Genetic Measurements

Motivations. Breast cancer is the second greatest cause of cancer mortality among women, according to the World Health Organization (WHO), and one of the most frequent illnesses among all women today. The influence is not confined to industrialized nations but also includes emerging countries since the authors believe that increased urbanization and adoption of Western lifestyles will lead to a rise in illness prevalence. Problem Statement. The breast cancer has become one of the deadliest diseases that women are presently facing. However, the causes of this disease are numerous and cannot be properly established. However, there is a huge difficulty in not accurately recognizing breast cancer in its early stages or prolonging the detection process. Methodology. In this research, machine learning is a field of artificial intelligence that employs a variety of probabilistic, optimization, and statistical approaches to enable computers to learn from past data and find and recognize patterns from large or complicated groups. The advantage is particularly well suited to medical applications, particularly those involving complicated proteins and genetic measurements. Result and Implications. However, when using the PCA method to reduce the features, the detection accuracy dropped to 89.9%. IG-ANFIS gave us detection accuracy (98.24%) by reducing the number of variables using the “information gain” method. While the ANFIS algorithm had a detection accuracy of 59.9% without utilizing features, J48, which is one of the decision tree approaches, had a detection accuracy of 92.86% without using features extraction methods. When applying PCA techniques to minimize features, the detection accuracy was lowered to the same way (91.1%) as the Naive Bayes detection algorithm (96.4%).

Identification of defected sensors in an array of amperometric gas sensors

Purpose Amperometric gas sensors are commonly used in air quality monitoring in long-term measurements. Baseline shift of sensor responses and power failure may occur over time, which is an obstacle for reliable operation of the entire system. The purpose of this study is to check the possibility of using PCA method to detect defected samples, identify faulty sensor and correct the responses of the sensor identified as faulty. Design/methodology/approach In this work, the authors present the results obtained with six amperometric sensors. An array of sensors was exposed to sulfur dioxide at the following concentrations: 0 ppm (synthetic air), 50 ppb, 100 ppb, 250 ppb, 500 ppb and 1000 ppb. The damage simulation consisted in adding to the sensor response a value of 0.05 and 0.1 µA and replacing the responses of one of sensors with a constant value of 0 and 0.15 µA. Sensor validity index was used to identify a damaged sensor in the matrix, and its responses were corrected via iteration method. Findings The results show that the methods used in this work can be potentially applied to detect faulty sensor responses. In the case of simulation of damage by baseline shift, it was possible to achieve 100% accuracy in damage detection and identification of the damaged sensor. The method was not very successful in simulating faults by replacing the sensor response with a value of 0 µA, due to the fact that the sensors mostly gave responses close to 0 µA, as long as they did not detect SO2 concentrations below 250 ppb and the failure was treated as a correct response. Originality/value This work was inspired by methods of simulating the most common failures that occurs in amperometric gas sensors. For this purpose, simulations of the baseline shift and faults related to a power failure or a decrease in sensitivity were performed.

Skeletal muscle interaction synergetic effects during a complex acyclic motor action performing

The study showed the sambo wrestlers’ muscle synergies’ spatial-temporal structure, extracted using the PCA method. We considered the individual periods of the "leg grabbing" throw coordination structure. It was revealed the electrical activity of extensive synergies changes depending on registered muscular efforts values, typical for different periods of the performed movement. The synergetic effects of skeletal muscle interaction demonstrate plasticity, manifested in typical patterns of spatial and temporal activation of revealed muscle synergies, which ensures reliable control of motor function in various periods of complex movement coordination performing. Key words: muscle synergies; synergetic effects; intermuscular coordination; motion control, skeletal muscle.

Reconstruction of Phase Space and Eigenvalue Decomposition from a Biological Time Series: A Malayalam Speech Signal Case Study

Our objective is to describe the speech production system from a non-linear physiological system perspective and reconstruct the attractor from the experimental speech data. Mutual information method is utilized to find out the time delay for embedding. The False Nearest Neighbour (FNN) method and Principal Component Analysis (PCA) method are used for optimizing the embedding dimension of time series. The time series obtained from the typical non-linear systems, Lorenz system and Rössler system, is used to standardize the methods and the Malayalam speech vowel time series of both genders of different age groups, sampled at three sampling frequencies (16[Formula: see text]kHz, 32[Formula: see text]kHz, 44.1[Formula: see text]kHz), are taken for analysis. It was observed that time delay varies from sample to sample and, it ought to be better to figure out the time delay with the embedding dimension analysis. The embedding dimension is shown to be independent of gender, age and sampling frequency and can be projected as five. Hence a five-dimensional hyperspace will probably be adequate for reconstructing attractor of speech time series.

A data-driven approach to measuring epidemiological susceptibility risk around the world

Epidemic outbreaks are extreme events that become more frequent and severe, associated with large social and real costs. It is therefore important to assess whether countries are prepared to manage epidemiological risks. We use a fully data-driven approach to measure epidemiological susceptibility risk at the country level using time-varying information. We apply both principal component analysis (PCA) and dynamic factor model (DFM) to deal with the presence of strong cross-section dependence in the data. We conduct extensive in-sample model evaluations of 168 countries covering 17 indicators for the 2010–2019 period. The results show that the robust PCA method accounts for about 90% of total variability, whilst the DFM accounts for about 76% of the total variability. Our index could therefore provide the basis for developing risk assessments of epidemiological risk contagion. It could be also used by organizations to assess likely real consequences of epidemics with useful managerial implications.

Fiscal Resilience Index - A Proposition and Evidence of Emerging Market

Fiscal resilience is essential to maintain economic stability and sustainability. Until now, there are no mutually agreed indicators to show a country's fiscal resilience. This study aims to explore the possibility of forming the index of fiscal resiliency that captures more than one underlying variable that are more comprehensive as opposed to the most current practices that use only one narrow variable. The Principal Component Analysis (PCA) method is applied to build the foundation of the index, whilst the trial is experimentally conducted as a case study of Indonesia as an emerging market in 1995-2020. Using the PCA method produces an index model of fiscal resiliency formed by the variables of government revenue, spending, debt, and macroeconomic conditions. The use of such Fiscal Resilience Index (FRI) as the case of Indonesia in the period 1995-2020 shows a reasonably consistent result which is in line with the underlying condition of the country during such period. It gives a negative figure, which means Indonesia is in a bad fiscal condition due to its budget deficit strategy.

CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network

Background The existing studies show that circRNAs can be used as a biomarker of diseases and play a prominent role in the treatment and diagnosis of diseases. However, the relationships between the vast majority of circRNAs and diseases are still unclear, and more experiments are needed to study the mechanism of circRNAs. Nowadays, some scholars use the attributes between circRNAs and diseases to study and predict their associations. Nonetheless, most of the existing experimental methods use less information about the attributes of circRNAs, which has a certain impact on the accuracy of the final prediction results. On the other hand, some scholars also apply experimental methods to predict the associations between circRNAs and diseases. But such methods are usually expensive and time-consuming. Based on the above shortcomings, follow-up research is needed to propose a more efficient calculation-based method to predict the associations between circRNAs and diseases. Results In this study, a novel algorithm (method) is proposed, which is based on the Graph Convolutional Network (GCN) constructed with Random Walk with Restart (RWR) and Principal Component Analysis (PCA) to predict the associations between circRNAs and diseases (CRPGCN). In the construction of CRPGCN, the RWR algorithm is used to improve the similarity associations of the computed nodes with their neighbours. After that, the PCA method is used to dimensionality reduction and extract features, it makes the connection between circRNAs with higher similarity and diseases closer. Finally, The GCN algorithm is used to learn the features between circRNAs and diseases and calculate the final similarity scores, and the learning datas are constructed from the adjacency matrix, similarity matrix and feature matrix as a heterogeneous adjacency matrix and a heterogeneous feature matrix. Conclusions After 2-fold cross-validation, 5-fold cross-validation and 10-fold cross-validation, the area under the ROC curve of the CRPGCN is 0.9490, 0.9720 and 0.9722, respectively. The CRPGCN method has a valuable effect in predict the associations between circRNAs and diseases.

A Methodology for Evaluation and Distribution of Patent Applications to INPI-BR Patent Examiners: A Case Study in the Electricity Field

This paper presents the development of a new methodology for evaluation and distribution of patent applications to the examiners at the Brazilian Patent Office considering a specific technological field, represented by classification of the application according to the International Patent Classification (IPC), and the variables corresponding to the volume of data of the application and its complexity for the examination process. After identifying the most relevant variables, such as the Specific Areas of Expertise (ZAE) of the examiners, a mathematical model was developed, including: (a) application of the principal component analysis (PCA) method; (b) calculation of a General Complexity Ratio (IGC); (c) classification into five classes (very light, light, moderate, heavy and very heavy) according to IGC average ranges and standard deviations; (d) implementation of a logic of distribution, compensating very heavy applications with very light ones, and light applications with heavy ones; and (e) calculation of a Distribution Balancing Ratio (IBD), considering the differences between the samples’ medians. The model was validated using a sample of patent applications including, in addition to the identified variables, the time for substantive examination by the examiner. Then, a correlation analysis of the variables with time and a comparison of the classifications according to the time and the IGC generated by the model were carried out. The results obtained showed a high correlation of the IGC with time, above 80%, as well as correct IGC classes in more than 80% of applications. The model proposed herein suggests that the three main relevant variables are: total number of pages, total number of claims, and total number of claim pages.