Tracking Down the Business Cycle: A Dynamic Factor Model For Germany 1820-1913

We use a Bayesian dynamic factor model to measure Germany’s pre World War I economic activity. The procedure makes better use of existing time series data than historical national accounting. To investigate industrialization we propose to look at comovement between sectors. We find that Germany’s industrial sector developed earlier than stated in the literature, since after the 1860s agricultural time series do not comove with the business cycle anymore. Also, the bulk of comovement between 1820 and 1913 can be traced back to five out of 18 series representing industrial production, investment and demand for industrial inputs. Our factor is impressingly confirmed by a stock price index, leading the factor by 1-2 years. We also find evidence for early market integration in the 1820s and 1830s. Our business cycle dating aims to resolve the debate on German business cycle history. Given the often unsatisfactory quality of national accounting data for the 19th century we show the advantage of dynamic factor models in making efficient use of rare historical time series.Business Cycle Chronology; Imperial Germany; Dynamic Factor Models; Industrialization.

The Response of Gossypium spp. to Biotic and Abiotic Stresses in Louisiana and the Modeling of Yarn Performance

Developing improved cotton cultivars depends on how cotton cultivars perform the best when under stresses. Reniform nematode is a major plant pathogen, causing 4-6% yield loss in southern United States. A variation in reproduction and pathogenicity across reniform isolates collected from Louisiana on susceptible cotton was reported. This study was conducted to determine the response of resistant/tolerant cotton genotypes to multiple reniform isolates by inoculating 10,000 juveniles into seven days old seedlings. Across genotypes, the Evan and Avoyelles isolates had significantly higher vermiform nematodes (33,793 and 27,800/250 g soil, respectively) than other isolates. Across isolates, the number of juveniles on A2-190 and Lonren-2 (5,573 and 6,013, respectively) were significantly lower than that on other genotypes. There was a significant interaction between the genotypes and isolates suggesting that the response of genotypes to reniform isolates was different. Salt stress is a major abiotic stress, affecting cotton production in the Macon Ridge and Red River regions in Louisiana. In a preliminary study, 150 day neutral primitive cotton accessions were screened at 0, 125, 250 mM NaCl under hydroponics. A promising subset was rescreened for salt tolerance in pot culture. MT11 had the lowest reduction in plant height and dry shoot weight (32% and 47%), significantly less than FM958 (43% and 66%) across salt concentrations. MT1219 had the lowest accumulation of Na+ (1,026.37 mM) at 250 mM NaCl, and significantly lower than FM958 (2,135.39 mM). Based on reduction in plant parameters, MT11, MT1219, MT45, and MT245 performed better than other genotypes. This study also showed that both hydroponics and pot culture are effective in the screening of a large number of cotton genotypes against elevated salt concentrations. In addition to stresses, cotton breeders are interested to develop a selection index, which aids in an efficient selection of multiple fibers traits. Using the data mining techniques, all developed models agreed that fiber length and strength are the most important fiber properties in determining the spinning consistency index (SCI). This study showed that SCI can be used as alternative selection index for combining the multiple fiber traits to enhance yarn spinning

Predicting the tear strength of woven fabrics via automated machine learning: an application of the CRISP-DM methodology

Textile and clothing is an important industry that is currently being transformed by the adoption of the Industry 4.0 concept. In this paper, we use the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology to model the textile testing process. Real-world data were collected from a Portuguese textile company. Predicting the outcome of a given textile test is beneficial to the company because it can reduce the number of physical samples that are needed to be produced when designing new fabrics. In particular, we target two important textile regression tasks: the tear strength in warp and weft directions. To better focus on feature engineering and data transformations, we adopt an Automated Machine Learning (AutoML) during the modeling stage of the CRISP-DM. Several iterations of the CRISP-DM methodology were employed, using different data preprocessing procedures (e.g., removal of outliers). The best predictive models were achieved after 2 (for warp) and 3 (for weft) CRISP-DM iterations.FEDER - European Regional Development Fund(P2020

Artificial Neural Networks and Their Applications in the Engineering of Fabrics

WOS: 00036372550000

A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics

In this paper, we propose an Intelligent Decision Support System (IDSS) for the design of new textile fabrics. The IDSS uses predictive analytics to estimate fabric properties (e.g., elasticity) and composition values (% cotton) and then prescriptive techniques to optimize the fabric design inputs that feed the predictive models (e.g., types of yarns used). Using thousands of data records from a Portuguese textile company, we compared two distinct Machine Learning (ML) predictive approaches: Single-Target Regression (STR), via an Automated ML (AutoML) tool, and Multi-target Regression, via a deep learning Artificial Neural Network. For the prescriptive analytics, we compared two Evolutionary Multi-objective Optimization (EMO) methods (NSGA-II and R-NSGA-II) when optimizing 100 new fabrics, aiming to simultaneously minimize the physical property predictive error and the distance of the optimized values when compared with the learned input space. The two EMO methods were applied to design of 100 new fabrics. Overall, the STR approach provided the best results for both prediction tasks, with Normalized Mean Absolute Error values that range from 4% (weft elasticity) to 11% (pilling) in terms of the fabric properties and a textile composition classification accuracy of 87% when adopting a small tolerance of 0.01 for predicting the percentages of six types of fibers (e.g., cotton). As for the prescriptive results, they favored the R-NSGA-II EMO method, which tends to select Pareto curves that are associated with an average 11% predictive error and 16% distance.This work was carried out within the project "TexBoost: less Commodities more Specialities" reference POCI-01-0247-FEDER-024523, co-funded by Fundo Europeu de Desenvolvimento Regional (FEDER), through Portugal 2020 (P2020)

Animal fibre length-diameter relationship and its effects on yarn properties

This study investigated how simultaneous changes in animal fibre diameter and length are adding value to luxury animal fibres and improving the quality of the resultant yarn.<br /

An online fabric database to link fabric drape and end-use properties

The main obstacle for adaptation of fabric selection through the Internet is that there is no objective selection method that is suitable for fashion fabrics. The purpose of this research is to develop an objective evaluation method for selecting fabrics through an online fabric database. The relationship between fabric mechanical properties and fabric drape was investigated. One hundred eighty-five commercial fabrics from different manufacturers were tested using the Kawabata fabric evaluation system (KES-FB) and Cusick drape tester. Applying regression analysis, the parameters that were significantly correlated with drape coefficient (DC) were determined. The test results, fabric structural parameters, and contact information for fabric manufacturers, were included in the database. A web-site with a user interface allowing users to implement various types of searches was published on the Internet. Fuzzy linear clustering technique was used to predict fabric drape property. The accuracy for predicting fabric drape using this technique was 94%. This means the model using fuzzy linear clustering is an efficient method to predict fabric end-use properties. Additionally, a new method to measure drape coefficient using Photo Shop was developed by this author. Instead of weighing paper rings, shaded drape area was used to calculate the drape coefficient. With the new Photo Shop method, the cost, testing time and human error was reduced while the accuracy of the test result was increased