Textile and clothing sector possesses a significant place in Turkish manufacturing industry as well as in exports, investments, gross national product and employment; also maintains its locomotive sector position in development for a long time with its established production potential and labor force. However, there are serious issues about the unionization of workers in the sector. On one hand, this situation causes an increment in social and economical issues of workers and enterprises and on the other hand, it damages democracy within the enterprise. In this context, this study aims to suggest tangible solutions by revealing the economical and social differences between unionization and non-unionization in the sector. Besides, the study differs from other studies and contributes to the literature due to its two-sided research structure (workers and employers) and analysis of unionization in textile and clothing sector in terms of economical and social aspects. In accordance with the aim of the research, two separate surveys are conducted for textile and clothing enterprises, which operate throughout Turkey, and for unionized and non-unionized blue-collar workers of these enterprises. The obtained data are analyzed by using descriptive statistics, exploratory factor analysis, and independent samples t-test. As stated by the research results, the perspectives of unionized and non-unionized participants differ with regard to positive and negative aspects (both economical and social aspects) of being a union member.
Miao embroidery of the southeast area of Guizhou province in China is a kind of precious intangible cultural heritage, as well as national costume handcrafts and textiles, with delicate patterns that require exquisite workmanship. There are various skills to make Miao embroidery; therefore, it is difficult to distinguish the categories of Miao embroidery if there is a lack of sufficient knowledge about it. Furthermore, the identification of Miao embroidery based on existing manual methods is relatively low and inefficient. Thus, in this work, a novel method is proposed to identify different categories of Miao embroidery by using deep convolutional neural networks (CNNs). Firstly, we established a Miao embroidery image database and manually assigned an accurate category label of Miao embroidery to each image. Then, a pre-trained deep CNN model is fine-tuned based on the established database to learning a more robust deep model to identify the types of Miao embroidery. To evaluate the performance of the proposed deep model for the application of Miao embroidery categories recognition, three traditional non-deep methods, that is, bag-of-words (BoW), Fisher vector (FV), and vector of locally aggregated descriptors (VLAD) are employed and compared in the experiment. The experimental results demonstrate that the proposed deep CNN model outperforms the compared three non-deep methods and achieved a recognition accuracy of 98.88%. To our best knowledge, this is the first one to apply CNNs on the application of Miao embroidery categories recognition. Moreover, the effectiveness of our proposed method illustrates that the CNN-based approach might be a promising strategy for the discrimination and identification of different other embroidery and national costume patterns.
In the recent years, robotic systems became more advanced and more accessible. This has led to their slow, but stable integration and use in different processes and applications, including in the agricultural domain. Nowadays, agricultural robots are developed with the aim to replace the human labour in the otherwise exhausting, time-consuming or dangerous activities. Agricultural robotic systems provide many advantages, which can differ based on the type of the robot and its sensors, actuators and communication systems. This paper presents the design, the construction process, the main characteristics and the evaluation of a prototype of a small-scale agricultural robot that can be used for some of the simplest activities in agricultural enterprises. The robot is designed as an end-user autonomous mobile system, which is capable of self-localization and can map or inspect a specific farming area. The decision-making capabilities of the robot are based on artificial intelligence (AI) algorithms, which allow it to perform specific actions in accordance to the situation and the surrounding environment. The presented prototype is in its early development and evaluation stages and the paper concludes with discussions on the possible further improvements of the platform.
Variable rate technology (VRT) in nutrient management has been developed in order to apply crop inputs according to the required amount of fertilizers. Meteorological conditions rarely differ within one field; however, differences in soil conditions responding to precipitation or evaporation results within field variations. These variations in soil properties such as moisture content, evapotranspiration ability, etc. requires site-specific treatments for the produced crops. There is an ongoing debate among experts on how to define management zones as well as how to define the required amount of fertilizers for phosphorus and nitrogen replenishment for winter wheat (Triticum aestivum L.) production. For management zone delineation, vegetation based or soil based data collection is applied, where various sensor technology or remote sensing is in help for the farmers. The objective of the study reported in this paper was to investigate the effect of soil moisture data derived from Sentinel-2 satellite images moisture index and variable rate phosphorus and nitrogen fertilizer by means of variable rate application (VRA) in winter wheat in Mezőföld, Hungary. Satellite based moisture index variance at the time of sowing has been derived, calculated and later used for data comparison. Data for selected points showed strong correlation (R2 = 0.8056; n = 6) between moisture index and yield, however generally for the whole field correlation does not appear. Vegetation monitoring has been carried out by means of NDVI data calculation. On the field level, as indicated earlier neither moisture index values at sowing nor vegetation index data was sufficient to determine yield. Winter wheat production based on VRA treatment resulted significant increase in harvested crop: 5.07 t/h in 2013 compared to 8.9 t/ha in 2018. Uniformly managed (control) areas provided similar yield as VRA treated areas (8.82 and 8.9 t/ha, respectively); however, the input fertilizer was reduced by 108 kg/ha N and increased by 37 kg/ha P.
Analyses based on precipitation data may be limited by the quality of the data, the size of the available historical series and the efficiency of the adopted methodologies; these factors are especially limiting when conducting analyses at the daily scale. Thus, methodologies are sought to overcome these barriers. The objective of this work is to develop a hybrid model through the maximum overlap discrete wavelet transform (MODWT) to estimate daily rainfall in homogeneous regions of the Tocantins-Araguaia Hydrographic Region (TAHR) in the Amazon (Brazil). Data series from the Climate Prediction Center morphing (CMORPH) satellite products and rainfall data from the National Water Agency (ANA) were divided into seasonal periods (dry and rainy), which were adopted to train the model and for model forecasting. The results show that the hybrid model had a good performance when forecasting daily rainfall using both databases, indicated by the Nash–Sutcliffe efficiency coefficients (0.81–0.95), thus, the hybrid model is considered to be potentially useful for modelling daily rainfall.
The article presented describes a new design of measuring chains in laboratory test equipment, which are used for testing the hydrostatic transducers and hydraulic fluids. Laboratory test equipment allows simultaneous observation of parameters of hydrostatic transducers and hydraulic fluids by simulating the operating conditions under laboratory conditions, what can significantly reduce the testing time and economic costs. The new design functionality was verified via measurement of the basic parameters of hydrostatic transducers and changing the load of hydraulic fluids. Based on the results measured, the flow efficiency of tested hydrostatic transducer UD-25R was calculated and compared with the transducer parameters specified by the manufacturer using different types of operating hydraulic fluids. Verification measurements of the unloaded hydrostatic transducer were performed at various rotation speeds: Q250 = 5.694 dm3·rpm at speed of n1 = 250 rpm; Q500 = 12.286 dm3·rpm at speed of n2 = 500 rpm; Q750 = 18.747 dm3·rpm at speed of n3 = 750 rpm. Based on the hydrostatic transducer flow rate, the UD-25R transducer flow efficiency was determined: at n1 = 250 rpm, the flow efficiency was η250 = 0.8946; at n2 = 500 rpm, the efficiency was η500 = 0.9651; at n3 = 750 rpm, the flow efficiency was η750 = 0.9812.
This work investigates the thermal decomposition of forest waste for a non-linear temperature distribution inside the pyrolysis reactor. Quantitative analysis of the distributed activation energy model is explained graphically. It has been assumed that thermal profile varies according to the general parabolic equation with the initial condition (0, T0). The approximated solution of the non-analytical integral is determined by the Laplace integral method. The integral limit for the distributed activation energy model (DAEM) is found to vary from 211 to 810 kJ·mol−1; whereas the frequency factor (the first-order reactions) for the corresponding range of the activation energy lies in the domain of 400–2000 min−1. The acceleration in the char formation has been found for the reactions other than that of the first order.
Recently water resources reanalysis (WRR) global streamflow products are emerging from high- resolution global models as a means to provide long and consistent global streamflow products for assessment of global challenge such as climate change. Like any other products, the newly developed global streamflow products have limitations accurately represent the dynamics of local streamflow hydrographs. There is a need to locally evaluate and apply correction factors for better representation and make use of the data. This research focuses on the evaluation and correction of the bias embedded in the global streamflow product (WRR, 0.25°) developed by WaterGAP3 hydrological model in the upper Blue Nile basin part of Ethiopia. Three spatiotemporal dynamical bias correction schemes (temporal-spatial variable, temporal-spatial constant and spatial variable) tested in twelve watersheds of the basin. The temporal-spatial variable dynamical bias correction scheme significantly improves the streamflow estimation. The Nash-Sutcliffe coefficient (NSCE) improves by 30% and bias decreases by 19% for the twelve streamflow gauging stations applying leave one out cross-validation approach in turn. Therefore, the temporal-spatial variable scheme is applicable and can use as one method for the bias correction to use the global data for local applications in the upper Blue Nile basin.