The European Journal of Research and Development

Investigation of Dyeing Synthetic Fabrics by Using Bacterial Colorants for More Sustainable Textile Production

Hülya Kıcık, Çağla Gökbulut — Sun, 31 Mar 2024 00:00:00 +0300

Polyester and polyamide fibers are the most commonly used textile fibre globally. For this reason, studies conducted on the production and dyeing of synthetic fabrics, especially polyester fabrics, have a significant environmental impact. In this study, it was aimed to put forward a more sustainable way to dye synthetic fabrics. Even if plant dyes are a good alternative for sustainable dyeing in the absence of synthetic dyestuff, the usage amount of plant dyestuffs is high and consistency is low. Bacterial dyeing can be a good alternative for more sustainable synthetic fabric dyeings due to consistent production and free of petroleum-based dyestuffs.

Within the scope of this study, 100% polyester and %100 polyamide woven fabric were dyed using 3 different receipts with 3 different bio-colors, the most suitable dyeing methods were determined for both polyester and polyamide fabric. Quality control parameters were checked regarding color depth, washing and rubbing fastness. The findings reveal that polyamide fabrics have higher K/S values compared with polyester fabrics while dyeing with the same receipt and process. pH adjustment and adding salt are necessary to get better results for dyeing pink and brown polyester. Only adding dyestuff to the dyeing bath is enough to dye polyester in blue color. On polyamide fabrics, most suitable dye bath includes salt and pH is 4 to dye pink and brown. But for the blue color, adding salt and adjusting pH to 4 gives better results in polyamide fabric.

Lidar Based Position Estimation in Warehouse Logistics

Hasan Ozcan, Gokhan Atali — Sun, 31 Mar 2024 00:00:00 +0300

This study introduces a lidar-based algorithm developed to overcome the difficulties encountered in localizing autonomous robots in complex environments. The testing procedure involves identifying lines coming from points, determining the intersections of these lines, and then calculating the location. The location calculation process was carried out by comparing the instantly obtained intersection points with the previous intersection points. The results obtained from the developed algorithm serve to explain the practical application of the algorithm and demonstrate its ability to achieve precise location detection in real-world scenarios. The findings highlight the effectiveness of the algorithm and its potential to contribute to the advancement of autonomous robot navigation in complex environments.

An Innovative Lift Control Board Design and Prototype Production Using CANbus Communication System and STM32 Microcontroller

İpek Sözer, Recep Can Başkurt, İsmail Ovalı, Engin Tan — Sun, 31 Mar 2024 00:00:00 +0300

In this study, Önder Group Inc. Design Center, with an innovative approach, designed controllers for the first time, and prototypes were produced for a wide range of goods lifts according to different needs. With the innovative board design, goods lift users can choose parameters such as the number of floors, door types, and lock types from a user-friendly menu to suit their needs. Within the scope of the study, all functions that may be needed were identified, and the hardware structure of the system was determined. By simply changing the defined parameters, the desired goods lift controller can be made ready in a very short time. Time savings were achieved by simply expanding the hardware structure with the design of the CANbus communication system according to the number of floors and stops of the elevators. Thus, a wide range of lifts can be used by simply increasing the number of floor announcer boards and selecting the menu. Additionally, thanks to the designed Wi-Fi plug-in software, operators will be able to monitor the malfunction and operating status of the lift. With this plug-in, technical service teams can intervene immediately in case of malfunction.

Development and Application of Reactive Dye Microcapsules for Cotton Fabric Dyeing

İsmet Ege Kalkan, Sevil Günç, Umut Kıvanç Şahin — Sun, 31 Mar 2024 00:00:00 +0300

Reactive dyestuffs used in textile efficiency cotton dyeing are generally applied as a triple mixture. Most of these dyestuffs are supplied to HT machines in powder form without being suitable for automation. 100% of the dyestuffs used in the dyeing recipe applied in reactive dyeing cannot adhere to cotton. Maximum 70% of it adheres to cotton. If the remaining 30% is dye, the substances pass into the water phase. And the dyestuffs that pass into this water phase include substances that are insoluble in water and difficult to purify. It also increases environmental waste burdens. In project scope; By encapsulating reactive dyes with microcapsule technology, it is aimed to reduce the limits of dyes used and reduce environmental waste. And in this context; Dye saving will be achieved.

Preliminary Study Based on Myocardial Infarction Classification of 12-Lead Electrocardiography Images with Deep Learning Methods

Sun, 31 Mar 2024 00:00:00 +0300

In contemporary medicine, the development of computer-aided diagnostic systems using Electrocardiography (ECG) signals has gained significance for the diagnosis of heart diseases. Myocardial infarction (MI) is recognized as the condition where blood flow to the heart muscle is obstructed due to blockages in coronary vessels. In this study, four deep learning approaches were employed to automatically identify different MI conditions (STEMI, NSTEMI, USAP) using images generated from 12-lead ECG signals. The utilized architectures include deep neural networks such as Visual Geometry Group-16 (VGG-16), AlexNet, Residual Neural Network (ResNet), SqueezeNet and an ensemble model composed of these networks. With the proposed method, classification was performed based on 10-second grayscale images of 12-lead ECG signals for HC-STEMI, HC-NSTEMI, HC-USAP, and NSTEMI-STEMI conditions. According to the obtained results, the HC-STEMI group achieved the highest performance with a cross-validated 0.8237 F1 score using the AlexNet architecture.

Among the novel contributions of this study is the image-based ECG classification method that can be more easily adapted to clinical applications and the analysis of the potential use of detecting different MI conditions in clinical practices. In conclusion, this study sheds light on future research by demonstrating the significant potential of using multi-channel ECG signals in image format for MI diagnosis, paving the way for advancements in this field.

Sustainable Denim Design Using Giza Cotton

Sun, 31 Mar 2024 00:00:00 +0300

Designing denim fabrics that will have a longer wear life by combining Giza cotton, one of the best cottons in the world, with yarn technology, without using any reinforced synthetic fibers. Giza cotton will be used in the products to be developed specifically for the project. High strength yarns will be obtained with the fiber length of Giza cotton and the appropriate twist value to be applied. It is aimed that the fabrics produced from these threads will have a longer lifespan than normal cotton threads. One of the most important steps for the continuity of sustainable production will be that the produced product is recyclable and can be used for a longer time. In this way, a significant decrease in the carbon and water footprint resulting from production will be achieved. The environmental impact of production will be minimized.

Deep Learning Approaches for Stream Flow and Peak Flow Prediction: A Comparative Study

Levent Latifoğlu, Emre Altuntaş — Sun, 31 Mar 2024 00:00:00 +0300

Stream flow prediction is crucial for effective water resource management, flood prevention, and environmental planning. This study investigates the performance of various deep neural network architectures, including LSTM, biLSTM, GRU, and biGRU models, in stream flow and peak stream flow predictions. Traditional methods for stream flow forecasting have relied on hydrological models and statistical techniques, but recent advancements in machine learning and deep learning have shown promising results in improving prediction accuracy. The study compares the performance of the models using comprehensive evaluations with 1-6 input steps for both general stream flow and peak stream flow predictions. Additionally, a detailed analysis is conducted specifically for the biLSTM model, which demonstrated high performance results. The biLSTM model is evaluated for 1-4 ahead forecasting, providing insights into its specific strengths and capabilities in capturing the dynamics of stream flow. Results show that the biLSTM model outperforms other models in terms of prediction accuracy, especially for peak stream flow forecasting. Scatter plots illustrating the forecasting performances of the models further demonstrate the effectiveness of the biLSTM model in capturing temporal dependencies and nonlinear patterns in stream flow data.

This study contributes to the literature by evaluating and comparing the performance of deep neural network models for general and peak stream flow prediction, highlighting the effectiveness of the biLSTM model in improving the accuracy and reliability of stream flow forecasts.

Design and Implementation of a Tailored Dealer Management System (DMS) for the Automotive Industry

Bora Beken, Muhammed Mustafa Temel, Özge Abduloğlu, Turgay Bilgin — Sun, 31 Mar 2024 00:00:00 +0300

This paper presents a thorough investigation into the design and implementation of a Dealer Management System tailored for the automotive industry. The DMS is intended to streamline after-sales processes such as inventory and expense management, service order creation, and financial data monitoring. The system integrates with a variety of other platforms, including ERP, CRM, and payment systems, which improves collaboration between dealers and distributors. The architecture of DMS, known as Copilot Next, is detailed, with a focus on the backend, frontend, and integration components. Sentinet and Dell Boomi are integration tools that allow for seamless connectivity with other systems. The findings show that the DMS effectively meets the needs of multiple vehicle brands, improves part ordering accuracy with AI tools, and ensures data consistency across systems. This study offers useful insights into the design and implementation of a DMS that can significantly improve dealership operations and customer service in the automotive industry.