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The increasing competition and rapid technological advancements in today's business world have raised customer expectations. People now expect quick delivery, low prices, and high-quality products. As a result, companies must adapt to this competitive environment to survive. Rework, which is a significant cost in production, increases expenses, reduces production efficiency, and can lead to customer attrition. Research shows various efforts across different sectors to reduce rework, although there is still a gap in the textile sector's fabric dyeing units. Common problems in these units include non-retentive colors, customer dissatisfaction with shades, and repeated dyeing due to environmental factors or dye vat issues. This study uses logistic regression and artificial neural networks models from machine learning to predict which fabrics will need rework, using data from a textile company in Bursa. The analysis indicates that artificial neural networks models perform better.

OBJECTIVE: A 1-day point prevalence study was planned to obtain country data by determining the clinical characteristics, follow-up and treatment methods of coronavirus disease 2019 (COVID-19) cases that required intensive care unit (ICU) treatment in the second year of the pandemic. MATERIAL AND METHODS: All patients who were hospitalized in the ICUs due to COVID-19 between March 11, 2022, 08.00 am, and March 12, 2022, 08.00 am, were included in the study. Demographic characteristics, intensive care and laboratory data, radiological characteristics, and follow-up results of the patients were recorded. RESULTS: A total of 811 patients from 59 centers were included in the study, 59% of the cases were male, and the mean age was 74 ± 14 years. At least one comorbid disease was present in 94% of the cases, and hypertension was the most common. When ICU weight scores were examined, Acute Physiology and Chronic Health Evaluation-II: 19 (15-27) and Sequential Organ Failure Assessment: 7 (4-10) were seen. Sepsis was present in 37% (n = 298) of cases. PaO2/FiO2 ratios of the patients were 190 the highest and 150 the lowest and 51% of the cases were followed via invasive mechanical ventilation. On the study day, 73% bilateral involvement was seen on chest x-ray, and ground-glass opacities (52%) were the most common on chest tomography. There was growth in culture in 40% (n = 318) of the cases, and the most common growth was in the tracheal aspirate (42%). CONCLUSION: The clinical course of COVID-19 is variable, and ICU follow-up was required due to advanced age, comorbidity, presence of respiratory symptoms, and widespread radiological involvement. The need for respiratory support and the presence of secondary infection are important issues to be considered in the follow-up. Despite the end of the second year of the pandemic and vaccination, the high severity of the disease as well as the need for follow-up in ICUs has shown that COVID-19 is an important health problem.

Abstract Background Interpersonal distance (IPD), which reflects the physical space between people, ensures the regulation of social behavior in interaction as part of nonverbal communication. Our research aims to reveal whether there is a difference in personal distance preference between individuals with hearing loss and normal hearing (NH). Methods Thirty-five adults (26.54 ± 7.05 years) were divided into three groups according to hearing status: hearing aid (HA) users, cochlear implant (CI) users, and NH individuals. The preferred interpersonal distance scale (PIPDS) and a stop distance paradigm were employed for measurements in various environments. Personal space violations were monitored using an electrodermal activity (EDA) wristband. Results Our findings showed a significant relationship between preferred interpersonal distance (PID) and hearing loss duration (r = 571; p