This paper proposes a new probability distribution, which belongs a member of the exponential family, defined on (0,1) unit interval. The new unit model has been defined by relation of a random variable defined on unbounded interval with respect to standard logistic function. Some basic statistical properties of newly defined distribution are derived and studied. The different estimation methods and some inferences for the model parameters have been derived. We assess the performance of the estimators of these estimation methods based on the three different simulation scenarios. The analysis of three real data examples which one is related to the coronavirus data, show better fit of proposed distribution than many known distributions on the unit interval under some comparing criteria.

E-commerce sites have generated large amounts of unstructured data as they allow millions of users to generate product reviews. Thus, although there have been significant improvements in the characteristics of big data, such as speed and volume, developing various analysis techniques to monitor, understand, and extract useful information from this web-based data has become challenging. This study aims to analyze cosmetic products on a Turkish-based e-commerce website with sentiment analysis and to create a new domain-specific Turkish sentiment dictionary model with manual labeling. In the study, a Turkish sentiment dictionary consisting of 65,378 words was created by manually labeling 875,455 product reviews for 24 cosmetic brands sold on the Turkey-based trendyol e-commerce site, and sentiment analysis was performed using this dictionary. The dataset, divided into seven product groups, was analyzed using K-NN, SVM, DT, RF, and LR algorithms to address three classification problems. The algorithms were evaluated with comparative analysis using accuracy, precision, recall, and f-1 score metrics. SVM gave the highest performance result with over 93% accuracy, 92% precision, 93% recall, and a 91% f-1 score in all product groups. The dictionary model created for the cosmetics industry in the study helps businesses and researchers to use their resources more efficiently and save time by performing fast and low-cost analyses on large datasets of product reviews. Moreover, by analyzing customer feedback, brands can offer long-lasting and environmentally friendly products that align with customers’ feelings. Thus, businesses have the opportunity to develop or improve products.

Bu çalışmada Türkiye’nin sağlık hizmetleri kapasitesi analiz edilmiştir. Türkiye’nin sağlık hizmetleri kapasitesi 13 farklı nicel değişken kullanılarak ‘Çok Boyutlu Ölçekleme Tekniği’ ile analiz edilmiştir. Analizlerde il bazında toplam; uzman hekim, pratisyen hekim, asistan hekim, diş hekimi, eczacı, hemşire, ebe, diğer sağlık personellerinin sayısı ile aile hekimliği birim sayısı, 112 istasyon sayısı, 112 ambulans sayısı, hastane sayısı, yatak sayısı, nitelikli yatak sayısı ve yoğun bakım yatak sayıları kullanılmıştır. Çalışmada illerin sağlık altyapılarının yeterli olup olmadığı, illere göre dağılımının adil olup olmadığı kanıta dayalı analizler ile incelenmiştir. Bulgular, Türkiye’deki 81 ilin sağlık hizmetleri kapasiteleri bakımından 7 gruba ayrıştığını göstermektedir. İstanbul, İzmir ve Ankara pozitif anlamda diğer illerden ayrışarak birinci grup iller arasında yer almıştır. Türkiye’deki toplam hekim sayısının yaklaşık %40’ı, toplam yoğun bakım yatak sayısının yaklaşık %30’u bu üç ilde yer almaktadır.

The aim of this study is to compare the results of MUST (Malnutrition Universal Screening Tool) and NRS-2002 (Nutritional Risk Screening 2002) nutritional screening scales which are widely used in nutritional risk assessment of inpatients and to investigate the harmony between them statistically. This study was carried out with a total of 220 people (124 male, 96 female) between the ages of 20 and 90 who were being treated in Republic of Turkey Ministry Of Health Adana Provincial Health Directorate Adana City Training and Research Hospital. Questionnaires applied to the participants include demographic questions, anthropometric measurements and questions found on the NRS-2002 and MUST nutrition screening scales. The Kappa analysis was used to determine whether there was a correlation between NRS-2002 and MUST tests for the distribution of nutritional status of individuals. In addition, the results obtained from dietary screening scales and whether there was a statistically significant difference between the demographic characteristics of the patients were analyzed with non-parametric statistical methods, Mann Whitney U and Kruskal Wallis tests. A statistically significant correlation was found between NRS-2002 and MUST tests in terms of distribution of nutritional status of individuals according to Kappa test results Furthermore, as the NRS-2002 scores increased, an increase in MUST scores was observed and a positive correlation was observed between them ( ). As a result, there was a positive correlation between NRS-2002 and MUST nutrition screening tests applied to individuals and there was a statistically significant correlation between the samples used in this study.

The purpose of this chapter is to provide an extensive overview of the underlying dynamics and socio-economic effects and consequences of the post-pandemic global tech layoffs while contributing to the literature by bringing forward pressing issues, like ‘backdoor layoffs’, and comprehensive insights while identifying the opportunities and challenges. Due to the multi-faceted nature of the phenomenon, an interdisciplinary approach is being employed, combining the empirical findings from various sources, including interviews, social media, and databases, into theoretical frameworks and real-world case studies. A multi-level analysis of the global tech layoffs is conducted through these case studies. As a result, this study reveals the root causes from the top and uncovers various challenges, like economic downturns, uncertainties, corporate strategies, and unethical practices. Moreover, companies are expected to follow the strategies of successful peers, and in the case of workforce reduction, small and medium enterprises follow different trajectories from each other but homogenous trends within their peer groups. This finding indicates that the global tech layoff has a multi-level nature, and each level has diverse needs and expectations to be addressed. At the social level, this chapter defines backdoor layoffs term and delves into their consequences that can exacerbate inequalities, especially for discrimination groups, and create barriers to re-employment. Addressing unethical backdoor layoff practices and creating a more sustainable tech labor market requires a wider participation of stakeholders. Finally, this chapter sheds light on the gaps in the mass layoffs scholarship, offering theoretical frameworks, definitions, and empirical evidence to expand the literature. <!----> <!----> <!----> <!---->

Siyasi partiler için ileriye yönelik olarak yapılacak doğru tahminler, seçim dönemi yönetim stratejilerinin güncellenmesine ve genel seçimlerde partinin daha büyük başarılara imza atmasına yardımcı olacaktır. Gelecek için tahminlemelerin yapılmasında Markov Zincirleri yöntemi yaygın olarak kullanılmaktadır. Bu çalışmada da bu yöntem kullanılarak uzun vadede seçmenlerin yüzde kaç olasılıkla hangi siyasi partiyi tercih edeceği hesaplanmıştır. Zaman ve maliyet açısından imkânların kısıtlı olması sebebiyle çalışma evreni Adana ili ile sınırlı tutulmuştur. Adana’nın ilçelerinin seçmen sayıları göz önünde bulundurularak örneklem büyüklükleri hesaplanmış ve bu doğrultuda rasgele seçilen kişilere yüz yüze anket yöntemi uygulanmıştır. Ön kontrollerden geçen ve çalışmanın kısıtlarına uyan 2848 anketten elde edilen verilerle çalışma gerçekleştirilmiştir. Verilerin analizleri neticesinde, Adana ili için Adalet ve Kalkınma Partisi seçmenin parti sadakatinin en yüksek olduğu (%87,06) ve uzun vadede Milliyetçi Hareket Partisi ile Cumhuriyet Halk Partisinin gelecek seçimlerde diğer partilere oranla oyunu daha çok arttıracağı görülmüştür.