Correlates of young children's screen time: Child-, parent-, and home-related factors

Okul öncesi çocukların günlerinin önemli bir kısmını ekran karşısında geçirdikleri bilinmektedir. Önceki çalışmalarda, çocukların ekran başında geçirdikleri sürenin çeşitli demografik, ailevi ve ev içi unsurlarla ilişkili olduğu gösterilmiştir. Bu bulguların çoğu, yüksek gelirli ve erken çocukluk eğitiminin yaygın olduğu gelişmiş ülkelerden gelmektedir. Farklı ülkelerdeki bulguların kıyaslanması ve çocukların ekran süresini belirleyebilecek yeni değişkenlerin alanyazına kazandırılması açısından ülkemizde yaşayan okul öncesi çocukların ekran süresi ile ilişkili unsurları tespit etmek önemlidir. Bu çalışmanın ilk amacı, çevrimiçi bir anket aracılığıyla okul öncesi çocukların ekran süresi ile ilişkili olabilecek çocuk (örn., yaş), ebeveyn (örn., stres seviyesi) ve ev ortamı (örn., arka plan televizyon sıklığı) ile ilgili unsurları incelemektir. Çalışmanın ikinci amacı, çocukların ekran süresi ile ebeveynin algıladığı sosyal destek arasın7 daki ilişkiyi alanyazında ilk kez incelemektir. Çalışmaya altı yaşından küçük çocuğu bulunan (Ort. = 41.5 ay, SS = 17.9) 647 ebeveyn katılmıştır. Çocukların ekran süresinin, ebeveynlerin çocukların teknoloji kullanımına yönelik olumlu tutumları, ebeveynlerin ekran süresi, çocuğun yaşı ve ebeveyn tarafından algılanan dikkat dağınıklığı ve arka plan televizyon sıklığı ile olumlu yönde ilişkili olduğu görülmüştür. Çocukların ekran süresinin, anne7baba eğitim seviyesi, hane geliri ve ebeveynin algıladığı sosyal destek ile olumsuz yönde ilişkili olduğu bulunmuştur. Hiyerarşik regresyon analizinde, arka plan televizyon sıklığı, ebeveynlerin çocukların teknoloji kullanımına yönelik olumlu tutumları ve çocuğun yaşı ekran süresinin en güçlü yordayıcıları olarak öne çıkmıştır. Ebeveynin algıladığı sosyal destek miktarının çocukların ekran süresi ile ilişkili bir değişken olduğu ilk kez mevcut çalışmada gösterilmiştir. Bulguların ülkemizdeki okul öncesi çocukların ekran süresini azaltmaya yönelik geliştirilecek girişimlere bilgi sağlayacağı düşünülmektedir.Young children spend a significant part of their day in front of screens. Existing literature has shown associationsbetween children's screen time and various demographic, parent7related, and home7related factors. Most evidencecomes from high7income, developed countries with access to early childcare options. Investigating these factors inT & uuml;rkiye is crucial to compare findings across countries and identify new variables that might influence children'sscreen time. The first goal of this study was to examine child7related factors (e.g., age), parent7related factors (e.g.,parental stress), and home7related factors (e.g., background television) that may be associated with young children'sscreen time through an online survey. The second goal was to investigate the relationship between children's screentime and parents' perceived social support for the first time in the literature. A total of 647 parents with children younger than six (M = 41.5, SD = 17.9) months) participated. Results revealed that children's screen time was positivelycorrelated with parents' positive attitudes toward children's use of technology, parents' own screen time, child ageand distractibility as perceived by the parents, and the frequency of background television at home. Conversely,children's screen time was negatively related to parental education, household income, and parents' perceived social support. Hierarchical regression analysis indicated that the frequency of background television at home, parents' positive attitudes toward children's use of technology, and child age emerged as the strongest predictors of children's screen time. This study is the first to propose and demonstrate the role of social support in determining children's screen time. Our findings may provide valuable insights for designing intervention strategies to reduce screen time among preschoolers.Publisher versio

Financial statement fraud detection via large language models

With the widespread adoption of Internet-based AI technologies, addressing financial fraud has become increasingly critical, particularly within the realm of machine learning. In this case, deep learning and natural language processing (NLP) techniques offer powerful means of detecting fraudulent activity by analyzing financial documents, thereby enhancing both the efficiency and precision of such assessments and supporting financial security. In this study, we introduce deep representation learning-based approaches relying mainly on large language models (LLMs) for identifying fraud in financial statements by examining temporal changes in the Management Discussion and Analysis (MD&A) sections of corporate disclosures. Departing from conventional techniques that rely only on word frequency analysis, we propose DeepFraud that combines time-evolving financial LLM embeddings, such as FinBERT, FinLlama, and FinGPT embeddings, of paragraphs and uses long short-term memory (LSTM) to predict frauds via historical textual embeddings. In addition to LLM embeddings, we also integrate (1) time-evolving word frequencies of words relevant to fraud detection, such as those expressing sentiment or uncertainty, and (2) time-evolving financial ratios. Trajectories of paragraph-level embeddings, frequencies, and ratios are used to construct a fraud detection model, which we evaluate against machine learning methods and deep time-series models. Using 30 years of financial report data (from 1995 to 2024), our experiments demonstrate that DeepFraud on average enhances fraud detection performance across a number of scenarios and on average outperforms the competing approaches as well as conventional word frequency approaches. Our framework introduces a novel direction for deep feature engineering in the field of financial statement fraud detection. © 2025 John Wiley & Sons Lt

Impact of long-duration earthquakes on site response: comparison of 1d and 3d approaches

This study presents a comparative analysis of multidirectional site response modeling conducted for a well-instrumented downhole array in Alaska, examining seven long-duration earthquake events. While traditional one-dimensional (1D) analyses provide valuable insight into vertical wave propagation, they often simplify real conditions by neglecting the full range of seismic input components that may influence ground motion characteristics. By incorporating all three earthquake directions as East-West, North-South, and Vertical in a multidirectional site response approach, we aim to achieve a more accurate evaluation of site amplification. The results reveal distinct differences in modelled ground motions between three-dimensional and one-dimensional models across various soil depths, demonstrating the improved reliability provided by a multidirectional framework. These findings emphasize the necessity of employing more comprehensive modeling strategies, particularly in high-seismicity regions like Alaska

Instrumented chair for sitting posture evaluation

An instrumented chair (IC) is proposed in this study to evaluate the sitting posture in a simple manner. This chair is capable of measuring the forces and pressure distribution during sitting in critical regions, i.e., under feet, sitting, and armrest regions, which are sensorized. One of the authors of this manuscript participated in these experiments. The results show that the difference in forces at the right and left side and also the pressure distribution at the hands and feet separately during sitting and standing using this relatively low-cost and accessible system

Shoplifting detection from customer behavior using deep learning

The increase in shoplifting in the retail market causes significant stock and profit losses. Existing security methods are often costly, prone to human error, and not applicable to all product types, highlighting the need for an innovative, low-cost, and effective solution against theft. This study presents a deep learning-based system for detecting shoplifting behavior from surveillance video footage. The system integrates four components: (1) person detection to identify customers approaching shelves, (2) activity recognition to analyze movements for suspicious behavior, (3) product detection to determine which items are taken, and (4) person re-identification model which matches suspicious customers when they arrive at the checkout were developed. A Time Distributed CNN-LSTM model was developed for activity recognition; YOLOv4 was fine-tuned for person and product detection, and Siamese Networks were used for person re-identification. Training and testing were conducted using a data set collected from both an office demo setup and a real retail environment, covering five different shoplifting scenarios. The dataset collected includes 1219 videos across five scenarios. The proposed system was evaluated on a custom-collected dataset and achieved 95% overall accuracy, with component-level accuracy of 85% for activity recognition, 97% for person and product detection, and 87% for person re-identification. In this paper, the authors suggested a model which primarily focuses on recognizing shoplifting actions. The originality of this study lies in the integrated system, including 4 components; person detection, activity recognition, product detection and person re-identification which work simultaneously to provide end-to-end solutions.TÜBİTA

Exploring business history of the middle east and north africa region

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Does geopolitical risk drive earnings management? Evidence from low- and middle-income countries

We examine the impact of geopolitical risk (GPR) on earnings management (EM) in low- and middle-income countries (LMICs). Using 257,659 firm-year observations from 16 LMICs (2002-2022) and System Generalized Method of Moments (System-GMM) estimations, we find that GPR significantly increases EM, especially under weak audit quality, low development, and high taxation. While EM buffers short-term shocks, it undermines transparency, underscoring the need for stronger institutions.Publisher versio

Diş problemleri tanısı için derin ögrenme tabanlı çoklu-hastalık tespiti

This work uses deep learning to automatically classify a set of dental pathologies from wide-field dental X-rays named panoramic radiographs. 9,573 adult and child patients' X-ray images form our dataset, each of which is manually annotated to 19 different dental pathologies. The proposed method leverages advanced deep learning models to diagnose a set of oral diseases and achieves state-of-the-art results. YOLO and DETR models are compared for their dental problem detection and classification accuracy. This complete AI-based method produces quick and ac-curate diagnoses of oral health, which allows dental practitioners to provide more informed decisions quickly and reliably. With evidence-based interpretation of AI results, we believe that the proposed method is a sensible way of supplementing dentists' diagnoses

In-depth analysis of Arabic-origin words in the Turkish morpholex

MorphoLex is an investigation that focuses on analyzing the roots, prefixes, and suffixes of words. Turkish Morpholex, for example, analyzes 48,472 Turkish words. Unfortunately, it lacks in-depth analysis of the Arabic-origin words, and does not include their accurate and correct roots. This study analyzes Arabic-origin words in the Turkish Morpholex, annotating their roots, morphological patterns, and semantic categories. The methodology developed for this work is adaptable to other languages influenced by Arabic, such as Urdu and Persian, offering broader implications for studying loanword integration across linguistic contexts.Publisher versio