Haliç University

Pelvic floor muscle exercises are widely recommended as an evidence-based, noninvasive, and cost-effective approach, particularly for the prevention and management of urinary incontinence. This study was conducted to compare the knowledge levels, application habits and recommendation behaviors of Physiotherapy and Rehabilitation and Nursing department students regarding Kegel exercises. This cross-sectional study included 250 undergraduate students (150 from Physiotherapy and Rehabilitation, 100 from Nursing) at a Turkish university. Data were collected via a structured questionnaire evaluating students' sociodemographic characteristics, knowledge, practice frequency, recommendation behaviors, and perceived effectiveness of Kegel exercises. Group comparisons were analyzed using independent t-tests and chi-square tests, with statistical significance set at p < 0.05. Physiotherapy and Rehabilitation students showed significantly higher scores in knowledge (p = 0.032), more frequent application (p = 0.008), and greater likelihood of recommending Kegel exercises (p = 0.001). They also reported higher effectiveness and overall engagement (p < 0.001). The findings indicate that Physiotherapy and Rehabilitation students possess higher levels of knowledge, are more likely to apply Kegel exercises themselves, and more frequently recommend them to others compared to Nursing students. This disparity may be attributed to curriculum differences, particularly the greater emphasis on musculoskeletal and pelvic health training within physiotherapy education. Integrating structured pelvic floor muscle training modules into nursing curricula may enhance students' competence and confidence, ultimately contributing to improved pelvic health awareness and patient care outcomes.

Purpose This study examines the impact of website metrics on e-commerce performance, focussing on average session duration (ASD) and e-commerce retail conversion rates. It aims to identify key metrics that drive user engagement and sales within a major online marketplace. The research seeks to provide actionable insights for optimising websites and improving conversion strategies by leveraging aggregated web analytics metrics. Design/methodology/approach Comprehensive website traffic data, encompassing a final sample of 1,315 pages, were collected for one month and analysed using SPSS 25.0. Multiple regression analysis examined how various web analytics metrics, ASD and e-commerce conversion rate are related. Hayes Process Model 1 investigated the moderation effect of page type on the relationship between session duration and conversion. Findings ASD positively predicted e-commerce conversion. Bounce rate (BR), new user ratio and pages per session significantly influenced session duration. Contrary to expectations, page type did not moderate the relationship between session duration and conversion rate, suggesting that engagement dynamics remained consistent across different page structures. Originality/value Unlike prior research that often relies on individual user clickstream data, this study uses comprehensive, site-wide traffic data, offering a broader perspective on website performance and user behaviour. This approach also incorporates key e-commerce metrics, such as BR and conversion rate, which have received limited attention in academic studies.

The increased amount of soccer data challenges performance evaluation using classical methods. Therefore, recent machine learning approaches can help address complex datasets in sports. The present study aims to determine which performance variables most strongly contribute to game outcomes in elite male soccer by using machine learning models trained under different venue conditions. Technical, tactical, and physical variables obtained from 542 matches played over two consecutive seasons were used to predict results for three venue conditions (all, home, and away). Variance Inflation Factor analysis and BorutaPy were applied before extreme gradient boost (XGBoost) modeling. Feature importance rankings and SHAP analysis were used to identify variables affecting model performance and outputs across different conditions. The models showed high accuracy on game outcome predictions, especially in the win and loss conditions (between 93.38-95.93%), while lower results in the draw (between 68.99-88.46%). The variables that most impacted the model's predictions were the Conversion rate, the Opponent's xG per goal, the Opponent's xG, and xG Conversion. The teams’ performance predictions for game outcomes differ, and draws are difficult to predict in this study's competition. The technical variables contributed the most to the models and outputs. Coaches should consider the structure and needs of their competitions while evaluating the data they possess. Future research could develop models for different tournaments, especially using time-related variables, if applicable.

Incisional hernia is a common postoperative complication, particularly following abdominal surgeries, and is frequently associated with recurrence and impaired healing due to postoperative infections. In this study, a dual-layered hernia repair biopatch was developed by integrating a 3D-printed polycaprolactone/gelatin (PCL/Ge) scaffold, providing mechanical support, with an electrospun nanofibrous layer composed of PCL/Ge/κ-carrageenan (κ-C) to promote wound healing. To impart antimicrobial functionality, the scaffolds were functionalized with eitherAgrimonia eupatoria(AE) extract or the clinically used antibiotic rifampicin (RIF). Commercial polypropylene (PP) meshes were employed as control groups in bothin vitroandin vivoevaluations. Mechanical testing demonstrated that the developed biopatches exhibited tensile strengths within a clinically relevant range, with values of 5.13 MPa and 2.49 MPa for the 3D-printed RIF-loaded and AE-loaded electrospun-coated scaffolds, respectively. Both AE- and RIF-loaded groups showed pronounced antibacterial activity againstS. aureus, a predominant pathogen associated with surgical site infections. Sustained and controlled release profiles were observed over 160 h, with cumulative release values of approximately 30%-35%.In vivoevaluation using a rat incisional hernia model revealed that AE exhibits strong potential as an alternative to conventional antibiotics, attributable to its phenolic-rich composition and associated anti-inflammatory and tissue-remodeling properties. Overall, these findings demonstrate that the proposed dual-layer biopatch, which integrates mechanical reinforcement with sustained antimicrobial activity, represents a promising and effective strategy for infection-resistant incisional hernia repair.

Introduction: Autism spectrum disorder (ASD) is a neurodevelopmental disorder that begins in early childhood and persists throughout life. This study aims to examine the job satisfaction and professional burnout levels of sports trainers providing services to individuals with ASD based on various variables (sex, age, years of experience).Method: The study included a total of 156 sports trainers working in autism sports club and associations, consisting of 79 females and 77 males. Participants completed a demographic information form, the Maslach Burnout Inventory (MBI), and the Minnesota Job Satisfaction Questionnaire (MSQ).Findings: No significant differences were found in the subscales of the MBI according to sex. However, depersonalization and personal accomplishment showed significant differences according to age, while personal accomplishment also differed significantly between trainers with 1-3 years and 7-9 years of experience. In the MSQ, significant differences were observed in the extrinsic satisfaction and general satisfaction subscales according to sex, and between the 20-25 age group and the 26-30 and 31+ age groups. However, no significant differences were found in job satisfaction levels based on years of experience. In the correlation analysis of the MBI and MSQ subscales, only personal accomplishment was found to be positively correlated with intrinsic, extrinsic, and general satisfaction.Discussion: As sports trainers age, their depersonalization and sense of personal accomplishment increase, and as their years of professional experience grow, their sense of personal accomplishment also improves. Male trainers reported higher external and general job satisfaction compared to female trainers. Additionally, job satisfaction levels increased with age but remained unaffected by years of experience. Furthermore, sports trainers working with individuals with ASD may exhibit emotionally detached behaviors due to excessive workload, and as their level of depersonalization increases, their intrinsic and extrinsic job satisfaction decreases.

ABSTRACT Multiaccess edge computing (MEC) reduces end‐to‐end (E2E) latency by offloading computation from user equipment (UE) to nearby edge hosts, but host‐selection policies in simulators such as Simu5G typically focus on service availability or CPU headroom. They do not explicitly account for dynamic UE–host latency and can therefore steer delay‐sensitive flows to topologically distant or temporarily congested hosts, inflating tail delay and limiting effective throughput. We extend Simu5G 's ETSI‐compliant MEC orchestrator with LatencyAwareSelectionBased , a lightweight host‐selection policy that jointly accounts for (i) a dynamic UE–host delay estimate derived from measurable run‐time delay statistics and smoothed via an exponential moving average and (ii) the host's normalized CPU load. These two signals are combined in a simple score, , controlled by a single trade‐off parameter . The policy is implemented as a plug‐in selection module activated through Simu5G 's native selectionPolicy hook, requiring only minimal changes to existing scenarios and preserving the standard orchestration pipeline. We evaluate the proposed policy in Simu5G 's MultiMecHost scenario under three operating points (Best, Moderate, and Worst) and an additional multitenant burst configuration with up to 20 UEs generating bursty arrivals. Using Simu5G scalar outputs, we take downlink MAC delay and per‐UE throughput as primary metrics and also examine CPU‐only, latency‐only, and mixed‐score variants for different values of . Across all configurations, the latency‐aware policy consistently maintains low tail latency (95th‐percentile downlink MAC delay below approximately ) while improving per‐UE throughput relative to Simu5G 's default CPU‐ and service‐based strategies and avoiding persistent host hot spots. The resulting design provides a simple, reproducible baseline for latency‐sensitive MEC orchestration and a practical integration point for future mobility‐aware and learning‐based host‐selection schemes.

<title>Abstract</title> In this paper, we tackle the challenge of short-term price prediction in the notoriously volatile cryptocurrency market. Our approach leverages inter-exchange signals within a machine-learning framework. Focusing on Ethereum (ETH), we analyze two complementary daily series: (i) high-granularity trading activity from Binance and (ii) a broad market index used as a benchmark for comparison. Our approach uses extensive feature engineering, including traditional technical indicators, volatility metrics, and new inter-exchange features that are specifically designed to capture possible lead-lag signals between markets, because these features are crucial for predicting the next day's closing price. We frame the task as supervised regression to predict the next day's closing price and compare three models - Linear Regression, Random Forest Regressor, and Gradient Boosting Regressor - with time-series split to mimic real-world deployment. Somewhat surprisingly, Linear Regression was the best-performing model, with an <italic>R</italic> 2 of 0.9894 and an RMSE of 55.49, indicating that the engineered features relate to the target in a largely linear fashion; therefore, a feature importance analysis was conducted, which further demonstrates that the general market price is the strongest predictor, confirming that inter-exchange information improves forecasting. Our results show that careful feature engineering, combined with simple linear and ensemble models, can produce highly accurate short-term cryptocurrency price predictions.

The control of quadrotors is inherently difficult due to their underactuated nature and highly coupled nonlinear dynamics. Although traditional control strategies often depend on model linearization, Deep Reinforcement Learning (DRL) has emerged as a robust, model-free approach suitable for handling complex aerodynamics and environmental uncertainties. Despite this potential, the identification of the most effective learning agent for specific flight regimes remains unresolved. This study conducts a comprehensive comparative assessment of three prominent algorithms: Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Deep Deterministic Policy Gradient (DDPG). Through rigorous simulation, we evaluate these algorithms based on convergence speed, sample efficiency, and steady-state tracking accuracy. Our findings reveal a distinct trade-off: while SAC exhibits faster convergence in early training phases, PPO ensures superior stability and reduced variance during aggressive maneuvering in the steady state. Additionally, the resilience of these policies to sensor noise and parametric uncertainty is investigated. By elucidating the performance disparities between on-policy and off-policy algorithms, this work establishes a practical framework for selecting DRL architectures for autonomous quadrotor deployment.

İkinci Dünya Savaşı’ndan sonra kurulan yeni dünya düzeni ticarette korumacılığın azaltılması ve öngörülebilirliğin arttırılması yönünde önemli bir başarı sağlamıştır. Fakat zaman zaman bazı ülkeler, çeşitli gerekçelerle, çok taraflı ticaret sisteminin esneklik hükümlerinden yararlanarak tekrar korumacılığa dönebiliyorlar. AB ile yapılan gümrük birliği neticesinde Türkiye’de sanayi sektörleri üçüncü ülkelerden gelen ithalat karşısında önemli ölçüde korumasız kalmıştır. AB ülkelerinden ve serbest ticaret anlaşması yapılan ülkelerden sanayi ürünleri ithalatı tamamen gümrük vergisi ve eş etkili mali yüklerden istisna tutulmuştur. Üçüncü ülkeler menşeli ithalata karşı uygulanan ortak gümrük tarifesi oranları Türk sanayiini rekabete karşı korumaktan oldukça uzaktır. Türkiye 2012 yılı başından itibaren seçilmiş tekstil ve hazır giyim ithalatına karşı ilave gümrük vergisi uygulamak suretiyle tedbir almaya başlamıştır. Daha sonra bu uygulamayı birçok imalat sanayi sektörüne yaygınlaştırmıştır. Türkiye İstatistik Kurumu’ndan alınan veri setleri üzerinden boylamsal metoda göre yapılan araştırmaya göre, 2012-2020 yılları arasında tekstil ve hazır giyim ithalatındaki uygulama kısmi bir etki göstermiştir.