Existence of positive solutions to nnth-order pp-Laplacian boundary value problems with integral boundary conditions

WOS: 000390602900029This work is devoted to the existence of positive solutions for an nth order p-Laplacian boundary value problem with integral boundary conditions. The proof of the main result is based on six functionals fixed point theorem. As an application, we give an example to illustrate the obtained result.TUBITAK, the Scientific and Technological Research Council of TurkeyTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)The second author was supported by the 2219 scholarship programme of TUBITAK, the Scientific and Technological Research Council of Turkey

Detection of bifid mandibular condyle using computed tomography

Objective: To determine the frequency and characteristics of bifid mandibular condyle (BMC) using computed tomography (CT) evaluation. Study Design: A retrospective study was carried out using the CT records of 550 patients referred to the Medical School of Erciyes University (Kayseri, Turkey) between 2007 and 2010. T-tests were used to compare frequency of BMC between the left and right sides and between female and male patients. Statistical analysis was performed using SPSS software and a chi-squared test. Results: Of the 550 Patients, 10 patients (1.82%) were found to have BMCs. Five patients were female (50%) and five were male (50%). Of these 10 patients, 7 (70%) had unilateral and 3 (30%) had bilateral BMCs. As a result, a total of 13 BMCs were found in 10 patients. No statistically significant differences were found between either the right- and left-sided BMCs or between female and male patients (p >.05). Conclusions: To our knowledge, this is the first retrospective study investigating the prevalence and characteristics of BMC using computed tomography. Although BMC is an uncommon anomaly, it may be a more frequent condition in the Turkish population. Further studies and research on the orientation of duplicated condylar heads should be carried out

Sağlık çalışanlarında etik iklim algısı ve yenilikçi çalışma davranışı ilişkisine yönelik bir araştırma

This study aims to reveal the relationship between ethical climate perception and the innovative work behaviors of health workers. Besides, the effects of ethical climate perception sub-dimensions on innovative work behavior were tried to be introduced in the study. In order to realize the objectives determined in the study, field research was conducted in a public hospital in Ankara. 280 public health workers participated in the study. The data obtained from the research were subjected to descriptive statistics analysis, correlation analysis, and multiple linear regression analysis. According to the results of the analysis, it is determined that there is a 21% statistically significant and positive relationship between the ethical climate perception and innovative working behavior levels of healthcare workers. Among the ethical climate perception sub-dimensions, benevolence and independence dimensions were found to be positively related to innovative work behavior. A 10 percent deviation in the innovative work behaviors of health workers is explained by ethical climate perception sub-dimensions. It was concluded that "benevolence" and "independence", the ethical climate sub-dimensions, affected the innovative working behavior by 17% and 23%, respectively. On the other hand, "laws, and codes", "rules", and "instrumental" dimensions were not found to have a statistically significant effect.Bu çalışmanın amacı, sağlık çalışanlarının etik iklim algısı ile yenilikçi çalışma davranışı düzeyi arasındaki ilişkiyi ortaya koymaktır. Ayrıca etik iklim algısı alt boyutlarının yenilikçi çalışma davranışı üzerindeki etkisi de ortaya konmaya çalışılmaktadır. Araştırmada belirlenen amaçları gerçekleştirmek için Ankara ilinde faaliyet gösteren bir kamu hastanesinde alan araştırması gerçekleştirilmiştir. Araştırmaya 280 kamu sağlık çalışanı katılmıştır. Araştırma neticesinde elde edilen veriler, tanımlayıcı istatistik analizi, korelâsyon analizi ve çoklu doğrusal regresyon analizine tâbi tutulmuştur. Analiz sonuçlarına göre, sağlık çalışanlarının etik iklim algısı ile yenilikçi çalışma davranışı düzeyleri arasında istatistiksel olarak anlamlı ve pozitif yönde yaklaşık %21 oranında ilişki olduğu belirlenmiştir. Etik iklim algısı alt boyutlarından başkalarının iyiliğini isteme ve bağımsızlık boyutlarının pozitif yönde yenilikçi çalışma davranışı ile ilişkili olduğu tespit edilmiştir. Sağlık çalışanlarının yenilikçi çalışma davranışlarının etik iklim algısı alt boyutları ile açıklanma oranı 0.10’dur. Etik iklim alt boyutlarından başkalarının iyiliğini isteme boyutunun yaklaşık %17 oranında, bağımsızlık boyutunun ise yaklaşık %23 oranında yenilikçi çalışma davranışını etkilediği sonucuna ulaşılmıştır

Exploring Mechanocardiography as a Tool to Monitor Systolic Function Improvement with Resynchronization Pacing

The thesis explores the utilization of mechanocardiography (MCG) as a novel approach to assess and quantify improvements in systolic cardiac function resulting from cardiac resynchronization therapy (CRT). The study focuses on patients with heart failure and reduced ejection fraction (HFrEF), a population commonly treated with CRT. The primary objective is to investigate the differences in MCG waveforms during CRT and single-chamber atrial (AAI) pacing, specifically comparing waveform characteristics. 10 patients with heart failure and previously implanted CRT pacemakers were included in the study. The MCG and ECG signals are recorded using accelerometers, gyroscopes, and Holter measurement unit placed on the lower chest and used in the analysis. ECG and MCG recordings were obtained during both CRT and AAI pacing at a consistent heart rate of 80 beats per minute. The analysis involved considering six MCG axes and three MCG vectors across various frequency ranges to derive key waveform characteristics such as energy, vertical range, electromechanical systole (QS2), and left ventricular ejection time (LVET). The results revealed significant differences between CRT and AAI pacing, with CRT consistently exhibiting higher energy and vertical range during systole across multiple axes. Notably, the study identified optimal differences in SCG-Y, GCG-X, and GCG-Y axes within the 6–90 Hz frequency range. However, any difference in QS2, LVET and waveform characteristics around aortic valve closure was not identified between the pacing modes. The findings suggest that MCG waveforms can serve as indicators of improved mechanical cardiac function during CRT. The use of accelerometers and gyroscopes may contribute to the development of a non-invasive and potentially predictive tool for optimizing CRT settings. The promising results underscore the need for further research to explore the differences in signal characteristics between responders and nonresponders to CRT. The overall aim is to enhance the clinical application of MCG, leveraging wearable technology and micro-electromechanical systems (MEMS), and ultimately improve the optimization and efficacy of CRT in heart failure (HF) management

Ransomware detection using stacked autoencoder for feature selection

The aim of this study is to propose and evaluate an advanced ransomware detection and classification method that combines a Stacked Autoencoder (SAE) for precise feature selection with a Long Short Term Memory (LSTM) classifier to enhance ransomware stratification accuracy. The proposed approach involves thorough pre processing of the UGRansome dataset and training an unsupervised SAE for optimal feature selection or fine tuning via supervised learning to elevate the LSTM model's classification capabilities. The study meticulously analyzes the autoencoder's learned weights and activations to identify essential features for distinguishing ransomware families from other malware and creates a streamlined feature set for precise classification. Extensive experiments, including up to 400 epochs and varying learning rates, are conducted to optimize the model's performance. The results demonstrate the outstanding performance of the SAE-LSTM model across all ransomware families, boasting high precision, recall, and F1 score values that underscore its robust classification capabilities. Furthermore, balanced average scores affirm the proposed model's ability to generalize effectively across various malware types. The proposed model achieves an exceptional 99% accuracy in ransomware classification, surpassing the Extreme Gradient Boosting (XGBoost) algorithm primarily due to its effective SAE feature selection mechanism. The model also demonstrates outstanding performance in identifying signature attacks, achieving a 98% accuracy rate

Digital Story-Based Problem Solving Applications: Preservice Primary Teachers’ Experiences and Future Integration Plans

This case study investigates how preservice primary school teachers describe their experiences with digital story-based problem solving applications and their plans for the future integration of this technology into their teaching. Totally 113 preservice primary school teachers participated in the study. Data collection tools included a questionnaire with three open-ended questions and focus group interviews. The data were analyzed using content analysis by combining manifest and latent techniques. Most of the preservice primary teachers described positive experiences about digital story-based problem solving applications by emphasizing on that they contribute to both their own and their students’ learning, development, and attitudes. Participants further described digital story (DS) integration as in line with behaviorist pedagogy. Study results revealed that most of the preservice primary school teachers planned to integrate DSs into their future classrooms for purposes such as capturing students’ attention and reinforcing, rewarding, or supporting learning

Ransomware Detection Using Stacked Autoencoder for Feature Selection

In response to the escalating malware threats, we propose an advanced ransomware detection and classification method. Our approach combines a stacked autoencoder for precise feature selection with a Long Short-Term Memory classifier which significantly enhances ransomware stratification accuracy. The process involves thorough preprocessing of the UGRansome dataset, training an unsupervised stacked autoencoder for optimal feature selection, and fine-tuning via supervised learning to elevate the Long Short-Term Memory model's classification capabilities. We meticulously analysed the autoencoder's learned weights and activations to pinpoint essential features for distinguishing 17 ransomware families from other malware and created a streamlined feature set for precise classification. Our results demonstrate the exceptional performance of the stacked autoencoder-based Long Short-Term Memory model across the 17 ransomware families. This model exhibits high precision, recall, and F1 score values. Furthermore, balanced average scores affirm its ability to generalize effectively across various malware types. To optimise the proposed model, we conducted extensive experiments, including up to 400 epochs, and varying learning rates and achieved an exceptional 98.5% accuracy in ransomware classification. These results surpass traditional machine learning classifiers. Moreover, the proposed model surpasses the Extreme Gradient Boosting (XGBoost) algorithm, primarily due to its effective stacked autoencoder feature selection mechanism and demonstrates outstanding performance in identifying signature attacks with a 98.5% accuracy rate. This result outperforms the XGBoost model, which achieved a 95.5% accuracy rate in the same task. In addition, a prediction of the ransomware financial impact using the proposed model reveals that while Locky, SamSam, and WannaCry still incur substantial cumulative costs, their attacks may not be as financially damaging as those of NoobCrypt, DMALocker, and EDA2