Trends in Turkish Science Education

The aim of the study is to determine the trends in Turkish Science Education on the basis of both master and doctoral theses involved. The researchers reviewed the online databases of the Higher Education Council and Proquest as well as the web page of graduate school of each university in Turkey which presents thesis archieve and investigated 444 graduate theses abstracts/fulltexts in regard to their created matrix (Year, Research Interest, Research Methodology and Sample). The document analysis has pointed out that in terms of research interest two general trends are apparent in Turkish science education research: (1) introducing science education between 1990 and 2000 (2) keeping up with new perspectives in the line of international trends. Also, in view of research methodology although interpretive research methodology has also been preffered since 1997, descriptive research design has still dominated in this context. Some suggestions were made for future research

Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction

6G – sixth generation – is the latest cellular technology currently under development for wireless communication systems. In recent years, machine learning (ML) algorithms have been applied widely in various fields, such as healthcare, transportation, energy, autonomous cars, and many more. Those algorithms have also been used in communication technologies to improve the system performance in terms of frequency spectrum usage, latency, and security. With the rapid developments of ML techniques, especially deep learning (DL), it is critical to consider the security concern when applying the algorithms. While ML algorithms offer significant advantages for 6G networks, security concerns on artificial intelligence (AI) models are typically ignored by the scientific community so far. However, security is also a vital part of AI algorithms because attackers can poison the AI model itself. This paper proposes a mitigation method for adversarial attacks against proposed 6G ML models for the millimeter-wave (mmWave) beam prediction using adversarial training. The main idea behind generating adversarial attacks against ML models is to produce faulty results by manipulating trained DL models for 6G applications for mmWave beam prediction. We also present a proposed adversarial learning mitigation method’s performance for 6G security in mmWave beam prediction application a fast gradient sign method attack. The results show that the defended model under attack’s mean square errors (i.e., the prediction accuracy) are very close to the undefended model without attack

A secure and efficient Internet of Things cloud encryption scheme with forensics investigation compatibility based on identity-based encryption

Data security is a challenge for end-users of cloud services as the users have no control over their data once it is transmitted to the cloud. A potentially corrupt cloud service provider can obtain the end-users’ data. Conventional PKI-based solutions are insufficient for large-scale cloud systems, considering efficiency, scalability, and security. In large-scale cloud systems, the key management requirements include scalable encryption, authentication, and non-repudiation services, as well as the ability to share files with different users and data recovery when the user keys of encrypted data are not accessible. Further requirements in cloud systems include the ability to provide the means for digital forensic investigations on encrypted data. Once data on the cloud is encrypted with a user's key it becomes impossible to access by forensic investigation teams. In this regard, distributing the trust of key management into multiple authorities is desirable. In the literature, there is no available secure cloud storage system with secure and efficient Type-3 pairings, supporting Encryption-as-a-Service (EaaS) and multiple Public Key Generators (PKGs). This paper proposes an efficient Identity-based cryptography (IBC) architecture for secure cloud storage, named Secure Cloud Storage System (SCSS), which supports distributed key management and encryption mechanisms and support for multiple PKGs. During forensic investigations, the legal authorities will be able to use the multiple PKG mechanism for data access, while an account locking mechanism prevents a single authority to access user data due to trust distribution. We also demonstrate that, the IBC scheme used in SCSS has better performance compared to similar schemes in the literature. For the security levels of 128-bits and above, SCSS has better scalability compared to existing schemes, with respect to encryption and decryption operations. Since the decryption operation is frequently needed for forensic analysis, the improved scalability results in a streamlined forensic investigation process on the encrypted data in the cloud

Assessment of ventricular septal defects by real-time three-dimensional echocardiography and comparison with surgical measurements

Peer reviewe

Towards robust autonomous driving systems through adversarial test set generation

Correct environmental perception of objects on the road is vital for the safety of autonomous driving. Making appropriate decisions by the autonomous driving algorithm could be hindered by data perturbations and more recently, by adversarial attacks. We propose an adversarial test input generation approach based on uncertainty to make the machine learning (ML) model more robust against data perturbations and adversarial attacks. Adversarial attacks and uncertain inputs can affect the ML model's performance, which can have severe consequences such as the misclassification of objects on the road by autonomous vehicles, leading to incorrect decision-making. We show that we can obtain more robust ML models for autonomous driving by making a dataset that includes highly-uncertain adversarial test inputs during the re-training phase. We demonstrate an improvement in the accuracy of the robust model by more than 12%, with a notable drop in the uncertainty of the decisions returned by the model. We believe our approach will assist in further developing risk-aware autonomous systems

Evaluation of the effect of mitral stenosis severity on the left ventricular systolic function using isovolumic myocardial acceleration

Background: Isovolumic acceleration (IVA) is a new tissue Doppler parameter in the as­sessment of systolic function of both left and right ventricles. It remains unaffected with the changes in pre- and after-load within the physiological range. The aim of our study was to assess the effect of mitral stenosis degree, which is determined by echocardiography, on the left ventricular (LV) function using IVA. Methods: A total number of 62 patients with mitral stenosis (MS) and 32 healthy controls were examined. The severity of MS (mild, moderate, and severe) was determined on the basis of mitral valve area (MVA) and the mean diastolic mitral gradient findings. The peak myocardial velocities during isovolumic contraction, systole, early diastole and late diastole were measured by using tissue Doppler imaging (TDI). Results: All TDI-derived global LV basal wall systolic (peak myocardial isovolumic contra­ction velocity, peak myocardial systolic velocity and IVA), and diastolic velocities (peak early and late diastolic velocities) were significantly decreased in the patients with MS, compared to the healthy patients (p < 0.001, for all). However, IVA was not different when the degree of MS was evaluated (p = 0.114). In addition, IVA was not correlated with the MVA (r = 0.185, p = 0.150). Conclusions: Left ventricular function is impaired in patients with MS regardless of the severity of the disease.

Elevated Blood Lead Concentrations in Essential Tremor: A Case–Control Study in Mersin, Turkey

Essential tremor (ET) is one of the most common neurologic disorders. Aside from underlying susceptibility genes, recent studies have also begun to focus on environmental toxic factors. Yet there remains a paucity of information on such factors, making studies of environmental factors important. A recent study in New York City found blood lead concentrations to be elevated in ET cases compared with matched controls. Chronic exposure to lead produces cerebellar damage, and this could predispose individuals to develop ET

Teaching the Academic Word List in Foreign Language Speaking Classes

WOS: 00043485270039

Determination of snow water equivalent over the eastern part of Turkey using passive microwave data

Information on regional snow water equivalent (SWE) is required for the management of water generated from snowmelt. Modeling of SWE in the mountainous regions of eastern Turkey, one of the major headwaters of Euphrates-Tigris basin, has significant importance in forecasting snowmelt discharge, especially for optimum water usage. An assimilation process to produce daily SWE maps is developed based on Helsinki University of Technology (HUT) model and AMSR-E passive microwave data. The characteristics of the HUT emission model are analyzed in depth and discussed with respect to the extinction coefficient function. A new extinction coefficient function for the HUT model is proposed to suit models for snow over mountainous areas. Performance of the modified model is checked against the original, other modified cases and ground truth data covering the 2003-2007 winter periods. A new approach to calculate grain size and density is integrated inside the developed data assimilation process. An extensive validation was successfully performed by means of snow data measured at ground stations during the 2008-2010 winter periods. The root mean square error of the data set for snow depth and SWE between January and March of the 2008-2010 periods compared with the respective AMSR-E footprints indicated that errors for estimated snow depth and predicted SWE values were 16.92cm and 40.91mm, respectively, for the 3-year period. Validation results were less satisfactory for SWE less than 75.0mm and greater than 150.0mm. An underestimation for SWE greater than 150mm could not be resolved owing to the microwave signal saturation that is observed for dense snowpack. Copyright (c) 2012 John Wiley & Sons, Ltd

Orbital decompression surgery for the treatment of Graves' ophthalmopathy: comparison of different techniques and long-term results.

To evaluate the long-term results of different orbital decompression techniques performed in patients with Graves' ophthalmopathy (GO)