Turkish Academic Network and Information Center

Generation Z is the youngest and popular generational cohort who started to be investigated with interest in researches since its impacts are distinctly seen in various fields, and it is entering on labour market now. Generation Z has been growing in an era where technological advancement is fostered, and they are able to have access to effortless and rapid information through technological tools, like the Internet and smartphones. Like other generational cohorts, Generation Z has unique habits and personality traits since they experienced different social, economic or historical circumstances depending on the time interval in which they raised, and the perception of work and occupational habits are affected by those particular characteristics. This study aims to explore dimensions that are related to work, such as work habits, motivations, expectations preferences and work ethics of Generation Z in order to build qualified and effective current and future workforce and satisfied individuals in career path. Generation Z is tech-savvy who is shaped by the peak in technology, and it is individualistic, entrepreneurial, moneyconscious, and a multitasker. They like working collaboratively with personal autonomy in a flexible workplace that allows work-life balance and ethical working, and they need monitoring and feedback at work by their executives.

Teachers play a central role in facilitating children’s cognitive, emotional, and behavioral development. Therefore, it is essential to investigate and understand the psychological factors that are associated with effective teaching and teacher wellbeing. The purpose of the present study is to present the association between teachers’ positive functioning at work and cognitive wellbeing in Turkish educators. Participants of the study comprised of 295 teachers (60.3% female), and they ranged in age from 23 to 55 years (M = 32.43, SD = 7.85). Findings from correlation analysis demonstrated the significant and positive association between cognitive wellbeing and school connectedness, teaching efficacy, and overall teacher functioning, ranging from moderate to large effect. Following, the outcomes indicated the significant effects of wellbeing groups on teachers’ functioning, and revealed that teachers with high levels of wellbeing had greater positive functioning at work than those with low and average wellbeing levels. Taken together, the results suggest that high level of wellbeing is associated with teachers’ healthy and successful functioning at work.

This paper represents the second part of an entire study which focuses on multi-time series and -time scale modeling in wind speed and wind power forecasting. In the first part of the entire study [1], firstly, moving average (MA), weighted moving average (WMA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models are introduced in-depth. Afterwards, the mentioned models are analyzed for very short-term and short-term forecasting scales, comprehensively. In this second part of the entire study, we address the medium-term and long-term prediction performance of MA, WMA, ARMA and ARIMA models in wind speed and wind power forecasting. Particularly, 3-h and 6-h time series forecasting models are constructed in order to carry out 9-h and 24-h ahead forecasting, respectively. Many valuable assessments are made for the employed statistical models in terms of medium-term and long-terms forecasting scales. Finally, many valuable achievements are discussed considering a detailed comparison chart of the entire study.

Today, peer-to-peer (P2P) traffic consumes the largest fraction of network bandwidth. The files shared by P2P communications are mostly copyright protected, and there are issues related to Quality of Service (QoS) support and billing of P2P traffic. Hence, scalable and accurate detection of peer-to-peer (P2P) traffic is a significant problem for network service providers. Flow-based detection methods employ characteristics of data flows such as the number of packets per flow to classify P2P and non-P2P traffic. Thus, they provide solutions to problems of port-based and signature-based detection such as P2P applications with dynamic ports, updating the signature database and encrypted packets. In this paper, a comparative evaluation of several flow-based P2P traffic detection methods that employ machine learning (ML) techniques is presented. Different from previous work, the effect of network parameters is taken into consideration in our evaluation. Furthermore a new verification approach based on custom-made data is presented which can circumvent the accuracy problems of the previous verification methods that use port-based or signature-based techniques for the accuracy evaluation.

Generation Z is the youngest and popular generational cohort who started to be investigated with interest in researches since its impacts are distinctly seen in various fields, and it is entering on labour market now. Generation Z has been growing in an era where technological advancement is fostered, and they are able to have access to effortless and rapid information through technological tools, like the Internet and smartphones. Like other generational cohorts, Generation Z has unique habits and personality traits since they experienced different social, economic or historical circumstances depending on the time interval in which they raised, and the perception of work and occupational habits are affected by those particular characteristics. This study aims to explore dimensions that are related to work, such as work habits, motivations, expectations preferences and work ethics of Generation Z in order to build qualified and effective current and future workforce and satisfied individuals in career path. Generation Z is tech-savvy who is shaped by the peak in technology, and it is individualistic, entrepreneurial, money-conscious, and a multitasker. They like working collaboratively with personal autonomy in a flexible workplace that allows work-life balance and ethical working, and they need monitoring and feedback at work by their executives.

Most algorithms that are used to predict the effects of variants rely on evolutionary conservation. However, a majority of such techniques compute evolutionary conservation by solely using the alignment of multiple sequences while overlooking the evolutionary context of substitution events. We had introduced PHACT, a scoring-based pathogenicity predictor for missense mutations that can leverage phylogenetic trees, in our previous study. By building on this foundation, we now propose PHACTboost, a gradient boosting tree-based classifier that combines PHACT scores with information from multiple sequence alignments, phylogenetic trees, and ancestral reconstruction. By learning from data, PHACTboost outperforms PHACT. Furthermore, the results of comprehensive experiments on carefully constructed sets of variants demonstrated that PHACTboost can outperform 40 prevalent pathogenicity predictors reported in the dbNSFP, including conventional tools, metapredictors, and deep learning-based approaches as well as more recent tools such as AlphaMissense, EVE, and CPT-1. The superiority of PHACTboost over these methods was particularly evident in case of hard variants for which different pathogenicity predictors offered conflicting results. We provide predictions of 215 million amino acid alterations over 20,191 proteins. PHACTboost is available at https://github.com/CompGenomeLab/PHACTboost. PHACTboost can improve our understanding of genetic diseases and facilitate more accurate diagnoses.

The share of peer-to-peer (P2P) protocol in the total network traffic grows day-by-day in the Turkish academic network (UlakNet) similar to the other networks in the world. This growth is mostly because of the popularity of the shared content and the great enhancement in the P2P protocol since it first came out with Napster. The shared files are generally both large and copyrighted. Motivated by the problems of UlakNet with the P2P traffic, we propose a novel method for P2P traffic-detection in the network backbone in this paper. Observing the similarity between detecting traffic that belongs to a specific protocol and detecting an intrusion in a computer system, we adopt an intrusion detection system (IDS) technique to detect P2P traffic. Our method is a passive detection procedure that uses traffic flows gathered from border routers. Hence, it is scalable and does not have the problems of other approaches that rely on packet pay load data or transport layer ports

Vascular diseases, such as abdominal aortic aneurysms, are associated with tissue degeneration of the aortic wall, resulting in variations in mechanical properties, such as tissue ultimate stress and a high slope. Variations in the mechanical properties of tissues may be associated with an increase in the number of collagen cross-links. Understanding the effect of collagen cross-linking on tissue mechanical properties can significantly aid in predicting diseased aortic tissue rupture and improve the clarity of decisions regarding surgical procedures. Therefore, this study focused on increasing the density of the aortic tissue through cross-linking and investigating the mechanical properties of the thoracic aortic tissue in relation to density. Uniaxial tensile tests were conducted on the porcine thoracic aorta in four test regions (anterior, posterior, distal, and proximal), two loading directions (circumferential and longitudinal), and density increase rates (0%-12%). As a result, the PPC (Posterior/Proximal/Circumferential) group experienced a higher ultimate stress than the PDC (Posterior/Distal/Circumferential) group. However, this relationship reversed when the specimen density exceeded 3%. In addition, the ultimate stress of the ADC (Anterior/Distal/Circumferential) and PPC group was greater than that of the APC (Anterior/Proximal/Circumferential) group, while these findings were reversed when the specimen density exceeded 6% and 9%, respectively. Finally, the high slope of the PDL (Posterior/Distal/Longitudinal) group was lower than that of the ADL (Anterior/Distal/Longitudinal) group, but the high slope of the PDL group appeared larger due to the stabilization treatment. This highlights the potential impact of density variations on the mechanical properties of specific specimen groups.

The idea that gender factor creates a difference on computer usage and computer-assisted instruction is based upon previous years. At that time, it was thought that some areas like engineering, science and mathematics were for males so it created a difference on the computer usage. Nevertheless, developing technology and females becoming more active in information era alter this imbalance. About analyzing this kind of studies, significant differences exist on behalf of males in some studies, while significant differences exist on behalf of females in some studies, and there is no significant difference in terms of genders in other studies. While gender variable has been dealt as sub-variable in studies conducted with teachers in terms of learning/teaching activities, the number of meta-analyses investigating related teaching method according to gender is limited. In this study, meta-analysis method which gathers the results of different studies on the same specific topic and analyzing these findings statistically, is used. Comp­rehensive Meta Analysis (CMA) Statistic Program was used for statistical analysis. The data was analyzed by using the method of study effect meta-analysis. Gender factor is a crucial variable for learning and teaching activities. However, with this research, it becomes obvious that it is not such an important factor that can create a huge difference. Publication type, sample type and geographical region are determined as moderator variables with the thought that they can create a difference. Any difference cannot be identified according to publication and sample type at the end of the research.

Cognitive behavioral therapy (CBT) is one of the most common used therapy techniques especially among people suffering depression. It was developed by Aeron Beck in 1960s and it has continued to improve itself on different relations with other therapeutic approaches. In addition, cognitive behavioral therapy (CBT) has been used for individual and group therapy sessions. Postpartum depression is a kind of depression which has specific interval of emergence. Almost every stage of pregnancy and after birth is inevitably important to intervene postpartum depression. With the development of technology, online platforms have become more prevalent and common including treatment of postpartum depression as a supplement to face to face therapy sessions. It is important to note that postpartum depression is vital not only mothers experience, but also children’s development. This study includes literature review about cognitive behavioral therapy (CBT) and postpartum depression from face to face group sessions to online group session. In the study, it is focused on firstly postpartum depression, cognitive behavioral (CBT) and online cognitive behavioral therapy (CBT) group interventions and finally alternative strategies to cognitive behavioral therapy (CBT) in treatment postpartum depression with different cultural perspectives as well. Outstanding alternatives to cognitive behavioral therapy (CBT) are interpersonal therapy group sessions, peer support groups and music and yoga.

The purpose of this paper is to discuss the evaluation of different alternatives for the implementation of Turkish army corps artillery ammunition supply system. The objective is to see whether the alternative systems operate properly and to select the best system design. We find that the first alternative system cannot supply the units for all phases of an eight-day battle time while the second, the third, and the fourth systems can supply and yield better results. The third system is less costly than the second and the fourth systems. However, it has the drawback of too many vehicles in the convoy (i.e. congestion) which makes it susceptible to the enemy long distance and air assaults. The fourth system is the best of all from the point of the performance it yields; but, it costs more compared to the other systems.

Asartepe Baraj Gölü’nde Mart 2015-Şubat 2016 tarihleri arasında yürütülen bu çalışmada Oxynoemacheilus angorae (Steindachner, 1897)’nın boy-ağırlık ilişkileri (LWR), kondisyon faktörü ve boy-boy ilişkileri araştırılmıştır. Elde edilen örneklerin total boyları 4-7,8 cm, toplam ağırlıkları ise 1-5 g arasında dağılım göstermiştir. Boy-ağırlık ilişkisinin fonksiyonel denklemi W = 0,00171×TL2,651 ve korelasyon değeri r2 = 0,80 olarak hesaplanmıştır. Elde edilen b değeri tüm bireylerde istatistiksel olarak 3’ten farklı çıkmamıştır (t-testi, P>0.05). Kondisyon faktörü değerlerinin 0,54-1,5625 arasında değiştiği belirlenmiştir. Elde edilen veriler son çalışmalarda bildirilen sonuçlarla karşılaştırılmıştır. Yapılan bu çalışma ile Asartepe Baraj Gölü’ndeki O. angorae popülasyonuna ait boy-ağırlık ilişkisi, kondisyon faktörü ve boy-boy ilişkisi ile ilgili ilk temel veriler belirlenerek sunulmuştur. Ayrıca, bu çalışma çeşitli ekolojik faktörlerin etkisiyle tehdit altında bu türün korunması ve balık faunası açısından da önem arz etmektedir.

Abstract Most algorithms that are used to predict the effects of variants rely on evolutionary conservation. However, a majority of such techniques compute evolutionary conservation by solely using the alignment of multiple sequences while overlooking the evolutionary context of substitution events. We had introduced PHACT, a scoring-based pathogenicity predictor for missense mutations that can leverage phylogenetic trees, in our previous study. By building on this foundation, we now propose PHACTboost, a gradient boosting tree-based classifier that combines PHACT scores with information from multiple sequence alignments, phylogenetic trees, and ancestral reconstruction. The results of comprehensive experiments on carefully constructed sets of variants demonstrated that PHACTboost can outperform 40 prevalent pathogenicity predictors reported in the dbNSFP, including conventional tools, meta-predictors, and deep learning-based approaches as well as state-of-the-art tools, AlphaMissense, EVE, and CPT-1. The superiority of PHACTboost over these methods was particularly evident in case of hard variants for which different pathogenicity predictors offered conflicting results. We provide predictions of 219 million missense variants over 20,191 proteins. PHACTboost can improve our understanding of genetic diseases and facilitate more accurate diagnoses.

Cognitive behavioral therapy (CBT) is one of the most common used therapy techniques especially among people suffering depression. It was developed by Aeron Beck in 1960s and it has continued to improve itself on different relations with other therapeutic approaches. In addition, cognitive behavioral therapy (CBT) has been used for individual and group therapy sessions. Postpartum depression is a kind of depression which has specific interval of emergence. Almost every stage of pregnancy and after birth is inevitably important to intervene postpartum depression. With the development of technology, online platforms have become more prevalent and common including treatment of postpartum depression as a supplement to face to face therapy sessions. It is important to note that postpartum depression is vital not only mothers experience, but also children’s development. This study includes literature review about cognitive behavioral therapy (CBT) and postpartum depression from face to face group sessions to online group session. In the study, it is focused on firstly postpartum depression, cognitive behavioral (CBT) and online cognitive behavioral therapy (CBT) group interventions and finally alternative strategies to cognitive behavioral therapy (CBT) in treatment postpartum depression with different cultural perspectives as well. Outstanding alternatives to cognitive behavioral therapy (CBT) are interpersonal therapy group sessions, peer support groups and music and yoga.

Talking about eating in the passive, as opposed to the active voice, (e.g., The cake will be eaten vs. I will eat the cake) can lead people to see the act of eating to be triggered by the food to a greater extent, leading to the continuation of past eating habits. Depending on whether or not the past habits are healthy, the motivation for healthy eating may change as a result. In study 1, writing passive sentences increased the motivation for healthy eating to the extent that people reported eating healthy in the past. Moreover, in study 2 across 127 languages spoken in 94 countries, when the acted-upons of actions (e.g., the food in the act of eating) became relatively more salient in a language, people became more likely to act on cultural habits that may be relatively healthier, decreasing unhealthy eating. The results are important for understanding the perceived role of food in starting eating as it impacts healthy eating across cultures.