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GÜREŞ GREKOROMEN MİLLİ SPORCULARININ İMGELEME VE BAŞARI MOTİVASYON DÜZEYLERİ ARASINDAKİ İLİŞKİNİN İNCELENMESİ
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Olive Leaf (Olea europaea L. folium): Potential Effects on Glycemia and Lipidemia
No full textBackground: Olive tree (Olea europaea, Oleaceae) leaves have been widely
used in traditional herbal medicine to prevent and treat various
diseases especially in Mediterranean countries. They contain several
potentially bioactive compounds that may have hypoglycemic and
hypolipidemic properties. Summary: The literature has recently been
attempting to define the relationship between olive leaf (Olea europaea
L. folium) polyphenols and a number of health problems. Oleuropein, the
basic phenolic compound of olive leaf and its extract, is responsible
for the characteristic bitter taste and unique aroma of olive fruits.
Furthermore, it is shown that oleuropein and its hydrolyzed products
have many beneficial effects on human health because of its antioxidant
characters. A number of studies report that olive leaf has potentially
positive effects on the parameters related to diabetes and
cardiovascular diseases by various mechanisms. Besides, toxicity studies
suggest that olive leaf is generally safe even at high doses. Key
messages: Although current results obtained until today seem promising,
the studies in this subject are usually on cell culture and animal
trials. Moreover, mostly the extract forms of olive leaves are used in
the studies. More randomized controlled human clinical trials with
extensive toxicity studies are needed to evaluate potential health
effects and safety. (c) 2020 S. Karger AG, Base
Microtensile bond strength and failure type analysis of selfetch adhesive systems on superficial and deep dentin after long-term water storage [Análise de resistência de adesão à microtração e tipo de falha de sistemas adesivos autocondicionantes em dentina superficial e profunda após armazenamento longitudinal em água]
No full textPre-transplant sTIM-3 levels may have a predictive impact on transplant outcome in acute leukemia patients
No full textA Review of Control Methods on Suppression of 2 < italic >\& x03C9;</italic > Ripple for Single-Phase Quasi-Z-Source Inverter
No full textOne of the disadvantages of the charging systems and voltage regulation
systems in alternative energy systems is that when the direct current
(DC)/alternative current (AC) or AC/DC conversion is made from the
energy source, the energy flow at frequency between the direct current
side and the alternative current side causes ripples in the DC,
inductors, and capacitors. The ripples that occur in frequency increase
the harmonic distortion and reduce the efficiency in the photovoltaic
energy system. For addressing this problem, different control methods
are evaluated in this paper for the elimination of ripples that occur at
frequency, both on the perspective of DC control side and AC control
side with a thorough comparative analysis with existing results in the
literature. In the present study, the model of Single-Phase Quasi-Z
Source (QZS) inverter and closed-loop control methods of the inverter
are examined and compared with pulse width modulation (PWM) methods. In
addition, the control methods are used to eliminate the ripples at
frequency in the use of inverters, and the advantages and disadvantages
of these methods are presented
Hierarchical behavior model for multi-agent system with evasion capabilities and dynamic memory
No full textDetecting DDoS Attacks in Software-Defined Networks Through Feature Selection Methods and Machine Learning Models
No full textSoftware Defined Networking (SDN) offers several advantages such as
manageability, scaling, and improved performance. However, SDN involves
specific security problems, especially if its controller is defenseless
against Distributed Denial of Service (DDoS) attacks. The process and
communication capacity of the controller is overloaded when DDoS attacks
occur against the SDN controller. Consequently, as a result of the
unnecessary flow produced by the controller for the attack packets, the
capacity of the switch flow table becomes full, leading the network
performance to decline to a critical threshold. In this study, DDoS
attacks in SDN were detected using machine learning-based models. First,
specific features were obtained from SDN for the dataset in normal
conditions and under DDoS attack traffic. Then, a new dataset was
created using feature selection methods on the existing dataset. Feature
selection methods were preferred to simplify the models, facilitate
their interpretation, and provide a shorter training time. Both
datasets, created with and without feature selection methods, were
trained and tested with Support Vector Machine (SVM), Naive Bayes (NB),
Artificial Neural Network (ANN), and K-Nearest Neighbors (KNN)
classification models. The test results showed that the use of the
wrapper feature selection with a KNN classifier achieved the highest
accuracy rate (98.3\%) in DDoS attack detection. The results suggest
that machine learning and feature selection algorithms can achieve
better results in the detection of DDoS attacks in SDN with promising
reductions in processing loads and times