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Netanya, Israel

Netanya Academic College is a private college based in Netanya, Israel. Established in 1994 by a team from Bar-Ilan University, it has an enrolment of around 4,000 undergraduate students. It is headed by its founder, Zvi Arad.The college offers Bachelor's and Master's degrees in several subjects, focusing on law, business administration, finance and computer science. Wikipedia.

Laufer A.,Netanya Academic College | Solomon Z.,Tel Aviv University
Journal of Religion and Health | Year: 2011

This study examined the effect of religiosity on youth's posttraumatic symptoms resulting from exposure to terror. Participants consisted of 1,973 Israeli high school students. Objective and subjective exposure (fear) to terror were positively associated with posttraumatic symptoms. Intrinsic religiosity was negatively associated with posttraumatic symptoms and found to decrease the effects of objective exposure. Personal extrinsic orientation and social extrinsic orientation were positively associated with posttraumatic symptoms, having no mediating effect. Theoretical implications regarding religiosity as a coping mechanism in light of exposure to terror are discussed. © 2009 Springer Science+Business Media, LLC. Source

Alpert Y.,Comsys Communication and Signal Processing Ltd. | Segev J.,Comsys Communication and Signal Processing Ltd. | Sharon O.,Netanya Academic College
Physical Communication | Year: 2010

In this paper we address some issues related to the mutual influence between the PHY layer building blocks (FEC blocks) and the MAC level allocations in IEEE 802.16e /WiMAX systems, in order to increase the overall PHY and MAC combined efficiency. In these systems transmissions are carried in physical Bursts, both on the Uplink and Downlink channels. Bursts are composed of slots, which are grouped into FEC blocks. The number of slots in a Burst determines the length and number of the FEC blocks. The FEC blocks have a direct influence on the probability that bits are received successfully, and thus on the Burst Goodput, which is defined as the ratio between the average number of bits in the Burst that arrive successfully at the receiver, to the Burst length. In this paper we address a new coupled PHY and MAC scheduling methodology by investigating the relationship between the Burst length and its Goodput in different Modulation/Coding schemes, and investigate, given a Burst, the most efficient such scheme. The outcomes of the paper are twofold: first we show that the Goodput of a Burst is almost not dependent on its length. Second, we show that in most cases, the most efficient Modulation/Coding scheme is the one that enables us to transmit the largest number of bits in a Burst. However, there are a few cases where this is not the case. We show these cases in the paper. © 2010 Elsevier B.V. Source

Sharon O.,Netanya Academic College | Alpert Y.,Lantiq
Physical Communication | Year: 2014

We compare between the Throughput performance of IEEE 802.11n and IEEE 802.11ac under the same PHY conditions and in the three aggregation schemes that are possible in the MAC layer of the two protocols. We find that for an error-free channel 802.11ac outperforms 802.11n due to its larger frame sizes, except for the case where there is a limit on the number of aggregated packets. In an error-prone channel the bit error rate sometimes determines the optimal frame sizes. Together with the limit on the number of aggregated packets, these two factors limit the advantage of 802.11ac. © 2014 Elsevier B.V. Source

Jbara A.,Netanya Academic College | Feitelson D.G.,Hebrew University of Jerusalem
22nd International Conference on Program Comprehension, ICPC 2014 - Proceedings | Year: 2014

It is naturally easier to comprehend simple code relative to complicated code. Regrettably, there is little agreement on how to effectively measure code complexity. As a result simple generalpurpose metrics are often used, such as lines of code (LOC), Mc- Cabe's cyclomatic complexity (MCC), and Halstead's metrics. But such metrics just count syntactic features, and ignore details of the code's global structure, which may also have an effect on understandability. In particular, we suggest that code regularity-where the same structures are repeated time after time-may significantly reduce complexity, because once one figures out the basic repeated element it is easier to understand additional instances. We demonstrate this by controlled experiments where subjects perform cognitive tasks on different versions of the same basic function. The results indicate that versions with significant regularity lead to better comprehension, while taking similar time, despite being longer and having higherMCC. These results indicate that regularity is another attribute of code that should be taken into account in the context of studying the code's complexity and comprehension. Moreover, the fact that regularity may compensate for LOC and MCC demonstrates that complexity cannot be decomposed into independently addable contributions by individual attributes. Copyright © 2014 ACM. Source

Natek S.,International School for Social and Business Studies | Zwilling M.,Netanya Academic College
Expert Systems with Applications | Year: 2014

Higher education institutions (HEIs) are often curious whether students will be successful or not during their study. Before or during their courses the academic institutions try to estimate the percentage of successful students. But is it possible to predict the success rate of students enrolled in their courses? Are there any specific student characteristics, which can be associated with the student success rate? Is there any relevant student data available to HEIs on the basis of which they could predict the student success rate? The answers to the above research questions can generally be obtained using data mining tools. Unfortunately, data mining algorithms work best with large data sets, while student data, available to HEIs, related to courses are limited and falls into the category of small data sets. Thus, the study focuses on data mining for small student data sets and aims to answer the above research questions by comparing two different data mining tools. The conclusions of this study are very promising and will encourage HEIs to incorporate data mining tools as an important part of their higher education knowledge management systems. © 2014 Elsevier Ltd. All rights reserved. Source

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