Browsing by Subject "Current situation"

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    Legal framework for the e-taxation in Turkey
    (IGI Global, 2013) Gerger G.C.
    This study analyzes the legal framework of e-taxation in the Turkish Republic. Tax service is commonly provided by using ICT in many countries. In e-government applications in Turkey, provision of e-tax service is one of the leading projects. Among the members of OECD, electronic tax return, payment systems and tax automation systems generated in this area have gained an increasing importance. Taxpayers fill the declarations electronically and also pay tax debts without going to the tax offices. E-taxation system is becoming widespread in Turkey. Implementation of the system in Turkey started in 1998 with VEDOP I and continued with 2004 VEDOP II and 2007 VEDOP III Projects. These applications are legislated by the Tax Procedure Law in Turkey. Thus, legal regulations on electronic recording are established on a legal basis. In this study, e-government tax applications in the Turkish Tax Law (e-tax return, e-books, e-signature, e-audit) and legal base of this application is examined. Information is given on how it is implemented by means of information technologies in Turkey. The main purpose of the study is to examine what legal regulations were enacted for registering and taxation in the use of information technologies and to determine the current situation in Turkey. © 2013 by IGI Global. All rights reserved.
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    Future perspective and current situation of maximum power point tracking methods in thermoelectric generators
    (Elsevier Ltd, 2022) Mamur H.; Üstüner M.A.; Bhuiyan M.R.A.
    One of the green technologies that can be used to increase energy efficiency by recovering a part of waste heat as electrical energy is thermoelectric generators (TEG) by using the Seebeck phenomenon. Conventional and modern maximum power point tracking (MPPT) methods used to deliver maximum power from energy sources. Conventional MPPT algorithms have disabilities such as a delay in reaching the maximum power point (MPP), certain oscillations around the MPP, being stuck at local MPP (LMPP), and not being able to find global MPP (GMPP). In order to overcome the drawbacks of conventional MPPT methods, methods using metaheuristic MPPT algorithms have come to the fore in recent years. However, the issue of determining the appropriate method among the increasing number and complexity of MPPT methods causes confusion. The aim of this study is to review more than sixty-two MPPT methods that have been used in TEGs in the last six years and have the potential to be adapted for TEGs and provide a reference for researchers. Eventually, this review will be a resource that introduces the next generation MPPT methods, presents MPPT methods with the potential to be adapted to TEGs, and will be a good reference for future studies. © 2021 Elsevier Ltd
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    DDSS: denge decision support system to recommend the athlete-specific workouts on balance data
    (Springer Science and Business Media Deutschland GmbH, 2022) Abidin D.; Cinsdikici M.G.
    Monitoring the balance conditions and physical abilities of athletes is important to track their current situations which enables us to apply appropriate training programs for recovery. For different branches of sports, there are three main balance board types to be used; not swaying board (i.e. Wii board), semi-spherical fulcrum (i.e. Wobble board), and springboard (i.e. Spring Balance Board). In this study, the Balance springboard, which is new to the literature, is used. The springboard equipped with sensors uses Bluetooth technology to transmit collected balance data. There are various previous studies developed for assessing the balance performance of athletes regarding the first two types of balance-boards. Most of them are based on statistical analysis and machine learning (ML) techniques. In this study, the usage of a shallow deep learning model trained with the balance data, which is a contribution to the literature, gathered from the springboard is introduced. This model (DDSS, Denge Decision Support System) is compared with the base ANN model -which leads the study to tend our DDSS model- and ML techniques. Our DDSS model outperforms when compared with the base ANN and ML techniques, Sequential Minimal Optimization and Random Forest, and offers appropriate training program suggestions with a success rate of 92.11%. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.