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It is of great interest for Mobile Network Operators (MNOs) to know how well their network infrastructure performance behaves in different geographical regions of their operating country compared to their horizontal competitors. However, traditional network monitoring and measurement methods of network infrastructure use limited numbers of measurement points that are insufficient for detailed analysis and expensive to scale using an internal workforce. On the other hand, the abundance of crowdsourced content can engender various unforeseen opportunities for MNOs to cope with this scaling problem. This paper investigates end-to-end reliability and packet loss (PL) performance comparisons of MNOs using a previously collected real-world proprietary crowdsourced dataset from a user application for 13 months' duration in Turkey. More particularly, a unified crowdsourced data-aided statistical MNO comparison framework is proposed, which consists of data collection and network performance analysis steps. Our results are statistically supported using confidence interval analysis for the mean difference of PL ratios and reliability levels of MNOs using unpaired number of observations statistical analysis. The network performance results indicate that significant performance differences in MNOs depending on different regions of the country exist. Moreover, we observe that the overall comparative ordered list of MNOs' reliability performance does not differ when both PL and latency requirements vary. [ABSTRACT FROM AUTHOR]

Retail companies monitor inventory stock levels regularly and manage them based on forecasted sales to sustain their market position. Inventory accuracy, defined as the difference between the warehouse stock records and the actual inventory, is critical for preventing stockouts and shortages. The root causes of inventory inaccuracy are the employee or customer theft, product damage or spoilage, and wrong shipments. In this paper, we aim at detecting inaccurate stocks of one of Turkey's largest supermarket chain using the variational autoencoder (VAE), which is an unsupervised learning method. Based on the findings, we showed that VAE is able to model the underlying probability distribution of data, regenerate the pattern from time series data, and detect anomalies. Hence, it reduces time and effort to manually label the inaccuracy in data. Since the distribution of inventory data depends on selected product/product categories, we had to use a parametric approach to handle potential differences. For individual products, we built univariate time series, whereas for product categories we built multivariate time series. The experimental results show that the proposed approaches can detect anomalies both in the low and high inventory quantities. [ABSTRACT FROM AUTHOR]

As fifth generation (5G) networks are starting to become commercial, user expectations in terms of new services become high as well. This signifies that mobile communications service providers need to build robust 5G new services as quickly and cost-efficiently as possible. Many new technologies rely on closed-loop (CL) and multiple input multiple output (MIMO) technologies due to emerging cooperation between nodes in next generation networks. In this paper, we first compare different multiantenna transmission modes namely: transmit diversity, open-loop (OL), and CL MIMO spatial multiplexing strategies to provide mobile network operator (MNO) services in terms of their characteristics, limitations and benefits. Later we investigate how launching a large-scale CL MIMO deployment strategy can affect the various key performance indicators (KPIs) of the existing services provided by Mobile Network Operators (MNOs) in real-operational network infrastructure in Turkey. Our practical experimental results indicate that, compared to OL MIMO system, CL MIMO can achieve large performance on a practical setup, where up to 3% improvement in cell average throughput, 9% in user throughput, 6% in spectrum efficiency, and 9% in channel quality indicator (CQI) and modulation coding scheme (MCS) are obtained, while reduction by 25% and 17% on sum delay and initial block error rates (IBLER) are observed. [ABSTRACT FROM AUTHOR]

In Turkey, Turkish Personal Data Protection Rule (PDPR) No. 6698, in force since 2016, provides protection to citizens for the legal existence of their personal data. Although the law provides excellent guidance, companies currently face challenges in complying with its regulations in terms of storing, sharing, or monitoring personal data. Since any specially designed software with wide industrial usage is not on the market, almost all of the companies have no other choice but to take expensive and error-prone operations manually to ensure their compliance. In this paper, we present an automated solution to facilitate and accelerate PDPR compliance. In a structured or unstructured document, words or phrases that could include personal data entities are tagged with the help of a Bi-LSTM based named entity recognition model and a rule-based matching component that employs contextual analysis. To find associations in personal data and obtain individual personal profiles, these entities are divided into categories according to their confidence levels. Personal profiles are constructed using an approach similar to clustering. It treats the personal data categories with high identification levels as separate clusters and finds related personal data entities at the left and/or right of its contexts. We evaluated the system on a data set formed of 70 documents of different types and personal data entities. We obtained 91.76 % micro-averaged F1-measure for personal data entity classification and 72.46 % accuracy for profile extraction. We also performed experiments related to the performance of the named entity recognition and to the time complexity of the overall system on a data set formed of 33K documents. [ABSTRACT FROM AUTHOR]

Wind power has become one of the cost-effective options for improving the energy mix, while the wind farm installations reach higher shares by new capacity additions in Turkey, similar to many other countries, in order to support the sustainable development targets. Various methodologies are implemented and applied for planning and optimizing energy systems of countries, taking into consideration the energy, economy, and ecology aspects as a whole. The objective of this paper is to demonstrate the implications of "2023 energy targets" policy in terms of wind power. A futuristic scenario was tailored and applied to a comprehensive energy model for the energy system of Turkey. The official wind energy vision and goal of Turkey were analyzed by applying a scenario based on 20,000 MW wind power capacity installation by the year 2023. The results indicated that, if applied, this action will increase the share of wind power utilization to the level of 16% in the total renewables while the installed wind capacity will reach 18%. Finally, the total cost of this strategic target which doubles the installed capacity and electricity generation by wind power, including the respective investment costs and subsidizations results, were calculated as $24.78 billion on the analysis horizon. [ABSTRACT FROM AUTHOR]

Enhancing the coverage and eliminating the poor performance is key to balance end-user experience and future network investments for mobile network operators (MNOs). Although vast amounts of infrastructure investments are provided by MNOs, there are still coverage and capacity planning problems at remote locations. This is because, in most cases, the population density and return-of-investments are low in those areas. In this paper, radio access network (RAN) sharing paradigm is utilized on experimental sites in Turkey to accommodate user equipment of multiple network operators under the same cell sites. We first investigate characteristics, benefits, and limitations of two different RAN sharing deployment scenarios. Then, a city-wide experimental RAN sharing study is conducted on live long-term evolution (LTE) networks between two MNOs in Turkey. Through experimental tests, we show overall performance gains of enabling RAN sharing feature in terms of observing various key performance indicators that are obtained from shared base stations. Our experimental results demonstrate that both downlink and uplink average user throughput values increased by 17.8% and 42.85%, respectively. After RAN sharing was enabled between MNOs, increase in the number of user equipment due to higher 4G coverage yielded a higher number of interradio access technology (inter-RAT) handover attempts. This caused inter-RAT handover out success rate to decrease by 70.66%. Intrafrequency handover out success rate, which indicates if the subscriber is using the same RAT type, increased by 358.33% and service drop rates dropped by 86.1%, respectively, after RAN sharing was enabled. Finally, we discuss and summarize the main takeaways of the outcome of the considered large-scale RAN sharing experiments. [ABSTRACT FROM AUTHOR]

As the traditional electricity grid transitions to the smart grid (SG), some emerging issues such as increased renewable energy penetration in the power system that cause load unbalances require new control methods. Storage of energy seems to be the best option to struggle with such issues. In this manner, energy storage technologies ensure the operating flexibility of the distribution system operator in the power system in terms of both sustainability of energy and peak load balancing. In this study, a grid condition monitoring user-interface and control algorithm is developed for the peak load reduction and supply-demand balancing in a SG system by using an energy storage unit. For this purpose, a battery energy storage system (BESS) is designed, scaled and integrated into the SG didactic test system, designed by the De Lorenzo Company. Online grid condition monitoring and control software is developed for gridconnected photovoltaic (PV) system and the BESS in the LabVIEW™ program. Moreover, an algorithm is developed that provides the conditions for the integration of the BESS into the system. The proposed algorithm is tested with real daily load data of the Manisa province in Turkey. Also, various case studies are performed to validate the effectiveness of the algorithm. Consequently, the proposed algorithm provides an average load factor improvement of 8.46% and the algorithm-controlled BESS increases the revenue of the system by 3.51% compared to the grid-connected PV system alone. [ABSTRACT FROM AUTHOR]

SURALP is a 29 degrees-of-freedom full-body walking humanoid robot designed and constructed at Sabanci University - Turkey. The human-sized robot is actuated by DC motors, belt and pulley systems and Harmonic Drive reduction gears. The sensory equipment consists of joint encoders, force/torque sensors, inertial measurement systems and cameras. The control hardware is based on a dSpace digital signal processor. This paper reviews the design of this robot and presents experimental walking results. A posture zeroing procedure is followed after manual zeroing of the robot joints. Controllers for landing impact reduction, early landing trajectory modification, foot-ground orientation compliance, body inclination and Zero Moment Point (ZMP) regulation, and independent joint position controllers are used in zeroing and walking. A smooth walking trajectory is employed. Experimental results indicate that the reference generation and control algorithms are successful in achieving a stable and continuous walk. [ABSTRACT FROM AUTHOR]

In this study, an experimental performance comparison of a 2-axis tracking system with a fixed panel in a solar renewable energy system is conducted. The paper mainly focuses on a cost-benefit analysis of fixed and tracking modules. For this aim, an experimental setup was built and periodical measurements were then obtained from the setup. To provide a better comparison, the panels were exposed to the same conditions during the measurements. By considering the real-time experimental results, the paper provides a quantitative analysis of the feasibility of tracking panels for domestic applications as well as an analysis of payback period if a tracking system is used for a home-building application instead of using a conventional fixed module. The study is restricted to the geographical region of Duzce, Turkey. [ABSTRACT FROM AUTHOR]

In this study multiple linear regression, multilayer perceptron (MLP) regression, and support vector regression (SVR) are used to make multivariate tourism forecasting for Turkey. This paper is a comparative study of data mining techniques based on multivariate regression modelling with monthly data points to forecast tourism demand; it focuses on Turkey. Both MLP and SVR methods are widely employed in the variety forecasting problems. Most of the previous research on tourism forecasting used univariate time series or a limited number of variables with mostly yearly or quarterly, and rarely monthly frequencies. However, the application of data mining techniques for multivariate forecasting in the context of tourism demand has not been widely explored. This paper differs from earlier research in two ways: 1) it proposes multivariate regression modelling with monthly data points to forecast tourism demand; and 2) it focuses on Turkey by using a dataset with the most recently accumulated (between January 1996 and Dec 2013) 67 time series with respect to Turkey and its top 26 major tourism clients. Comparison of forecasting performances in terms of relative absolute error (RAE) and root relative squared error (RRSE) measurements shows that the SVR model, with RAE = 12.34% and RRSE = 14.02%, gives a better performance. The results obtained in this study provide information for researchers interested in applying data mining techniques to tourism demand forecasting and help policy makers, government bodies, investors, and managers for their regularization, planning, and investments by way of accurate tourism demand forecasting. [ABSTRACT FROM AUTHOR]

This paper presents findings on performance benchmarking of cellular network operators in Turkey. Benchmarking is based on measurements of standard key performance indicators (KPIs) in one of the metropolitan cities of Turkey, Ankara. Performance benchmarking is formulated by incorporating customer perception by conducting surveys on how important KPIs are from the user's point of view. KPIs are measured, with standard test equipment, by drive test method on specified routes. According to the performance benchmarking results, the GSM and UMTS network operators achieving the best performance were determined in Ankara. Speech qualities of network operators, as the most popular service, were also evaluated by several statistical methods including pdf/cdf analysis and chi-square and Fisher's exact tests. The network operator providing the highest speech quality in Ankara was determined with the methods applied. Overall, the results and approaches on benchmarking of cellular networks in Turkey are reported for the first time in this paper. The approaches proposed in this paper could be adapted to wide-scale benchmarking of services in cellular networks. [ABSTRACT FROM AUTHOR]

This paper deals with the exergoeconomic analysis of a 750-Wp solar photovoltaic (PV) system that is constructed at the Meteorology Park of İstanbul Technical University (41.102° N, 29.02° E) in Istanbul, Turkey. It is known that seasonal differences cause a variation in the exergoeconomic parameters (energy and exergy loss rates) of solar PV systems. Due to this perspective, for regional climatic parameters 2 months are selected: April to represent the spring and August to represent the summer. Within this research, first the exergy analysis is performed on the system and then the economic values of the results are evaluated using the exergy, cost, energy, and mass (EXCEM) method. The energy efficiency of the considered system in August and April varies between 4.5% and 7.3% and 5.5% and 8.5%, respectively. Additionally, the exergy efficiency of the system in August and April varies between 3% and 5.3% and 3.8% and 6.5%, respectively. The average unit cost of the exergy values for August and April are calculated based on the EXCEM model as 0.214 W/$ and 0.129 W/$. Moreover, the average unit cost of the energy values for August and April are estimated as 0.222 W/$ and 0.134 W/$, respectively. [ABSTRACT FROM AUTHOR]

The importance of sustainable development and energy efficiency efforts has increased in the construction sector. Energy-saving efforts for buildings have become more important due to the global energy crisis, and energy rating systems have been developed globally for assessment of energy performance. In this study, international green building certificate and energy performance certificate (EPC) systems are presented. The official EPC system for buildings in Turkey is illustrated with case studies. Energy consumption and greenhouse gas emissions from residential buildings are calculated using the official national software, which is called BEP-TR. The impact of lighting on the total energy performance of residential buildings is also examined. Changes in energy performance values are discussed for various cases. As a result of this study, it was determined that residential buildings constructed before 2000 consume more energy than those constructed after 2000; they are a lower energy performance class of buildings. Use of energy-saving lamps has also had a positive impact on both lighting and total energy performance classes. This paper aims to contribute to the promotion of building energy certification systems and energy efficiency issues for the construction sector. [ABSTRACT FROM AUTHOR]

The privatization of electricity distribution companies in the state monopoly has increased significantly in recent years. Privatization is expected to improve the quality of services in the electric distribution sector and to provide additional income to the state. In this paper the privatization revenues of the 18 electricity distribution companies in Turkey are calculated through regression equations. The privatization revenues of the electricity distribution companies are identified by 5 models containing microeconomic and macroeconomic variables (parameters). The study indicates a correlation between the considered variables and the privatization revenues of the electricity distribution companies and useful parametric formulations are proposed. The proposed models and regression equations obtained can be employed for future predictions of privatization contributions after making the necessary adjustments by considering the real exchange rates, which is helpful in determining tender offer prices. [ABSTRACT FROM AUTHOR]

TV users have an abundance of different movies they could choose from, and with the quantity and quality of data available both on user behavior and content, better recommenders are possible. In this paper, we evaluate and combine different content-based and collaborative recommendation methods for a Turkish movie recommendation system. Our recommendation methods can make use of user behavior, different types of content features, and other users' behavior to predict movie ratings. We gather different types of data on movies, such as the description, actors, directors, year, and genre. We use natural language processing methods to convert the Turkish movie descriptions into keyword vectors. Then, for each user, we use the content features and the user's past implicit ratings to produce content feature-based user profiles. In order to have more reliable profiles, we do feature selection on these profiles. We show that for each feature space, such as actor, director, or keyword, a different amount of feature selection may be optimal. Different recommenders may also perform best for a different number of movies available as training data for a user. We also combine different content-based recommenders and collaborative recommenders using an aggregation or the best of the available recommenders. Experimental results on a dataset with hundreds of users and movies show that, especially for users who have watched a small number of movies in the past, feature selection can increase recommendation success. [ABSTRACT FROM AUTHOR]

There is a trend toward decentralized systems, but these systems are developed without conducting enough software tests. Also, the performance, scalability, and sustainability of the decentralized systems are not taken into account. One of the reasons is the time-consuming testing process. The other is the hardware requirements and the complexity of the software installations of the testing environment. Developer communities need stable and secure research networks to test and develop prototypes before releasing the working versions. Cloud-based blockchain test networks are available, but it allows using a specific framework. Also, users are required to learn how to use each framework. Blockchain test networks use public ledgers, and this is also not preferable in many project implementations because of privacy reasons. Testbeds are inflexible and unrealistic in terms of scalability and performance. There is a need for a physical test network to create a realistic test environment. This study aims to form a sustainable and scalable research environment where decentralized technologies are researched and developed. The method of this study will include three stages to create a decentralized research network. The first stage is to develop the decentralized foundation of the network by using the Quorum blockchain framework and RAFT consensus protocol. Node requirements are simplified to run on low-cost devices. The second stage will be to develop a web-based platform to serve as an efficient way for the developers to deploy smart contracts easily. The final stage will be ensuring the sustainability of the system. Usage policy is formed that includes management of the network, the system usage rules, and regulations. A decentralized governance model is proposed. Smart contracts are developed to manage and ensure the sustainability of the research network. The available test tools were not sufficient and not flexible to use in this system, so a new test tool was developed for this project. The new test tool will verify the research environment with scalability, latency, and performance tests. As a result, the "Decentralized Solutions for Humanity" (DS4H) research network is deployed in four different regions of Turkey. This network is tested with four initial nodes. New nodes are to be added worldwide. The developed tools named Chainex, GoHammer, and Scanner are shared as open-source. Future works will include adding multiple blockchain network support, working on consensus protocols and new decentralized services. Finally, the DS4H research network provides a test and development research environment for blockchain projects with sustainable and low-cost maintenance. [ABSTRACT FROM AUTHOR]

Due to the coronavirus, millions of people worldwide carry out their work, education, shopping, culture, and entertainment activities from their homes now using the advantages of today's technology. Apart from this, patient care and follow-up are carried out with the help of electronic equipment especially in the institutions where health services are provided. It is important to provide a reliable electricity supply for humanity so that people can perform all these services. In this study, the outlook of energy in Turkey was examined. The current energy consumption and investments were examined. Then, the precautions by the government in the pandemic period according to the occurrence and spread of COVID-19 in the country are given in chronological order. The actual electricity consumption data were obtained daily across the country, after all these precautions. It was observed that electricity consumption decreased significantly, especially on restricted days. It is inarguable that energy consumption estimation should be made in the short term so that the energy sector is not adversely affected by this situation. In this study, more accurate short-term consumption forecasting methods were developed during the COVID-19 pandemic period: nonlinear autoregressive (NARX) and long short-term memory (LSTM) artificial neural networks (ANNs). Between January and April 2019 electrical consumption data were used to train and validate the forecast prediction. The NARX and LSTM are potential candidates for effective forecasting of electricity consumption. However, the obtained LSTM results suggest that the proposed method performs better than the NARX ANN. [ABSTRACT FROM AUTHOR]

Mechanical cooling is responsible for a significant fraction of the energy consumption of data centers (DCs). Free cooling systems take advantage of ambient conditions to reduce the need for compressor-based cooling. This study utilizes thermodynamic models of major free cooling systems such as the direct air-side economizer (ASE), indirect air-side economizer (IASE), indirect evaporative cooler (IEC), and indirect water-side economizer (WSE) integrated with the existing cooling infrastructure of a typical 1 MW IT load DC. Proposed models utilize hourly weather data of various cities in Turkey to compute annual energy consumption and cost-saving potentials of each free cooling method with respect to the baseline DC with both open aisle (OA) and enclosed aisle (EA). Results confirm the energy-saving potential by IEC leading to less than 10% of annual chiller hours across Turkey and less than 1% in half of the ten cities studied, especially in EA DCs. However, despite greater energy-saving potential, IEC has more extended payback periods of 1.5 to 3.7 years due to the high capital investment compared with those of ASE and WSE with less than 1.4 years. [ABSTRACT FROM AUTHOR]

Colon cancer is one of the major causes of human mortality worldwide and the same can be said for Turkey. Various methods are used for the determination of cancer. One of these methods is Fourier transform infrared (FTIR) spectroscopy, which has the ability to reveal biochemical changes. The most common features used to distinguish patients with cancer and healthy subjects are peak densities, peak height ratios, and peak area ratios. The greatest challenge of studies conducted to distinguish cancer patients from healthy subjects using FTIR signals is that the signals of cancer patients and healthy subjects are similar. In the current study, a method in which the area and height ratios of the FTIR signal, as well as various statistical features, are proposed in order to overcome this difficulty. Blood samples (plasma) were collected from 30 colon cancer patients and 40 healthy subjects, and FTIR measurements were performed. A total of 16 features were obtained, including five height ratios, five area ratios, and six statistical features, from each FTIR signal. The 16 features were classified with a multilayer perceptron neural network and support vector machines using cross-validation and their performances were then compared. The current study demonstrated that different features obtained from plasma FTIR spectra can be used together in order to distinguish colon cancer patients from healthy individuals. [ABSTRACT FROM AUTHOR]

The timely and accurate diagnosis of plant diseases plays an important role in preventing the loss of productivity and loss or reduced quantity of agricultural products. In order to solve such problems, methods based on machine learning can be used. In recent years, deep learning, which is especially widely used in image processing, offers many new applications related to precision agriculture. In this study, we evaluated the performance results using different approaches of nine powerful architectures of deep neural networks for plant disease detection. Transfer learning and deep feature extraction methods are used, which adapt these deep learning models to the problem at hand. The utilized pretrained deep models are considered in the presented work for feature extraction and for further fine-tuning. The obtained features using deep feature extraction are then classified by support vector machine (SVM), extreme learning machine (ELM), and K-nearest neighbor (KNN) methods. The experiments are carried out using data consisting of real disease and pest images from Turkey. The accuracy, sensitivity, specificity, and F1-score are all calculated for performance evaluation. The evaluation results show that deep feature extraction and SVM/ELM classification produced better results than transfer learning. In addition, the fc6 layers of the AlexNet, VGG16, and VGG19 models produced better accuracy scores when compared to the other layers. [ABSTRACT FROM AUTHOR]

In this study, the energy demand of a house in Gebze, Turkey, is met by using wind energy as a primary energy source combined with rechargeable batteries. The wind-battery system is simulated by MATLAB while considering that energy continuity is maintained by using a sufficient number of batteries. A life-cycle cost analysis is carried out over a 20-year system lifetime because the operating time of the hybrid system is subject to the turbine life, usually assumed to be 20 years. The wind turbine generators considered are of various nominal powers, ranging from 0.6 to 450 kW. For each wind turbine, the necessary number of batteries to continuously supply the house with energy is calculated and an economic analysis of each system is performed. According to the simulation results, among the wind turbines considered in this study, Proven 2.5 (with a nominal power of 2.5 kW) appears to be the most economical turbine and produces electricity at a cost of US$0.82/kWh, while the optimum battery number for the Proven 2.5 wind turbine is 44. The total system cost obtained is $17,438. [ABSTRACT FROM AUTHOR]

In 2001, Engineering Faculty of Dokuz Eylül University has started to change its undergraduate education in a radical way. As being among the first examples in the world, the Departments of Electrical and Electronics, Geological and Geophysics Engineering in 2002 and Mining Engineering Department in 2003 have adopted the so-called "modular-staged problem based active learning system". The system is based on real engineering problems which values teamwork and the integration of information from different disciplines and it places the student at the center of the learning process. The faculty has learned many things and it needs to learn many other things on how to write a real world engineering scenario, to facilitate the construction of learning environment that the students must do by themselves, to assess students' performances in a compatible way to the implemented education system, to evaluate educational programs for continuous improvement and to integrate educational activities with research done in the departments for mutual benefits of students and faculty staff. [ABSTRACT FROM AUTHOR]