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Copyright of Turkish Journal of Neurology / Turk Noroloji Dergisi is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

The heuristic method is one of the methods used for the scheduling of resource-constrained projects. This method is commonly used in programming the projects with high number of activities and resources such as construction investments. This paper investigates the effectiveness of three heuristic method priority rules applied in the resource scheduling of ten Turkish housing estate projects which were scheduled according to three preselected priority rules [maximum remaining path length (MRPL), latest finish time (LFT), and minimum slack time (MNSLCK)] in resource-constrained conditions. The performance of each priority rule was evaluated in relation to the duration of the project. The results revealed that MRPL priority reduced the project duration to minimum in six projects, whereas LFT priority yielded the best duration results in three projects and MNSLCK priority in only one project. [ABSTRACT FROM AUTHOR]

This paper presents an object-oriented two-dimensional (2-D) overland flow model and its application in simulating flood flows over Ulus basin, located in the north of Turkey adjacent to the Black Sea. A new coding implementation according to the class environment created in object oriented C++ programming language is carried out in structuring and building the solver. The model is based on the Godunov type finite volume scheme on unstructured triangular meshes. A mass balance preserving wet/dry boundary solution algorithm is integrated in the numerical scheme to satisfy the positive-depth condition and minimize the numerical instability when treating the propagation of wave front in regions of dry bed. The balance between bed slope and flux terms is also preserved for still water conditions on irregular topography. The 2-D solver is verified by simulating selected dam break cases, where good agreement with measured data is achieved. For the simulation of flood flows in the Ulus basin, in general, the simulated outflow hydrograph is found to compare well with the recorded data. A selected inundation map that is extracted from the model results is also presented to show the water surface level in the Floodplain. [ABSTRACT FROM AUTHOR]

Land use/land cover (LULC) maps represent a primary requirement for several geospatial applications around the world such as change detection, time series analysis, environment, and urban researches. Mapping LULC from remotely sensed data based on satellite image classification handle the rapid changes in extensive geographical areas. Several effective and efficient mechanisms suggested for supervised satellite image classification. The neural networks machine learning algorithm became a major method in supervised satellite image classification. The objective of this article is to employ neural networks as a machine learning algorithm for LULC mapping. The study applied in Ankara area, which is the capital city of Turkey. This work utilized a free Landsat 8 satellite image with the Operational Land Imager OLI sensor to implement the analysis. The image was obtained and processed in ArcGIS software. Then, the machine learning data set developed using Python scripting language. Every band out of 8 bands from Landsat 8 image considered as an explanatory variable, while the output variable defined based on visual interpretation. The training dataset built based on the signature file and random sample points. The training dataset divided into three sections, for training, for validation and the last section for testing. The training and testing processes were implemented using Google-Tensor Flow Keres library from Anaconda distribution. Feedforward neural network structure implemented with 500 neurons in the hidden layer. Confusion matrix used as accuracy assessment metrics to measure the performance of the developed model. The overall accuracy of the developed model was 92%. In terms of overall accuracy and robustness, the neural networks algorithm was effectively implemented and the LULC map produces. The model gained high accuracy that it is satisfied with the geospatial accuracy target. The consequence showed the competence of neural networks algorithm to generating LULC maps from Landsat 8 satellite images. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a population based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces, and has been successfully used to solve several kinds of problems. In this paper, differential evolution is used for quantitative interpretation of self-potential data in geophysics. Six parameters are estimated including the electrical dipole moment, the depth of the source, the distance from the origin, the polarization angle and the regional coefficients. This study considers three kinds of data from Turkey: noise-free data, contaminated synthetic data, and Field example. The differential evolution and the corresponding model parameters are constructed as regards the number of the generations. Then, we show the vibration of the parameters at the vicinity of the low misfit area. Moreover, we show how the frequency distribution of each parameter is related to the number of the DE iteration. Experimental results show the DE can be used for solving the quantitative interpretation of self-potential data efficiently compared with previous methods. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Civil Engineering is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Land consolidation (LC) is a widely used spatial land management tool. The most essential stage of the LC process is land reallocation, which comprises the exchange of property rights. For this reason, land reallocation may be a potential source of dissatisfaction among farmers, and thus, optimization techniques are continually being developed to minimize these dissatisfactions. The goal of this study was to develop a novel preference‐based artificial‐bee‐colony (ABC) algorithm for use in the land reallocation project for DOT village. In the study, the farmer preferences were prioritized. Specifically, 70% of the lands were preference‐based, with the remainder being random. Furthermore, the ABC algorithm's performance was assessed using three limit parameter (LP) values and two probability functions. The best success was attained when the LP was set to 50 and the roulette‐wheel probability function was employed. The success of land reallocation was defined by the occupancy rate in the blocks and the reallocation rates based on the preferences of the farmer's lands. The occupancy rate in all the blocks using the ABC algorithm was 95.54%, the full occupancy rate of blocks according to farmer preferences was 38%, and the land reallocation rate based on the farmers' preferences was 73%. The remaining lands were distributed to the block areas at random using the ABC method. Moreover, the success of the preference‐based population surpassed that of randomly formed populations. [ABSTRACT FROM AUTHOR]

Background. Cardiovascular diseases (CVD) are chronic diseases that can be asymptomatic for a long time, and the first symptom may be sudden death. Objectives. This study was designed to draw attention to the frequency of both individual and occupational cardiovascular risk factors and to warn health professionals about variable risk factors. Material and methods. This research was conducted between 01.03.2022–01.09.2022. 160 participants were included in the study. The questionnaire form in which sociodemographic data was asked, the international physical activity questionnaire (short) form and the work stress scale form were directed to the participants. Blood pressure, height, weight and waist circumference were measured, and CVD risks were calculated using the SCORE (Systematic Coronary Risk Evaluation) 2 cardiovascular risk estimation algorithm. Results. Medium, high and very high CVD risks were determined in 41.8% of the employees. The risk was found to be significantly different among occupational groups (p < 0.001) and economic status (p = 0.036). Considering the relationship between shift work status and CVD risk, the risk was found to be significantly higher in those working only during day shifts compared to those working during alternating day and night shifts (p = 0.033). It has been shown that work stress does not increase the CVD risk of healthcare workers (HCW) (p = 0.857). However, it was observed that work stress significantly increases LDL and total cholesterol (p = 0.026 and p = 0.018). Conclusions. In this study, it is emphasised that work-related risks should be taken into consideration, as well as individual CVD risks. [ABSTRACT FROM AUTHOR]

Introduction: Medical tourism, one of the most profitable industries, has been growing rapidly in recent years. Especially Turkey, which has a high ranking among medical travel destinations, has some advantages that can become preferable for international patients. This study is among the first few studies which examine affecting factors in patients' medical travel destination choices with Data Mining techniques. Methods: The data were obtained from patients who came to Ankara from abroad for treatment in May 2015 through a question-naire. Cross-industry Standard Process for data mining, known as the CRISP-DM method, is used in this study. After cleaning out the missing data, the models were created using classification algorithms. Results: Models including Generalized Linear Model, Deep Learning, Decision Tree, Random Forest, Gradient Boosted Trees, and Support Vector Machine (SVM) were compared, and SVM reached the best performance with 0.2% Relative Error, 0.014 Root Mean Squared Error and 0.998 Correlation. As a result of the SVM model, effective attributes in patients' satisfaction level include low price advantage, advertisement, doctors with high-quality education, trained assistant staff, relatives living in Turkey, and high technology of medical equipment, respectively. Conclusion: Special attention should be paid to these factors in developing plans and policies for the health tourism sector. However, the importance of related socio-demographic variables was indicated in detail. Eventually, some suggestions were presented to improve the weaknesses in the health tourism sector. [ABSTRACT FROM AUTHOR]

Home healthcare services are public or private service that aims to provide health services at home to socially disadvantaged, sick, needy, disabled, and elderly individuals. This study aims to increase the quality of home healthcare practice by analyzing the factors affecting it. In Megacity Istanbul, data from 1707 patients were used by considering 14 different input variables affecting home healthcare practice. The demographic, geographic, and living conditions of patients and healthcare professionals who take an active role in home healthcare practice constituted the central theme of the input parameters of this study. The regression method was used to look at the factors that affect the length of time a patient needs home healthcare, which is the study's output variable. This article provides short planning times and flexible solutions for home healthcare practice by showing how to avoid planning patient healthcare applications by hand using methods that were developed for home health services. In addition, in this research, the AB, RF, GB, and NN algorithms, which are among the machine learning algorithms, were developed using patient and personnel data with known input parameters to make home healthcare application planning correct. These algorithms' accuracy and error margins were calculated, and the algorithms' results were compared. For the prediction data, the AB model showed the best performance, and the R2 value of this algorithm was computed as 0.903. The margins of error for this algorithm were found to be 0.136, 0.018, and 0.043 for the RMSE, MSE, and MAE, respectively. This article provides short planning times and flexible solutions in home healthcare practice by avoiding manual patient healthcare application planning with the methods developed in the context of home health services. [ABSTRACT FROM AUTHOR]

The Global Positioning System (GPS), although it has existed for only 30 years, is an important source for active tectonics, resulting in estimates of plate motions very close to geologic estimates over millions of years. GPS is also used for elastic block models to calculate slip rates for a better understanding of Earth's active crustal deformation. GPS-derived velocity fields may be used as the basis for clustering analysis to create a preliminary definition of block geometry. In this study, we used published horizontal velocity fields to evaluate the effects of data dependences on determining the optimum number of clusters with algorithms. For this purpose, we used different variations of velocity fields in Turkey and tested four different algorithms that are Davies–Bouldin index, the elbow method, GAP statistics algorithm and the silhouette method. We also clustered velocity components with the k -means technique and compared the results with previous studies. [ABSTRACT FROM AUTHOR]

Forests cover nearly a third of the Earth's land area, and they are a key factor for all life on Earth, but unfortunately, forest fires are the greatest danger to their presence. The wildfires jeopardize general wellbeing, security, and require high levels of government resources. They also lead to noteworthy debasement of nature, property loss, and high rates of human death and injury. This paper proposes an algorithm to use and route unmanned aerial vehicles (UAVs) to mitigate forest fire risks. The developed matheuristic algorithm hybridizes simulated annealing and local search metaheuristics with an integer linear programming model. The mathematical model was developed to solve the distance-constrained multi-based multi-UAV routing problem, and because of the complexity of the problem, the generated metaheuristics helps the model to find better solutions. The effectiveness of the proposed matheuristic is tested with a real-life case study for Turkey and is also compared with a genetic algorithm. The Turkish State Meteorological Service generates forest fire-risk maps countrywide every day to predict fire risks 3 days later by using meteorological data. These maps are used to generate the risky regions to be visited by the UAVs, and the existing airports are considered for the UAVs to take off and land. The algorithm is coded using MATLAB and ILOG. The metaheuristics are designed with problem-based operators, and their parameters are tuned by experiments. Computational results demonstrate the effectiveness of the algorithm and the hybridization procedures. Results demonstrate that the CPU times for the methods are acceptable. • Defines a distance-constrained multi-based multi-UAV routing problem. • Routes UAVs for managing forest fire risks in a countrywide manner. • Includes a matheuristic with metaheuristics and integer programming. • Tests the effectiveness of the method with real-life case scenarios from Turkey. • Illustrates the improvements achieved by the hybridization procedures. [ABSTRACT FROM AUTHOR]

Intermodal transportation (IMT) combines two or more different modes of transportation without changing the packaging of the freight transported in order to minimize the transportation cost and to utilize the benefits of modes that are used. One of the most common intermodal freight transportation systems is the Rolling Highway (Ro-La), where a special train system is used to carry highway vehicles on railway cars. This paper reviews IMT and proposes a system in which the facility layout problems are solved simultaneously with the scheduling problems arising in Ro-La transportation. In this context, the best station layouts are obtained by applying a layout improvement algorithm to several initial layouts with respect to different scoring functions. One of the important questions to answer in the IMT problem pertains to the number of loading and unloading platforms. Using the output of the layout improvement algorithm, the train scheduling model is solved to find the minimum number of platforms in a station, the number of trains, and the departures with the carried trucks and trailers, while scheduling train operations, with the objective of minimizing operation costs. The proposed IMT system is applied to the Marmaray Undersea Railway Tunnel Project with regard to different Ro-La systems, and its effectiveness is discussed. [ABSTRACT FROM AUTHOR]

The studies conducted so far on the history of the clinical algorithm have not noticed to the works of Ahmad Al Hayati Ibn Muhammad al- Qurashi (917 AH- 1511 AD), who, to the best of our knowledge, was the first person who applied multiple branching algorithms (arborization) to medical books, and his book named Shajarat al-Tibb (lit. a tree of medicine) followed the dendritic method (multiple branching algorithms (arborization) in presenting medical information. The book includes brief, yet useful, information, coordinated in a wonderful arrangement of the medical science rules, such as naturals, manuals, symptoms, and treatment arts. Moreover, the book, written at the time of Sultan Beyazid II, was mentioned in the Index of Medical Manuscripts in Turkey. It can be concluded that Ḥayati, Aḥmad ibn Muḥammad was the first medical professor to adopt the use of clinical algorithms. If the translation of his books was available, his research and expertise would be known to more researchers as well. [ABSTRACT FROM AUTHOR]

Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS. [ABSTRACT FROM AUTHOR]

Load information plays an important role in deregulated electricity markets, since it is the primary factor to make critical decisions on production planning, day-to-day operations, unit commitment and economic dispatch. Being able to predict the load for a short term, which covers one hour to a few days, equips power generation facilities and traders with an advantage. With the deregulation of electricity markets, a variety of short term load forecasting models are developed. Deregulation in Turkish Electricity Market has started in 2001 and liberalization is still in progress with rules being effective in its predefined schedule. However, there is a very limited number of studies for Turkish Market. In this study, we introduce two different models for current Turkish Market using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) and present their comparative performances. Building models that cope with the dynamic nature of deregulated market and are able to run in real-time is the main contribution of this study. We also use our ANN based model to evaluate the effect of several factors, which are claimed to have effect on electrical load. [ABSTRACT FROM AUTHOR]

The security-level detection of a confidential document is a vital task for organizations to protect their confidential information. Diverse classification rules and techniques are being applied by human experts. Increasing number of confidential information in organizations is making difficult to classify all the documents carefully with human effort. The recommended frameworks in this study classify the internal documents of TUBITAK UEKAE (National Research Institute of Electronics and Cryptology of Turkey) by using classification algorithms naïve Bayes, support vector machines (SVMs) and adaptive neuro-fuzzy inference systems (ANFISs). A hybrid approach involving support vector classifiers and adaptive neuro-fuzzy classifiers exposes the most successful accuracy rates of expert system classification. This study also states preprocessing tasks required for document classification with natural language processing. To represent term-document relations, a recommended metric TF-IDF was chosen to construct a weight matrix. Agglutinative nature of Turkish documents is handled by Turkish stemming algorithms. At the end of the article, some experimental results and success metrics are projected with accuracy rates and receiver operating characteristic (ROC) curves. [ABSTRACT FROM AUTHOR]

The ensemble method has long been used to reduce the errors that are caused by initial conditions and/or parameterizations of models in forecasting problems. In this study, neural network (NN) simulations are applied to ensemble weather forecasting. Temperature forecasts averaged over 2 weeks from four different forecasts are used to develop the NN model. Additionally, an ensemble mean of bias-corrected data is used as the control experiment. Overall, ensemble forecasts weighted by NN with feed forward backpropagation algorithm gave better root mean square error, mean absolute error, and same sign percent skills compared to those of the control experiment in most stations and produced more accurate weather forecasts. [ABSTRACT FROM AUTHOR]

Copyright of Transport (16484142) is the property of Vilnius Gediminas Technical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Moving military troops, which is critical to tactical success, depends on providing large quantities of fuel. We used simulation to model and analyze the Turkish army's fuel-supply system that consists of sea-going tankers, tank fields, pipelines, and depots. We measured performance of the existing and proposed systems under various scenarios. We developed a simulation optimization model based on a genetic algorithm (GA) to optimize system performance. Based on the results of extensive simulation experiments, we proposed a number of changes. We recommended that the army should open the existing fuel-supply system and that it establish commercial use in peacetime to obtain additional operating revenue. New trigger levels (or fuel-replenishment policies) for wartime allow the fuel-supply system to survive much longer even in the most severe war conditions. These specific recommendations are being considered by the top army officials to obtain benefits worth millions of dollars. [ABSTRACT FROM AUTHOR]