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Many companies prefer to use third party logistics firms to deliver their goods and as such planning the return of the vehicles to the depot is not required. This is called open vehicle routing problem (OVRP). In literature, the OVRP is handled with minimum distance as objective function like vehicle routing problem. But in the real world, the objective function achieves minimum many costs like standard routing cost, stopping by cost and the deviation cost. The standard routes are previously defined under free market conditions by third party logistic firms. The deviation from the standard route is required to arrive cities which are not on the standard route. The stop by cost occurs on the delivery points. In this paper mentioned three costs are considered in the objective function while many papers consider only distance related costs in the literature. This paper proposes a new mathematical model for the OVRP. In the constraints, the last points of the routes are researched in detail. The standard route costs are determined by considering the last point of the route. Because of the NP-hard structure of the OVRP, the proposed mathematical model is solved with a hybrid metaheuristic called Civilized Genetic Algorithm (CGA). CGA is developed by hybridizing a modified genetic algorithm and a local search algorithm. The application of this study is implemented for the delivery routing of a combi boiler producer in Turkey. The third party logistic firms may use this proposed model and the solution approach for the real life applications. [ABSTRACT FROM AUTHOR]

In this study, reversed-phase HPLC procedure was conducted for the synchronized amount of water-soluble such vitamins as thiamine (B1), riboflavin (B2), niacin (B3) pyridoxine (B6), and cobalamin (B12) in four Meat Based Ethnic Foods (MBEF) of Turkey. In this paper, we investigated MBEF Kelle-Paça, Arnavut Ciğeri, Erzurum Cağ Kebabı, Keşkek respectively. In this paper, rapid and a simple method was constructed for the determination of some water-soluble vitamins (B1, B2, B3, B4, B12), by High-performance liquid chromatography. The B2, B3, B4, B6, and B12 content was determined high in Arnavut Ciğeri 498.68 µg/portion, 2.9857 µg/portion, 24.607 µg/portion, 2.2149 µg/portion, 178.36 µg/portion respectively a significant amount B6 ingredient in Erzurum Cağ Kebab 1.5320 µg/portion. The results of investigation showed that among these meat based ethnic foods are rich sources of vitamins Arnavut Ciğeri. In the food sector, the successful implementation of a simple, low-cost, and time-efficient approach is used to determine these vitamins. We have indicated our meat based ethnic foods are healthy and available. And also, the most commonly used method in the determination of vitamins of the B-groups is RP-HPLC is most suitable procedure. The procedures validation indicated that it is extremely sensitive, selective, and linear. [ABSTRACT FROM AUTHOR]

Earthquake is one of the most destructive disasters for people, both materially and morally. Some precautions to be taken before an earthquake reduce this harmful effect. Earthquake risk assessment is one of these precautions. Earthquake risk assessment, which is an interdisciplinary topic, is a problem suitable for clustering and multi-criteria decision-making (MCDM) techniques, as it includes more than one criterion and alternative. In this study, decision model was proposed for earthquake risk prioritization of twenty-nine provinces with high earthquake risk in Turkey. In the proposed model, provinces were clustered via Two-Step Cluster Analysis. Indicators determined in the Two-Step Cluster Analysis were defined as criteria, and criteria weighting was made via SWARA method. After weighing the criteria, the ELECTRE I method was used for earthquake risk ranking of the clustered provinces. In the proposed model for earthquake risk assessment, the similarity of provinces can be defined, and the impact of indicators can be examined. For this purpose, as the innovative aspect of this paper, while evaluating the clustering success, it was proposed to examine the coefficients of variation for continuous variables. In the Two-Step Cluster Analysis, clusters were formed in different ways and the risk rankings for provinces divided into six sub-clusters in total were presented. As a result of the Two-Step Cluster Analysis, two clusters consisting of six provinces, two clusters consisting of five provinces, one cluster consisting of four provinces and one cluster consisting of three provinces were obtained. The rankings of these provinces within clusters were obtained via ELECTRE I method. The aim of the study is to guide the decision makers working on earthquake risk assessment in the practical world by providing the hybridization of the specified clustering and multi-criteria decision-making methods. [ABSTRACT FROM AUTHOR]

The aim o f this paper is to e xamine the n et single premiums o f multiple l ife annuities using stochastic rates of return and dynamic life table under the assumption of dependency of spouses' future lifetimes. In order to calculate the present value of the annuity or the net single premium, two parameters are needed: survival rate and the rate of return. For the survival rates, we used a life table with a time dimension for Turkey, in which mortality rates follow a declining pattern, a major indicator of longevity. For the rate of return, two portfolios were created, low and high risk portfolios that include assets with different ratios and AR(1) process was used to model the rates of return based on both portfolios. To assess the dependency, future lifetimes of spouses were assumed to follow Frank's copula model. The effects of longevity, stochastic rates of return and dependency of future lifetimes on these net single premiums were analyzed. [ABSTRACT FROM AUTHOR]

Multi-Criteria Decision Making (MCDM) methods help researchers in solving many problems in terms of numerical analysis. However, MCDM methods have not been very popular in the health sector. In this study, five ones of Turkey's most intense and highly populated cities were selected and the risk of the spread of Covid-19 disease was evaluated on the basis of seven criteria. The PROMETHEE and the ELECTRE methods were conducted to rank the cities in terms of the spread of Covid-19. The PROMETHEE method correctly ranked the most risky city as Istanbul, but ELECTRE ranked Istanbul the second most risky. The results of the methods are compared with real data. PROMETHEE gave more convenient results than ELECTRE. Also, this paper offers a new field of study to the literature. [ABSTRACT FROM AUTHOR]

Maritime transport is one of the traditional alternatives for moving heavy loads around the world. Providing long ranges with high capacity and low costs are regarded to be the most important superficies of the marine transport. However, similar to the other transporting facilities some environmental impacts arise from shipping and port operations. Waste reception facilities are essential in order to minimize and manage the environmental impacts on marine ecosystems. This work aims to evaluate the practices at a waste reception plant serving for iron and steel industry. In the study, the maximum waste amounts were determined individually for all types of vessels handled at the port. The calculated quantities of ship sewage, solid waste, bilge, sludge, and waste oil were approximately 17483, 59, 616, 1715, and 123 m3 /month, respectively. The actual and calculated amounts of ship-sourced solid waste were found to be 607 and 708 m3 /year, respectively. The closer values indicate that the current disposal facility is sufficient for the management of solid wastes. On the other hand, a significant difference was found between the actual and calculated data for liquid waste. The present situation and ways to improve the overall efficiency of the plant were evaluated in detail within the study [ABSTRACT FROM AUTHOR]

Daily consumption of various teas is a popular tradition in many countries especially in Turkey and although it shouldn’t be in tea, carbonate can be added to obtain more brew from teas especially in local coffeehouses. In this study, the effect of carbonate addition on the essential element concentrations of some black teas (black, earl grey and green tea), herbal teas (fennel, mint and sage tea) and fruit teas (apple, lemon and rosehip) is analysed by inductively coupled plasma optical emission spectrometer (ICP-OES), after the infusion process of tea brewing with carbonate. Afterwards, the total element contents of the tea grains in are determined by again ICP-OES prior to microwave digestion process. Considering the data obtained from the experiments, carbonate addition increased or decreased some elements with respect to the tea types. According to the results obtained after the microwave digestion method, although the major elements in all types of teas are found as Ca, K, P, Mg and Mn, which do not reach 2% brewing from the tea grains, carbonated infusion is not recommended due to its increasing effect on the concentration of the major elements contained in teas. [ABSTRACT FROM AUTHOR]

Modeling the number of air passengers correctly is essential for management policy in the global world. Based on seasonality (depending on the season of the year), data about the number of air passengers are heteroscedastic. Heteroscedasticity violates "Homoscedasticity" which is one of the central assumptions of linear regression analysis. In this study, a new weighting approach called "Weighting Absolute Centered External Variable" (WCEV) is applied to the Turkish total monthly air passenger's data to obtain correct statistical inference and forecasting. Besides scatter plot months vs. studentized residuals, the homoscedasticity assumption is checked with the studentized RCEV test as well. Consequently, the WCEV method is shown superior performance against multiple linear regressions and exponential weighted moving average (EWMA) methods. The study also provides insights into the seasonal patterns of air passenger demand in Turkey, with passenger mobility increasing in the last quarter of each year and the lowest demand in January and February. This information can be used to optimize airport and airplane maintenance schedules and increase capacity during peak months. [ABSTRACT FROM AUTHOR]

This study proposes a new approach to reduce the information redundancy at Air Pollution Monitoring Networks (APMNs) and costs required for monitoring them. Proposed approach is based on Vector Autoregressive (VAR) model which describes the relationship between multivariate time series and consists of three main steps: In the first step, VAR model between two or more than two time series consisting of air pollutant observations is estimated. This step is repeated as the number of monitoring stations (n) under study and thus, n parameter vectors are obtained. In the second step, parameters vectors are divided into homogenous groups by using clustering analysis. The objective of this step is to identify the similar monitoring stations in terms of the relationship. Last step is to calculate the reduced information redundancy and the monitoring costs. To evaluate the efficiency of proposed approach, data sets consisting of PM10 and SO2 time series obtained from 116 APMNs at Türkiye are used. Fuzzy K-Medoids (FKM) as clustering method Xie-Beni (XB) index as cluster validity index are preferred. Experimental results showed that information redundancy and monitoring cost in PM10 and SO2 stations can reduced at the rate of 63.36 by following proposed approach. [ABSTRACT FROM AUTHOR]

In this study, antioxidant (TAS), oxidant (TOS) and antimicrobial activities of samples collected from Osmaniye province, which is located in the Eastern Mediterranean Region of Turkey and where Arum dioscoridis Sm. consumption is common, were determined. Water extracts of leaf parts of plant samples were obtained in a soxhlet device. TAS and TOS status were determined using Rel Assay kits. Antimicrobial activities of plant samples were determined against standard bacterial and fungal strains using agar dilution method. At the end of the studies, the highest TAS value among the plant samples was determined as 6.486±0.182. The highest TOS value was determined as 15.576±0.235. The highest OSI (oxidative stress index) value was determined as 0.285±0.007. In addition, A. dioscoridis extracts were determined to be effective against test microorganisms at 50-800 µg/mL concentrations. As a result, it was determined that the samples collected from different regions of the A. dioscoridis samples used in our study showed variability. In addition, it has been determined that A. dioscoridis may be an important antioxidant and antimicrobial source. [ABSTRACT FROM AUTHOR]

Amasya is a province of Turkey, situated on the Yeşil River in the Black Sea Region to the north of the country. In Family Health Centers (FHC), in the province of Amasya, a different method is applied for blood tests according to hospitals. Assay tubes are sent to the central laboratory outside the FHC or to the hospital laboratory in the city center by road transportations. This method brings time loss and financial burden. Theory of Innovative Problem Solving (TRIZ)-based solution proposal is presented as an alternative approach for the transport of assay tubes by taking into consideration the family health centers in Amasya. The problem was transformed into a TRIZ problem and solutions were produced using appropriate principles. Drone usage is proposed as a solution. If drone usage is preferred over vehicle use, it has been observed that it provides 93% financial and time savings. Today, the great impact of the COVID-19 pandemic on global healthcare systems has led to seeking new tools and methods to overcome the crisis. The use of drone technology has become even more important in order to provide fast and wide-ranging service, especially in the period of medical crisis, by reducing contact from person to person. [ABSTRACT FROM AUTHOR]

Viruses are obligate intracellular parasites that can infect animals, plants, bacteria and many more microorganisms. Viruses are spread by aphids and insects in plants, and by bloodsucking insects in animals. In humans, they can cause flu by spreading through respiratory, human-to-human contact, water and food. Some viruses, however rare, infect humans from animals. Coronavirus is one of the viruses transmitted from animals to humans. The new type of coronavirus (Covid-19), which is on the world agenda, is similar to seasonal flu, previously unidentified and contagious virus in humans. In this study, new types of coronavirus and its effects were investigated and studies related to coronavirus conducted worldwide were analyzed. As the treatment process in the world continues, every country has started to take its measures. The measures taken by countries, especially Turkey, have been identified. It is aimed to calculate the importance and priority values of the measures with the Analytic Network Process and Fuzzy Analytic Hierarchy Process methods, which is one of the multi-criteria decision-making methods. As a result of the values obtained, it is planned to prioritize the measures taken while struggling coronavirus and contribute to the struggle against coronavirus by giving emphasis to the priority measures. [ABSTRACT FROM AUTHOR]

The diagnosis and follow-up of focal liver lesions have an important place in radiology practice and in planning the treatment of patients. Lesions detected in the liver can be benign or malign. While benign lesions do not require any treatment, some treatments and surgical operations may be required for malign lesions. Magnetic resonance imaging provides some advantages over other imaging modalities in the detection and characterization of focal liver lesions with its superior soft tissue contrast. Additionally, different phases help make a clear diagnosis of different contrast agent retention properties in magnetic resonance imaging. This study aims to classify focal liver lesions based on convolutional neural networks by fusing magnetic resonance liver images obtained in pre-contrast, venous, arterial, and delayed phases. Magnetic resonance imaging data were obtained from Selcuk University, Faculty of Medicine, Department of Radiology in Turkey. The experiments were performed using 460 magnetic resonance images in four phases of 115 patients. Two experiments were conducted. Two-dimensional discrete wavelet transform was used to fuse the phases in both experiments. In the first experiment, the best model was determined using the original data, different number of convolution layers and different activation functions. In the second experiment, the best-found model was used. Additionally, the number of data was increased using data augmentation methods in this experiment. The results were compared with other state-ofthe art methods and the superiority of the proposed method was proved. As a result of the classification, 96.66% accuracy, 86.67% sensitivity and 98.76% specificity rates were obtained. When the results are examined, CNN efficiency increases by fusing MR liver images taken in different phases. [ABSTRACT FROM AUTHOR]

Highway geometric standards are defined depending on the road users -both passengers and drivers- and vehicle characteristics, as well as the land structure on which the route is located. The limit values for the radius of the horizontal curves, the maximum flow rate, and the combination of the curve characteristics are calculated according to the safety and comfort parameters. As the comfort parameter, lateral acceleration acting on the passenger while traveling on a curved section or the change of this lateral acceleration in unit time is used. In the current situation, the limit value of the lateral acceleration used in Turkey is 1.47 m/s2 [1]. However, it is not known if this value is still suitable considering the technology enhancements and the changes in people's expectations. The limit value of lateral acceleration brings out negative economic consequences for both the users and constructers when it is lower than necessary. Too safe standards force constructers to increase the radii of the curves which increases the construction costs or creates a need for speed limitation which increases the fuel costs via braking and accelerating. This study aims to evaluate the suitability of the existing limit value with a case study involving test drives and a survey. During the test drives different levels of lateral acceleration geometrically permissible within the vehicle are produced and the level of perception of this lateral acceleration is determined. Within the scope of the project, the case study is done with university students. In the survey, participants are asked to answer demographical questions and rate their level of comfort perception. The perception levels are considered in five different categories according to the level of discomfort. Data such as age, vehicle ownership, gender, weight, height, and transportation mode choices for their regular trips were also taken from the participants and the relationship between these and comfort perception was investigated. The results obtained from this study were investigated to determine whether it is necessary to disseminate the study to a broader group and whether the limit value is suitable for the age group we studied with. The results of this study, which will contribute to the literature in terms of evaluating this common standard, showed that the lateral acceleration limit value is lower than necessary and an advanced study with larger groups may suggest changing the standard. [ABSTRACT FROM AUTHOR]

Leaks occurring in distribution systems constitute a significant part of water losses. Active leak control methods (district metered area, minimum night flow analysis, repair speed and quality) are applied to manage and control the leaks. It is very important to use appropriate indicators to determine and monitor the performance in the process, and compare the systems with each other. In this study, it was aimed to use the infrastructure leakage index (ILI) proposed by International Water Association (IWA) and considered as the unique indicator that is used to compare systems with each other in order to analyze and monitor system performance in leakage management for pilot regions. However, the use of this indicator, which is preferred by a limited number of utilities in Turkey due to the lack of information and awareness, and lack of technical infrastructure, requires basic data representing the system characteristics. Based on the pilot case studies, the role and advantage of this indicator in leakage management, the problems experienced and the interpretation of the results were discussed. With this indicator, it is possible to determine the leakage level in the current conditions and to determine the most suitable process accordingly. It is thought that this study will make a significant contribution to technical personnel in terms of leakage management. [ABSTRACT FROM AUTHOR]

Rapid increase in the number of universities in Turkey resulted in competition among higher education institutes to recruit the best students. Student requirements are important information for universities because if universities have a better understanding of students' requirements, they would be in a better position to recruit them and manage the educational service quality. The purpose of this study is to determine the most important requirements of engineering students in a Turkish university. The proposed methodology integrates Service Quality (SERVQUAL), Analytic Hierarchy Process (AHP) and Quality Function Deployment (QFD) methods. Results show that (1) engineering students value contemporary education system more than traditional education system (2) they are demanding partnership from their instructors based on trust, respect and guidance (3) curriculum should be designed with courses that are industry oriented, interactive and case study based with practical information provided to participate in real-life engineering projects. In addition, institutions should recognize the importance of investing in academic faculty when they are trying to improve the quality of their programs and recruit the best students. [ABSTRACT FROM AUTHOR]

Agriculture is one of the main economic sectors in Türkiye. Therefore, a performance evaluation is essential for an improvement in agricultural sector. In this study, the productivity of Turkish agricultural sector was analyzed for the years between 2006-2015. A non-parametric technique, Data Envelopment Analysis (DEA) based Malmquist index method, was applied to calculate the Total Factor Productivity (TFP) indices for agriculture in 26 NUTS2 (The Nomenclature of Territorial Units for Statistics) regions of Türkiye for the selected 10-year period. Total agricultural production value is used as the output variable and six input variables are selected as: land, labor, machine, livestock and government investment. The analysis was conducted via the computer program DEAP2.1. The result reveals that agricultural TFP of regions has decreased by 2% annually on average. The maximum TFP growth in agriculture occurred between 2007 and 2008 with a mean increase of 12% in overall TFP of regions. On the other hand, the greatest regression in the overall TFP was observed in 2010-2011 period by a decrease of 13%. [ABSTRACT FROM AUTHOR]

On microblogging sites, which are gaining more and more users every day, a wide range of ideas are quickly emerging, spreading, and creating interactive environments. In some cases, in Turkey as well as in the rest of the world, it was noticed that events were published on microblogging sites before appearing in visual, audio and printed news sources. Thanks to the rapid flow of information in social networks, it can reach millions of people in seconds. In this context, social media can be seen as one of the most important sources of information affecting public opinion. Since the information in social networks became accessible, research started to be conducted using the information on the social networks. While the studies about spam detection and identification of opinion leaders gained popularity, surveys about these topics began to be published. This study also shows the importance of spam detection and identification of opinion leaders in social networks. It is seen that the data collected from social platforms, especially in recent years, has sourced many state-of-art applications. There are independent surveys that focus on filtering the spam content and detecting influencers on social networks. This survey analyzes both spam detection studies and opinion leader identification and categorizes these studies by their methodologies. As far as we know there is no survey that contains approaches for both spam detection and opinion leader identification in social networks. This survey contains an overview of the past and recent advances in both spam detection and opinion leader identification studies in social networks. Furthermore, readers of this survey have the opportunity of understanding general aspects of different studies about spam detection and opinion leader identification while observing key points and comparisons of these studies. [ABSTRACT FROM AUTHOR]

Ground-Penetrating Radar (GPR) can successfully image the buried archaeological findings depending on changes in the electromagnetic features of the researched materials. The GPR survey in the Akçakale (Kordyle) castle built in the 13th century was presented in this study. The castle is located at Akçakale district of Trabzon province on the Eastern Black Sea Coast of Turkey. The key focus of this research is to investigate whether there are archaeological findings in the inner area of the castle with GPR. For this purpose, GPR data were collected by using 250 and 500 MHz antennas at 95 transects within grid lengths ranging 30-50m in the study area. Anomalies considered to be important by evaluating the filtered data were marked on a sketch where the measurement lines are located. On this sketch, the overlapping areas of the anomalies obtained from the data in different directions on the measurement lines were shown by ellipses with red-cut as priority possible anomaly areas. Possible archaeological structures were been successfully determined from 2D and 3D images obtained GPR data in the study area. As a result, it has been suggested that archaeological excavations which will be planned in the study area should be conducted by considering these areas. After the excavations, archaeological findings which are compatible with anomalies were found in the studied area. Thus, it was seen that successful results were obtained with the GPR method in the study area. [ABSTRACT FROM AUTHOR]

Strawberry is a delicious fruit that can be consumed fresh or/and can be used in the industry. One of the important factors that affect yield is the plant protection problems. Fungicides because of the metabolites they produce, especially their presence in foods, pose a threat to health and also cause economic problems. Botrytis cinerea causes grey mold disease in over 220 plant species. Samples were collected from five different localities from Aydın and Mersin province. Samples were incubated on um and then isolated from mixed cultures. Morphological and molecular identification of the samples were made. Transposon sites were amplified with PCR. In total 154 B.cinerea samples were isolated of which Transposa 20.1%, Boty 46.1%, Flipper 10.4% and Vacuma 23.4%, in Aydın population Transposa 19.5%, Boty 48.3%, Flipper 9.2% and Vacuma 23.0%, in Mersin Transposa 20.9%, Boty 43.3%, Flipper 11.9% and Vacuma 23.9% were found. In the fungicide resistant test, in spore tests, Cyprodinil was found most effective while fenhexamid was found less effective. In mycelium tests, fenhexamid was most effective at the lowest concentration applied while carbendazim was found less effective even at the highest concentration. Although short-term results are limited, in long-term transposon profiling can help with disease management. [ABSTRACT FROM AUTHOR]

The coronavirus infection outbreak started in Wuhan city, China, in December 2019 (COVID-19) and affected more than 200 countries in a year. The number of patients dy in g from and infected with COVID-19 is increasing at an alarming rate in almost all affected countries. One of the most important factors in the COVID-19 death and case rates is the care of intensive care patients. The management of COVID-19 patients who need acute and/or critical respiratory care has created a significant difficulty for h ealthcare systems worldwide. To prevent the further spread of COVID-19 around the world and to fight the disease, nonclinical computer-aided quick solutions such as artificial intelligence and machine learning are needed. Prediction techniques evaluate past situations and enable predictions about the future situation. In this study, using the dataset created from the data received from the World Health Organization and national database, the numbers of intensive care, intubated patients, and deaths from COVID-19 in Turkey were predicted by the random forest, bagging, support vector regression, classification and regression trees, and k-nearest neighbors machine learning regression methods. In this study, the random forest method has been the most successful algorithm for predicting the number of intensive care patients (r = 0.8698, RMSE = 188.5, MAE = 135.1, MAPE = 13%), the number of intubated patients (r = 0.9846, RMSE = 47.1, MAE = 39.7, MAPE = 9.2%), and the number of deaths (r = 0.9994, RMSE = 1.2, MAE = 0.9, MAPE = 3.5%). The results in this study, it has been shown that machine learning methods, which have been successfully applied in other epidemic diseases, will be successfully applied in the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Deep learning methods are the subfield of the machine learning models that have spread rapidly in the field of engineering in the last decade. But, these methods are a fairly new in educational literature. The aim of this study was modeling and predicting mathematics achievement of successful and unsuccessful students via deep learning methods. For this purpose, Turkey's Programme for International Student Assessment (PISA 2018) survey data was used. Deep learning methods were displayed comparable performance to multi-layer perceptron and logistic regression. Jordan neural network method was found the most successful method among Elman neural network, Logistic regression and multi-layer perceptron methods with 0.826 accuracy and 0.739 area under curve scores. It was understood that deep learning methods can be used in the modelling and predicting of students' mathematics achievement. [ABSTRACT FROM AUTHOR]