Renewable energy sources as a solution for energy security risk: Empirical evidence from OECD countries

This study analyzes the impact of renewable energy on energy security risk for 23 OECD countries over the 1985-2016 period using second generation panel data techniques. To our knowledge, this study is the first to empirically analyze the impact of renewable energy sources on energy security risk for OECD countries. According to the findings obtained from the Augmented Mean Group (AMG) estimates for OECD countries, we have concluded that wind, hydroelectric, and total renewable energy reduce energy security risk, whereas biomass and solar sources do not have a significant effect on energy security. According to the Dumitrescu-Hurlin panel causality test results, we found unidirectional causality running from biomass and hydroelectricity to energy security risk and bidirectional causality between industrial production, total energy, economic integration, urbanization, and wind and energy security risk. The study also made country-specific estimates which show significant differences in terms of the direction of the relationship, significance, and coefficient size by country. The findings demonstrate that wind, hydroelectricity, and total renewable energy reduce energy security risk for OECD countries. However, these positive effects are not valid for all OECD countries. According to these results, OECD countries should implement policies aiming to reduce their energy security risk specific to countries' unique characteristics. (c) 2021 Elsevier Ltd. All rights reserved

Effect of temperature on wear behavior of multilayered thin cr-coated acrylonitrile-butadiene-styrene polymer: An experimental and prediction study

The wear behavior of thin chromium-coated and uncoated acrylonitrile-butadiene-styrene (ABS) samples were investigated by using a pin-on-disk test apparatus with a heat chamber for different temperatures of 30°C, 50°C, 70°C, 90°C, and 110°C. The wear tracks were characterized with a scanning electron microscope, energy dispersive X-ray spectroscopy, and widefield confocal microscope. The tests showed that the wear of thin chromium-coated ABS samples exhibited better results under high temperature in comparison with uncoated samples. Then, the experimental data are used to create Nonlinear AutoRegressive with eXogenous inputs (NARX) and transfer function prediction models for wear experiments. It is observed that the NARX model matches quite well with the experimental data. Copyright © 2021 by ASTM International.The author acknowledges PGS Plastics Wood Metal Coating Industry and Trade Ltd. Co. for the support

Picture Fuzzy Edas Method for Team Leader Selection in International Audit Firm

International Conference on Intelligent and Fuzzy Systems, INFUS 2021 -- 24 August 2021 through 26 August 2021 -- 264409Today, many companies are turning to teamwork rather than individual efforts. It seems to be much more effective, creating a common synergy by working in a team after an appropriate job sharing. The efficiency of team work properly depends on what the team leader guides in this regard. For this reason, the selection of a team leader is also extremely important for effective and efficient teamwork. The team leader selection can be called as a MCDM problem because it depends on numerous selection criteria. In this research, a decision support model is provided in order to help an independent firm, specializing in audit services selecting the most appropriate candidate. In the model proposed, considering the fuzziness of the evaluation processus Picture Fuzzy Edas method is applied. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG

A Comparative Study of Amino Acid Encoding Methods for Predicting Drug-Target Interactions in COVID-19 Disease

Identifying drug-target interactions plays an important role in discovering drugs. Identifying, finding, and preparing drug molecule targets is the key for modern drug discovery. However, potential drug-target interactions are usually determined experimental approaches (in vivo and in vitro). Experimental approaches are expensive, require a lot of manpower, and the data are complex, making it difficult to use these methods effectively. For these reasons, the importance of simulation-based methods (in-silico) has increased and computational methods have started to be used more actively. In addition, more computational methods need to be developed to validate the interactions between drugs and their targets. In order to predict and validate the interactions between drugs and their targets by computational methods, both drugs and targets need to be mapped and to be classified with artificial intelligence techniques. As it is known, targets consist of proteins and protein sequences consist of letters. Furthermore, drug compounds are expressed in molecular codes. It is not possible to determine the interactions between drugs and their targets by computational methods without any pre-processing (mapping). The performance of the DTI (Drug-Target Interaction) prediction process varies according to the protein mapping and artificial intelligence approaches selected thus, it is important to choose the right methods in such applications. There are a number of protein mapping techniques and artificial intelligence algorithms in the literature. In this study, prediction of drug-target interactions carried out for COVID-19 disease by using certain protein mapping techniques and a deep learning. The proposed method consists of 5 stages. In the first stage, drug-target interactions were obtained from the DrugBank database. In the second stage, mapping of drug compounds and target proteins was made. While PubChem fingerprinting method was used for the mapping of drug compounds, target proteins were mapped with 6 different methods; Meiler parameters, Atchley factors, PAM250, BLOSUM62, Miyazawa energies and Micheletti potentials. In the third stage, the mapped drug compounds and the mapped target proteins were combined and a one-dimensional feature space was obtained. In the fourth stage, the one-dimensional feature that was generated before was classified with the LSTM (Long-Short Term Memory) deep learning model and the prediction was performed. In the last stage, the performance of the protein mapping methods was determined and compared with accuracy, precision, recall, f1-score, and ROC (Receiver Operating Characteristic) evaluation matrices. When the application results were examined, it was seen that all protein mapping techniques performed above 85%. The best accuracy and ROC scores were obtained from Atchley factors and Meiler parameters. With Atchley factors, an average of 92% accuracy and 98% ROC were obtained. With the Meiler parameters, the ROC value did not change, but the accuracy value was measured as 91%. Afterwards, it was observed that Micheletti potentials and Miyazawa energies showed the second best performance. On average, 90 and 91% accuracy values were obtained, respectively. ROC values were calculated to be close to each other and 98% ROC value was obtained for Micheletti potentials, while this ratio decreased to 96% with Miyazawa energies. BLOSUM62 and PAM250 protein mapping methods were more ineffective than other methods. While BLOSM62 showed an average accuracy of 87%, PAM250 predicted drug-target interactions an average of 91% accuracy. While the ROC value of the BLOSUM62 method was 89%, this rate increased in PAM250 and a ROC value of 92% was obtained. Contributions obtained by the end of the study can be expressed as follows; with this study for the first time, drug-target interactions of COVID-19 were predicted by protein mapping techniques. In addition, the most effective protein mapping method among protein mapping techniques was determined. It was demonstrated that the selected protein mapping techniques are important in determining drug-target interactions. Additionally, it has been observed that computational-based methods can be at least as effective as experimental approaches. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG

A comparative study on appliance recognition with power parameters by using machine learning algorithms

Recently, machine Learning algorithms are widely used in many fields. Especially, they are reallygood to create prediction models for problems which are not easy to solve with conventionalprogramming techniques. Although, there are many different kinds of machine learningalgorithms, results of applications are varying depend on type of data and correlation ofinformation. In this study, different machine learning algorithms have been used for appliancerecognition. The measurement data of Appliance Consumption Signatures database and somederivative values have been used for training and testing. Additionally, a data pre-processingtechnique and its effects on results have been presented. Filtering corrupted data and removinguncertain measurement value has improved the quality of machine learning. Combination ofmachine learning algorithms is best way to work with uncertain values. Different feature extractionmethods and data pre-processing techniques are crucial in machine learning. Therefore, this studyaims to develop a high accurate appliance recognition technique by combining grey relationalanalysis and an ensemble classification method. The results of this new method have beenpresented comparatively to show the improvement for itself and previous studies that uses thesame database

Characterization of gamma-ray and neutron radiation absorption properties of synthesized quinoline derivatives and their genotoxic potential

In this study, six different types of quinoline derivatives were synthesized, which can be used as an anti-radiation drug for active matter and its effects against radiation was determined. The effective removal cross-sections, mean free path, halfvalue layer and neutron transmission number were calculated for fast neutron radiation using the Monte Carlo simulation GEANT4 and FLUKA codes. Gamma radiation shielding parameters such as mass attenuation coefficient (MAC), effective atom number (Zeff), mean free path (MFP), exposure buildup factor (EBF) and halfvalue layer (HVL) were theoretically calculated using WinXCom and Phy-X/PSD software. Neutron equivalent dose measurements were performed using a 241Am-Be fast neutron source and a BF3 gas detector with 4.5 MeV of energy and 74 GBq activity. Both simulation and experimental measurements were compared with paraffin and conventional concrete. It was found that the quinoline derivative shielding material absorbed radiation much better than these reference materials. Additionally, the genotoxic potentials of the derivatives were assessed by using the yeast DEL assay and the results revealed that the derivatives can be considered genotoxically safe at the tested concentrations (up to 5 mM). It has been suggested that this new radiation shielding derivatives material can be used as active ingredients for a drug to be developed against both neutron and gamma radiation

A study on investigating socio-demographic characteristics affecting life satisfaction of tourist guides

Bu araştırmanın temel amacı; turist rehberlerinin sosyo-demografik özellikleri ile yaşamdoyumları arasındaki ilişkinin tespit edilmesidir. Araştırmanın evrenini, eylemli ve seyahatacentasına bağlı şekilde çalışan turist rehberleri oluşturmaktadır. Araştırmada anket tekniğikullanılmış ve 416 turist rehberine anket uygulanmıştır. Araştırmaya katılan turist rehberlerinin yaşam doyumlarının ölçülmesi için ilgili alanyazında yaygın olarak kabul görmüş olanDiener vd. (1985) tarafından geliştirilen yaşam doyumu ölçeği kullanılmıştır. Araştırma sonucunda; turist rehberlerinin yaşam doyumlarının “medeni durum” ve “bağlı oldukları turistrehberleri odası” değişkenleri açısından anlamlı farklılıklar gösterdiği tespit edilmiştirThe main purpose of this study is to determine the relationship between the socio-demographic characteristics and life satisfaction of tourist guides. The population of the study consists of the tourist guide who are active and working for travel agencies. Questionnaire technique has been used in the study and a questionnaire has been applied to 416 tourist guides. The life satisfaction scale developed by Diener et al (1985), which is widely accepted in the relevant literature, has been used to measure the life satisfaction of the tourist guides participating in the study. As a result of this study; it has been determined that life satisfaction of the tourist guides shows significant differences in terms of “marital status” and “the tourist guide chamber which they are affiliated

Disaster and emergency preparedness in Istanbul museums

Istanbul is a high-risk city in terms of disasters, as well as having museums hosting many cultural heritage artifacts. The purpose of this study is to analyze the preparation level of museums in Istanbul for disaster and emergencies, to determine the problems in planning, and to evaluate the point of view of museum staff on disaster management. A mixed method that includes qualitative and quantitative data about state and private museums in Istanbul was used in the research. A form that included a questionnaire and open-ended interview questions was used for data collection. According to the results, it has been determined that state museums have problems such as testing the disaster plan and giving practical training to staff, and those who define themselves as small among private museums do not have a disaster plan due to lack of staff and insufficient financing. Disaster management is a neglected issue and lags behind other museum activities. This research, which provides valuable information about the disaster preparedness of museums in Istanbul, has implications for disaster planning for all museums. Since the problems in planning are also seen in cultural institutions in different parts of the world, recommendations can be adapted to these institutions

The effect of collector parameters on nanofiber yarns produced by electro yarn spinning machine with conical collector

In this work, electrospun yarns were produced by a nanofiber yarn spinning machine with a conical collector. In the first part, two different polymers (PAN and PVDF-HFP) were used in nanofiber yarn spinning with two different types of conical collector which are made of stainless steel and aluminium. In the second part, four different types of aluminium collector having different wall thickness and surface properties were used. For all yarns produced, the effect of different parameters has been analyzed in terms of fibre fineness distribution, yarn fineness and yarn linear density, yarn strength and breaking elongation. The results show that yarns produced by using aluminium collector are finer and have higher strength compare to the ones produced by stainless steel collector while PVDF-HFP nanofiber yarns are finer and have higher tensile strength compare to PAN. The results also show that yarns get coarser while fibres get finer as the collector wall thickness is increased in general.Scientific and Technological Research Council of Turkey (TuBTAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [117M166]We would like to gratefully acknowledge Scientific and Technological Research Council of Turkey (TuBTAK) for its support (Project no: 117M166). We also would like to thank Dr. Ylmaz Guven for his support during electric field simulation

ON SOME HUMAN VALUES IN SOCIAL LIFE DURING THE COLLAPSE OF THE TURKEY SELJUKS

Bu çalışmada Kösedağ Savaşı ve sonrasında Anadolu’da yaşanan sosyal buhran ile Anadolu’dakiinsani değerlerin bu buhranın atlatılmasındaki etkisi incelenecektir. Kösedağ bozgunu (641/1243) ilebirlikte iktisadi, siyasî ve en önemlisi sosyal buhran yaşanmıştır. Reaya, ekonominin ve güvenliğinbozulması ile otorite boşluğunun sonuçlarını yaşayarak deneyimlemiş ve dertlerini anlatacakları birmerci bulmakta zorlanmıştır. Şehirli ve göçer tabakaları arasındaki sosyal farkın yanı sıra Anadolu’yayoğun bir göçün de gelmesi var olan keşmekeşin artmasına sebep olmuş, müslim-gayrimüslim tümreaya var olma mücadelelerini bu karmaşa ortamında vermek zorunda kalmışlardır.İnsanlık tarihine birer hediye olan değerlerin oluşması genellikle böyle buhran zamanlarındansonra vuku’ bulmuştur. Bu dönemde Anadolu’da İslâm algısı kendine has bir anlayış geliştirmiştir.Bu anlayışa göre en yüce değer insan sevgisidir. İnsan, müslim-gayrimüslim ayırt edilmeksizindeğerlidir, sevgi ve saygıyı hak etmektedir. Bunların yanında din, dil, ırk ayrımı yapılmaksızın dinîliderlerin ve özellikle Türkmen dervişlerinin tüm halka eşit davrandığı ve herkesin sığınağı olduğu,dinî önderlerin dinler üstü bir pozisyonda bulunduğu izlenmektedir.In this study, the social crisis experienced in Anatolia after the Kösedağ War and its aftermath and the impact of human values in Anatolia on overcoming this crisis will be examined. After the Kösedağ defeat (641/1243) an economic, political and most importantly social crisis was experienced. The people experienced the consequences of the deterioration of the economy and security, and the lack of authority, and had a hard time finding an authority to tell about their troubles. In addition to the social difference between urban and nomadic strata, the intense immigration to Anatolia caused the existing chaos to increase, and all Muslim and non-muslim citizens had to struggle to exist in this chaos. Sentimental values which were presented to the history of human, are formed usually after such times of depression. In this period, the perception of Islam in Anatolia developed a unique understanding. According to this understanding, the highest value is humanity. Human beings are valuable, regardless of whether they are Muslim or non-muslim, and deserve love and respect. In addition to these, it is observed that religious leaders and especially Turkmen dervishes treat all people equally, regardless of religion, language or race, and are a shelter for everyone, and especially religious leaders have a supra-religious position