Miracle-dz/UniFAR-FTUs huggingface dataset:
Denetimde Standartlaşma Çabaları: Motorlu Taşıt Sürücüleri Kursu Denetim Rehberi Örneği

Bu arastirmanin amaci, denetimde uygulama birligi ve standartlasmayi saglamak uzere hazirlanmis motorlu tasitlar surucu kursu denetim rehberinin temel bolumlerinin egitim denetimi alanyazini ve yasal mevzuata gore tutarliliginin incelenmesidir. Calismada, nitel arastirma yaklasimi benimsenmis olup dokuman analizi teknigi kullanilmistir. Bu dogrultuda Milli Egitim Bakanligi tarafindan maarif mufettisleri baskanliklari icin olusturulan denetim rehberleri icerisinden motorlu tasitlar surucu kurslari icin hazirlanan dokuman incelenmistir. Arastirmanin verileri dokuman analizi teknigine gore cozumlenmis ve elde edilen bulgular “Denetim Rehberinin Yasal Belgelerle Iliskisi” ve “Denetim Rehberinin Alanyazinla Iliskisi” olmak uzere iki tema altinda sunulmustur. Arastirmanin sonuclari, rehberdeki ifadeler ile yasal dayanagin cogunlukla ortustugunu gostermistir. Ote yandan, yasal dayanaga gore rehberde eksik ifade edilmis maddeler de bulunmaktadir. Ayrica, denetim rehberinin yayimlanmasindan sonra yasal belgeler uzerinde yapilan guncellemelerin bu rehbere yansitilmadigi anlasilmistir. Denetim rehberindeki maddeler alanyazin acisindan degerlendirildiginde bazi maddeler icin degerlendirme olcutlerinin yer almadigi saptanmistir. Arastirma kapsaminda Milli Egitim Bakanliginin ilgili yonetmeliklerde yaptigi duzenlemeleri denetim rehberine yansitmasi onerilmistir.
Prediction of Credit Card Using the Naïve Bayes Method and C4.5 Algorithm

Prediction credit card submission is a system that is able to provide an assessment of alternatives in order to help credit card applicants in making decisions. Many methods can be used in building a prediction system for credit card submissions. This research will compare two methods in predicting credit card submission, namely the naïve bayes method and c4.5 algorithm, case study of Credit Card Submission Prediction, the results of the research are, knowing the level of accuracy of the two methods. Criteria that form the basis of decision making include age, sex, recent education, marital status, number of dependents, type of company, monthly income, and salary slip. The final result found that the naïve bayes method and the c4.5 algorithm are relatively the same.
Choice of R&D strategy and asymmetric cost behaviour

ABSTRACT This study investigates the effect of corporate R&D investment on the asymmetric behaviour of SG&A costs. Managers decide how much resources to invest in R&D, based on the strategic objectives of the company. We found that if more resources are invested in R&D, the company reserves more resources when sales decrease. Additionally, we distinguished capitalised R&D from expensed R&D to demonstrate that the effect on SG&A cost behaviour is different according to disclosure policies. This will help in understanding the decision-making process which is dependent on the management’s motivation or perception of R&D investment.
Exploring Creative Tourism Potential in Protected Areas: The Kruger National Park Case

Creative tourism strategies are often adopted by destinations as a result of its appeal to policy makers, without a serious assessment of tourists’ needs. Although there are significant cultural and heritage resources in and on the periphery of the Kruger National Park (KNP), these resources seem to be inadequately harnessed. The aim of the article is thus to explore the potential of creative tourism as a medium to harness cultural and heritage resources in the KNP by measuring the importance of local community (cultural tourism) elements to tourists when visiting the KNP. Convenience sampling is used to distribute questionnaires to tourists, and 201 responses are used in the data analysis. The results indicate that respondents have a neutral opinion regarding the importance of local community aspects, and recommendations are made to exploit the untapped potential for creative tourism. This study adds to the current debate on creative tourism by assessing its role in protected areas.
Conception and development of inductive reasoning and mathematical induction in the context of written argumentations

Abstract: Nowadays, mathematical reasoning and making proof have taken importance for all students from the grade level of elementary education to university. More specifically, mathematical induction (MI) is a kind of proof and reasoning strategy taking place nearly all grade levels. Moreover, teachers are important factors affecting student learning and they can acquire necessary knowledge and skills developmentally in their teacher education programs. This paper makes contributions to domain of research by investigating the development of PMT’s conception of MI in the context of written argumentations encouraging MI. In other words, the purpose of this multiple case study is to explore how PMT’s conception of mathematical induction develop through their written argumentations. These cases show that there exist improvements in PMT’s written argumentations, conception of MI and proof construction activities related to MI. In other words, the more organized and structured they produced written argumentation, the more successfully they use and make mathematical induction.
Sensing Occupancy through Software: Smart Parking Proof of Concept

In order to detect the vehicle presence in parking slots, different approaches have been utilized, which range from image recognition to sensing via detection nodes. The last one is usually based on getting the presence data from one or more sensors (commonly magnetic or IR-based), controlled and processed by a micro-controller that sends the data through radio interface. Consequently, given nodes have multiple components, adequate software is required for its control and state-machine to communicate its status to the receiver. This paper presents an alternative, cost-effective beacon-based mechanism for sensing the vehicle presence. It is based on the well-known effect that, once the metallic obstacle (i.e., vehicle) is on top of the sensing node, the signal strength will be attenuated, while the same shall be recognized at the receiver side. Therefore, the signal strength change conveys the information regarding the presence. Algorithms processing signal strength change at the receiver side to estimate the presence are required due to the stochastic nature of signal strength parameters. In order to prove the concept, experimental setup based on LoRa-based parking sensors was used to gather occupancy/signal strength data. In order to extract the information of presence, the Hidden Markov Model (HMM) was employed with accuracy of up to 96%, while the Neural Network (NN) approach reaches an accuracy of up to 97%. The given approach reduces the costs of the sensor production by at least 50%.
Neuromodulation of premotor and posterior parietal cortices for enhancing explicit motor sequence learning in healthy individuals: a randomized, sham-controlled crossover trial

Background: Anodal transcranial Direct Current Stimulation (tDCS) has been shown to be effective in improving human motor learning when applied over the contralateral primary motor cortex (M1). However, the stimulation of other cortical areas, such as the posterior parietal (PPC) and premotor (PMC) cortices, may be also beneficial. Methods: The present study (crossover design) investigated the effects of tDCS applied over PPC, PMC, and M1 on the acquisition and retention of a new motor skill, and on the generalization of such learned skill in healthy individuals. During a sequential finger-tapping task (FTT), performed with the non-dominant (left) hand, participants received real or sham anodal tDCS (1.5 mA, 20 min) over PPC, PMC, and the M1 of the right hemisphere. Explicit motor sequence learning was measured online (during the training with tDCS; primary outcome) and 24 hours after tDCS (retention, secondary outcome). A new, untrained, sequence was used to assess generalization effects (secondary outcome). Results: Anodal tDCS of M1 improved both online learning and retention. PMC tDCS facilitated the generalization of the learning effect to the untrained motor sequence. In contrast, neuromodulation of the PPC does not influence motor sequence learning. Conclusions: These findings show that, in addition to M1, higher-order associative cortical regions (PMC and PPC) are involved in explicit online motor sequence learning, retention and generalization playing different roles, as indicated by the differential modulatory effects of anodal tDCS.
Intelligibility of L2 speech in ELF

This paper investigates the phonology of L2 speech and its impact on intelligibility in English as a Lingua Franca (ELF) contexts. Many studies have considered speaker-related characteristics, such as speech styles and pronunciation features, that influence the intelligibility of L2 speech for both nonnative speakers (NNS) and native speakers (NS). However, only a handful of studies have considered what impact listener-related conditions, such as a shared first language (L1) background or L1 typology between NNSs, may have on the intelligibility of their speech. Therefore, an online survey was used to study the intelligibility of Mandarin-English, French-English, Japanese-English, and German-English from the perspective of 100 NNSs. It was hypothesized that a shared L1 or shared L1 typology between two NNSs will enhance the intelligibility of their speech. However, the findings did not support this supposition. For example, the Mandarin speakers did not find Mandarin-English to be more intelligible than Japanese-English or any of the other two accent types. Similarly, the results indicated that a shared L1 typology between two NNSs does not improve the intelligibility of their speech. The data did suggest however that a listeners’ familiarity to one accent type may improve the intelligibility of a typologically related novel L2 accent. The limitations of the findings are discussed along with their implications for future research directions.
Pharmacokinetic and bioavailability study of lercanidipine hydrochloride fast disintegrating tablets in rabbits by high-performance liquid chromatography

In the present study, fast disintegrating tablets of Lercanidipine Hydrochloride (LFDT) were tested in vivo in the buccal cavity of the rabbits. Various pharmacokinetic parameters were analysed in the study, including maximum measured plasma concentration (Cmax), time of maximum measured plasma concentration (tmax) and area under the plasma concentration vs time curve (AUC). Also, the comparative study of the Lercanidipine Hydrochloride fast disintegrating tablets (LFDT) was performed with the marketed conventional tablets of the drug (LMKT). The technique selected for the bioanalytical analysis of the blood samples of the rabbits for pharmacokinetic data computation was High-Performance Liquid Chromatography. An already well-established and validated method was used to analyse the blood samples of the rabbits. The results revealed that the rate of absorption was improved for fast disintegrating tablets of Lercanidipine Hydrochloride (LFDT) as compared to the marketed conventional tablets of the drug (LMKT). This indicated that drug was rapidly absorbed from the fast disintegrating tablet and attained elevated plasma concentration in a short interval after dosing than the marketed formulation. However, the value of tmax was drastically shorter for LFDT than the LMKT. The average peak plasma concentration also designated a rise in the extent of absorption (AUC). From the present study, it was concluded that the fast disintegrating tablet batch (LFDT) had much more improved pharmacokinetic parameters as compared to its conventional marketed counterpart (LMKT).
Evaluation of the Relationship between Silent Cerebral Lesions and Triglyceride/HDL-Cholesterol in Patients with First Stroke Attack

Aim: Triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio is defined as the serum atherogenicity index. High TG/HDL-C ratio is related with vascular diseases, insulin resistance and metabolic syndrome. The TG/HDL-C ratio in cerebrovascular diseases, especially in silent cerebral lesions hasn’t been well studied. The aim of this study was to evaluate the frequency of silent cerebral ischemia (SCI) and leukoaraiosis (LA), and its relationship with TG/HDL-C ratio in patients admitted with the first ischemic stroke attack. Material and Methods: We retrospectively evaluated 200 patients who admitted to Bulent Ecevit University Faculty of Medicine, Department of Neurology with the diagnosis of acute first ischemic stroke. Silent cerebral lesions were defined as LA and SCI with magnetic resonance imaging. TG/HDL-C ratio was calculated by dividing TG levels by HDL-C levels. Results: Silent cerebral lesions were detected 124 (83.2%) of 149 patients. LA severity was evaluated according to Fazekas score, 22 (14.8%) of patients were grade 0, 49 (32.9%) of them were grade 1, and 78 (52.3%) of them were advanced periventricular white matter hyperintensity (adv-PWMH) group. TG/HDL-C ratio in SCI group was higher than the group without SCI, but it wasn’t statistically significant (p=0.091). A significant increase was observed in the TG/HDL-C ratio, as LA severity increased. TG/HDL-C ratio was significantly higher in adv-PWMH group (p=0.050). Conclusion: High serum atherogenicity index is associated with atherosclerosis and vascular endothelial dysfunction. With this simple, inexpensive and effective test method, high-risk group of LA and SCI could be identified.
A Review on Lightweight Block Ciphers

Lightweight cryptography has been very important for the last few years. It became one the most important block of a secure embedded systems because it has been a strong requirement for any resource constrained devices, and has several features like, it consumes only less power, less memory, etc. This paper is a survey of various lightweight block ciphers such as PRESENT, GIFT, RECTANGLE, PICO and LED. The strength and weaknesses of each algorithm and the several attacks acting on them is also summarized.
Incremental Detection of Remote Sensing Objects With Feature Pyramid and Knowledge Distillation

When a detection model that has been well-trained on a set of classes faces new classes, incremental learning is always necessary to adapt the model to detect the new classes. In most scenarios, it is required to preserve the learned knowledge of the old classes during incremental learning rather than reusing the training data from the old classes. Since the objects in remote sensing images often appear in various sizes, arbitrary directions, and dense distribution, it further makes incremental learning-based object detection more difficult. In this article, a new architecture for incremental object detection is proposed based on feature pyramid and knowledge distillation. Especially, by means of a feature pyramid network (FPN), the objects with various scales are detected in the different layers of the feature pyramid. Motivated by Learning without Forgetting (LwF), a new branch is expended in the last layer of FPN, and knowledge distillation is applied to the outputs of the old branch to maintain the old learning capability for the old classes. Multitask learning is adopted to jointly optimize the losses from two branches. Experiments on two widely used remote sensing data sets show our promising performance compared with state-of-the-art incremental object detection methods.
Simulation for senior undergraduate education of robot engineering based on Webots

In this paper, a comprehensive simulation framework is presented for senior undergraduates of robot engineering based on open‐source simulation software, Webots. In our curriculum, a virtual quadruped robot is used to test different theories. First, students should design the physical structure of the robot and select proper drivers according to the requirements on its motion ability. Then, the kinematics and workspace of each leg should be analyzed. To overcome different terrains, terrain recognition, and motion planning methods are presented. Finally, the processes of building a virtual robot and its test environment are introduced. Simulation results demonstrate that it is possible to test different robot techniques in our proposed simulation framework. Teaching practice and feedback from students show their promotion of understanding of robot theories. Their interest in robots are also increased.
Criteria of evaluating augmented reality applications

Introduction . The field of augmented reality (AR) is growing rapidly and has great advances in interaction, navigation and tracking. Nowadays there are a lot of trends for AR applications in different areas (education, entertainment, business, medicine, etc.). However, there is a lack of research to provide the evaluating AR apps framework to support developers when creating suitable AR applications for specific needs. We provide a practical approach to quantify some of the AR applications features. We focus on the development of criteria for evaluating augmented reality applications. We discuss the criteria of choosing dimensions for that space such as standards for AR, tools for AR development, navigation and tracking, content management, usability. We provide analysis and evaluation of AR apps through each characteristic using guidelines which we have developed. Materials and Methods . An AR application is a software application that integrates digital visual, audio and other types of content into a real-world environment. The software quality and performance are the main characteristics of the application, which are key factors for AR applications. The analysis of scientific papers, documents and standards made it possible to determine characteristics that are the most significant quality indicators based on well-grounded users’ needs and demands. Results. The criteria we have developed for evaluating applications with augmented reality enable developers to create their own software products in stages, based on step-by-step requirements for them, evaluating the development process by characteristics. This approach will allow you to create high-quality software products using standardized, modern development tools. Discussion and Conclusions . In addition, developers will have a detailed understanding of each stage of creating the application and the necessary development tools and technologies to obtain the highest quality result. That will give an opportunity to decide on specific development tools, methods, models and technologies before starting work on a project. As a result, it will provide the final high-quality software product with good extensibility and compliance with the modern requirements of the digital industry market.
Distinct Imaging Modalities in Head and Neck Cancer: Benefits and Drawbacks

This review mainly focuses on the distinct imaging techniques in for head and neck cancer (HNC), its altered techniques used in diagnosis and its applications. It also depicts the upcoming imaging methods present in the field of HNC causing areas. It explains precise determination regarding the degree and extends of neoplasm. We mainly look on to the MRI(Magnetic resonance imaging), PET(Positron emission tomography), CT (computed tomography)imaging biomarkers for the management of HNC. It plays an important role in the therapy selection strategies and also enhances the therapeutic ratio in the management of HNC. The role of imaging techniques become increasingly more crucial in the management process in locally progressed head and neck squamous cell carcinoma(HNSCC). In this structure, PET allows non-invasive assessment of a range of tumour biomarkers such as metabolism, hypoxia and proliferation. MRI techniques such as can characterize different tissues by probing into their microstructure, providing a novel methodology in oncological imaging. CT, MRI, and PET/CT are widely used to determine the presence and extent of the tumours before and after treatment. This review depicts a synopsis of the most recent imaging strategies and imaging recommendations for every one of the different strides along the clinical way of patients with head and neck malignant growth.
Sensitivity Based Congestion Management in a Deregulated Power System by Optimal Allocation & Parameter Setting of TCSC using Grey Wolf Optimization

In a deregulated power system as the power demand is increasing rapidly, with a limited power system framework, it is very difficult to transmit bulk power. This limitation is due to the thermal constraints of the transmission line. There is a need to increase the loadability of the line without hitting the thermal limits of the system. Thyristor Controlled Series Compensator (TCSC) is used in this paper to reduce congestion in the system by reducing losses and increasing the loadability of the lines. The sensitivity factor applied to optimally allocate TCSC is Disparity Line Utilization Factor (DLUF) and parameter setting is optimized using Grey Wolf Optimisation (GWO). The lines are sorted in terms of the degree of congestion with the help of LUF and then optimal location of TCSC is obtained using DLUF to minimize congestion. TCSC parameters are optimally calculated by the multivariable objective function applying GWO. The method is validated on IEEE 30 bus system. The congestion is reduced and the loadability of a line is increased by the algorithm used in this paper. Effects of congestion alleviation by proposed method are suitably demonstrated in this paper for reduced real and reactive power loss, reduced voltage deviation and incremented security margin of the test system. Results obtained by the proposed algorithm is establishing an upper hand over other state of art algorithms in terms of solution of problem formulated for congestion alleviation
Experimental Investigation on Improvement of Wet Cooling Tower Efficiency with Diverse Packing Compaction Using ANN-PSO Algorithm

In this study, a numerical and empirical scheme for increasing cooling tower performance is developed by combining the particle swarm optimization (PSO) algorithm with a neural network and considering the packing’s compaction as an effective factor for higher accuracies. An experimental setup is used to analyze the effects of packing compaction on the performance. The neural network is optimized by the PSO algorithm in order to predict the precise temperature difference, efficiency, and outlet temperature, which are functions of air flow rate, water flow rate, inlet water temperature, inlet air temperature, inlet air relative humidity, and packing compaction. The effects of water flow rate, air flow rate, inlet water temperature, and packing compaction on the performance are examined. A new empirical model for the cooling tower performance and efficiency is also developed. Finally, the optimized performance conditions of the cooling tower are obtained by the presented correlations. The results reveal that cooling tower efficiency is increased by increasing the air flow rate, water flow rate, and packing compaction.