Convergence in house prices across OECD countries: A panel data analysis

This study examines whether housing prices converged in OECD countries over the 1996–2015 period. The unconditional and conditional convergence hypotheses are tested via the system-GMM method using five-year span panel data of twenty OECD countries. The results reveal that there exists a significant convergence process within this country group. To test the conditional convergence hypothesis, the convergence equation is estimated also with some control variables that may reflect market activity and demand side impacts such as income level, construction, unemployment rate, permits for dwellings and share prices. The findings show that the speed of convergence is even higher when the above-mentioned variables are controlled

DEVELOPMENT OF AN INDUSTRIAL ROBOTIC ARM EDUCATION KIT BASED ON OBJECT RECOGNITION AND ROBOT KINEMATICS FOR ENGINEERS

DEVELOPMENT OF AN INDUSTRIAL ROBOTIC ARM EDUCATION KIT BASED ON OBJECT RECOGNITION AND ROBOT KINEMATICS FOR ENGINEERSAbstractRobotic vision makes systems in the industry more advantageous regarding practicality and flexibility. For this reason, it is essential to provide the necessary training for the standard use of vision based robotic systems on production lines. In this article, it is aimed to design a low cost computer vision based industrial robotic arm education kit with eye-to-hand configuration. This kit is based on classifying and stacking products in random locations in a short time, making them ready for industrial operations or logistics. In the development phase of the system, firstly, motion simulation of the robotic arm was performed and then, experimental setup was established, and the performance of the system was tested by experimental studies. This system, which operates with a great success rate, has been made available for use within the scope of education. Regarding the use of the system for educational purposes, this kit supports theoretical lessons by reviewing object recognition (vision systems), forward - inverse kinematics, and trajectory planning (robot kinematics) and running the system several times. Thus, engineering students are expected to approach the industry more consciously and to develop the industry. It can also be used for training of relevant engineers in the institution where vision based robotic systems are available.Keywords: Education Kit, Stereo Vision, Robotic Arm, Object Recognition and Classification, Pick-and-Place Tas

Development of new membrane materials for direct methanol fuel cells

Development of new membrane materials for direct methanol fuel cells\ud Direct methanol fuel cells (DMFCs) can convert the chemical energy of a fuel directly into electrical energy with high efficiency and low emission of pollutants. DMFCs can be used as the power sources to portable electronic devices like laptop computers, cellular phones and, to a less degree, vehicular applications. \ud In order to compete with Li-ion batteries for portable applications higher power densities must be achieved. For that reason DMFCs should be operated at high methanol concentrations. The proton exchange membrane (PEM) is the key component of the DMFC. Currently Perfluorosulfonated ionomer (PFSI) membranes, like DuPont’s Nafion® and Asahi Chemical’s Aciplex®, are used as a PEM in DMFCs due to their excellent proton conductivity, mechanical strength and thermal and chemical stability. However, their high methanol cross over, especially at high methanol concentrations and, high cost (US$700/m2) due to the expensive fluorination step, severely limit their commercialization in fuel cells. Therefore there is a strong need to develop new polymeric membranes. To achieve this, various strategies were employed:\ud • Impregnation of conductive polymer into a porous support to obtain composite membranes with low methanol crossover and long term stability\ud • Incorporation of inorganic fillers into the bulk phase of conductive polymer to increase the proton conductivity and at the same time decrease the methanol cross-over\ud • Surface micro-structuring of the conductive polymer by hot embossing to increase the effective catalytic surface area without increasing the geometric one.\ud Impregnated membranes have high dimensional stability and low methanol cross-over leading to a higher DMFC performance at low and high methanol concentrations.\ud Membranes with inorganic fillers have lower methanol permeability and higher proton conductivity than the pure membranes. The membrane with the highest zeolite content (5wt.%) has the best performance; namely high power density and stable performance in time with low fluctuations.\ud Micro-structured membranes have less water content and methanol flux and at the same time similar resistances in comparison to the flat membranes. Owing to these properties, micro-structured membranes performed the best in the DMFC system

Competitiveness of Turkey’s Automotive Industry

This paper attempts to answer the question whether Turkey’s Automotive Industry, which was, in 2016, 14th largest automotive industry in the world, and the 5th in Europe, is competitive or not. To measure the competitiveness of the industry, Revealed Comparative Advantage index (RCA) has been benefited. What differs this study from the previous ones are the facts that: first, the time span that the study which covers a larger period than most of other studies benefiting RCA index, namely, the time coverage of the study is from 2001 to 2016, thus the results will also account for the RCA values of 2001 and 2008, which are the economic crisis years. Second, in addition to having a large time coverage, to the best of the author’s knowledge, this study’s results provides the most up-to-date RCA values for the industry

Do we need early ankle arthroscopy for the patients with acute lateral ankle instability

Background: Lateral ankle injury is the most frequently encountered trauma in physically active populations. The general approach to acute lateral ankle instabilities is conservative treatment.Methods: We investigated the effects of compliance to conservative treatment on permanent instability and other intra-articular pathologies and the outcomes of insufficiently treated lateral ankle instability. These patients underwent conservative treatment for at least 3 months. At the end of this period, anterior ankle arthroscopy was performed for patients who continued to report ankle problems. The patients were grouped according to compliance and noncompliance with conservative treatment.Results: The rate of compliance for conservative treatment was 41.4%. Arthroscopy revealed that the rate of osteochondral lesions of the talus was 45.3%; 51.6% of the patients had partial or complete lateral ankle ligament injury. The rate of lateral ankle instability was significantly lower in patients who were compliant with conservative treatment (39.6% vs. 60%, p<0.05). Lateral ankle instability was accompanied by osteochondral defects in only 5 patients who were compliant with conservative treatment.Conclusions: For ankle injuries associated with lateral ankle instability, conservative treatment can decrease instability levels and other pathologies, which may become chronic over time. However, arthroscopy may be required due to ankle pathologies accompanying instability and an early decision for arthroscopy may reduce the incidence of permanent lateral ankle instability

Subdivision of the spectra for factorable matrices on cc and ellpell ^{p}

There are many different ways to subdivide the spectrum of a bounded linear operator; some of them are motivated by applications to physics (in particular, quantum mechanics). In a series of papers, B.E. Rhoades and M. Yildirim previously investigated the spectra and fine spectra for factorable matrices, considered as bounded operators over various sequence spaces. In the present paper, approximation point spectrum, defect spectrum and compression spectrum of factorable matrices are investigated

Endothelial nitric oxide synthase activity involves in the protective effect of ascorbic acid against penicillin-induced epileptiform activity

AbstractAscorbic acid and nitric oxide are known to play important roles in epilepsy. The aim of present study was to identify the involvement of nitric oxide (NO) in the anticonvulsant effects of ascorbic acid on penicillin-induced epileptiform activity in rats. Intracortical injection of penicillin (500, International Units (IU)) into the left sensorimotor cortex induced epileptiform activity within 2–5min. Thirty minutes after penicillin injection, nitric oxide synthase (NOS) inhibitor, NG-nitro-l-arginine methyl ester (l-NAME, 100mg/kg), neuronal nitric oxide synthase (nNOS) inhibitor 7-nitroindazole (7-NI, 40mg/kg), NO substrate, l-arginine (500mg/kg) were administered with the most effective dose of ascorbic acid (100mg/kg) intraperitoneally (i.p.). The administration of l-arginine significantly decreased the frequency of epileptiform activity while administration of l-NAME did not influence the mean frequency of epileptiform activity. Injection of 7-NI decreased the mean frequency of epileptiform activity but did not influence amplitude. Ascorbic acid decreased both the mean frequency and amplitude of penicillin-induced epileptiform activity in rats. The application of l-NAME partially and temporarily reversed the anticonvulsant effects of ascorbic acid. The results support the hypothesis of neuro-protective role for NO and ascorbic acid. The protective effect of ascorbic acid against epileptiform activity was partially and temporarily reversed by nonspecific nitric oxide synthase inhibitor l-NAME, but not selective neuronal nitric oxide synthase inhibitor 7-NI, indicating that ascorbic acid needs endothelial-NOS/NO route to decrease penicillin-induced epileptiform activity

Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach

The dynamic nature of driving environments and the presence of diverse road users pose significant challenges for decision-making in autonomous driving. Deep reinforcement learning (DRL) has emerged as a popular approach to tackle this problem. However, the application of existing DRL solutions is mainly confined to simulated environments due to safety concerns, impeding their deployment in real-world. To overcome this limitation, this paper introduces a novel neuro-symbolic model-free DRL approach, called DRL with Symbolic Logics (DRLSL) that combines the strengths of DRL (learning from experience) and symbolic first-order logics knowledge-driven reasoning) to enable safe learning in real-time interactions of autonomous driving within real environments. This innovative approach provides a means to learn autonomous driving policies by actively engaging with the physical environment while ensuring safety. We have implemented the DRLSL framework in autonomous driving using the highD dataset and demonstrated that our method successfully avoids unsafe actions during both the training and testing phases. Furthermore, our results indicate that DRLSL achieves faster convergence during training and exhibits better generalizability to new driving scenarios compared to traditional DRL methods.Comment: 15 pages, 9 figures, 1 table, 1 algorithm. Under review as a journal paper at IEEE transactions on Intelligent Transportation System