EMPIRICAL ANALYSIS OF THE FINANCIAL BEHAVIOR OF INVESTORS WITH BRAND APPROACH (CASE STUDY: TEHRAN STOCK EXCHANGE)

Behavioral science in the field of finance and investment is among new topics raised in recent years. The relationship between financial sciences and other fields of social sciences such as financial psychology has caused researchers to do many researches regarding the behavior of investors in the financial markets and their reactions to different situations. Based on the theories of financial behavior, shareholders' decision to buy and sell stocks is under the influence of internal and external psychological factors. Through designing and experimental testing of the model of investors' financial behavior in the Tehran Stock Exchange with an emphasis on brand, this study was an attempt to investigate the influence of these factors. To this end, financial, psychological and social factors were considered as the most important external factors influencing the behavior of investors and, considering the mediating role of brand awareness, their impact on perceived risk and perceived return as well as investment intention was tested. The research population consisted of all individual investors in the Tehran Stock Exchange. In order to determine the sample size, considering unlimited population, Cochran formula was used and hence the sample size was determined to be 145. For data collection, standard questionnaire was used. Confirmatory factor analysis was used to test the reliability of the questionnaire and the research hypotheses were tested using path analysis. The results showed that psychological factors have a positive impact on perceived risk and returns. Financial factors had a positive impact on perceived risk but no impact on perceived return. The impact of social factors on perceived risk and perceived return was not confirmed. Moreover, the results showed that brand awareness has a moderating role in the relationship between social factors and perceived risk and return. However, its moderating role was not confirmed in the relationship between the psychological and financial factors and perceived risk and return. Perceived risk had a positive effect on attitude toward the brand. However, the impact of perceived return on attitude toward the brand was not significant. Finally, the attitude toward the brand had a positive effect on shareholders' investment intention.JEL Codes - G0

Drug-Refractory Trigeminal Neuralgia: Treatment via Botulinum Toxin Type A

Trigeminal neuralgia (TN) is a disorder characterized by severe abrupt lancinating pains, limited to areas of distribution of the fifth cranial nerve—the trigeminal nerve. Numerous modals have been used to reduce or alleviate the intensity and frequency of pain. Drug therapy with anticonvulsive drugs is still the first choice. Migraine and occipital neuralgia have been treated via botulinum toxin type A (BTX-A). Symptoms of TN (pain duration, initiating factors, affected nerve branch, frequency of attacks, and severity of pain) are assessed before injections, and evaluated 1 week, 1 month, and 6 months after injection of 50 U reconstituted BTX-A solution in the trigger zones. Patients generally improve with regard to frequency and severity of pain attacks and in many, the pain is completely eradicated and there is no need for further medication. In some patients, nonsteroidal anti-inflammatory drugs (NSAIDs) may be needed to alleviate pain attacks. All patients develop higher pain thresholds after injections. Complications of BTX therapy include transient paresis of the facial nerve. BTX-A therapy is a minimally invasive method that can play a role in treating TN before other more invasive therapies, i.e., radiofrequency and surgery, are sought. In this chapter, we discuss the indication and method to treat TN via BTX-A in patients refractory to medical treatment

Robot Motion Prediction by Channel State Information

Autonomous robotic systems have gained a lot of attention, in recent years. However, accurate prediction of robot motion in indoor environments with limited visibility is challenging. While vision-based and light detection and ranging (LiDAR) sensors are commonly used for motion detection and localization of robotic arms, they are privacy-invasive and depend on a clear line-of-sight (LOS) for precise measurements. In cases where additional sensors are not available or LOS is not possible, these technologies may not be the best option. This paper proposes a novel method that employs channel state information (CSI) from WiFi signals affected by robotic arm motion. We developed a convolutional neural network (CNN) model to classify four different activities of a Franka Emika robotic arm. The implemented method seeks to accurately predict robot motion even in scenarios in which the robot is obscured by obstacles, without relying on any attached or internal sensors.Comment: 6 pages, 10 figures, 2 tables, MLSP Conferenc

Robustness Evaluation of Machine Learning Models for Robot Arm Action Recognition in Noisy Environments

In the realm of robot action recognition, identifying distinct but spatially proximate arm movements using vision systems in noisy environments poses a significant challenge. This paper studies robot arm action recognition in noisy environments using machine learning techniques. Specifically, a vision system is used to track the robot's movements followed by a deep learning model to extract the arm's key points. Through a comparative analysis of machine learning methods, the effectiveness and robustness of this model are assessed in noisy environments. A case study was conducted using the Tic-Tac-Toe game in a 3-by-3 grid environment, where the focus is to accurately identify the actions of the arms in selecting specific locations within this constrained environment. Experimental results show that our approach can achieve precise key point detection and action classification despite the addition of noise and uncertainties to the dataset.Comment: Accepted at ICASS

Chemical composition and antibacterial activity of some herbal essential oils against Streptococcus mutans

Background and aims: One of the most common chronic diseases in the world is tooth decay. A variety of bacteria are involved in this disorder of which Streptococcus mutants is the most common. Essential oils are considered as new natural compounds for use in combating drug-resistant bacteria. This study was aimed to evaluate the antibacterial activity of some essential oils prepared from Eucalyptus caesia Benth, Cuminum cyminum L. and Satureja hortensis L. on S. mutants. Methods: In this study, essential oils were extracted by hydrodistillation method. E. caesia Benth, C. cyminum L. and S. hortensis L. were characterized by using gas chromatography‒mass spectrophotometry (GC‒MS). Antibacterial activity indices including minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC) and zone of inhibition for the above essential oils against Streptococcus mutans were determined using broth macro-dilution and disk diffusion methods. Data analysis was performed using one-way ANOVA and Tukey test. Results:Results showed that all three extracts had antibacterial activity against S. mutants. S. hortensis L. essential oil with the lowest MIC and MBC value (13.2 and 18.4 µg/ml, respectively) and the biggest inhibition zone showed the strongest antibacterial effect against S. mutants in all exposure times and at all concentrations, compared with two other essential oils. Furthermore, C. cyminum L. essential oil had higher anti-bacterial activity against S. mutant than E. caesia Benth essential oil. Conclusions:The essential oils used in the present study with different components showed antibacterial activity (especially S. hortensis L essential oil), and therefore they can be used as a new antibacterial substance. Keywords: Dental caries, Streptococcus mutans, Essential oils, Antimicrobial